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Sample records for brain tumour classification

  1. Brain tumour classification using Gaussian decomposition and neural networks.

    Science.gov (United States)

    Arizmendi, Carlos; Sierra, Daniel A; Vellido, Alfredo; Romero, Enrique

    2011-01-01

    The development, implementation and use of computer-based medical decision support systems (MDSS) based on pattern recognition techniques holds the promise of substantially improving the quality of medical practice in diagnostic and prognostic tasks. In this study, the core of a decision support system for brain tumour classification from magnetic resonance spectroscopy (MRS) data is presented. It combines data pre-processing using Gaussian decomposition, dimensionality reduction using moving window with variance analysis, and classification using artificial neural networks (ANN). This combination of techniques is shown to yield high diagnostic classification accuracy in problems concerning diverse brain tumour pathologies, some of which have received little attention in the literature.

  2. An Improved Brain Tumour Classification System using Wavelet Transform and Neural Network.

    Science.gov (United States)

    Dhas, DAS; Madheswaran, M

    2015-06-09

    An improved brain tumour classification system using wavelet transform and neural network is developed and presented in this paper. The anisotropic diffusion filter is used for image denoising and the performance of oriented rician noise reducing anisotropic diffusion (ORNRAD) filter is validated. The segmentation of the denoised image is carried out by Fuzzy C-means clustering. The features are extracted using Symlet and Coiflet Wavelet transform and Levenberg Marquardt algorithm based neural network is used to classify the magnetic resonance imaging (MRI) images. This MRI classification technique is tested and analysed with the existing methodologies and its performance is found to be satisfactory with a classification accuracy of 93.02%. The developed system can assist the physicians for classifying the MRI images for better decision-making.

  3. An Improved Image Mining Technique For Brain Tumour Classification Using Efficient Classifier

    OpenAIRE

    Rajendran, P.; M.Madheswaran

    2010-01-01

    An improved image mining technique for brain tumor classification using pruned association rule with MARI algorithm is presented in this paper. The method proposed makes use of association rule mining technique to classify the CT scan brain images into three categories namely normal, benign and malign. It combines the low-level features extracted from images and high level knowledge from specialists. The developed algorithm can assist the physicians for efficient classification with multiple ...

  4. An Improved Image Mining Technique For Brain Tumour Classification Using Efficient Classifier

    Directory of Open Access Journals (Sweden)

    P. Rajendran

    2009-12-01

    Full Text Available An improved image mining technique for brain tumor classification using pruned association rule with MARI algorithm is presented in this paper. The method proposed makes use of association rule mining technique to classify the CT scan brain images into three categories namely normal, benign and malign. It combines the low-level features extracted from images and high level knowledge from specialists. The developed algorithm can assist the physicians for efficient classification with multiple keywords per image to improve the accuracy. The experimental result on pre-diagnosed database of brain images showed 96% and 93% sensitivity and accuracy respectively.Keywords- Data mining; Image ming; Association rule mining; Medical Imaging; Medical image diagnosis; Classification;

  5. of brain tumours

    African Journals Online (AJOL)

    'psychiatric' indicators of possible brain tumour are sudden ... found to have weakness and/or loss of sensation in the lower extremities. Even when there is no clear weakness or hearing impairment, they may respond poorly, or not at all,.

  6. Primary brain tumours in adults.

    Science.gov (United States)

    Ricard, Damien; Idbaih, Ahmed; Ducray, François; Lahutte, Marion; Hoang-Xuan, Khê; Delattre, Jean-Yves

    2012-05-26

    Important advances have been made in the understanding and management of adult gliomas and primary CNS lymphomas--the two most common primary brain tumours. Progress in imaging has led to a better analysis of the nature and grade of these tumours. Findings from large phase 3 studies have yielded some standard treatments for gliomas, and have confirmed the prognostic value of specific molecular alterations. High-throughput methods that enable genome-wide analysis of tumours have improved the knowledge of tumour biology, which should lead to a better classification of gliomas and pave the way for so-called targeted therapy trials. Primary CNS lymphomas are a group of rare non-Hodgkin lymphomas. High-dose methotrexate-based regimens increase survival, but the standards of care and the place of whole-brain radiotherapy remain unclear, and are likely to depend on the age of the patient. The focus now is on the development of new polychemotherapy regimens to reduce or defer whole-brain radiotherapy and its delayed complications.

  7. The Heidelberg classification of renal cell tumours

    NARCIS (Netherlands)

    Kovacs, G; Akhtar, M; Beckwith, BJ; Bugert, P; Cooper, CS; Delahunt, B; Eble, JN; Fleming, S; Ljungberg, B; Medeiros, LJ; Moch, H; Reuter, VE; Ritz, E; Roos, G; Schmidt, D; Srigley, [No Value; Storkel, S; VandenBerg, E; Zbar, B

    1997-01-01

    This paper presents the conclusions of a workshop entitled 'Impact of Molecular Genetics on the Classification of Renal Cell Tumours', which was held in Heidelberg in October 1996, The focus on 'renal cell tumours' excludes any discussion of Wilms' tumour and its variants, or of tumours metastatic t

  8. Brain tumour-associated status epilepticus.

    Science.gov (United States)

    Goonawardena, Janindu; Marshman, Laurence A G; Drummond, Katharine J

    2015-01-01

    We have reviewed the scant literature on status epilepticus in patients with brain tumours. Patients with brain tumour-associated epilepsy (TAE) appear less likely to develop status epilepticus (TASE) than patients with epilepsy in the general population (EGP) are to develop status epilepticus (SEGP). TASE is associated with lesions in similar locations as TAE; in particular, the frontal lobes. However, in contrast to TAE, where seizures commence early in the course of the disease or at presentation, TASE is more likely to occur later in the disease course and herald tumour progression. In marked contrast to TAE, where epilepsy risk is inversely proportional to Word Health Organization tumour grade, TASE risk appears to be directly proportional to tumour grade (high grade gliomas appear singularly predisposed). Whilst anti-epileptic drug (AED) resistance is more common in TAE than EGP (with resistance directly proportional to tumour grade and frontal location), TASE appears paradoxically more responsive to simple AED regimes than either TAE or SEGP. Although some results suggest that mortality may be higher with TASE than with SEGP, it is likely that (as with SEGP) the major determinant of mortality is the underlying disease process. Because all such data have been derived from retrospective studies, because TASE and SEGP are less common than TAE and EGP, and because TASE and SEGP classification has often been inconsistent, findings can only be considered preliminary: multi-centre, prospective studies are required. Whilst preliminary, our review suggests that TASE has a distinct clinical profile compared to TAE and SEGP.

  9. Prognosis of Brain Tumours with Epilepsy

    OpenAIRE

    1991-01-01

    The prognosis of 560 patients with a clinical and CT diagnosis of intrinsic supratentorial brain tumour was examined retrospectively at the Department of Neurosciences, Walton Hospital, Liverpool, England.

  10. Ex-vivo HRMAS of adult brain tumours: metabolite quantification and assignment of tumour biomarkers

    Directory of Open Access Journals (Sweden)

    Wilson M

    2010-03-01

    Full Text Available Abstract Background High-resolution magic angle spinning (HRMAS NMR spectroscopy allows detailed metabolic analysis of whole biopsy samples for investigating tumour biology and tumour classification. Accurate biochemical assignment of small molecule metabolites that are "NMR visible" will improve our interpretation of HRMAS data and the translation of NMR tumour biomarkers to in-vivo studies. Results 1D and 2D 1H HRMAS NMR was used to determine that 29 small molecule metabolites, along with 8 macromolecule signals, account for the majority of the HRMAS spectrum of the main types of brain tumour (astrocytoma grade II, grade III gliomas, glioblastomas, metastases, meningiomas and also lymphomas. Differences in concentration of 20 of these metabolites were statistically significant between these brain tumour types. During the course of an extended 2D data acquisition the HRMAS technique itself affects sample analysis: glycine, glutathione and glycerophosphocholine all showed small concentration changes; analysis of the sample after HRMAS indicated structural damage that may affect subsequent histopathological analysis. Conclusions A number of small molecule metabolites have been identified as potential biomarkers of tumour type that may enable development of more selective in-vivo 1H NMR acquisition methods for diagnosis and prognosis of brain tumours.

  11. Movement disorders caused by brain tumours.

    Directory of Open Access Journals (Sweden)

    Bhatoe H

    1999-01-01

    Full Text Available Movement disorders are uncommon presenting features of brain tumours. Early recognition of such lesions is important to arrest further deficit. We treated seven patients with movement disorders secondary to brain tumours over a period of seven years. Only two of these were intrinsic thalamic tumours (astrocytomas while the rest were extrinsic tumours. The intrinsic tumours were accompanied by hemichorea. Among the extrinsic tumours, there was one pituitary macroadenoma with hemiballismus and four meningiomas with parkinsonism. Symptoms were unilateral in all patients except one with anterior third falcine meningioma who had bilateral rest tremors. There was relief in movement disorders observed after surgery. Imaging by computed tomography or magnetic resonance imaging is mandatory in the evaluation of movement disorders, especially if the presentation is atypical, unilateral and/or accompanied by long tract signs.

  12. Symptoms and time to diagnosis in children with brain tumours

    DEFF Research Database (Denmark)

    Klitbo, Ditte Marie; Nielsen, Rine; Illum, Niels Ove;

    2011-01-01

    Clinical symptoms in brain tumours in children are variable at onset and diagnosis is often delayed. Symptoms were investigated with regard to brain tumour localisation, prediagnostic symptomatic intervals and malignancy.......Clinical symptoms in brain tumours in children are variable at onset and diagnosis is often delayed. Symptoms were investigated with regard to brain tumour localisation, prediagnostic symptomatic intervals and malignancy....

  13. 'Pseudo-Alzheimer's' and primary brain tumour.

    OpenAIRE

    O'Mahony, D; Walsh, J. B.; Coakley, D.

    1992-01-01

    Primary brain tumour may present in the elderly purely as a dementing illness before the onset or detection of sensorimotor neurological symptoms or signs. Although neurological examination may indicate no definite signs, close attention to accepted DSM-IIIR and NINCDS-ADRDA diagnostic criteria for primary degenerative dementia and 'probable' Alzheimer's disease respectively will suggest a process other than a degenerative one. This was the case in two patients with primary brain tumour prese...

  14. Oncogenic extracellular vesicles in brain tumour progression

    Directory of Open Access Journals (Sweden)

    Esterina eD'Asti

    2012-07-01

    Full Text Available The brain is a frequent site of neoplastic growth, including both primary and metastatic tumours. The clinical intractability of many brain tumours and their distinct biology are implicitly linked to the unique microenvironment of the central nervous system (CNS and cellular interactions within. Among the most intriguing forms of cellular interactions is that mediated by membrane-derived extracellular vesicles (EVs. Their biogenesis (vesiculation and uptake by recipient cells serves as a unique mechanism of intercellular trafficking of complex biological messages including the exchange of molecules that cannot be released through classical secretory pathways, or that are prone to extracellular degradation. Tumour cells produce EVs containing molecular effectors of several cancer-related processes such as growth, invasion, drug resistance, angiogenesis, and coagulopathy. Notably, tumour-derived EVs (oncosomes also contain oncogenic proteins, transcripts, DNA and microRNA (miR. Uptake of this material may change properties of the recipient cells and impact the tumour microenvironment. Examples of transformation-related molecules found in the cargo of tumour-derived EVs include the oncogenic epidermal growth factor receptor (EGFRvIII, tumour suppressors (PTEN and oncomirs (miR-520g. It is postulated that EVs circulating in blood or cerebrospinal fluid (CSF of brain tumour patients may be used to decipher molecular features (mutations of the underlying malignancy, reflect responses to therapy or molecular subtypes of primary brain tumours (e.g. glioma or medulloblastoma. It is possible that metastases to the brain may also emit EVs with clinically relevant oncogenic signatures. Thus EVs emerge as a novel and functionally important vehicle of intercellular communication that can mediate multiple biological effects. In addition, they provide a unique platform to develop molecular biomarkers in brain malignancies.

  15. Anatomical and biochemical investigation of primary brain tumours

    Energy Technology Data Exchange (ETDEWEB)

    Del Sole, A. [Univ. di Milano (Italy); Falini, A. [Univ. Vita e Salute (Italy). IRCCS; Ravasi, L.; Ottobrini, L.; Lucignani, G. [Univ. di Milano (Italy). Ist. di Scienze Radiologiche; De Marchis, D. [Univ. di Milano-Bicocca (Italy); Bombardieri, E. [Istituto Nazionale dei Tumori, Milano (Italy)

    2001-12-01

    Cancerous transformation entails major biochemical changes including modifications of the energy metabolism of the cell, e.g. utilisation of glucose and other substrates, protein synthesis, and expression of receptors and antigens. Tumour growth also leads to heterogeneity in blood flow owing to focal necrosis, angiogenesis and metabolic demands, as well as disruption of transport mechanisms of substrates across cell membranes and other physiological boundaries such as the blood-brain barrier. All these biochemical, histological and anatomical changes can be assessed with emission tomography, X-ray computed tomography (CT), magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). Whereas anatomical imaging is aimed at the diagnosis of brain tumours, biochemical imaging is better suited for tissue characterisation. The identification of a tumoural mass and the assessment of its size and vascularisation are best achieved with X-ray CT and MRI, while biochemical imaging can provide additional information that is crucial for tumour classification, differential diagnosis and follow-up. As the assessment of variables such as water content, appearance of cystic lesions and location of the tumour are largely irrelevant for tissue characterisation, a number of probes have been employed for the assessment of the biochemical features of tumours. Since biochemical changes may be related to the growth rate of cancer cells, they can be thought of as markers of tumour cell proliferation. Biochemical imaging with radionuclides of processes that occur at a cellular level provides information that complements findings obtained by anatomical imaging aimed at depicting structural, vascular and histological changes. This review focusses on the clinical application of anatomical brain imaging and biochemical assessment with positron emission tomography, single-photon emission tomography and MRS in the diagnosis of primary brain tumours, as well as in follow-up. (orig.)

  16. Primary brain tumours, meningiomas and brain metastases in pregnancy

    DEFF Research Database (Denmark)

    Verheecke, Magali; Halaska, Michael J; Lok, Christianne A

    2014-01-01

    to obtain better insight into outcome and possibilities of treatment in pregnancy. METHODS: We collected all intracranial tumours (primary brain tumour, cerebral metastasis, or meningioma) diagnosed during pregnancy, registered prospectively and retrospectively by international collaboration since 1973......, respectively. Eight patients (30%) underwent brain surgery, seven patients (26%) had radiotherapy and in three patients (11%) chemotherapy was administered during gestation. Two patients died during pregnancy and four pregnancies were terminated. In 16 (59%) patients elective caesarean section was performed...... were reassuring. CONCLUSION: Adherence to standard protocol for the treatment of brain tumours during pregnancy appears to allow a term delivery and a higher probability of a vaginal delivery....

  17. Lymphoreticular cells in human brain tumours and in normal brain.

    OpenAIRE

    1982-01-01

    The present investigation, using various rosetting assays of cell suspensions prepared by mechanical disaggregation or collagenase digestion, demonstrated lymphoreticular cells in human normal brain (cerebral cortex and cerebellum) and in malignant brain tumours. The study revealed T and B lymphocytes and their subsets (bearing receptors for Fc(IgG) and C3) in 5/14 glioma suspensions, comprising less than 15% of the cell population. Between 20-60% of cells in tumour suspensions morphologicall...

  18. Ex-vivo HRMAS of adult brain tumours: metabolite quantification and assignment of tumour biomarkers.

    NARCIS (Netherlands)

    Wright, A.J.; Fellows, G.A.; Griffiths, J.R.; Wilson, M.; Bell, B.A.; Howe, F.A.

    2010-01-01

    BACKGROUND: High-resolution magic angle spinning (HRMAS) NMR spectroscopy allows detailed metabolic analysis of whole biopsy samples for investigating tumour biology and tumour classification. Accurate biochemical assignment of small molecule metabolites that are "NMR visible" will improve our inter

  19. Ex-vivo HRMAS of adult brain tumours: metabolite quantification and assignment of tumour biomarkers.

    NARCIS (Netherlands)

    Wright, A.J.; Fellows, G.A.; Griffiths, J.R.; Wilson, M.; Bell, B.A.; Howe, F.A.

    2010-01-01

    BACKGROUND: High-resolution magic angle spinning (HRMAS) NMR spectroscopy allows detailed metabolic analysis of whole biopsy samples for investigating tumour biology and tumour classification. Accurate biochemical assignment of small molecule metabolites that are "NMR visible" will improve our inter

  20. Hepatic mitochondrial function and brain tumours.

    Science.gov (United States)

    Pouliquen, Daniel L

    2007-07-01

    Therapeutic advances remain modest for patients with malignant brain tumours, due in part to inadequate ability of in-vitro models to mimic the consequences of tumour progression in vivo, which include profound immunosuppression, cytokine dysregulation and microvascular proliferation. This review summarizes recent findings on the wasting consequences of glioma growth, including changes in hepatic metabolism caused by the tumour. Release of proinflammatory cytokines by gliomas leads to anorexia, a sensation of tiredness and fatigue associated with sleep deprivation. The cachexia and associated decrease in relative liver mass that are observed in rats with the most aggressive gliomas may be accounted for by increased activity of the Cori cycle, with the intermediary metabolism of the glioma-influenced liver being directed toward energy utilization rather than energy storage. In these conditions, liver mitochondria exhibit abnormal biogenesis, together with modifications to water dynamics and ion content. Improved patient care will result from better understanding of the interactions between brain tumour cells and the immune system, and use of nutritional metabolic therapy to protect tumour-influenced hepatocytes and their mitochondria may improve outcomes.

  1. Prophylactic Anticonvulsants in patients with brain tumour

    Energy Technology Data Exchange (ETDEWEB)

    Forsyth, P.A. [Depts. of Oncology and Clinical Neurosciences, Univ. of Calgary, Calgary, Alberta (Canada); Tom Baker Cancer Centre, Calgary, Alberta (Canada); Weaver, S. [Depts. of Neurology and Medicine, Albany Medical College, Albany, New York (United States); Fulton, D. [Dept. of Radiation Oncology, Cross Cancer Institute and Dept. of Medicine/Neurology, Univ. of Alberta, Edmonton, Alberta (Canada)

    2003-05-01

    We conducted a clinical trial to determine if prophylactic anticonvulsants in brain tumour patients (without prior seizures) reduced seizure frequency. We stopped accrual at 100 patients on the basis of the interim analysis. One hundred newly diagnosed brain tumour patients received anticonvulsants (AC Group) or not (No AC Group) in this prospective randomized unblinded study. Sixty patients had metastatic, and 40 had primary brain tumours. Forty-six (46%) patients were randomized to the AC Group and 54 (54%) to the No AC Group. Median follow-up was 5.44 months (range 0.13 -30.1 months). Seizures occurred in 26 (26%) patients, eleven in the AC Group and 15 in the No AC Group. Seizure-free survivals were not different; at three months 87% of the AC Group and 90% of the No AC Group were seizure-free (log rank test, p=0.98). Seventy patients died (unrelated to seizures) and survival rates were equivalent in both groups (median survival = 6.8 months versus 5.6 months, respectively; log rank test, p=0.50). We then terminated accrual at 100 patients because seizure and survival rates were much lower than expected; we would need {>=}900 patients to have a suitably powered study. These data should be used by individuals contemplating a clinical trial to determine if prophylactic anticonvulsants are effective in subsets of brain tumour patients (e.g. only anaplastic astrocytomas). When taken together with the results of a similar randomized trial, prophylactic anticonvulsants are unlikely to be effective or useful in brain tumour patients who have not had a seizure. (author)

  2. Imaging biomarkers in primary brain tumours

    Energy Technology Data Exchange (ETDEWEB)

    Lopci, Egesta; Chiti, Arturo [Humanitas Clinical and Research Center, Nuclear Medicine Department, Rozzano, MI (Italy); Franzese, Ciro; Navarria, Pierina; Scorsetti, Marta [Humanitas Clinical and Research Center, Radiosurgery and Radiotherapy, Rozzano, MI (Italy); Grimaldi, Marco [Humanitas Clinical and Research Center, Radiology, Rozzano, MI (Italy); Zucali, Paolo Andrea; Simonelli, Matteo [Humanitas Clinical and Research Center, Medical Oncology, Rozzano, MI (Italy); Bello, Lorenzo [Humanitas Clinical and Research Center, Neurosurgery, Rozzano, MI (Italy)

    2015-04-01

    We are getting used to referring to instrumentally detectable biological features in medical language as ''imaging biomarkers''. These two terms combined reflect the evolution of medical imaging during recent decades, and conceptually comprise the principle of noninvasive detection of internal processes that can become targets for supplementary therapeutic strategies. These targets in oncology include those biological pathways that are associated with several tumour features including independence from growth and growth-inhibitory signals, avoidance of apoptosis and immune system control, unlimited potential for replication, self-sufficiency in vascular supply and neoangiogenesis, acquired tissue invasiveness and metastatic diffusion. Concerning brain tumours, there have been major improvements in neurosurgical techniques and radiotherapy planning, and developments of novel target drugs, thus increasing the need for reproducible, noninvasive, quantitative imaging biomarkers. However, in this context, conventional radiological criteria may be inappropriate to determine the best therapeutic option and subsequently to assess response to therapy. Integration of molecular imaging for the evaluation of brain tumours has for this reason become necessary, and an important role in this setting is played by imaging biomarkers in PET and MRI. In the current review, we describe most relevant techniques and biomarkers used for imaging primary brain tumours in clinical practice, and discuss potential future developments from the experimental context. (orig.)

  3. Pancreatic neuroendocrine tumours: correlation between MSCT features and pathological classification

    Energy Technology Data Exchange (ETDEWEB)

    Luo, Yanji; Dong, Zhi; Li, Zi-Ping; Feng, Shi-Ting [The First Affiliated Hospital, Sun Yat-Sen University, Department of Radiology, Guangzhou, Guangdong (China); Chen, Jie [The First Affiliated Hospital, Sun Yat-Sen University, Department of Gastroenterology, Guangzhou, Guangdong (China); Chan, Tao; Chen, Minhu [Union Hospital, Hong Kong, Medical Imaging Department, Shatin, N.T. (China); Lin, Yuan [The First Affiliated Hospital, Sun Yat-Sen University, Department of Pathology, Guangzhou, Guangdong (China)

    2014-11-15

    We aimed to evaluate the multi-slice computed tomography (MSCT) features of pancreatic neuroendocrine neoplasms (P-NENs) and analyse the correlation between the MSCT features and pathological classification of P-NENs. Forty-one patients, preoperatively investigated by MSCT and subsequently operated on with a histological diagnosis of P-NENs, were included. Various MSCT features of the primary tumour, lymph node, and distant metastasis were analysed. The relationship between MSCT features and pathologic classification of P-NENs was analysed with univariate and multivariate models. Contrast-enhanced images showed significant differences among the three grades of tumours in the absolute enhancement (P = 0.013) and relative enhancement (P = 0.025) at the arterial phase. Univariate analysis revealed statistically significant differences among the tumours of different grades (based on World Health Organization [WHO] 2010 classification) in tumour size (P = 0.001), tumour contour (P < 0.001), cystic necrosis (P = 0.001), tumour boundary (P = 0.003), dilatation of the main pancreatic duct (P = 0.001), peripancreatic tissue or vascular invasion (P < 0.001), lymphadenopathy (P = 0.011), and distant metastasis (P = 0.012). Multivariate analysis suggested that only peripancreatic tissue or vascular invasion (HR 3.934, 95 % CI, 0.426-7.442, P = 0.028) was significantly associated with WHO 2010 pathological classification. MSCT is helpful in evaluating the pathological classification of P-NENs. (orig.)

  4. Automated EEG signal analysis for identification of epilepsy seizures and brain tumour.

    Science.gov (United States)

    Sharanreddy, M; Kulkarni, P K

    2013-11-01

    Abstract Electroencephalography (EEG) is a clinical test which records neuro-electrical activities generated by brain structures. EEG test results used to monitor brain diseases such as epilepsy seizure, brain tumours, toxic encephalopathies infections and cerebrovascular disorders. Due to the extreme variation in the EEG morphologies, manual analysis of the EEG signal is laborious, time consuming and requires skilled interpreters, who by the nature of the task are prone to subjective judegment and error. Further, manual analysis of the EEG results often fails to detect and uncover subtle features. This paper proposes an automated EEG analysis method by combining digital signal processing and neural network techniques, which will remove error and subjectivity associated with manual analysis and identifies the existence of epilepsy seizure and brain tumour diseases. The system uses multi-wavelet transform for feature extraction in which an input EEG signal is decomposed in a sub-signal. Irregularities and unpredictable fluctuations present in the decomposed signal are measured using approximate entropy. A feed-forward neural network is used to classify the EEG signal as a normal, epilepsy or brain tumour signal. The proposed technique is implemented and tested on data of 500 EEG signals for each disease. Results are promising, with classification accuracy of 98% for normal, 93% for epilepsy and 87% for brain tumour. Along with classification, the paper also highlights the EEG abnormalities associated with brain tumour and epilepsy seizure.

  5. Residential Radon and Brain Tumour Incidence in a Danish Cohort

    DEFF Research Database (Denmark)

    Bräuner, Elvira V.; Andersen, Zorana J.; Andersen, Claus Erik;

    2013-01-01

    (CI) for the risk of primary brain tumours associated with residential radon exposure with adjustment for age, sex, occupation, fruit and vegetable consumption and traffic-related air pollution. Effect modification by air pollution was assessed. Results: Median estimated radon was 40.5 Bq/m3......Background: Increased brain tumour incidence over recent decades may reflect improved diagnostic methods and clinical practice, but remain unexplained. Although estimated doses are low a relationship between radon and brain tumours may exist. Objective: To investigate the long-term effect...... of exposure to residential radon on the risk of primary brain tumour in a prospective Danish cohort. Methods: During 1993–1997 we recruited 57,053 persons. We followed each cohort member for cancer occurrence from enrolment until 31 December 2009, identifying 121 primary brain tumour cases. We traced...

  6. Burnout in Mothers and Fathers of Children Surviving Brain Tumour

    OpenAIRE

    Lindahl Norberg, Annika

    2007-01-01

    The aim of this paper was to investigate the occurrence of burnout among parents of brain tumour survivors. Burnout was assessed in 24 mothers and 20 fathers of childhood brain tumour survivors, using the Shirom–Melamed Burnout Questionnaire. Parents of children with no history of chronic or serious diseases served as a reference group. Mothers’ burnout scores were significantly higher compared with reference mothers. For fathers, no relation between burnout and being a parent of a brain tumo...

  7. MicroRNA Regulation of Brain Tumour Initiating Cells in Central Nervous System Tumours

    Directory of Open Access Journals (Sweden)

    Neha Garg

    2015-01-01

    Full Text Available CNS tumours occur in both pediatric and adult patients and many of these tumours are associated with poor clinical outcome. Due to a paradigm shift in thinking for the last several years, these tumours are now considered to originate from a small population of stem-like cells within the bulk tumour tissue. These cells, termed as brain tumour initiating cells (BTICs, are perceived to be regulated by microRNAs at the posttranscriptional/translational levels. Proliferation, stemness, differentiation, invasion, angiogenesis, metastasis, apoptosis, and cell cycle constitute some of the significant processes modulated by microRNAs in cancer initiation and progression. Characterization and functional studies on oncogenic or tumour suppressive microRNAs are made possible because of developments in sequencing and microarray techniques. In the current review, we bring recent knowledge of the role of microRNAs in BTIC formation and therapy. Special attention is paid to two highly aggressive and well-characterized brain tumours: gliomas and medulloblastoma. As microRNA seems to be altered in the pathogenesis of many human diseases, “microRNA therapy” may now have potential to improve outcomes for brain tumour patients. In this rapidly evolving field, further understanding of miRNA biology and its contribution towards cancer can be mined for new therapeutic tools.

  8. Discrimination of paediatric brain tumours using apparent diffusion coefficient histograms

    Energy Technology Data Exchange (ETDEWEB)

    Bull, Jonathan G.; Clark, Christopher A. [UCL Institute of Child Health, Imaging and Biophysics Unit, London (United Kingdom); Saunders, Dawn E. [Great Ormond Street Hospital for Children NHS Trust, Department of Radiology, London (United Kingdom)

    2012-02-15

    To determine if histograms of apparent diffusion coefficients (ADC) can be used to differentiate paediatric brain tumours. Imaging of histologically confirmed tumours with pre-operative ADC maps were reviewed (54 cases, 32 male, mean age 6.1 years; range 0.1-15.8 years) comprising 6 groups. Whole tumour ADC histograms were calculated; normalised for volume. Stepwise logistic regression analysis was used to differentiate tumour types using histogram metrics, initially for all groups and then for specific subsets. All 6 groups (5 dysembryoplastic neuroectodermal tumours, 22 primitive neuroectodermal tumours (PNET), 5 ependymomas, 7 choroid plexus papillomas, 4 atypical teratoid rhabdoid tumours (ATRT) and 9 juvenile pilocytic astrocytomas (JPA)) were compared. 74% (40/54) were correctly classified using logistic regression of ADC histogram parameters. In the analysis of posterior fossa tumours, 80% of ependymomas, 100% of astrocytomas and 94% of PNET-medulloblastoma were classified correctly. All PNETs were discriminated from ATRTs (22 PNET and 4 supratentorial ATRTs) (100%). ADC histograms are useful in differentiating paediatric brain tumours, in particular, the common posterior fossa tumours of childhood. PNETs were differentiated from supratentorial ATRTs, in all cases, which has important implications in terms of clinical management. (orig.)

  9. Residential Radon and Brain Tumour Incidence in a Danish Cohort

    DEFF Research Database (Denmark)

    Bräuner, Elvira V.; Andersen, Zorana J.; Andersen, Claus Erik;

    2013-01-01

    Background: Increased brain tumour incidence over recent decades may reflect improved diagnostic methods and clinical practice, but remain unexplained. Although estimated doses are low a relationship between radon and brain tumours may exist. Objective: To investigate the long-term effect...... of exposure to residential radon on the risk of primary brain tumour in a prospective Danish cohort. Methods: During 1993–1997 we recruited 57,053 persons. We followed each cohort member for cancer occurrence from enrolment until 31 December 2009, identifying 121 primary brain tumour cases. We traced...... residential addresses from 1 January 1971 until 31 December 2009 and calculated radon concentrations at each address using information from central databases regarding geology and house construction. Cox proportional hazards models were used to estimate incidence rate-ratios (IRR) and 95% confidence intervals...

  10. Quantitation of glial fibrillary acidic protein in human brain tumours

    DEFF Research Database (Denmark)

    Rasmussen, S; Bock, E; Warecka, K

    1980-01-01

    The glial fibrillary acidic protein (GFA) content of 58 human brain tumours was determined by quantitative immunoelectrophoresis, using monospecific antibody against GFA. Astrocytomas, glioblastomas, oligodendrogliomas, spongioblastomas, ependymomas and medulloblastomas contained relatively high...... amounts of GFA, up to 85 times the concentration in parietal grey substance of normal human brain. GFA was not found in neurinomas, meningiomas, adenomas of the hypophysis, or in a single case of metastasis of adenocarcinoma. Non-glial tumours of craniopharyngioma and haemangioblastoma were infiltrated...

  11. Classification of Medical Brain Images

    Institute of Scientific and Technical Information of China (English)

    Pan Haiwei(潘海为); Li Jianzhong; Zhang Wei

    2003-01-01

    Since brain tumors endanger people's living quality and even their lives, the accuracy of classification becomes more important. Conventional classifying techniques are used to deal with those datasets with characters and numbers. It is difficult, however, to apply them to datasets that include brain images and medical history (alphanumeric data), especially to guarantee the accuracy. For these datasets, this paper combines the knowledge of medical field and improves the traditional decision tree. The new classification algorithm with the direction of the medical knowledge not only adds the interaction with the doctors, but also enhances the quality of classification. The algorithm has been used on real brain CT images and a precious rule has been gained from the experiments. This paper shows that the algorithm works well for real CT data.

  12. Residential radon and brain tumour incidence in a Danish cohort.

    Directory of Open Access Journals (Sweden)

    Elvira V Bräuner

    Full Text Available BACKGROUND: Increased brain tumour incidence over recent decades may reflect improved diagnostic methods and clinical practice, but remain unexplained. Although estimated doses are low a relationship between radon and brain tumours may exist. OBJECTIVE: To investigate the long-term effect of exposure to residential radon on the risk of primary brain tumour in a prospective Danish cohort. METHODS: During 1993-1997 we recruited 57,053 persons. We followed each cohort member for cancer occurrence from enrolment until 31 December 2009, identifying 121 primary brain tumour cases. We traced residential addresses from 1 January 1971 until 31 December 2009 and calculated radon concentrations at each address using information from central databases regarding geology and house construction. Cox proportional hazards models were used to estimate incidence rate-ratios (IRR and 95% confidence intervals (CI for the risk of primary brain tumours associated with residential radon exposure with adjustment for age, sex, occupation, fruit and vegetable consumption and traffic-related air pollution. Effect modification by air pollution was assessed. RESULTS: Median estimated radon was 40.5 Bq/m(3. The adjusted IRR for primary brain tumour associated with each 100 Bq/m(3 increment in average residential radon levels was 1.96 (95% CI: 1.07; 3.58 and this was exposure-dependently higher over the four radon exposure quartiles. This association was not modified by air pollution. CONCLUSIONS: We found significant associations and exposure-response patterns between long-term residential radon exposure radon in a general population and risk of primary brain tumours, adding new knowledge to this field. This finding could be chance and needs to be challenged in future studies.

  13. Epstein-Barr virus-associated smooth muscle tumour presenting as a parasagittal brain tumour.

    Science.gov (United States)

    Ibebuike, K E; Pather, S; Emereole, O; Ndolo, P; Kajee, A; Gopal, R; Naidoo, S

    2012-11-01

    Dural-based brain tumours, apart from meningiomas, are rare. Epstein-Barr virus (EBV)-associated smooth muscle tumor (SMT) is a documented but rare disease that occurs in immunocompromized patients. These tumours may be located at unusual sites including the brain. We present a 37-year-old patient, positive for the human immunodeficiency virus (HIV), who was admitted after generalized tonic-clonic seizures. MRI and CT scan revealed a dural-based brain tumour, intraoperatively thought to be a meningioma, but with an eventual histological diagnosis of EBV-SMT. Clinically the patient was well postoperatively with a Glasgow coma scale score of 15/15 and no focal neurologic deficit. This case confirms the association between EBV and SMT in patients with HIV/acquired immunodeficiency syndrome (AIDS). It also highlights the need to include EBV-SMT in the differential diagnosis of intracranial mass lesions in patients with HIV/AIDS.

  14. A rare metastasis from a rare brain tumour

    DEFF Research Database (Denmark)

    Aabenhus, Kristine; Hahn, Christoffer Holst

    2014-01-01

    This case report presents the story of a patient with an oligodendroglioma metastasizing to the bone marrow and to lymph nodes of the neck. The patient had undergone primary brain surgery 13 years prior to the discovery of metastases and radiotherapy directed at the brain tumour two months prior........ Oligodendroglioma are rare primary brain tumours of which extraneural metastasis is even more rare. The incidence of cases like this may be increasing because of better treatment and thus longer survival of patients with oligodendroglioma....

  15. Cognitive deficits in adult patients with brain tumours.

    NARCIS (Netherlands)

    Taphoorn, M.J.B.; Klein, M.

    2004-01-01

    Cognitive function, with survival and response on brain imaging, is increasingly regarded as an important outcome measure in patients with brain tumours. This measure provides us with information on a patient's clinical situation and adverse treatment effects. Radiotherapy has been regarded as the m

  16. High resolution magic angle spinning 1H NMR of childhood brain and nervous system tumours

    Directory of Open Access Journals (Sweden)

    Davies Nigel P

    2009-02-01

    Full Text Available Abstract Background Brain and nervous system tumours are the most common solid cancers in children. Molecular characterisation of these tumours is important for providing novel biomarkers of disease and identifying molecular pathways which may provide putative targets for new therapies. 1H magic angle spinning NMR spectroscopy (1H HR-MAS is a powerful tool for determining metabolite profiles from small pieces of intact tissue and could potentially provide important molecular information. Methods Forty tissue samples from 29 children with glial and primitive neuro-ectodermal tumours were analysed using HR-MAS (600 MHz Varian gHX nanoprobe. Tumour spectra were fitted to a library of individual metabolite spectra to provide metabolite values. These values were then used in a two tailed t-test and multi-variate analysis employing a principal component analysis and a linear discriminant analysis. Classification accuracy was estimated using a leave-one-out analysis and B632+ bootstrapping. Results Glial tumours had significantly (two tailed t-test p Conclusion HR-MAS identified key differences in the metabolite profiles of childhood brain and nervous system improving the molecular characterisation of these tumours. Further investigation of the underlying molecular pathways is required to assess their potential as targets for new agents.

  17. Semi-supervised analysis of human brain tumours from partially labeled MRS information, using manifold learning models.

    Science.gov (United States)

    Cruz-Barbosa, Raúl; Vellido, Alfredo

    2011-02-01

    Medical diagnosis can often be understood as a classification problem. In oncology, this typically involves differentiating between tumour types and grades, or some type of discrete outcome prediction. From the viewpoint of computer-based medical decision support, this classification requires the availability of accurate diagnoses of past cases as training target examples. The availability of such labeled databases is scarce in most areas of oncology, and especially so in neuro-oncology. In such context, semi-supervised learning oriented towards classification can be a sensible data modeling choice. In this study, semi-supervised variants of Generative Topographic Mapping, a model of the manifold learning family, are applied to two neuro-oncology problems: the diagnostic discrimination between different brain tumour pathologies, and the prediction of outcomes for a specific type of aggressive brain tumours. Their performance compared favorably with those of the alternative Laplacian Eigenmaps and Semi-Supervised SVM for Manifold Learning models in most of the experiments.

  18. Recurrent brain tumour: the impact of illness on patient's life.

    Science.gov (United States)

    Lamperti, Elena; Pantaleo, Giuseppe; Finocchiaro, Claudia Yvonne; Silvani, Antonio; Botturi, Andrea; Gaviani, Paola; Sarno, Lucio; Salmaggi, Andrea

    2012-06-01

    Despite advances in therapies that offer improved survival rates, clinical course of brain tumours leads to a progressive functional deterioration in patients with modifications in their psychological reaction to the disease. Patients with brain tumours are rarely assessed for quality of life and psychological variables, and even fewer studies have assessed patients who have experienced a recurrence of brain tumours. Therefore, the aim of the present study is to investigate the patients with recurrent brain tumours and their reaction to the illness. We enrolled 81 patients with recurrent CNS tumours. Karnofsky Performance Status scale (KPS) was used to evaluate functional status of patients; the multidimensional aspect of quality of life was assessed through "Functional Assessment of Cancer Therapy-Brain" (FACT-Br), "Hospital Anxiety and Depression Scale" and "Psychological Distress Inventory". These were all used as tests of psychological well-being. Distress and almost all mean FACT-Br subscale scores seemed to be significantly lower in patients, in comparison with normative data. Surprisingly, the emotional well-being mean score was significantly higher in our recurrence sample than in patients with brain tumours at first diagnosis. Anxiety seemed not to be influenced by a relapse diagnosis; instead, depression was higher and differed significantly from normative data. Low correlation between KPS and FACT-Br total and some sub-scores was found. Apparent dissociation between patients' judgment on their quality of life (bad except for emotional) and their reported distress (low) is the most intriguing finding, suggesting highly preserved coping strategies in the emotional sphere, despite intact judgment and disease awareness.

  19. A reproducible brain tumour model established from human glioblastoma biopsies

    Directory of Open Access Journals (Sweden)

    Li Xingang

    2009-12-01

    Full Text Available Abstract Background Establishing clinically relevant animal models of glioblastoma multiforme (GBM remains a challenge, and many commonly used cell line-based models do not recapitulate the invasive growth patterns of patient GBMs. Previously, we have reported the formation of highly invasive tumour xenografts in nude rats from human GBMs. However, implementing tumour models based on primary tissue requires that these models can be sufficiently standardised with consistently high take rates. Methods In this work, we collected data on growth kinetics from a material of 29 biopsies xenografted in nude rats, and characterised this model with an emphasis on neuropathological and radiological features. Results The tumour take rate for xenografted GBM biopsies were 96% and remained close to 100% at subsequent passages in vivo, whereas only one of four lower grade tumours engrafted. Average time from transplantation to the onset of symptoms was 125 days ± 11.5 SEM. Histologically, the primary xenografts recapitulated the invasive features of the parent tumours while endothelial cell proliferations and necrosis were mostly absent. After 4-5 in vivo passages, the tumours became more vascular with necrotic areas, but also appeared more circumscribed. MRI typically revealed changes related to tumour growth, several months prior to the onset of symptoms. Conclusions In vivo passaging of patient GBM biopsies produced tumours representative of the patient tumours, with high take rates and a reproducible disease course. The model provides combinations of angiogenic and invasive phenotypes and represents a good alternative to in vitro propagated cell lines for dissecting mechanisms of brain tumour progression.

  20. Predicting parenting stress in caregivers of children with brain tumours.

    Science.gov (United States)

    Bennett, Emily; English, Martin William; Rennoldson, Michael; Starza-Smith, Arleta

    2013-03-01

    The purpose of the study was to identify factors that contribute to parenting stress in caregivers of children diagnosed with brain tumours. The study was cross-sectional and recruited 37 participants from a clinical database at a specialist children's hospital. Parents were sent questionnaires, which were used to measure factors related to stress in caregivers of children diagnosed with a brain tumour. Stress levels were measured using the Parenting Stress Index-Short Form (PSI/SF). Correlation analysis and multiple linear regression were used to examine the associations between parenting stress and coping styles, locus of control, parent-perceived child disability and time since diagnosis. Results revealed that 51% of parents were experiencing clinically significant levels of stress. The mean stress level of parents in the study was significantly higher than the PSI/SF norms (t = 4.7, p parenting stress. Other styles of coping, child behaviour problems and the amount of time since diagnosis were not found to be predictive of levels of parenting stress. There was a high prevalence of parenting stress in caregivers of children with a brain tumour. An external locus of control and coping by accepting responsibility increased the likelihood of elevated levels of stress. Results emphasised the importance of ongoing support for parents of children with brain tumours. Intervention might helpfully be centred on strategies to increase parents' internal locus of control. Copyright © 2012 John Wiley & Sons, Ltd.

  1. Improved classification, diagnosis and prognosis of canine round cell tumours

    NARCIS (Netherlands)

    Cangul, Taci

    2001-01-01

    As the name suggests, canine round cell tumour (RCTs) are composed of cells with a round morphology. There is some discrepancy amongst authors as to which tumours belong to this category, but most designate lymphomas, melanomas, plasmacytomas, transmissible venereal tumours (TVTs), histiocytomas, an

  2. Mobile phone use and risk of brain tumours

    Energy Technology Data Exchange (ETDEWEB)

    Lahkola, A.

    2010-05-15

    Mobile phone use has increased rapidly worldwide since the 1990's. As mobile telephones are used close to the head, the exposure to the radiofrequency radiation emitted by mobile phones has been suggested as a possible risk factor for brain tumours. The effect of mobile phone use on risk of brain tumours, particularly gliomas and meningiomas as well as acoustic neuromas, was evaluated using both a case-control approach and a meta-analysis. In addition, one of the most important sources of error in a case-control study, selection bias due to differential participation, was assessed in a subset of the case-control data. The risk of glioma and meningioma in relation to mobile phone use was investigated in population-based case-control studies conducted in five North European countries. All these countries used a common protocol and were included in a multinational study on mobile phone use and brain tumours, the INTERPHONE study, coordinated by the International Agency for Research on Cancer (IARC). Cases (1,521 gliomas and 1,209 meningiomas) were identified mostly from hospitals and controls (3,299) from national population registers or general practitioners' patient lists. Detailed history of mobile phone use was obtained in personal interviews. Mobile phone use was assessed using several exposure indicators, such as regular use (phone use at least once a week for at least six months), duration of use as well as cumulative number of hours and calls. To comprehensively evaluate the effect of mobile phone use on risk of brain tumours, the existing evidence from the epidemiological studies published on the issue was combined using meta-analysis. In the analysis, a pooled estimate was calculated for all brain tumours combined, and also separately for the three most common tumour types, glioma, meningioma and acoustic neuroma using inverse variance-weighted method. Pooled estimate was also obtained for different telephone types (NMT and GSM) and by the location

  3. Classification of tubulo-papillary renal cortical tumours using estimates of nuclear volume

    DEFF Research Database (Denmark)

    Brooks, B; Sørensen, Flemming Brandt; Olsen, S

    1993-01-01

    The classification of renal cortical tumours is problematic, with no clear division of benign from malignant tumours. Unbiased stereological estimates of volume-weighted nuclear volume (nuclear vv) were obtained by point sampling of nuclear intercepts in a retrospective study of 36 variably sized...

  4. Pancreatic neuroendocrine tumour: Correlation of apparent diffusion coefficient or WHO classification with recurrence-free survival.

    Science.gov (United States)

    Kim, Mimi; Kang, Tae Wook; Kim, Young Kon; Kim, Seong Hyun; Kwon, Wooil; Ha, Sang Yun; Ji, Sang A

    2016-03-01

    To evaluate the correlation between grade of pancreatic neuroendocrine tumours (pNETs) based on the 2010 World Health Organization (WHO) classification and the apparent diffusion coefficient (ADC), and to assess whether the ADC value and WHO classification can predict recurrence-free survival (RFS) after surgery for pNETs. This retrospective study was approved by the Institutional Review Board. The requirement for informed consent was waived. Between March 2009 and November 2014, forty-nine patients who underwent magnetic resonance (MR) imaging with diffusion-weighted image and subsequent surgery for single pNETs were included. Correlations among qualitative MR imaging findings, quantitative ADC values, and WHO classifications were assessed. An ordered logistic regression test was used to control for tumour size as a confounding factor. The association between ADC value (or WHO classification) and RFS was analysed. All tumors (n=49) were classified as low- (n=29, grade 1), intermediate- (n=17, grade 2), and high-grade (n=3, grade 3), respectively. The mean ADC of pNETs was moderately negatively correlated with WHO classification before and after adjustment for tumour size (ρ=-0.64, pcorrelated with WHO tumour grade, regardless of tumour size. However, the WHO tumour classification of pNET may be more suitable for predicting RFS than the ADC value. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  5. Endocrine disorders following treatment of childhood brain tumours.

    OpenAIRE

    Livesey, E A; Hindmarsh, P C; Brook, C G; Whitton, A. C.; Bloom, H. J.; Tobias, J. S.; Godlee, J. N.; Britton, J.

    1990-01-01

    We have studied the long-term endocrine effects of treatment on 144 children treated for brain tumours. All received cranial irradiation, 86 also received spinal irradiation and 34 chemotherapy. Almost all patients (140 of 144) had evidence of growth hormone insufficiency. Treatment with growth hormone was effective in maintaining normal growth but could not restore a deficit incurred by delay in instituting treatment. The effect of spinal irradiation on spinal growth was not corrected by gro...

  6. STUDY OF BRAIN TUMOURS BY NOVE L MAGNETIC RESONANCE TECHNIQUE

    OpenAIRE

    Mohammad Shamim; Reyaz; Anju; Dinesh Kumar; Paricharak

    2015-01-01

    In the present study , thirty patients in the age range of 22 to 63 years of age were included after being diagnosed to be having brain tumour on CT scan or conventional MRI. In addition DWI , MRS , and PWI were carried out i n these patients. All the patients with suspicious malignant lesions were then subjected to FDG - PET examination . Histopathological correlation was obtained in all the patients to serve as gold standard against which other m...

  7. Automatic Brain Tumour Detection Using Symmetry Information

    Directory of Open Access Journals (Sweden)

    Mr.Mubarak Jamadar

    2015-07-01

    Full Text Available Image segmentation is used to separate an image into several “meaningful” parts. Image segmentation is identification of homogeneous regions in the image. Many algorithms have been elaborated for gray scale images. However, the problem of segmentation for color images, which convey much more information about objects in scenes, has received much less attention of scientific community. While several surveys of monochrome image segmentation techniques were published, similar surveys for color images did not emerge. Image segmentation is a process of pixel classification. An image is segmented into subsets by assigning individual pixels to classes. It is an important step towards pattern detection and recognition. Segmentation is one of the first steps in image analysis. It refers to the process of partitioning a digital image into multiple regions (sets of pixels. Each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. The level of segmentation is decided by the particular characteristics of the problem being considered. Image segmentation could be further used for object matching between two images. An object of interest is specified in the first image by using the segmentation result of that image; then the specified object is matched in the second image by using the segmentation result of that image

  8. Alterations of monocarboxylate transporter densities during hypoxia in brain and breast tumour cells

    DEFF Research Database (Denmark)

    Cheng, Chang; Edin, Nina F Jeppesen; Lauritzen, Knut H

    2012-01-01

    Tumour cells are characterized by aerobic glycolysis, which provides biomass for tumour proliferation and leads to extracellular acidification through efflux of lactate via monocarboxylate transporters (MCTs). Deficient and spasm-prone tumour vasculature causes variable hypoxia, which favours...... tumour cell survival and metastases. Brain metastases frequently occur in patients with advanced breast cancer.Effective treatment strategies are therefore needed against brain metastasis from breast carcinoma....

  9. Pharmacokinetics and pharmacodynamics of propofol : Changes in patients with frontal brain tumours

    NARCIS (Netherlands)

    Sahinovic, M. M.; Eleveld, D. J.; Miyabe-Nishiwaki, T.; Struys, M. M. R. F.; Absalom, A. R.

    2017-01-01

    Background: Models of propofol pharmacokinetics and pharmacodynamics developed in patients without brain pathology are widely used for target-controlled infusion (TCI) during brain tumour excision operations. The goal of this study was to determine if the presence of a frontal brain tumour

  10. Automated identification of brain tumours from single MR images based on segmentation with refined patient-specific priors

    Directory of Open Access Journals (Sweden)

    Ana eSanjuán

    2013-12-01

    Full Text Available Brain tumours can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI procedure which enables brain tumour identification from single MR images. Our method rests on (A a modified segmentation-normalisation procedure with an explicit extra prior for the tumour and (B an outlier detection procedure for abnormal voxel (i.e. tumour classification. To minimise tissue misclassification, the segmentation-normalisation procedure requires prior information of the tumour location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers’ manual tracings. The automated procedure identified the tumours successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03. The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behaviour mapping studies, or when lesion identification and/or spatial normalisation are problematic.

  11. Development of luciferase tagged brain tumour models in mice for chemotherapy intervention studies.

    Science.gov (United States)

    Kemper, E M; Leenders, W; Küsters, B; Lyons, S; Buckle, T; Heerschap, A; Boogerd, W; Beijnen, J H; van Tellingen, O

    2006-12-01

    The blood-brain barrier (BBB) is considered one of the major causes for the low efficacy of cytotoxic compounds against primary brain tumours. The aim of this study was to develop intracranial tumour models in mice featuring intact or locally disrupted BBB properties, which can be used in testing chemotherapy against brain tumours. These tumours were established by intracranial injection of suspensions of different tumour cell lines. All cell lines had been transfected with luciferase to allow non-invasive imaging of tumour development using a super-cooled CCD-camera. Following their implantation, tumours developed which displayed the infiltrative, invasive or expansive growth patterns that are also found in primary brain cancer or brain metastases. Contrast-enhanced magnetic resonance imaging showed that the Mel57, K1735Br2 and RG-2 lesions grow without disruption of the BBB, whereas the BBB was leaky in the U87MG and VEGF-A-transfected Mel57 lesions. This was confirmed by immunohistochemistry. Bioluminescence measurements allowed the visualisation of tumour burden already within 4 days after injection of the tumour cells. The applicability of our models for performing efficacy studies was demonstrated in an experiment using temozolomide as study drug. In conclusion, we have developed experimental brain tumour models with partly disrupted, or completely intact BBB properties. In vivo imaging by luciferase allows convenient follow-up of tumour growth and these models will be useful for chemotherapeutic intervention studies.

  12. Proton magnetic resonance spectroscopy in brain tumours: clinical applications

    Energy Technology Data Exchange (ETDEWEB)

    Burtscher, I.M.; Holtaas, S. [Lund Univ. (Sweden). Dept. of Diagnostic Radiology

    2001-05-01

    Parallel to the rapid development of clinical MRI, MR spectroscopy (MRS) has, after starting as an analytical tool used in chemistry and physics, evolved to a noninvasive clinical examination. Most common neuroradiological diagnostic indications for MRS are functional inborn errors, neonatal hypoxia, ischaemia, metabolic diseases, white matter and degenerative diseases, epilepsy, inflammation, infections and intracranial neoplasm. Compared to CT and MRI, well-established morphological diagnostic tools, MRS provides information on the metabolic state of brain tissue. We review the clinical impact of MRS in diagnosis of tumours and their differentiation from non-neoplastic lesions. (orig.)

  13. Strategy in clinical practice for classification of unselected colorectal tumours based on mismatch repair deficiency

    DEFF Research Database (Denmark)

    Jensen, Lars Henrik; Lindebjerg, J; Byriel, L

    2007-01-01

    nonpolyposis colon cancer or Lynch syndrome), but most are epigenetic changes of sporadic origin. The aim of this study was to define a robust and inexpensive strategy for such classification in clinical practice. Method Tumours and blood samples from 262 successive patients with colorectal adenocarcinomas...... to be sporadic. Results Thirty-nine (14.9%) of the tumours showed MMR deficiency by IHC or by microsatellite analysis. Sporadic inactivation by methylation of MLH1 promoter was found in 35 patients whereby the BRAF activating V600E mutation, indicating sporadic origin, was found in 32 tumours. On the basis...

  14. Small Intestinal Tumours: An Overview on Classification, Diagnosis, and Treatment

    Directory of Open Access Journals (Sweden)

    Chiara Notaristefano

    2014-12-01

    Full Text Available The small intestinal neoplasia group includes different types of lesions and are a relatively rare event, accounting for only 3-6% of all gastrointestinal (GI neoplasms and 1-3% of all GI malignancies. These lesions can be classified as epithelial and mesenchymal, either benign or malignant. Mesenchymal tumours include stromal tumours (GIST and other neoplasms that might arise from soft tissue throughout the rest of the body (lipomas, leiomyomas and leiomyosarcomas, fibromas, desmoid tumours, and schwannomas. Other lesions occurring in the small bowel are carcinoids, lymphomas, and melanomas. To date, carcinoids and GIST are reported as the most frequent malignant lesions occurring in the small bowel. Factors that predispose to the development of malignant lesions are different, and they may be hereditary (Peutz-Jeghers syndrome, familial adenomatous polyposis, hereditary non-polyposis colorectal cancer, neuroendocrine neoplasia Type 1, von Hippel-Lindau disease, and neurofibromatosis Type 1, acquired (sporadic colorectal cancer and small intestine adenomas, coeliac disease, Crohn’s disease, or environmental (diet, tobacco, and obesity. Small bowel tumours present with different and sometimes nonspecific symptoms, and a prompt diagnosis is not always so easily performed. Diagnostic tools, that may be both radiological and endoscopic, possess specificity and sensitivity, as well as different roles depending on the type of lesion. Treatment of these lesions may be different and, in recent years, new therapies have enabled an improvement in life expectancy.

  15. Thallium uptake and biological behaviour in childhood brain tumours

    Energy Technology Data Exchange (ETDEWEB)

    Bernard, E.J.; Howman-Giles, R.; Kellie, S.; Uren, R.F. [Royal Alexandra Hospital for Children, Sydney, NSW (Australia)

    1998-03-01

    Full text: The histopathological grade and radiological appearance of the diverse cerebral neoplasms in childhood frequently poorly reflect their biological behaviour. We examined thallium accumulation prior to treatment (and in several cases, at intervals there after) in 13 children to determine its usefulness as a tumour marker. 23 SPECT studies were acquired 20 minutes after the injection of 1-3 mCi of {sup 201}TI. Thallium index (TI), the ratio of counts in tumour/normal brain, was calculated. No uptake was seen in two patients (pts) with a Grade 1 cerebellar astrocytomas (disease free at 4/12 f/u). Three pts with medulloblastomas were studied. One pt showed intense uptake (Tl =12). His tumour (proliferative antigen stain Ki67 = 50%) recurred early after debulking surgery (Tl +ve prior to CT or MRI changes). The second pt was imaged at relapse (Ki67 = 60%) and showed intense uptake, Tl = 17. The third pt showed lower level uptake (Tl = 2), Ki67 = 5%, and is disease-free at 5/12 (as per {sup 201}TI and MRI). One pt with a Grade 1 brainstem glioma showed Tl = 5 and has progressed rapidly despite low grade histology. Four pts with chiasmatic-hypothalamic gliomas have been studied. Although these neoplasms are usually low grade histologically, their growth properties vary greatly. Two pts with Tl<2.5 have been conservatively managed because of slow tumour growth. The other two pts have Tl>3.5 and have required aggressive treatment for rapid disease progression. One pt with a large pilocytic astrocytoma of the optic chiasm showed Tl = 9.5. Active treatment was not undertaken. One pt with a pineal germ cell tumour showed avid {sup 201}TI uptake (Tl not performed) and has had two normal studies, and is clinically well, since BMT. Avid {sup 201}TI uptake also seen in one pt with cerebral neuroblastoma. (Died at 8/12 after Dx.) Thus, {sup 201}TI accumulates in histologically diverse paediatric neoplasms. The Tl appears to reflect biological behaviour in the limited

  16. Value of C-11-methionine PET in imaging brain tumours and metastases

    NARCIS (Netherlands)

    Glaudemans, Andor W J M; Enting, Roeline; Heesters, Martinus; Dierckx, Rudi A J O; van Rheenen, Ronald W J; Walenkamp, Annemiek M E; Slart, Riemer H J A

    2013-01-01

    C-11-methionine (MET) is the most popular amino acid tracer used in PET imaging of brain tumours. Because of its characteristics, MET PET provides a high detection rate of brain tumours and good lesion delineation. This review focuses on the role of MET PET in imaging cerebral gliomas. The Introduct

  17. Development of luciferase tagged brain tumour models in mice for chemotherapy intervention studies.

    NARCIS (Netherlands)

    Kemper, E.M.; Leenders, W.P.J.; Kusters, B.; Lyons, S.; Buckle, T.; Heerschap, A.; Boogerd, W.; Beijnen, J.H.; Tellingen, O.

    2006-01-01

    The blood-brain barrier (BBB) is considered one of the major causes for the low efficacy of cytotoxic compounds against primary brain tumours. The aim of this study was to develop intracranial tumour models in mice featuring intact or locally disrupted BBB properties, which can be used in testing ch

  18. A PROSPECTIVE HISTOPATHOLOGICAL-BASED STUDY OF BRAIN TUMOURS IN A REFERRAL CENTRE

    Directory of Open Access Journals (Sweden)

    Prathima Gujjaru

    2016-07-01

    Full Text Available BACKGROUND Brain neoplasms occur at all ages and account for around 2-3 percent of all deaths in adults. In children, the frequency increases to more than twenty percent. In children, it forms the second most common type of malignancy. Most of the tumours encountered are not related to any identifiable risk factors except for irradiation and some hereditary syndromes like subependymal giant cell astrocytoma, glioblastoma multiforme, cerebellar haemangioblastoma, meningioma, Schwannoma of 7 th cranial nerve. Gliomas constitute fifty percent of the brain tumours and sixty percent of all gliomas are glioblastoma multiforme. Meningiomas constitute twenty percent and cerebral metastasis is seen in fifteen percent of the cases. Seventy percent of supratentorial tumours are found in adults and seventy percent of brain tumours in children are infratentorial. The three common tumours of cerebellum are medulloblastoma, haemangioblastoma and juvenile pilocytic astrocytoma. Brain tumours are space occupying lesions and cause compression and destruction of adjacent structures, brain oedema (Peritumoural tissue, infarction and ischaemia of brain by compressing/infiltrating cerebral blood vessels, obstruction of CSF flow causing hydrocephalus, and rise in intracranial pressure with herniations. Tumours can undergo ischaemic necrosis and necrotic tumours tend to bleed. Brain tumours generally do not metastasise. Schwannoma and meningioma are benign tumours. Medulloblastoma of childhood may have drop metastasis via CSF. A sincere effort has been put in this study to identify the incidence of each variety of brain tumour among the fifty confirmed and identified cases of brain tumours. METHODS The age range of the cases in present study was 5-72 years with a mean age of occurrence of 44.11 years and the peak age group affected were in the 3 rd and 4 th decades. Cerebral hemisphere was the commonest site for intracranial tumours. RESULT In the present study, fifty

  19. Perioperative intensive care in patients with brain tumours

    Directory of Open Access Journals (Sweden)

    Mariana A. Aquafredda

    2011-04-01

    Full Text Available The surgery of brain tumours is not free from complications, above all taking into account that today the patients operated are even older and with multiple comorbidities associated. The multidisciplinary preoperative evaluation aims at minimising the risks; nevertheless this evaluation has not yet been defined and is not based on a strong evidence. The detailed clinical history, the physical examination including functional status and the neuroimaging are the fundamental pillars.The more critical complications occur in the immediate postoperative period: cerebral oedema, postoperative haemorrhage, intracranial hypertension and convulsions; other complications, such as pulmonary thromboembolism or infections, develop lately but are not less severe. Every surgical approach has its own complications in addition to the ones common to the whole neurosurgery.

  20. Guiding intracortical brain tumour cells to an extracortical cytotoxic hydrogel using aligned polymeric nanofibres

    Science.gov (United States)

    Jain, Anjana; Betancur, Martha; Patel, Gaurangkumar D.; Valmikinathan, Chandra M.; Mukhatyar, Vivek J.; Vakharia, Ajit; Pai, S. Balakrishna; Brahma, Barunashish; MacDonald, Tobey J.; Bellamkonda, Ravi V.

    2014-03-01

    Glioblastoma multiforme is an aggressive, invasive brain tumour with a poor survival rate. Available treatments are ineffective and some tumours remain inoperable because of their size or location. The tumours are known to invade and migrate along white matter tracts and blood vessels. Here, we exploit this characteristic of glioblastoma multiforme by engineering aligned polycaprolactone (PCL)-based nanofibres for tumour cells to invade and, hence, guide cells away from the primary tumour site to an extracortical location. This extracortial sink is a cyclopamine drug-conjugated, collagen-based hydrogel. When aligned PCL-nanofibre films in a PCL/polyurethane carrier conduit were inserted in the vicinity of an intracortical human U87MG glioblastoma xenograft, a significant number of human glioblastoma cells migrated along the aligned nanofibre films and underwent apoptosis in the extracortical hydrogel. Tumour volume in the brain was significantly lower following insertion of aligned nanofibre implants compared with the application of smooth fibres or no implants.

  1. Combined radiotherapy and chemotherapy for high-grade brain tumours

    Science.gov (United States)

    Barazzuol, Lara

    Glioblastoma (GBM) is the most common primary brain tumour in adults and among the most aggressive of all tumours. For several decades, the standard care of GBM was surgical resection followed by radiotherapy alone. In 2005, a landmark phase III clinical trial coordinated by the European Organization for Research and Treatment of Cancer (EORTC) and the National Cancer Institute of Canada (NCIC) demonstrated the benefit of radiotherapy with concomitant and adjuvant temozolomide (TMZ) chemotherapy. With TMZ, the median life expectancy in optimally managed patients is still only 12-14 months, with only 25% surviving 24 months. There is an urgent need for new therapies in particular in those patients whose tumour has an unmethylated methylguanine methyltransferase gene (MGMT) promoter, which is a predictive factor of benefit from TMZ. In this dissertation, the nature of the interaction between TMZ and radiation is investigated using both a mathematical model, based on in vivo population statistics of survival, and in vitro experimentation on a panel of human GBM cell lines. The results show that TMZ has an additive effect in vitro and that the population-based model may be insufficient in predicting TMZ response. The combination of TMZ with particle therapy is also investigated. Very little preclinical data exists on the effects of charged particles on GBM cell lines as well as on the concomitant application of chemotherapy. In this study, human GBM cells are exposed to 3 MeV protons and 6 MeV alpha particles in concomitance with TMZ. The results suggest that the radiation quality does not affect the nature of the interaction between TMZ and radiation, showing reproducible additive cytotoxicity. Since TMZ and radiation cause DNA damage in cancer cells, there has been increased attention to the use of poly(ADP-ribose) polymerase (PARP) inhibitors. PARP is a family of enzymes that play a key role in the repair of DNA breaks. In this study, a novel PARP inhibitor, ABT-888

  2. GLCM textural features for Brain Tumor Classification

    Directory of Open Access Journals (Sweden)

    N S Zulpe

    2012-05-01

    Full Text Available Automatic recognition system for medical images is challenging task in the field of medical image processing. Medical images acquired from different modalities such as Computed Tomography (CT, Magnetic Resonance Imaging (MRI, etc which are used for the diagnosis purpose. In the medical field, brain tumor classification is very important phase for the further treatment. Human interpretation of large number of MRI slices (Normal or Abnormal may leads to misclassification hence there is need of such a automated recognition system, which can classify the type of the brain tumor. In this research work, we used four different classes of brain tumors and extracted the GLCM based textural features of each class, and applied to two-layered Feed forward Neural Network, which gives 97.5% classification rate.

  3. Cellular immortality in brain tumours: an integration of the cancer stem cell paradigm.

    Science.gov (United States)

    Rahman, Ruman; Heath, Rachel; Grundy, Richard

    2009-04-01

    Brain tumours are a diverse group of neoplasms that continue to present a formidable challenge in our attempt to achieve curable intervention. Our conceptual framework of human brain cancer has been redrawn in the current decade. There is a gathering acceptance that brain tumour formation is a phenotypic outcome of dysregulated neurogenesis, with tumours viewed as abnormally differentiated neural tissue. In relation, there is accumulating evidence that brain tumours, similar to leukaemia and many solid tumours, are organized as a developmental hierarchy which is maintained by a small fraction of cells endowed with many shared properties of tissue stem cells. Proof that neurogenesis persists throughout adult life, compliments this concept. Although the cancer cell of origin is unclear, the proliferative zones that harbour stem cells in the embryonic, post-natal and adult brain are attractive candidates within which tumour-initiation may ensue. Dysregulated, unlimited proliferation and an ability to bypass senescence are acquired capabilities of cancerous cells. These abilities in part require the establishment of a telomere maintenance mechanism for counteracting the shortening of chromosomal termini. A strategy based upon the synthesis of telomeric repeat sequences by the ribonucleoprotein telomerase, is prevalent in approximately 90% of human tumours studied, including the majority of brain tumours. This review will provide a developmental perspective with respect to normal (neurogenesis) and aberrant (tumourigenesis) cellular turnover, differentiation and function. Within this context our current knowledge of brain tumour telomere/telomerase biology will be discussed with respect to both its developmental and therapeutic relevance to the hierarchical model of brain tumourigenesis presented by the cancer stem cell paradigm.

  4. Glioblastoma brain tumours: estimating the time from brain tumour initiation and resolution of a patient survival anomaly after similar treatment protocols.

    Science.gov (United States)

    Murray, J D

    2012-01-01

    A practical mathematical model for glioblastomas (brain tumours), which incorporates the two key parameters of tumour growth, namely the cancer cell diffusion and the cell proliferation rate, has been shown to be clinically useful and predictive. Previous studies explain why multifocal recurrence is inevitable and show how various treatment scenarios have been incorporated in the model. In most tumours, it is not known when the cancer started. Based on patient in vivo parameters, obtained from two brain scans, it is shown how to estimate the time, after initial detection, when the tumour started. This is an input of potential importance in any future controlled clinical study of any connection between cell phone radiation and brain tumour incidence. It is also used to estimate more accurately survival times from detection. Finally, based on patient parameters, the solution of the model equation of the tumour growth helps to explain why certain patients live longer than others after similar treatment protocols specifically surgical resection (removal) and irradiation.

  5. Radiotherapy of primary brain tumours in the region of the third ventricle

    NARCIS (Netherlands)

    Heesters, M A; Struikmans, H

    1990-01-01

    Patients (n = 18) with a primary brain tumour near the third ventricle and treated by radiotherapy were retrospectively analysed. Four different subgroups of patients, according to the histology (germ cell tumours, astrocytomas, other histologies, no histology) were separately discussed. Third ventr

  6. Brain perfusion CT compared with ¹⁵O-H₂O PET in patients with primary brain tumours

    DEFF Research Database (Denmark)

    Grüner, Julie Marie; Paamand, Rune Tore; Kosteljanetz, Michael;

    2012-01-01

    Perfusion CT (PCT) measurements of regional cerebral blood flow (rCBF) have been proposed as a fast and easy method for identifying angiogenically active tumours. In this study, quantitative PCT rCBF measurements in patients with brain tumours were compared to the gold standard PET rCBF with (15)O...

  7. Targeting breast to brain metastatic tumours with death receptor ligand expressing therapeutic stem cells.

    Science.gov (United States)

    Bagci-Onder, Tugba; Du, Wanlu; Figueiredo, Jose-Luiz; Martinez-Quintanilla, Jordi; Shah, Khalid

    2015-06-01

    Characterizing clinically relevant brain metastasis models and assessing the therapeutic efficacy in such models are fundamental for the development of novel therapies for metastatic brain cancers. In this study, we have developed an in vivo imageable breast-to-brain metastasis mouse model. Using real time in vivo imaging and subsequent composite fluorescence imaging, we show a widespread distribution of micro- and macro-metastasis in different stages of metastatic progression. We also show extravasation of tumour cells and the close association of tumour cells with blood vessels in the brain thus mimicking the multi-foci metastases observed in the clinics. Next, we explored the ability of engineered adult stem cells to track metastatic deposits in this model and show that engineered stem cells either implanted or injected via circulation efficiently home to metastatic tumour deposits in the brain. Based on the recent findings that metastatic tumour cells adopt unique mechanisms of evading apoptosis to successfully colonize in the brain, we reasoned that TNF receptor superfamily member 10A/10B apoptosis-inducing ligand (TRAIL) based pro-apoptotic therapies that induce death receptor signalling within the metastatic tumour cells might be a favourable therapeutic approach. We engineered stem cells to express a tumour selective, potent and secretable variant of a TRAIL, S-TRAIL, and show that these cells significantly suppressed metastatic tumour growth and prolonged the survival of mice bearing metastatic breast tumours. Furthermore, the incorporation of pro-drug converting enzyme, herpes simplex virus thymidine kinase, into therapeutic S-TRAIL secreting stem cells allowed their eradication post-tumour treatment. These studies are the first of their kind that provide insight into targeting brain metastasis with stem-cell mediated delivery of pro-apoptotic ligands and have important clinical implications.

  8. Medical exposure to ionising radiation and the risk of brain tumours

    DEFF Research Database (Denmark)

    Blettner, Maria; Schlehofer, Brigitte; Samkange-Zeeb, Florence

    2007-01-01

    BACKGROUND: The role of exposure to low doses of ionising radiation in the aetiology of brain tumours has yet to be clarified. The objective of this study was to investigate the association between medically or occupationally related exposure to ionising radiation and brain tumours. METHODS: We...... used self-reported medical and occupational data collected during the German part of a multinational case-control study on mobile phone use and the risk of brain tumours (Interphone study) for the analyses. RESULTS: For any exposure to medical ionising radiation we found odds ratios (ORs) of 0.63 (95...... regions. CONCLUSION: We did not find any significant increased risk of brain tumours for exposure to medical ionising radiation....

  9. Tissue tracking: applications for brain MRI classification

    Science.gov (United States)

    Melonakos, John; Gao, Yi; Tannenbaum, Allen

    2007-03-01

    Bayesian classification methods have been extensively used in a variety of image processing applications, including medical image analysis. The basic procedure is to combine data-driven knowledge in the likelihood terms with clinical knowledge in the prior terms to classify an image into a pre-determined number of classes. In many applications, it is difficult to construct meaningful priors and, hence, homogeneous priors are assumed. In this paper, we show how expectation-maximization weights and neighboring posterior probabilities may be combined to make intuitive use of the Bayesian priors. Drawing upon insights from computer vision tracking algorithms, we cast the problem in a tissue tracking framework. We show results of our algorithm on the classification of gray and white matter along with surrounding cerebral spinal fluid in brain MRI scans. We show results of our algorithm on 20 brain MRI datasets along with validation against expert manual segmentations.

  10. Primary malignant rhabdoid tumour of the brain in an adult

    Energy Technology Data Exchange (ETDEWEB)

    Arrazola, J.; Pedrosa, I.; Mendez, R. [Radiology Department, Hospital Clinico San Carlos, Madrid (Spain); Saldana, C. [Neurosurgery Department, Hospital Clinico San Carlos, Madrid (Spain); Scheithauer, B.W. [Division of Anatomic Pathology, Mayo Clinic, Rochester, MN (United States); Martinez, A. [Anatomic Pathology Department, Hospital Clinico San Carlos, Madrid (Spain)

    2000-05-01

    We report a mass in the left cerebral hemisphere of a 20-year-old man. Histological, ultrastructural and immunohistochemical features of the tumour were consistent with primary malignant rhabdoid tumour. The age of presentation, imaging features prior to histological examination, and prognosis in this case were unusual. (orig.)

  11. Brain perfusion CT compared with {sup 15}O-H{sub 2}O PET in patients with primary brain tumours

    Energy Technology Data Exchange (ETDEWEB)

    Gruener, Julie Marie; Paamand, Rune; Hoejgaard, Liselotte; Law, Ian [University of Copenhagen, Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen (Denmark); Kosteljanetz, Michael [University of Copenhagen, Department of Neurosurgery, Rigshospitalet, Copenhagen (Denmark); Broholm, Helle [University of Copenhagen, Department of Neuropathology, Rigshospitalet, Copenhagen (Denmark)

    2012-11-15

    Perfusion CT (PCT) measurements of regional cerebral blood flow (rCBF) have been proposed as a fast and easy method for identifying angiogenically active tumours. In this study, quantitative PCT rCBF measurements in patients with brain tumours were compared to the gold standard PET rCBF with {sup 15}O-labelled water ({sup 15}O-H{sub 2}O). On the same day within a few hours, rCBF was measured in ten adult patients with treatment-naive primary brain tumours, twice using {sup 15}O-H{sub 2}O PET and once with PCT performed over the central part of the tumour. Matching rCBF values in tumour and contralateral healthy regions of interest were compared. PCT overestimated intratumoural blood flow in all patients with volume-weighted mean rCBF values of 28.2 {+-} 18.8 ml min{sup -1} 100 ml{sup -1} for PET and 78.9 {+-} 41.8 ml min{sup -1} 100 ml{sup -1} for PCT. There was a significant method by tumour grade interaction with a significant tumour grade rCBF difference for PCT of 32.9 {+-} 15.8 ml min{sup -1} 100 ml{sup -1} for low-grade (WHO I + II) and 81.5 {+-} 15.4 ml min{sup -1} 100 ml{sup -1} for high-grade (WHO III + IV) tumours, but not for PET. The rCBF PCT and PET correlation was only significant within tumours in two patients. Although intratumoural blood flow measured by PCT may add valuable information on tumour grade, the method cannot substitute quantitative measurements of blood flow by PET and {sup 15}O-H{sub 2}O PET in brain tumours. (orig.)

  12. The 2016 World Health Organization Classification of tumours of the Central Nervous System: what the paediatric neuroradiologist needs to know.

    Science.gov (United States)

    Chhabda, Sahil; Carney, Olivia; D'Arco, Felice; Jacques, Thomas S; Mankad, Kshitij

    2016-10-01

    The recently published 2016 World Health Organization (WHO) classification of tumours of the Central Nervous System (CNS) introduces a number of significant changes from the previous edition. Based on an improved understanding of the genetic and molecular basis of tumorigenesis there has been a shift towards defining tumours by means of these characteristics in addition to their histological features, thus providing an integrated diagnosis. In this article, we will provide a concise overview of the salient changes in the 2016 WHO classification of tumours of the CNS that are of relevance to the paediatric neuroradiologist when it comes to day-to-day reporting.

  13. Deep learning for brain tumor classification

    Science.gov (United States)

    Paul, Justin S.; Plassard, Andrew J.; Landman, Bennett A.; Fabbri, Daniel

    2017-03-01

    Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is to apply deep learning methods to classify brain images with different tumor types: meningioma, glioma, and pituitary. A dataset was publicly released containing 3,064 T1-weighted contrast enhanced MRI (CE-MRI) brain images from 233 patients with either meningioma, glioma, or pituitary tumors split across axial, coronal, or sagittal planes. This research focuses on the 989 axial images from 191 patients in order to avoid confusing the neural networks with three different planes containing the same diagnosis. Two types of neural networks were used in classification: fully connected and convolutional neural networks. Within these two categories, further tests were computed via the augmentation of the original 512×512 axial images. Training neural networks over the axial data has proven to be accurate in its classifications with an average five-fold cross validation of 91.43% on the best trained neural network. This result demonstrates that a more general method (i.e. deep learning) can outperform specialized methods that require image dilation and ring-forming subregions on tumors.

  14. Quantitative MR imaging and spectroscopy of brain tumours: a step forward?

    Energy Technology Data Exchange (ETDEWEB)

    Wagnerova, Dita; Herynek, Vit; Dezortova, Monika; Jiru, Filip; Skoch, Antonin; Hajek, Milan [Institute for Clinical and Experimental Medicine, Department of Diagnostic and Interventional Radiology, Prague (Czech Republic); Malucelli, Alberto; Bartos, Robert; Sames, Martin [JE Purkyne University and Masaryk Hospital, Department of Neurosurgery, Usti nad Labem (Czech Republic); Vymazal, Josef [Na Homolce Hospital, Department of Radiology, Prague (Czech Republic); Urgosik, Dusan [Na Homolce Hospital, Stereotactic and Radiation Neurosurgery, Prague (Czech Republic); Syrucek, Martin [Na Homolce Hospital, Department of Pathology, Prague (Czech Republic)

    2012-11-15

    A prospective quantitative MR study of brain tumours was performed to show the potential of combining different MR techniques to distinguish various disease processes in routine clinical practice. Twenty-three patients with various intracranial tumours before treatment (diagnosis confirmed by a biopsy) and 59 healthy subjects were examined on a 3-T system by conventional MR imaging, 1H spectroscopic imaging, diffusion tensor imaging and T2 relaxometry. Metabolic concentrations and their ratios, T2 relaxation times and mean diffusivities were calculated and correlated on a pixel-by-pixel basis and compared to control data. Different tumour types and different localisations revealed specific patterns of correlations between metabolic concentrations and mean diffusivity or T2 relaxation times. The patterns distinguish given tissue states in the examined area: healthy tissue, tissue infiltrated by tumour, active tumour, oedema infiltrated by tumour, oedema, etc. This method is able to describe the complexity of a highly heterogeneous tissue in the tumour and its vicinity, and determines crucial parameters for tissue differentiation. A combination of different MR parameters on a pixel-by-pixel basis in individual patients enables better identification of the tumour type, direction of proliferation and assessment of the tumour extension. (orig.)

  15. Adaptive multiclass classification for brain computer interfaces.

    Science.gov (United States)

    Llera, A; Gómez, V; Kappen, H J

    2014-06-01

    We consider the problem of multiclass adaptive classification for brain-computer interfaces and propose the use of multiclass pooled mean linear discriminant analysis (MPMLDA), a multiclass generalization of the adaptation rule introduced by Vidaurre, Kawanabe, von Bünau, Blankertz, and Müller (2010) for the binary class setting. Using publicly available EEG data sets and tangent space mapping (Barachant, Bonnet, Congedo, & Jutten, 2012) as a feature extractor, we demonstrate that MPMLDA can significantly outperform state-of-the-art multiclass static and adaptive methods. Furthermore, efficient learning rates can be achieved using data from different subjects.

  16. {sup 1}H magnetic resonance spectroscopy in the diagnosis of paediatric low grade brain tumours

    Energy Technology Data Exchange (ETDEWEB)

    Orphanidou-Vlachou, E., E-mail: eleni.orphanidou@googlemail.com [School of Cancer Sciences, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT (United Kingdom); Birmingham Children' s Hospital NHS Foundation Trust, Whittall Street, Birmingham, B4 6NH (United Kingdom); Auer, D., E-mail: dorothee.auer@nottingham.ac.uk [Division of Academic Radiology, School of Medical and Surgical Sciences, The University of Nottingham, University Park, Nottingham, NG7 2RD (United Kingdom); Children' s Brain Tumour Research Centre, Queens Medical Centre, University of Nottingham (United Kingdom); Brundler, M.A., E-mail: marie-anne.brundler@bch.nhs.uk [Birmingham Children' s Hospital NHS Foundation Trust, Whittall Street, Birmingham, B4 6NH (United Kingdom); Davies, N.P., E-mail: nigel.davies@nhs.net [School of Cancer Sciences, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT (United Kingdom); Birmingham Children' s Hospital NHS Foundation Trust, Whittall Street, Birmingham, B4 6NH (United Kingdom); Department of Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Mindelsohn Way, Edgbaston, Birmingham, B15 2WB (United Kingdom); Jaspan, T., E-mail: tim.jaspan@nuh.nhs.uk [Children' s Brain Tumour Research Centre, Queens Medical Centre, University of Nottingham (United Kingdom); MacPherson, L., E-mail: Lesley.MacPherson@bch.nhs.uk [Birmingham Children' s Hospital NHS Foundation Trust, Whittall Street, Birmingham, B4 6NH (United Kingdom); Natarajan, K., E-mail: Kal.Natarajan@uhb.nhs.uk [Birmingham Children' s Hospital NHS Foundation Trust, Whittall Street, Birmingham, B4 6NH (United Kingdom); Department of Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Mindelsohn Way, Edgbaston, Birmingham, B15 2WB (United Kingdom); and others

    2013-06-15

    Introduction: Low grade gliomas are the commonest brain tumours in children but present in a myriad of ways, each with its own treatment challenges. Conventional MRI scans play an important role in their management but have limited ability to identify likely clinical behaviour. The aim of this study is to investigate {sup 1}H magnetic resonance spectroscopy (MRS) as a method for detecting differences between the various low grade gliomas and related tumours in children. Patients and methods: Short echo time single voxel {sup 1}H MRS at 1.5 or 3.0 T was performed prior to treatment on children with low grade brain tumours at two centres and five MR scanners, 69 cases had data which passed quality control. MRS data was processed using LCModel to give mean spectra and metabolite concentrations which were compared using T-tests, ANOVA, Receiver Operator Characteristic curves and logistic regression in SPSS. Results: Significant differences were found in concentrations of key metabolites between glioneuronal and glial tumours (T-test p < 0.05) and between most of the individual histological subtypes of low grade gliomas. The discriminatory metabolites identified, such as choline and myoinositol, are known tumour biomarkers. In the set of pilocytic astrocytomas and unbiopsied optic pathway gliomas, significant differences (p < 0.05, ANOVA) were found in metabolite profiles of tumours depending on location and patient neurofibromatosis type 1 status. Logistic regression analyses yielded equations which could be used to assess the probability of a tumour being of a specific type. Conclusions: MRS can detect subtle differences between low grade brain tumours in children and should form part of the clinical assessment of these tumours.

  17. In vivo magnetic resonance imaging and 31P spectroscopy of large human brain tumours at 1.5 tesla

    DEFF Research Database (Denmark)

    Thomsen, C; Jensen, K E; Achten, E

    1988-01-01

    31P MR spectroscopy of human brain tumours is one feature of magnetic resonance imaging. Eight patients with large superficial brain tumours and eight healthy volunteers were examined with 31P spectroscopy using an 8 cm surface coil for volume selection. Seven frequencies were resolved in our spe...

  18. In vivo magnetic resonance imaging and 31P spectroscopy of large human brain tumours at 1.5 tesla

    DEFF Research Database (Denmark)

    Thomsen, C; Jensen, K E; Achten, E;

    1988-01-01

    31P MR spectroscopy of human brain tumours is one feature of magnetic resonance imaging. Eight patients with large superficial brain tumours and eight healthy volunteers were examined with 31P spectroscopy using an 8 cm surface coil for volume selection. Seven frequencies were resolved in our spe...

  19. Local Kernel for Brains Classification in Schizophrenia

    Science.gov (United States)

    Castellani, U.; Rossato, E.; Murino, V.; Bellani, M.; Rambaldelli, G.; Tansella, M.; Brambilla, P.

    In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorso-lateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.

  20. Human cytomegalovirus tegument protein pp65 is detected in all intra- and extra-axial brain tumours independent of the tumour type or grade.

    Directory of Open Access Journals (Sweden)

    Sylwia Libard

    Full Text Available Human cytomegalovirus (HCMV has been indicated being a significant oncomodulator. Recent reports have suggested that an antiviral treatment alters the outcome of a glioblastoma. We analysed the performance of commercial HCMV-antibodies applying the immunohistochemical (IHC methods on brain sample obtained from a subject with a verified HCMV infection, on samples obtained from 14 control subjects, and on a tissue microarray block containing cores of various brain tumours. Based on these trials, we selected the best performing antibody and analysed a cohort of 417 extra- and intra-axial brain tumours such as gliomas, medulloblastomas, primary diffuse large B-cell lymphomas, and meningiomas. HCMV protein pp65 immunoreactivity was observed in all types of tumours analysed, and the IHC expression did not depend on the patient's age, gender, tumour type, or grade. The labelling pattern observed in the tumours differed from the labelling pattern observed in the tissue with an active HCMV infection. The HCMV protein was expressed in up to 90% of all the tumours investigated. Our results are in accordance with previous reports regarding the HCMV protein expression in glioblastomas and medulloblastomas. In addition, the HCMV protein expression was seen in primary brain lymphomas, low-grade gliomas, and in meningiomas. Our results indicate that the HCMV protein pp65 expression is common in intra- and extra-axial brain tumours. Thus, the assessment of the HCMV expression in tumours of various origins and pathologically altered tissue in conditions such as inflammation, infection, and even degeneration should certainly be facilitated.

  1. Respiratory Deleted in Malignant Brain Tumours 1 (DMBT1) levels increase during lung maturation and infection

    DEFF Research Database (Denmark)

    Müller, H; End, C; Weiss, C;

    2007-01-01

    Deleted in Malignant Brain Tumours 1 (DMBT1) is a secreted scavenger receptor cysteine-rich protein that binds and aggregates various bacteria and viruses in vitro. Studies in adults have shown that DMBT1 is expressed mainly by mucosal epithelia and glands, in particular within the respiratory...

  2. The risk of brain tumours in hereditary non-polyposis colorectal cancer (HNPCC)

    NARCIS (Netherlands)

    Vasen, HFA; Sanders, EACM; Taal, BG; Nagengast, FM; Griffioen, G; Menko, FH; Kleibeuker, JH; HouwingDuistermaat, JJ; Khan, PM

    1996-01-01

    Hereditary non-polyposis colorectal cancer (HNPCC) is known to be associated with several extracolonic cancers, e.g., cancers of the endometrium, stomach, urinary tract, small bowel and ovary. An association between HNPCC and brain tumours has also been reported, although previous risk analysis did

  3. Magnetic fields and brain tumour risks in UK electricity supply workers.

    Science.gov (United States)

    Sorahan, T

    2014-04-01

    To investigate whether brain tumour risks are related to occupational exposure to low-frequency magnetic fields. Brain tumour risks experienced by 73 051 employees of the former Central Electricity Generating Board of England and Wales were investigated for the period 1973-2010. All employees were hired in the period 1952-82 and were employed for at least 6 months with some employment in the period 1973-82. Detailed calculations had been performed by others to enable an assessment to be made of exposures to magnetic fields. Poisson regression was used to calculate relative risks (rate ratios) of developing a brain tumour (or glioma or meningioma) for categories of lifetime, distant (lagged) and recent (lugged) exposure. Findings for glioma and for the generality of all brain tumours were unexceptional; risks were close to (or below) unity for all exposure categories and there was no suggestion of risks increasing with cumulative (or recent or distant) magnetic field exposures. There were no statistically significant dose-response effects shown for meningioma, but there was some evidence of elevated risks in the three highest exposure categories for exposures received >10 years ago. This study found no evidence to support the hypothesis that exposure to magnetic fields is a risk factor for gliomas, and the findings are consistent with the hypotheses that both distant and recent magnetic field exposures are not causally related to gliomas. The limited positive findings for meningioma may be chance findings; national comparisons argue against a causal interpretation.

  4. The risk of brain tumours in hereditary non-polyposis colorectal cancer (HNPCC)

    NARCIS (Netherlands)

    Vasen, HFA; Sanders, EACM; Taal, BG; Nagengast, FM; Griffioen, G; Menko, FH; Kleibeuker, JH; HouwingDuistermaat, JJ; Khan, PM

    1996-01-01

    Hereditary non-polyposis colorectal cancer (HNPCC) is known to be associated with several extracolonic cancers, e.g., cancers of the endometrium, stomach, urinary tract, small bowel and ovary. An association between HNPCC and brain tumours has also been reported, although previous risk analysis did

  5. X-ray fluorescence study of the concentration of selected trace and minor elements in human brain tumours

    Science.gov (United States)

    Wandzilak, Aleksandra; Czyzycki, Mateusz; Radwanska, Edyta; Adamek, Dariusz; Geraki, Kalotina; Lankosz, Marek

    2015-12-01

    Neoplastic and healthy brain tissues were analysed to discern the changes in the spatial distribution and overall concentration of elements using micro X-ray fluorescence spectroscopy. High-resolution distribution maps of minor and trace elements such as P, S, Cl, K, Ca, Fe, Cu and Zn made it possible to distinguish between homogeneous cancerous tissue and areas where some structures could be identified, such as blood vessels and calcifications. Concentrations of the elements in the selected homogeneous areas of brain tissue were compared between tumours with various malignancy grades and with the controls. The study showed a decrease in the average concentration of Fe, P, S and Ca in tissues with high grades of malignancy as compared to the control group, whereas the concentration of Zn in these tissues was increased. The changes in the concentration were found to be correlated with the tumour malignancy grade. The efficacy of micro X-ray fluorescence spectroscopy to distinguish between various types of cancer based on the concentrations of studied elements was confirmed by multivariate discriminant analysis. Our analysis showed that the most important elements for tissue classification are Cu, K, Fe, Ca, and Zn. This method made it possible to correctly classify histopathological types in 99.93% of the cases used to build the model and in as much as 99.16% of new cases.

  6. Texture analysis in quantitative MR imaging. Tissue characterisation of normal brain and intracranial tumours at 1.5 T

    DEFF Research Database (Denmark)

    Kjaer, L; Ring, P; Thomsen, C

    1995-01-01

    of common first-order and second-order grey level statistics. Tissue differentiation in the images was estimated by the presence or absence of significant differences between tissue types. A fine discrimination was obtained between white matter, cortical grey matter, and cerebrospinal fluid in the normal...... brain, and white matter was readily separated from the tumour lesions. Moreover, separation of solid tumour tissue and peritumoural oedema was suggested for some tumour types. Mutual comparison of all tumour types revealed extensive differences, and even specific tumour differentiation turned out...

  7. Spurious leptomeningeal enhancement on immediate post-operative MRI for paediatric brain tumours

    Energy Technology Data Exchange (ETDEWEB)

    Widjaja, Elysa; Connolly, Daniel J.A. [Royal Hallamshire Hospital, Department of Radiology, Sheffield (United Kingdom); Gatscher, Sylvia; McMullen, John [Royal Hallamshire Hospital, Department of Neurosurgery, Sheffield (United Kingdom); Griffiths, Paul D. [University of Sheffield, Academic section of Radiology, Sheffield (United Kingdom)

    2005-03-01

    Immediate post-operative MRI has been recommended as an accurate and robust method to assess residual brain tumour. Early enhancement at the resection margin and in the dura is well recognized, but we describe two cases of enhancement in the basal cisterns on immediate post-operative MRI that resolved on follow-up. The underlying cause of the enhancement remains to be elucidated, but it should be recognized that leptomeningeal enhancement may occur after surgery and that this does not necessarily imply tumour spread. (orig.)

  8. {sup 1}H MR spectroscopy of human brain tumours: a practical approach

    Energy Technology Data Exchange (ETDEWEB)

    Callot, Virginie [Centre de Resonance Magnetique Biologique et Medicale (CRMBM), UMR 6612, CNRS - Universite de la Mediterranee, 27 Boulevard Jean Moulin, 13385 Marseille Cedex 05 (France)], E-mail: virginie.callot@univmed.fr; Galanaud, Damien [Centre de Resonance Magnetique Biologique et Medicale (CRMBM), UMR 6612, CNRS - Universite de la Mediterranee, 27 Boulevard Jean Moulin, 13385 Marseille Cedex 05 (France); Departement de Neuroradiologie, Hopital La Pitie-Salpetriere, Paris (France); Le Fur, Yann; Confort-Gouny, Sylviane; Ranjeva, Jean-Philippe; Cozzone, Patrick J. [Centre de Resonance Magnetique Biologique et Medicale (CRMBM), UMR 6612, CNRS - Universite de la Mediterranee, 27 Boulevard Jean Moulin, 13385 Marseille Cedex 05 (France)

    2008-08-15

    Magnetic resonance spectroscopy (MRS) is proposed in addition to magnetic resonance imaging (MRI) to help in the characterization of brain tumours by detecting metabolic alterations that may be indicative of the tumour class. MRS can be routinely performed on clinical magnets, within a reasonable acquisition time and if performed under adequate conditions, MRS is reproducible and thus can be used for longitudinal follow-up of treatment. MRS can also be performed in clinical practice to guide the neurosurgeon into the most aggressive part of the lesions or to avoid unnecessary surgery, which may furthermore decrease the risk of surgical morbidity.

  9. Nutritional Status and Body Composition of Adult Patients with Brain Tumours Awaiting Surgical Resection.

    Science.gov (United States)

    McCall, Michele; Leone, Ashley; Cusimano, Michael D

    2014-09-01

    To measure the prevalence of malnutrition, risk factors for poor dietary intake and body composition in patients with brain tumours admitted to hospital for surgical resection. In this study, 316 patients admitted for brain tumour resection to the Neurosurgical service at St. Michael's Hospital were screened. Assessment tools included the Subjective Global Assessment (SGA) for nutritional status and Bioelectrical Impedance Analysis (BIA) for body composition. All measurements were performed by one research dietitian. Information regarding medical history, symptomology, and tumour pathology was recorded. One hundred and nine participants were recruited. Malnutrition was present in 17.6% of patients, of whom 94.7% were moderately malnourished (SGA-B) and 5.3% severely malnourished (SGA-C). Key symptoms contributing to malnutrition included weight loss, nausea, vomiting, dysphagia, headaches, and fatigue. Patients with malignant tumors were more likely to have weight loss and lower fat mass. This study demonstrated that patients admitted for brain tumour resection have a low prevalence of malnutrition compared with other cancer populations. Useful parameters for nutritional screening of inpatient admissions include weight loss >5% of usual weight, nausea, vomiting, dysphagia, and headaches.

  10. Classification of CT brain images based on deep learning networks.

    Science.gov (United States)

    Gao, Xiaohong W; Hui, Rui; Tian, Zengmin

    2017-01-01

    While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great extent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early diagnosis of Alzheimer's disease. Towards this end, three categories of CT images (N = 285) are clustered into three groups, which are AD, lesion (e.g. tumour) and normal ageing. In addition, considering the characteristics of this collection with larger thickness along the direction of depth (z) (~3-5 mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification accuracy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, lesion and normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6 ± 1.10, 86.3 ± 1.04, 85.2 ± 1.60, 83.1 ± 0.35 for 2D CNN, 2D SIFT, 2D KAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information

  11. Long-term exposure to ambient air pollution and incidence of brain tumours: The Danish Nurse Cohort

    DEFF Research Database (Denmark)

    Jørgensen, Jeanette Therming; Johansen, Martin Søes; Ravnskjær, Line

    2016-01-01

    -reported information on lifestyle was collected. We obtained data on the incidence of brain tumours until 2013 from the Danish Cancer Register, and estimated annual mean concentrations of particulate matter with diameter... by location (brain or meninges), and by malignancy (malignant or benign), and estimated hazard ratios and 95% confidence intervals per increase in interquartile range of exposure. RESULTS: Of 25,143 tumour-free nurses at recruitment, 121 developed brain cancer during 15.7 years of follow-up. We found a weak......BACKGROUND: Air pollution has been considered a potent environmental risk factor for neuropathology through neuroinflammation and oxidative stress, which might also cause brain tumour formation. However, epidemiological evidence on the association between air pollution and brain tumours in humans...

  12. Development of a positron probe for localization and excision of brain tumours during surgery

    Energy Technology Data Exchange (ETDEWEB)

    Bogalhas, F; Charon, Y; Duval, M-A; Lefebvre, F; Pinot, L; Siebert, R; Menard, L [Laboratoire Imagerie et Modelisation en Neurobiologie et Cancerologie (UMR 8165), Campus d' Orsay, 91406 Orsay Cedex (France); Palfi, S [Service de neurochirurgie, CHU Henri Mondor, 94010 Creteil Cedex (France) and URA CEA-CNRS 2210, 4 Place du General Leclerc, 91401 Orsay Cedex (France)], E-mail: menard@imnc.in2p3.fr

    2009-07-21

    The survival outcome of patients suffering from gliomas is directly linked to the complete surgical resection of the tumour. To help the surgeons to delineate precisely the boundaries of the tumour, we developed an intraoperative positron probe with background noise rejection capability. The probe was designed to be directly coupled to the excision tool such that detection and removal of the radiolabelled tumours could be simultaneous. The device consists of two exchangeable detection heads composed of clear and plastic scintillating fibres. Each head is coupled to an optic fibre bundle that exports the scintillating light to a photodetection and processing electronic module placed outside the operative wound. The background rejection method is based on a real-time subtraction technique. The measured probe sensitivity for {sup 18}F was 1.1 cps kBq{sup -1} ml{sup -1} for the small head and 3.4 cps kBq{sup -1} ml{sup -1} for the large head. The mean spatial resolution was 1.6 mm FWHM on the detector surface. The {gamma}-ray rejection efficiency measured by realistic brain phantom modelling of the surgical cavity was 99.4%. This phantom also demonstrated the ability of the probe to detect tumour discs as small as 5 mm in diameter (20 mg) for tumour-to-background ratios higher than 3:1 and with an acquisition time around 4 s at each scanning step. These results indicate that our detector could be a useful complement to existing techniques for the accurate excision of brain tumour tissue and more generally to improve the efficiency of radio-guided cancer surgery.

  13. Study of bantam miRNA expression in brain tumour resulted due to loss of polarity modules in Drosophila melanogaster

    Indian Academy of Sciences (India)

    ANIMESH BANERJEE; JAGAT K. ROY

    2017-06-01

    Disturbance of delicate concordance between stem cell proliferation, specification and differentiation during brain development leads to several neural disorders including tumours. Accumulating evidences have demonstratedinvolvement of short noncoding microRNAs (miRNAs) in governing several biological as well as pathological processes, including tumourigenesis across various species. Drosophila bantam miRNA, known to regulate critical physiological functions is reported to have elevated expression in ovarian tumour. Here, we provide an update on the expression of bantam miRNA in Drosophila brain tumour background resulting due to loss of well characterized polarity proteins, Brat, Lgl and Scrib. Since, both miRNA TaqMan assay and bantam sensor assay showed elevated expression of bantam in brain tumour background, it clearly reflects presence of an antagonistic relationship between polarity proteins and bantam miRNA indicating of its involvement in tumour progression.

  14. In vivo proton magnetic resonance spectroscopy of intraventricular tumours of the brain

    Energy Technology Data Exchange (ETDEWEB)

    Majos, Carles; Aguilera, Carles [Hospital Universitari de Bellvitge, Institut de Diagnostic per la Imatge (IDI). Centre Bellvitge, L' Hospitalet de Llobregat, Barcelona (Spain); Biomateriales y Nanomedicina (CIBER-BBN), Centro de Investigacion Biomedica en Red en Bioingenieria, Cerdanyola del Valles (Spain); Cos, Monica; Camins, Angels; Samitier, Alex; Castaner, Sara; Sanchez, Juan J. [Hospital Universitari de Bellvitge, Institut de Diagnostic per la Imatge (IDI). Centre Bellvitge, L' Hospitalet de Llobregat, Barcelona (Spain); Candiota, Ana P.; Delgado-Goni, Teresa [Biomateriales y Nanomedicina (CIBER-BBN), Centro de Investigacion Biomedica en Red en Bioingenieria, Cerdanyola del Valles (Spain); Unitat de Bioquimica de Biociencies, Department de Bioquimica i Biologia Molecular, Cerdanyola del Valles (Spain); Mato, David [Hospital Universitari de Bellvitge, Department of Neurosurgery, L' Hospitalet de Llobregat, Barcelona (Spain); Acebes, Juan J. [Hospital Universitari de Bellvitge, Department of Neurosurgery, L' Hospitalet de Llobregat, Barcelona (Spain); Biomateriales y Nanomedicina (CIBER-BBN), Centro de Investigacion Biomedica en Red en Bioingenieria, Cerdanyola del Valles (Spain); Arus, Carles [Unitat de Bioquimica de Biociencies, Department de Bioquimica i Biologia Molecular, Cerdanyola del Valles (Spain); Biomateriales y Nanomedicina (CIBER-BBN), Centro de Investigacion Biomedica en Red en Bioingenieria, Cerdanyola del Valles (Spain)

    2009-08-15

    The aim of this study was to assess the usefulness of proton MR spectroscopy in the diagnosis of intraventricular tumours. Fifty-two intraventricular tumours pertaining to 16 different tumour types were derived from our database. All cases had single-voxel proton MR spectroscopy performed at TE at both 30 and 136 ms at 1.5 T. The Mann-Whitney U test was used to search for the most discriminative datapoints each tumour type. Characteristic trends were found for some groups: high Glx and Ala in meningiomas (p<0.001 and p<0.01, respectively), high mobile lipids in metastasis (p<0.001), high Cho in PNET (p<0.001), high mI+Gly in ependymoma (p<0.001), high NAC (p<0.01) in the absence of the normal brain parenchyma pattern in colloid cysts, and high mI/Gly and Ala in central neurocytoma. Proton MR spectroscopy provides additional metabolic information that could be useful in the diagnosis of intraventricular brain tumors. (orig.)

  15. Active video gaming improves body coordination in survivors of childhood brain tumours

    DEFF Research Database (Denmark)

    Sabel, M.; Sjölund, A.; Broeren, J.

    2016-01-01

    Purpose: We investigated whether active video gaming (AVG) could bring about regular, enjoyable, physical exercise in children treated for brain tumours, what level of physical activity could be reached and if the children’s physical functioning improved. Methods: Thirteen children, aged 7–17 years......-blinded assessments of physical functioning were done, using the Bruininks–Osteretsky Test of Motor Performance, second edition, evaluating participants before and after the intervention period, as well as comparing the randomisation groups after the first period. Results: All patients completed the study. AVG...... compared to their healthy peers. Active video gaming (AVG), supported by Internet coaching, is a feasible home-based intervention in children treated for brain tumours, promoting enjoyable, regular physical exercise of moderate intensity. In this pilot study, AVG with Nintendo Wii improved Body...

  16. Bevacizumab plus irinotecan in the treatment patients with progressive recurrent malignant brain tumours

    DEFF Research Database (Denmark)

    Poulsen, H.S.; Grunnet, K.; Sorensen, M.

    2009-01-01

    MATERIAL AND METHODS: We retrospectively determined the efficacy and safety of a combination of bevacizumab and irinotecan in a consecutive series of 52 heavily pre-treated patients with recurrent high-grade brain tumours. Patients received bevacizumab (10 mg/kg) and irinotecan [340 mg/m(2...... glioma and 32 weeks for grade III glioma. Four patients discontinued treatment because of unmanageable toxicity: cerebral haemorrhage, cardiac arrhythmia, intestinal perforation and diarrhoea, the latter resulting in death. DISCUSSION: We conclude that the combination of bevacizumab and irinotecan shows...... acceptable safety and is a clinically relevant choice of therapy in heavily pre-treated patients with recurrent high-grade brain tumours Udgivelsesdato: 2009...

  17. Neural correlates of delayed visual-motor performance in children treated for brain tumours.

    Science.gov (United States)

    Dockstader, Colleen; Gaetz, William; Bouffet, Eric; Tabori, Uri; Wang, Frank; Bostan, Stefan R; Laughlin, Suzanne; Mabbott, Donald J

    2013-09-01

    Both structural and functional neural integrity is critical for healthy cognitive function and performance. Across studies, it is evident that children who are affected by neurological insult commonly demonstrate impaired cognitive abilities. Children treated with cranial radiation for brain tumours suffer substantial structural damage and exhibit a particularly high correlation between the degree of neural injury and cognitive deficits. However the pathophysiology underlying impaired cognitive performance in this population, and many other paediatric populations affected by neurological injury or disease, is unknown. We wished to investigate the characteristics of neuronal function during visual-motor task performance in a group of children who were treated with cranial radiation for brain tumours. We used Magnetoencephalography to investigate neural function during visual-motor reaction time (RT) task performance in 15 children treated with cranial radiation for Posterior Fossa malignant brain tumours and 17 healthy controls. We found that, relative to controls, the patient group showed: 1) delayed latencies for neural activation in both visual and motor cortices; 2) muted motor responses in the alpha (8-12Hz) and beta (13-29Hz) bandwidths, and 3) potentiated visual and motor responses in the gamma (30-100Hz) bandwidth. Collectively these observations indicate impaired neural processing during visual-motor RT performance in this population and that delays in the speed of visual and motor neuronal processing both contribute to the delays in the behavioural response. As increases in gamma activity are often observed with increases in attention and effort, increased gamma activities in the patient group may reflect compensatory neural activity during task performance. This is the first study to investigate neural function in real-time during cognitive performance in paediatric brain tumour patients. Copyright © 2012 Elsevier Ltd. All rights reserved.

  18. Active video gaming improves body coordination in survivors of childhood brain tumours.

    Science.gov (United States)

    Sabel, Magnus; Sjölund, Anette; Broeren, Jürgen; Arvidsson, Daniel; Saury, Jean-Michel; Blomgren, Klas; Lannering, Birgitta; Emanuelson, Ingrid

    2016-10-01

    We investigated whether active video gaming (AVG) could bring about regular, enjoyable, physical exercise in children treated for brain tumours, what level of physical activity could be reached and if the children's physical functioning improved. Thirteen children, aged 7-17 years, were randomised to either AVG or waiting-list. After 10-12 weeks they crossed-over. Weekly Internet coaching sessions were used to sustain motivation and evaluate enjoyment. Energy expenditure (EE) levels were measured as Metabolic Equivalent of Task (MET), using a multisensory activity monitor. Single-blinded assessments of physical functioning were done, using the Bruininks-Osteretsky Test of Motor Performance, second edition, evaluating participants before and after the intervention period, as well as comparing the randomisation groups after the first period. All patients completed the study. AVG sessions (mean duration 47 minutes) were performed on 72% of all days. Mean EE level during AVG sessions was 3.0 MET, corresponding to moderate physical activity. The Body Coordination score improved by 15% (p = 0.021) over the intervention period. In this group of childhood brain tumour survivors, home-based AVG, supported by a coach, was a feasible, enjoyable and moderately intense form of exercise that improved Body Coordination. Implications for Rehabilitation Childhood brain tumour survivors frequently have cognitive problems, inferior physical functioning and are less physically active compared to their healthy peers. Active video gaming (AVG), supported by Internet coaching, is a feasible home-based intervention in children treated for brain tumours, promoting enjoyable, regular physical exercise of moderate intensity. In this pilot study, AVG with Nintendo Wii improved Body Coordination.

  19. The response of bispectral index to laryngoscopy, comparison between hemispheres in patients with a brain tumour versus a healthy control group.

    NARCIS (Netherlands)

    Wyler, B.; Wyffels, P.; de Hert, S.; Okito, Jean Pierre Kalala; Struys, Michel; Vereecke, Hugo Eric Marc

    2013-01-01

    Background and Goal of Study: Electroencephalogram during anaesthesia may be affected by brain tumour.(1) We studied whether patients with a brain tumour have different BIS responses after laryngoscopy (LAR). We compared tumour patients with healthy control patients. Materials and Methods: After EC

  20. In vivo magnetic resonance imaging and 31P spectroscopy of large human brain tumours at 1.5 tesla

    DEFF Research Database (Denmark)

    Thomsen, C; Jensen, K E; Achten, E

    1988-01-01

    31P MR spectroscopy of human brain tumours is one feature of magnetic resonance imaging. Eight patients with large superficial brain tumours and eight healthy volunteers were examined with 31P spectroscopy using an 8 cm surface coil for volume selection. Seven frequencies were resolved in our...... and after chemotherapy. The spectra showed considerable changes during chemotherapy. It is concluded that 31P spectroscopy using surface coils is of limited value for tumour characterization, but may add useful information in monitoring the effect of chemotherapy....

  1. Multimodal magnetic resonance imaging increases the overall diagnostic accuracy in brain tumours: Correlation with histopathology

    Directory of Open Access Journals (Sweden)

    Kasim Abul-Kasim

    2013-03-01

    Full Text Available Background: The aim of this retrospective study was to assess the contribution of multimodal MRI techniques, specifically perfusion-weighted imaging (PWI, and/or MR spectroscopy (MRS, in increasing the diagnostic accuracy of MRI in brain tumours.Methods: Forty-four patients with suspected brain tumours (27 (61% patients male, mean age 58±17 (mean±SD years were included in this retrospective analysis. Patients were examined with conventional MR sequences, DWI, and with PWI and/or MRS. The concordance between the diagnoses obtained with multimodal MRI and with the conventional MR sequences, and the final diagnosis obtained by biopsy, was estimated. Fisher’s exact test and/or chi-square test was performed to estimate the added utility of multimodal MRI. Statistical significance was set at p<0.05.Results: With multimodal MRI, the diagnosis in 41 (93% patients was the same as that obtained by biopsy, compared with 39% (17/44 patients when the readers were allowed to give one diagnostic possibility during the evaluation of the conventional MR sequences alone (p<0.001. The concordance between the diagnoses provided by evaluating the multimodal MRIs and the final diagnoses was almost perfect (κ value 0.92, 95% CI 0.82 - 1. PWI primarily helped to differentiate lymphomas from other solid tumours, whereas MRS helped to differentiate malignant glioma from metastasis. Both PWI and MRS helped in grading astrocytomas.Conclusion: Multimodal MRI increases diagnostic accuracy and should, wherever available, be performed in the work-up of brain tumours, although this entails increased examination cost and time.

  2. Gene expression-based classifications of fibroadenomas and phyllodes tumours of the breast.

    Science.gov (United States)

    Vidal, Maria; Peg, Vicente; Galván, Patricia; Tres, Alejandro; Cortés, Javier; Ramón y Cajal, Santiago; Rubio, Isabel T; Prat, Aleix

    2015-06-01

    Fibroepithelial tumors (FTs) of the breast are a heterogeneous group of lesions ranging from fibroadenomas (FAD) to phyllodes tumors (PT) (benign, borderline, malignant). Further understanding of their molecular features and classification might be of clinical value. In this study, we analysed the expression of 105 breast cancer-related genes, including the 50 genes of the PAM50 intrinsic subtype predictor and 12 genes of the Claudin-low subtype predictor, in a panel of 75 FTs (34 FADs, 5 juvenile FADs, 20 benign PTs, 5 borderline PTs and 11 malignant PTs) with clinical follow-up. In addition, we compared the expression profiles of FTs with those of 14 normal breast tissues and 49 primary invasive ductal carcinomas (IDCs). Our results revealed that the levels of expression of all breast cancer-related genes can discriminate the various groups of FTs, together with normal breast tissues and IDCs (False Discovery Rate expression of proliferation-related genes (e.g. CCNB1 and MKI67) and mesenchymal/epithelial-related (e.g. CLDN3 and EPCAM) genes were found to be most discriminative. As expected, FADs showed the highest and lowest expression of epithelial- and proliferation-related genes, respectively, whereas malignant PTs showed the opposite expression pattern. Interestingly, the overall profile of benign PTs was found more similar to FADs and normal breast tissues than the rest of tumours, including juvenile FADs. Within the dataset of IDCs and normal breast tissues, the vast majority of FADs, juvenile FADs, benign PTs and borderline PTs were identified as Normal-like by intrinsic breast cancer subtyping, whereas 7 (63.6%) and 3 (27.3%) malignant PTs were identified as Claudin-low and Basal-like, respectively. Finally, we observed that the previously described PAM50 risk of relapse prognostic score better predicted outcome in FTs than the morphological classification, even within PTs-only. Our results suggest that classification of FTs using gene expression

  3. Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features.

    Science.gov (United States)

    Pinto, Adriano; Pereira, Sergio; Correia, Higino; Oliveira, J; Rasteiro, Deolinda M L D; Silva, Carlos A

    2015-08-01

    Gliomas are among the most common and aggressive brain tumours. Segmentation of these tumours is important for surgery and treatment planning, but also for follow-up evaluations. However, it is a difficult task, given that its size and locations are variable, and the delineation of all tumour tissue is not trivial, even with all the different modalities of the Magnetic Resonance Imaging (MRI). We propose a discriminative and fully automatic method for the segmentation of gliomas, using appearance- and context-based features to feed an Extremely Randomized Forest (Extra-Trees). Some of these features are computed over a non-linear transformation of the image. The proposed method was evaluated using the publicly available Challenge database from BraTS 2013, having obtained a Dice score of 0.83, 0.78 and 0.73 for the complete tumour, and the core and the enhanced regions, respectively. Our results are competitive, when compared against other results reported using the same database.

  4. Intraoperative probe detecting β- decays in brain tumour radio-guided surgery

    Science.gov (United States)

    Solfaroli Camillocci, E.; Bocci, V.; Chiodi, G.; Collamati, F.; Donnarumma, R.; Faccini, R.; Mancini Terracciano, C.; Marafini, M.; Mattei, I.; Muraro, S.; Recchia, L.; Rucinski, A.; Russomando, A.; Toppi, M.; Traini, G.; Morganti, S.

    2017-02-01

    Radio-guided surgery (RGS) is a technique to intraoperatively detect tumour remnants, favouring a radical resection. Exploiting β- emitting tracers provides a higher signal to background ratio compared to the established technique with γ radiation, allowing the extension of the RGS applicability range. We developed and tested a detector based on para-terphenyl scintillator with high sensitivity to low energy electrons and almost transparent to γs to be used as intraoperative probe for RGS with β- emitting tracer. Portable read out electronics was customised to match the surgeon needs. This probe was used for preclinical test on specific phantoms and a test on ;ex vivo; specimens from patients affected by meningioma showing very promising results for the application of this new technique on brain tumours. In this paper, the prototype of the intraoperative probe and the tests are discussed; then, the results on meningioma are used to make predictions on the performance of the probe detecting residuals of a more challenging and more interesting brain tumour: the glioma.

  5. Cl- and K+ channels and their role in primary brain tumour biology.

    Science.gov (United States)

    Turner, Kathryn L; Sontheimer, Harald

    2014-03-19

    Profound cell volume changes occur in primary brain tumours as they proliferate, invade surrounding tissue or undergo apoptosis. These volume changes are regulated by the flux of Cl(-) and K(+) ions and concomitant movement of water across the membrane, making ion channels pivotal to tumour biology. We discuss which specific Cl(-) and K(+) channels are involved in defined aspects of glioma biology and how these channels are regulated. Cl(-) is accumulated to unusually high concentrations in gliomas by the activity of the NKCC1 transporter and serves as an osmolyte and energetic driving force for volume changes. Cell volume condensation is required as cells enter M phase of the cell cycle and this pre-mitotic condensation is caused by channel-mediated ion efflux. Similarly, Cl(-) and K(+) channels dynamically regulate volume in invading glioma cells allowing them to adjust to small extracellular brain spaces. Finally, cell condensation is a hallmark of apoptosis and requires the concerted activation of Cl(-) and Ca(2+)-activated K(+) channels. Given the frequency of mutation and high importance of ion channels in tumour biology, the opportunity exists to target them for treatment.

  6. Simple Fully Automated Group Classification on Brain fMRI

    Energy Technology Data Exchange (ETDEWEB)

    Honorio, J.; Goldstein, R.; Honorio, J.; Samaras, D.; Tomasi, D.; Goldstein, R.Z.

    2010-04-14

    We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.

  7. Towards the introduction of the 'Immunoscore' in the classification of malignant tumours

    NARCIS (Netherlands)

    Galon, J.; Mlecnik, B.; Bindea, G.; Angell, H.K.; Berger, A.; Lagorce, C.; Lugli, A.; Zlobec, I.; Hartmann, A.; Bifulco, C.; Nagtegaal, I.D.; Palmqvist, R.; Masucci, G.V.; Botti, G.; Tatangelo, F.; Delrio, P.; Maio, M.; Laghi, L.; Grizzi, F.; Asslaber, M.; D'Arrigo, C.; Vidal-Vanaclocha, F.; Zavadova, E.; Chouchane, L.; Ohashi, P.S.; Hafezi-Bakhtiari, S.; Wouters, B.G.; Roehrl, M.; Nguyen, L; Kawakami, Y.; Hazama, S.; Okuno, K.; Ogino, S.; Gibbs, P.; Waring, P.; Sato, N.; Torigoe, T.; Itoh, K.; Patel, P.S.; Shukla, S.N.; Wang, Y.; Kopetz, S.; Sinicrope, F.A.; Scripcariu, V.; Ascierto, P.A.; Marincola, F.M.; Fox, B.A.; Pages, F.

    2014-01-01

    The American Joint Committee on Cancer/Union Internationale Contre le Cancer (AJCC/UICC) TNM staging system provides the most reliable guidelines for the routine prognostication and treatment of colorectal carcinoma. This traditional tumour staging summarizes data on tumour burden (T), the presence

  8. Perinatal and early postnatal risk factors for malignant brain tumours in New South Wales children.

    Science.gov (United States)

    McCredie, M; Maisonneuve, P; Boyle, P

    1994-01-02

    A population-based case-control study of incident primary malignant brain tumours diagnosed during 1985-1989 in children aged 0 to 14 years was carried out in the coastal conurbation of New South Wales comprising Sydney, Wollongong and Newcastle in the period 1988 to 1990. Personal interviews were conducted using a structured questionnaire with mothers of 82 cases and 164 control children individually matched to the cases by sex and age. Among the hypotheses examined were those related to: N-nitroso compounds (sources included diet, dummies, medications, tobacco smoke); factors associated with the birth of the child; trauma to the head; and irradiation (X-rays and electromagnetic radiation through electric blankets or water beds). Reported ever-use of a dummy increased the risk of childhood brain tumours (OR = 2.9, 95% CI 1.6 to 5.4), although there did not appear to be any consistent indication of rising risk with reported increased levels of use. Compared with children who had never used a dummy, categories of use during the first year of life of a maximum of "no more than 1 hour per day or night", "several hours per day or night", and "most of the day or night" had statistically significant odds ratios of 2.6, 3.4, and 2.7 respectively. Consumption of fruit by the child before the age of one appeared to be protective. No association was found between childhood brain tumours and birth weight, being the first-born child, or factors linked with the child's birth; head injuries; exposure to X-rays; contact with horses, or living on a farm; pesticide treatment of the house during the child's lifetime; or exposure to burning incense.

  9. Brain Tumor Detection and Classification Using Deep Learning Classifier on MRI Images

    Directory of Open Access Journals (Sweden)

    V.P. Gladis Pushpa Rathi

    2015-05-01

    Full Text Available Magnetic Resonance Imaging (MRI has become an effective tool for clinical research in recent years and has found itself in applications such as brain tumour detection. In this study, tumor classification using multiple kernel-based probabilistic clustering and deep learning classifier is proposed. The proposed technique consists of three modules, namely segmentation module, feature extraction module and classification module. Initially, the MRI image is pre-processed to make it fit for segmentation and de-noising process is carried out using median filter. Then, pre-processed image is segmented using Multiple Kernel based Probabilistic Clustering (MKPC. Subsequently, features are extracted for every segment based on the shape, texture and intensity. After features extraction, important features will be selected using Linear Discriminant Analysis (LDA for classification purpose. Finally, deep learning classifier is employed for classification into tumor or non-tumor. The proposed technique is evaluated using sensitivity, specificity and accuracy. The proposed technique results are also compared with existing technique which uses Feed-Forward Back Propagation Network (FFBN. The proposed technique achieved an average sensitivity, specificity and accuracy of 0.88, 0.80 and 0.83, respectively with the highest values as about 1, 0.85 and 0.94. Improved results show the efficiency of the proposed technique.

  10. Endocannabinoid metabolism in human glioblastomas and meningiomas compared to human non-tumour brain tissue

    DEFF Research Database (Denmark)

    Petersen, G.; Moesgaard, B.; Hansen, Harald S.

    2005-01-01

    The endogenous levels of the two cannabinoid receptor ligands 2-arachidonoyl glycerol and anandamide, and their respective congeners, monoacyl glycerols and N-acylethanolamines, as well as the phospholipid precursors of N-acylethanolamines, were measured by gas chromatography-mass spectrometry in...... in glioblastoma (WHO grade IV) tissue and meningioma (WHO grade I) tissue and compared with human non-tumour brain tissue. Furthermore, the metabolic turnover of N-acylethanolamines was compared by measurements of the enzymatic activity of N-acyltransferase, N...

  11. Brain tumor classification of microscopy images using deep residual learning

    Science.gov (United States)

    Ishikawa, Yota; Washiya, Kiyotada; Aoki, Kota; Nagahashi, Hiroshi

    2016-12-01

    The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.

  12. Childhood brain tumours and use of mobile phones: comparison of a case–control study with incidence data

    Directory of Open Access Journals (Sweden)

    Aydin Denis

    2012-05-01

    Full Text Available Abstract The first case–control study on mobile phone use and brain tumour risk among children and adolescents (CEFALO study has recently been published. In a commentary published in Environmental Health, Söderqvist and colleagues argued that CEFALO suggests an increased brain tumour risk in relation to wireless phone use. In this article, we respond and show why consistency checks of case–control study results with observed time trends of incidence rates are essential, given the well described limitations of case–control studies and the steep increase of mobile phone use among children and adolescents during the last decade. There is no plausible explanation of how a notably increased risk from use of wireless phones would correspond to the relatively stable incidence time trends for brain tumours among children and adolescents observed in the Nordic countries. Nevertheless, an increased risk restricted to heavy mobile phone use, to very early life exposure, or to rare subtypes of brain tumours may be compatible with stable incidence trends at this time and thus further monitoring of childhood brain tumour incidence rate time trends is warranted.

  13. [Low field intra-operative magnetic resonance imaging for brain tumour surgery: preliminary experience].

    Science.gov (United States)

    Roldán, Pedro; García, Sergio; González, Josep; Reyes, Luis Alberto; Torales, Jorge; Valero, Ricard; Oleaga, Laura; Enseñat, Joaquim

    Intra-operative magnetic resonance imaging (iMRI) is a recently introduced tool in the most advanced neurosurgical operating rooms worldwide. We present our preliminary experience in brain tumour surgery with low field PoleStar N30® intraoperative MRI since its introduction in 2013 in the Barcelona Clinic Hospital. A prospective non-randomised study was conducted on cases operated on using iMRI and intention of complete removal up to October 2015. A record was made of the data as regards surgical times, resection rates, histological diagnosis, hospital stay, and survival rates during follow-up. The study included 50 patients, with a mean age of 55 years (±13.7), a preoperative mean Karnofsky of 92 (being 81 post-operatively), and a mean follow-up of 10.5 months (±6.5). There were 26% re-operations due to recurrence. High-grade gliomas were reported in 56%, low-grade gliomas in 24%, and 20% "Other" tumours. Overall hospital stay was 10 days (±4.5). Depending on the histologiacl diagnosis, the "Others" group had a longer hospital stay. Overall, there were 52% complete removal, 18% of maximum removals, and 30% of partial removals. The overall survival rates during follow-up was 84%. iMRI is a safe and effective tool for brain tumour surgery. Its use allows an increase in resection rates, and minimises post-operative complications. Its implementation involves an increase in surgical time, which improves with the characteristic learning curve. More studies are needed to establish its role in the long-term survival of patients. Copyright © 2016 Sociedad Española de Neurocirugía. Publicado por Elsevier España, S.L.U. All rights reserved.

  14. Estimating progression-free survival in paediatric brain tumour patients when some progression statuses are unknown

    Science.gov (United States)

    Yuan, Ying; Thall, Peter F.; Wolff, Johannes E.

    2012-01-01

    Summary In oncology, progression-free survival time, which is defined as the minimum of the times to disease progression or death, often is used to characterize treatment and covariate effects. We are motivated by the desire to estimate the progression time distribution on the basis of data from 780 paediatric patients with choroid plexus tumours, which are a rare brain cancer where disease progression always precedes death. In retrospective data on 674 patients, the times to death or censoring were recorded but progression times were missing. In a prospective study of 106 patients, both times were recorded but there were only 20 non-censored progression times and 10 non-censored survival times. Consequently, estimating the progression time distribution is complicated by the problems that, for most of the patients, either the survival time is known but the progression time is not known, or the survival time is right censored and it is not known whether the patient’s disease progressed before censoring. For data with these missingness structures, we formulate a family of Bayesian parametric likelihoods and present methods for estimating the progression time distribution. The underlying idea is that estimating the association between the time to progression and subsequent survival time from patients having complete data provides a basis for utilizing covariates and partial event time data of other patients to infer their missing progression times. We illustrate the methodology by analysing the brain tumour data, and we also present a simulation study. PMID:22408277

  15. Metastatic disease of the brain: extra-axial metastases (skull, dura, leptomeningeal) and tumour spread

    Energy Technology Data Exchange (ETDEWEB)

    Maroldi, Roberto; Ambrosi, Claudia; Farina, Davide [University of Brescia, Department of Radiology, Brescia, BS (Italy)

    2005-03-01

    Extra-axial intracranial metastases may arise through several situations. Hematogenous spread to the meninges is the most frequent cause. Direct extension from contiguous extra-cranial neoplasms, secondary invasion of the meninges by calvarium and skull base metastases, and migration along perineural or perivascular structures are less common. Leptomeningeal invasion gives rise to tumour cell dissemination by the cerebrospinal fluid (CSF), eventually leading to neoplastic coating of brain surfaces. Contrast-enhanced magnetic resonance (MR) imaging is complementary to CSF examinations and can be invaluable, detecting up to 50% of false-negative lumbar punctures. MR findings range from diffuse linear leptomeningeal enhancement to multiple enhancing extra-axial nodules, obstructive communicating and non-communicating hydrocephalus. Both calvarial and epidural metastases infrequently transgress the dura, which acts as a barrier against tumour spread. Radionuclide bone studies are still a valuable screening test to detect bone metastases. With computed tomography (CT) and MR, bone metastases extending intracranially and primary dural metastases show the characteristic biconvex shape, usually associated with brain displacement away from the inner table. Although CT is better in detecting skull base erosion, MR is more sensitive and provides more detailed information about dural involvement. Perineural and perivascular spread from head and neck neoplasms require thin-section contrast-enhanced MR. (orig.)

  16. Image-guided microbeam irradiation to brain tumour bearing mice using a carbon nanotube x-ray source array

    Science.gov (United States)

    Zhang, Lei; Yuan, Hong; Burk, Laurel M.; Inscoe, Christy R.; Hadsell, Michael J.; Chtcheprov, Pavel; Lee, Yueh Z.; Lu, Jianping; Chang, Sha; Zhou, Otto

    2014-03-01

    Microbeam radiation therapy (MRT) is a promising experimental and preclinical radiotherapy method for cancer treatment. Synchrotron based MRT experiments have shown that spatially fractionated microbeam radiation has the unique capability of preferentially eradicating tumour cells while sparing normal tissue in brain tumour bearing animal models. We recently demonstrated the feasibility of generating orthovoltage microbeam radiation with an adjustable microbeam width using a carbon nanotube based x-ray source array. Here we report the preliminary results from our efforts in developing an image guidance procedure for the targeted delivery of the narrow microbeams to the small tumour region in the mouse brain. Magnetic resonance imaging was used for tumour identification, and on-board x-ray radiography was used for imaging of landmarks without contrast agents. The two images were aligned using 2D rigid body image registration to determine the relative position of the tumour with respect to a landmark. The targeting accuracy and consistency were evaluated by first irradiating a group of mice inoculated with U87 human glioma brain tumours using the present protocol and then determining the locations of the microbeam radiation tracks using γ-H2AX immunofluorescence staining. The histology results showed that among 14 mice irradiated, 11 received the prescribed number of microbeams on the targeted tumour, with an average localization accuracy of 454 µm measured directly from the histology (537 µm if measured from the registered histological images). Two mice received one of the three prescribed microbeams on the tumour site. One mouse was excluded from the analysis due to tissue staining errors.

  17. Case-control study on the use of cellular and cordless phones and the risk for malignant brain tumours.

    Science.gov (United States)

    Hardell, L; Mild, K H; Carlberg, M

    2002-10-01

    To investigate the use of cellular and cordless phones and the risk for malignant brain tumours. A case-control study was performed on 649 patients aged 20-80 years of both sexes with malignant brain tumour diagnosed from 1 January 1997 to 30 June 2000. All patients were alive during the time of the study and had histopathology verified brain tumours. One matched control to each case was selected from the Swedish Population Register. The study area was the Uppsala-Orebro, Stockholm, Linköping and Göteborg medical regions of Sweden. Exposure was assessed by a questionnaire answered by 588 (91%) cases and 581 (90%) controls. Phone usage was defined as 'ever use' and usage starting within 1 year before diagnosis was disregarded. Overall, no significantly increased risks were found: analogue cellular phones yielded an odds ratio (OR)=1.13, 95% confidence interval (CI)=0.82-1.57, digital cellular phones OR=1.13, CI=0.86-1.48, and cordless phones OR=1.13, CI=0.85-1.50. For ipsilateral (same side) radiofrequency exposure, analogue mobile phones gave OR=1.85, CI=1.16-2.96, for all malignant brain tumours. For astrocytoma, this risk was OR=1.95, CI=1.12-3.39. For all malignant brain tumours, digital mobile phones yielded OR=1.59, CI=1.05-2.41, and cordless phones yielded OR=1.46, CI=0.96-2.23, in the analysis of ipsilateral exposure. The ipsilateral use of an analogue cellular phone yielded a significantly increased risk for malignant brain tumours.

  18. A novel brain tumour model in zebrafish reveals the role of YAP activation in MAPK- and PI3K-induced malignant growth

    Science.gov (United States)

    Mayrhofer, Marie; Gourain, Victor; Reischl, Markus; Affaticati, Pierre; Jenett, Arnim; Joly, Jean-Stephane; Benelli, Matteo; Demichelis, Francesca; Poliani, Pietro Luigi; Sieger, Dirk

    2017-01-01

    ABSTRACT Somatic mutations activating MAPK and PI3K signalling play a pivotal role in both tumours and brain developmental disorders. We developed a zebrafish model of brain tumours based on somatic expression of oncogenes that activate MAPK and PI3K signalling in neural progenitor cells and found that HRASV12 was the most effective in inducing both heterotopia and invasive tumours. Tumours, but not heterotopias, require persistent activation of phospho (p)-ERK and express a gene signature similar to the mesenchymal glioblastoma subtype, with a strong YAP component. Application of an eight-gene signature to human brain tumours establishes that YAP activation distinguishes between mesenchymal glioblastoma and low grade glioma in a wide The Cancer Genome Atlas (TCGA) sample set including gliomas and glioblastomas (GBMs). This suggests that the activation of YAP might be an important event in brain tumour development, promoting malignant versus benign brain lesions. Indeed, co-expression of dominant-active YAP (YAPS5A) and HRASV12 abolishes the development of heterotopias and leads to the sole development of aggressive tumours. Thus, we have developed a model proving that neurodevelopmental disorders and brain tumours might originate from the same activation of oncogenes through somatic mutations, and established that YAP activation is a hallmark of malignant brain tumours. PMID:27935819

  19. A novel brain tumour model in zebrafish reveals the role of YAP activation in MAPK- and PI3K-induced malignant growth

    Directory of Open Access Journals (Sweden)

    Marie Mayrhofer

    2017-01-01

    Full Text Available Somatic mutations activating MAPK and PI3K signalling play a pivotal role in both tumours and brain developmental disorders. We developed a zebrafish model of brain tumours based on somatic expression of oncogenes that activate MAPK and PI3K signalling in neural progenitor cells and found that HRASV12 was the most effective in inducing both heterotopia and invasive tumours. Tumours, but not heterotopias, require persistent activation of phospho (p-ERK and express a gene signature similar to the mesenchymal glioblastoma subtype, with a strong YAP component. Application of an eight-gene signature to human brain tumours establishes that YAP activation distinguishes between mesenchymal glioblastoma and low grade glioma in a wide The Cancer Genome Atlas (TCGA sample set including gliomas and glioblastomas (GBMs. This suggests that the activation of YAP might be an important event in brain tumour development, promoting malignant versus benign brain lesions. Indeed, co-expression of dominant-active YAP (YAPS5A and HRASV12 abolishes the development of heterotopias and leads to the sole development of aggressive tumours. Thus, we have developed a model proving that neurodevelopmental disorders and brain tumours might originate from the same activation of oncogenes through somatic mutations, and established that YAP activation is a hallmark of malignant brain tumours.

  20. [Ovarian tumor in a koi carp (Cyprinus carpio): Diagnosis, surgery, postoperative care and tumour classification].

    Science.gov (United States)

    Lewisch, E; Reifinger, M; Schmidt, P; El-Matbouli, M

    2014-01-01

    Although ovarian tumour in the koi (Cyprinus carpio) does not appear to be an uncommon condition, its occurrence and therapy has rarely been reported. In the present case, the decision for surgery was based on clinical and sonographic findings of an intracoelomic mass. We used tricaine methansulfonate for the anaesthesia. Laparotomy was performed by ventral access and an ovarian tumour of 12-cm diameter was removed. The wound was sutured in two layers using Vicryl®. In addition to the application of an analgesic, an antibiotic and vitamins, the postoperative conditions the patient was kept under were adapted to support wound healing. The fish recovered uneventfully and was clinically healthy during the 16-month observation period. Based on the histological findings, the tumour was diagnosed as a thecoma. Investigations using antibodies against vimentin, cytokeratin, S 100 and glial fibrillary acidic protein (GFAP) failed to provide reliable results.

  1. The role of CXC chemokine ligand (CXCL)12-CXC chemokine receptor (CXCR)4 signalling in the migration of neural stem cells towards a brain tumour

    NARCIS (Netherlands)

    van der Meulen, A. A. E.; Biber, K.; Lukovac, S.; Balasubramaniyan, V.; den Dunnen, W. F. A.; Boddeke, H. W. G. M.; Mooij, J. J. A.

    2009-01-01

    Aims: It has been shown that neural stem cells (NSCs) migrate towards areas of brain injury or brain tumours and that NSCs have the capacity to track infiltrating tumour cells. The possible mechanism behind the migratory behaviour of NSCs is not yet completely understood. As chemokines are involved

  2. Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra.

    NARCIS (Netherlands)

    Tate, A.R.; Underwood, J.; Acosta, D.M.; Julia-Sape, M.; Majos, C.; Moreno-Torres, A.; Howe, F.A.; Graaf, M. van der; Lefournier, V.; Murphy, M.M.; Loosemore, A.; Ladroue, C.; Wesseling, P.; Luc Bosson, J.; Cabanas, M.E.; Simonetti, A.W.; Gajewicz, W.; Calvar, J.; Capdevila, A.; Wilkins, P.R.; Bell, B.A.; Remy, C.; Heerschap, A.; Watson, D.; Griffiths, J.R.; Arus, C.

    2006-01-01

    A computer-based decision support system to assist radiologists in diagnosing and grading brain tumours has been developed by the multi-centre INTERPRET project. Spectra from a database of 1H single-voxel spectra of different types of brain tumours, acquired in vivo from 334 patients at four differe

  3. Classification of Brain Tumor Using Support Vector Machine Classfiers

    Directory of Open Access Journals (Sweden)

    Dr.D. J. Pete

    2014-03-01

    Full Text Available Magnetic resonance imagi ng (MRI is an imaging technique that has played an important role in neuro science research for studying brain images. Classification is an important part in order to distinguish between normal patients and those who have the possibility of having abnormalities or tumor. The proposed method consists of two stages: feature extraction and classification. In first stage features are extracted from images using GLCM. In the next stage, extracted features are fed as input to Kernel-Based SVM classifier. It classifies the images between normal and abnormal along with Grade of tumor depending upon features. For Brain MRI images; features extracted with GLCM gives 98% accuracy with Kernel-Based SVM Classifiesr. Software used is MATLAB R2011a.

  4. Unsupervised classification of operator workload from brain signals

    Science.gov (United States)

    Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

    2016-06-01

    Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects’ error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

  5. Leveraging Human Brain Activity to Improve Object Classification

    OpenAIRE

    Fong, Ruth Catherine

    2015-01-01

    Today, most object detection algorithms differ drastically from how humans tackle visual problems. In this thesis, I present a new paradigm for improving machine vision algorithms by designing them to better mimic how humans approach these tasks. Specifically, I demonstrate how human brain activity from functional magnetic resonance imaging (fMRI) can be leveraged to improve object classification. Inspired by the graduated manner in which humans learn, I present a novel algorithm that sim...

  6. Evolution of growth hormone neurosecretory disturbance after cranial irradiation for childhood brain tumours: a prospective study

    Energy Technology Data Exchange (ETDEWEB)

    Spoudeas, H.A.; Hindmarsh, P.C.; Brook, C.G.D. [Middlesex Hospital, London (United Kingdom); Matthews, D.R. [Radcliffe Infirmary, Oxford (United Kingdom)

    1996-08-01

    To determine the aetiopathology of post-irradiation growth hormone (GH) deficiency, we performed a mixed longitudinal analysis of 56 24 h serum GH concentration profiles and 45 paired insulin-induced hypoglycaemia tests (ITT) in 35 prepubertal children, aged 1.5-11.8 years, with brain tumours in the posterior foss (n = 25) or cerebral hemispheres (n 10). Assessments were made before (n = 16), 1 year (n = 25) and 2 to 5 years (n = 15) after a cranial irradiation (DXR) dose of at least 30 Gy. Fourier transforms, occupancy percentage, first-order derivatives (FOD) and mean concentrations were determined from the GH profiles taken after neurosurgery but before radiotherapy (n = 16) and in three treatment groups: Group 1: neurosurgery only without DXR (9n 9); Group 2: {>=} 30 Gy DXR only (n = 22); Group 3: {>=} 30 Gy DXR with additional chemotherapy (n = 9). Results were compared with those from 26 short normally growing (SN) children. (author).

  7. Lipopolysaccharide induces expression of tumour necrosis factor alpha in rat brain : inhibition by methylprednisolone and by rolipram

    NARCIS (Netherlands)

    Buttini, M; Mir, A; Appel, K; Wiederhold, KH; Limonta, S; GebickeHaerter, PJ; Boddeke, HWGM

    1997-01-01

    1 We have investigated the effects of the phosphodiesterase (PDE) type TV inhibitor rolipram and of the glucocorticoid methylprednisolone on the induction of tumour necrosis factor alpha (TNF-alpha) mRNA and protein in brains of rats after peripheral administration of lipopolysaccharide (LPS). 2 Aft

  8. Lipopolysaccharide induces expression of tumour necrosis factor alpha in rat brain : inhibition by methylprednisolone and by rolipram

    NARCIS (Netherlands)

    Buttini, M; Mir, A; Appel, K; Wiederhold, KH; Limonta, S; GebickeHaerter, PJ; Boddeke, HWGM

    1997-01-01

    1 We have investigated the effects of the phosphodiesterase (PDE) type TV inhibitor rolipram and of the glucocorticoid methylprednisolone on the induction of tumour necrosis factor alpha (TNF-alpha) mRNA and protein in brains of rats after peripheral administration of lipopolysaccharide (LPS). 2 Aft

  9. Radioisotope scanning of brain, liver, lung and bone with a note on tumour localizing agents

    Science.gov (United States)

    Lavender, J. P.

    1973-01-01

    Radioisotopic scanning of brain, liver, lungs and the skeleton is briefly reviewed with a survey of recent developments of clinical significance. In brain scanning neoplasm detection rates of greater than 90% are claimed. The true figure is probably 70-80%. Autopsy data shows a number of false negatives, particularly with vascular lesions. Attempts to make scanning more specific in differentiating neoplasm from vascular lesions by rapid sequence blood flow studies are reviewed. In liver scanning by means of colloids again high success rate is claimed but small metastases are frequently missed and the false negative scan rate is probably quite high. Lung scanning still has its main place in investigating pulmonary embolic disease. Ventilation studies using Xenon 133 are useful, particularly combined with perfusion studies. The various radiopharmaceuticals for use in bone scanning are reviewed. The appearance of technetium labelled phosphate compounds will probably allow much wider use of total skeletal scanning. Research into tumour localizing agents continues, the most recent and interesting being Gallium citrate and labelled bleomycin. Neither agent is predictable however although Gallium may have a place in Hodgkins disease and bronchogenic neoplasm and both may have a place in the detection of cerebral tumours. ImagesFig. 1Fig. 2Fig. 3p452-bFig. 3bFig. 4Fig. 5Fig. 5bFig. 6Fig. 7Fig. 8Fig. 9Fig. 10Fig. 11Fig. 12Fig. 12c & 12dFig. 13Fig. 13 b,c,dFig. 14Fig. 14bFig. 15Fig. 15bFig. 16Fig. 17Fig. 18 PMID:4602127

  10. 5-Amino-4-oxopentanoic acid photodynamic diagnosis guided microsurgery and photodynamic therapy on VX2 brain tumour implanted in a rabbit model

    Institute of Scientific and Technical Information of China (English)

    XIAO Hong; LIAO Qiong; CHENG Ming; LI Fei; XIE Bing; LI Mei; FENG Hua

    2009-01-01

    Background Complete tumour resection is important for improving the prognosis of brain tumour patients. However,extensive resection remains controversial because the tumour margin is difficult to be distinguished from surrounding brain tissue. It has been established that 5-amino-4-oxopentanoic acid (5-aminolevulinic acid, ALA) can be used as a photodynamic diagnostic marker and a photosensitizer for photodynamic therapy in surgical treatment of brain tumours. We investigated the efficacy of ALA photodynamically guided microsurgery and photodynamic therapy on VX2 brain tumour implanted in a rabbit model.Methods Eighty New Zealand rabbits implanted with VX2 brain tumours were randomly assigned to five groups: control, conventional white light microsurgery, a photodynamic therapy group, a photodynamically guided microsurgery group and a group in which guided microsurgery was followed by photodynamic therapy. The VX2 tumour was resected under a surgical microscope. The tumour resection was confirmed with histological analysis. All animals were examined with MRI for presence of any residual tumour tissue. The survival time of each rabbit was recorded.Results All treatment groups showed a significantly extended survival time compared with the control group.Photodynamically guided microsurgery combined with photodynamic therapy significantly prolonged survival time, compared with guided microsurgery alone. MRI and the autopsy results confirmed removal of most of the tumours.Conclusions Our results suggest that photodynamically guided surgery and photodynamic therapy significantly reduce or delay local recurrence, increase the effectiveness of radical resection and prolong the survival time of tumour bearing rabbits, Their combination has the potential to be used as a rapid and highly effective treatment of metastatic brain tumours.

  11. Amplification, enhanced expression and possible rearrangement of EGF receptor gene in primary human brain tumours of glial origin.

    Science.gov (United States)

    Libermann, T A; Nusbaum, H R; Razon, N; Kris, R; Lax, I; Soreq, H; Whittle, N; Waterfield, M D; Ullrich, A; Schlessinger, J

    Epidermal growth factor (EGF), through interaction with specific cell surface receptors, generates a pleiotropic response that, by a poorly defined mechanism, can induce proliferation of target cells. Subversion of the EGF mitogenic signal through expression of a truncated receptor may be involved in transformation by the avian erythroblastosis virus (AEV) oncogene v-erb-B, suggesting that similar EGF receptor defects may be found in human neoplasias. Overexpression of EGF receptors has been reported on the epidermoid carcinoma cell line A431, in various primary brain tumours and in squamous carcinomas. In A431 cells the receptor gene is amplified. Here we show that 4 of 10 primary brain tumours of glial origin which express levels of EGF receptors that are higher than normal also have amplified EGF receptor genes. Amplified receptor genes were not detected in the other brain tumours examined. Further analysis of EGF receptor defects may show that such altered expression and amplification is a particular feature of certain human tumours.

  12. A multinational case-control study on childhood brain tumours, anthropogenic factors, birth characteristics and prenatal exposures

    DEFF Research Database (Denmark)

    Vienneau, Danielle; Infanger, Denis; Feychting, Maria

    2016-01-01

    complemented with data from birth registries and validated by assessing agreement (Cohen's Kappa). We used conditional logistic regression models matched on age, sex and geographical region (adjusted for maternal age and parental education) to explore associations between birth factors and childhood brain...... during pregnancy was indicative of a protective effect (OR 0.75, 95%-CI: 0.56-1.01). No association was seen for maternal smoking during pregnancy or working during pregnancy. We found little evidence that the considered birth factors were related to brain tumour risk among children and adolescents.......Little is known about the aetiology of childhood brain tumours. We investigated anthropometric factors (birth weight, length, maternal age), birth characteristics (e.g. vacuum extraction, preterm delivery, birth order) and exposures during pregnancy (e.g. maternal: smoking, working, dietary...

  13. Perinatal tumours: the contribution of radiology to management

    Energy Technology Data Exchange (ETDEWEB)

    Donoghue, Veronica; Ryan, Stephanie; Twomey, Eilish [Children' s University Hospital, Radiology Department, Dublin (Ireland)

    2008-06-15

    A formal classification does not exist and they are probably best classified by their location. Overall the most common neoplasms are - Extracranial teratoma - Neuroblastoma - Soft-tissue tumours - Brain tumours - Leukaemia - Renal tumours - Liver tumours - Retinoblastoma. The prognosis is generally poor, although there are some exceptions such as congenital neuroblastoma and hepatoblastoma. These tumours have a tendency to regress and have a benign clinical course despite a clear malignant histological picture. Other tumours, though histologically benign, may be fatal because of their size and location. Large benign masses may cause airway or cardiovascular compromise and death. Others may cause significant mass effect preventing normal organ development. As normal embryonic cells have a high mitotic rate it is not surprising that perinatal tumours may have a rapid growth rate and become enormous in size. (orig.)

  14. Challenges in providing culturally-competent care to patients with metastatic brain tumours and their families.

    Science.gov (United States)

    Longo, Lianne; Slater, Serena

    2014-01-01

    Being diagnosed with a metastatic brain tumour can be devastating as it is characterized by very low cure rates, as well as significant morbidity and mortality. Given the poor life expectancy and progressive disability that ensues, patients and family members experience much turmoil, which includes losses that bring about changes to family roles, routines and relationships. Crisis and conflict are common during such major disruptions to a family system, as individual members attempt to make sense of the illness experience based on cultural and spiritual beliefs, past experiences and personal philosophies. It is imperative health care providers strive towards increased awareness and knowledge of how culture affects the overall experience of illness and death in order to help create a mutually satisfactory care plan. Providing culturally-competent care entails the use of proper communication skills to facilitate the exploration of patient and family perspectives and allows for mutual decision making. A case study will illustrate the challenges encountered in providing culturally-competent care to a woman with brain cancer and her family. As the patient's health declined, the family entered into a state of crisis where communication between family members and health care professionals was strained; leading to conflict and sub-optimal outcomes. This paper will address the ethical dilemma of providing culturally-competent care when a patient's safety is at risk, and the nursing implications of upholding best practices in the context of differing beliefs and priorities.

  15. Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis.

    Science.gov (United States)

    Su, Zhengyu; Zeng, Wei; Wang, Yalin; Lu, Zhong-Lin; Gu, Xianfeng

    2015-01-01

    Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory. By Poincare uniformization theorem, all shapes can be conformally deformed to one of the three canonical spaces: the unit sphere, the Euclidean plane or the hyperbolic plane. The uniformization map will distort the surface area elements. The area-distortion factor gives a probability measure on the canonical uniformization space. All the probability measures on a Riemannian manifold form the Wasserstein space. Given any 2 probability measures, there is a unique optimal mass transport map between them, the transportation cost defines the Wasserstein distance between them. Wasserstein distance gives a Riemannian metric for the Wasserstein space. It intrinsically measures the dissimilarities between shapes and thus has the potential for shape classification. To the best of our knowledge, this is the first. work to introduce the optimal mass transport map to general Riemannian manifolds. The method is based on geodesic power Voronoi diagram. Comparing to the conventional methods, our approach solely depends on Riemannian metrics and is invariant under rigid motions and scalings, thus it intrinsically measures shape distance. Experimental results on classifying brain cortical surfaces with different intelligence quotients demonstrated the efficiency and efficacy of our method.

  16. Improved Classification Methods for Brain Computer Interface System

    Directory of Open Access Journals (Sweden)

    YI Fang

    2012-03-01

    Full Text Available Brain computer interface (BCI aims at providing a new communication way without brain’s normal output through nerve and muscle. The electroencephalography (EEG has been widely used for BCI system because it is a non-invasive approach. For the EEG signals of left and right hand motor imagery, the event-related desynchronization (ERD and event-related synchronization(ERS are used as classification features in this paper. The raw data are transformed by nonlinear methods and classified by Fisher classifier. Compared with the linear methods, the classification accuracy can get an obvious increase to 86.25%. Two different nonlinear transform were arised and one of them is under the consideration of the relativity of two channels of EEG signals. With these nonlinear transform, the performance are also stable with the balance of two misclassifications.

  17. Dosimetric and geometric evaluation of an open low-field magnetic resonance simulator for radiotherapy treatment planning of brain tumours

    DEFF Research Database (Denmark)

    Kristensen, B.H.; Laursen, F.J.; Logager, V.

    2008-01-01

    distortion within radial distances below 12 cm (2% are observed in low dose areas. Monte Carlo simulations with 4 MV photons show large deviations in dose (>2%) just behind the skull if bone is not segmented. Conclusions: It is feasible to use an MR...... patients with brain tumours are both CT and MR scanned and the defined tumour volumes are compared. Image distortions and dose calculations based on CT density correction, MR unit density and MR bulk density, bone segmentation are performed. Monte Carlo simulations using 4 and 8 MV beams on homogeneous...... and bone segmented mediums are performed. Results: Mean MR and CT tumour volumes of approximately the same size ((V-MR) over bar = 55 +/- 34 cm(3) and (V-CT) over bar = 51 +/- 32 cm(3)) are observed, but for individual patients, small intersection volumes are observed. The MR images show negligible...

  18. Application of machine learning on brain cancer multiclass classification

    Science.gov (United States)

    Panca, V.; Rustam, Z.

    2017-07-01

    Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

  19. EMOTION INTERACTION WITH VIRTUAL REALITY USING HYBRID EMOTION CLASSIFICATION TECHNIQUE TOWARD BRAIN SIGNALS

    National Research Council Canada - National Science Library

    Faris A. Abuhashish; Jamal Zraqou; Wesam Alkhodour; Mohd S. Sunar; Hoshang Kolivand

    2015-01-01

    .... Last decade many researchers focused on emotion classification in order to employ emotion in interaction with virtual reality, the classification will be done based on Electroencephalogram (EEG) brain signals...

  20. Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation

    Directory of Open Access Journals (Sweden)

    Richard eBeare

    2016-03-01

    Full Text Available Measuring the distribution of brain tissue types (tissue classification in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation, which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF, hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks’ gestation acquired at 30 weeks’ corrected gestational age (n= 5, coronal T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5 and axial T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5. The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR group, consisted of T2-weighted images of preterm infants (born <30 weeks’ gestation acquired shortly after birth (n= 12, preterm infants acquired at term-equivalent age (n= 12, and healthy term-born infants (born ≥38 weeks’ gestation acquired within the first nine days of life (n= 12. For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for

  1. Long-term use of cellular phones and brain tumours: increased risk associated with use for > or =10 years.

    Science.gov (United States)

    Hardell, Lennart; Carlberg, Michael; Söderqvist, Fredrik; Mild, Kjell Hansson; Morgan, L Lloyd

    2007-09-01

    To evaluate brain tumour risk among long-term users of cellular telephones. Two cohort studies and 16 case-control studies on this topic were identified. Data were scrutinised for use of mobile phone for > or =10 years and ipsilateral exposure if presented. The cohort study was of limited value due to methodological shortcomings in the study. Of the 16 case-control studies, 11 gave results for > or =10 years' use or latency period. Most of these results were based on low numbers. An association with acoustic neuroma was found in four studies in the group with at least 10 years' use of a mobile phone. No risk was found in one study, but the tumour size was significantly larger among users. Six studies gave results for malignant brain tumours in that latency group. All gave increased odd ratios (OR), especially for ipsilateral exposure. In a meta-analysis, ipsilateral cell phone use for acoustic neuroma was OR = 2.4 (95% CI 1.1 to 5.3) and OR = 2.0, (1.2 to 3.4) for glioma using a tumour latency period of > or =10 years. Results from present studies on use of mobile phones for > or =10 years give a consistent pattern of increased risk for acoustic neuroma and glioma. The risk is highest for ipsilateral exposure.

  2. Correlation of nodal mast cells with clinical outcome in dogs with mast cell tumour and a proposed classification system for the evaluation of node metastasis.

    Science.gov (United States)

    Weishaar, K M; Thamm, D H; Worley, D R; Kamstock, D A

    2014-11-01

    Lymph node metastasis in dogs with mast cell tumour has been reported as a negative prognostic indicator; however, no standardized histological criteria exist to define metastatic disease. The primary aim of this study was to determine whether different histological patterns of node-associated mast cells correlate with clinical outcome in dogs with mast cell tumour. A secondary goal was to propose a criteria-defined classification system for histological evaluation of lymph node metastasis. The Colorado State University Diagnostic Medicine Center database was searched for cases of canine mast cell tumours with reported lymph node metastasis or evidence of node-associated mast cells. Additional cases were obtained from a clinical trial involving sentinel lymph node mapping and node extirpation in dogs with mast cell neoplasia. Forty-one cases were identified for inclusion in the study. Demographic data, treatment and clinical outcome were collected for each case. Lymph nodes were classified according to a novel classification system (HN0-HN3) based on the number of, distribution of, and architectural disruption by, nodal mast cells. The findings of this study indicate that characterization of nodal mast cells as proposed by this novel classification system correlates with, and is prognostic for, clinical outcome in dogs with mast cell tumours.

  3. Cerebellar mutism syndrome in children with brain tumours of the posterior fossa

    OpenAIRE

    Wibroe, Morten; Cappelen, Johan; Castor, Charlotte; Clausen, Niels; Grillner, Pernilla; Gudrunardottir, Thora; Gupta, Ramneek; Gustavsson, Bengt; Heyman, Mats; Holm, Stefan; Karppinen, Atte; Klausen, Camilla; Lonnqvist, Tuula; Mathiasen, Rene; Nilsson, Pelle

    2017-01-01

    Background Central nervous system tumours constitute 25% of all childhood cancers; more than half are located in the posterior fossa and surgery is usually part of therapy. One of the most disabling late effects of posterior fossa tumour surgery is the cerebellar mutism syndrome (CMS) which has been reported in up to 39% of the patients but the exact incidence is uncertain since milder cases may be unrecognized. Recovery is usually incomplete. Reported risk factors are tumour type, midline lo...

  4. Myoepithelial and epithelial-myoepithelial, mesenchymal and fibroepithelial breast lesions: updates from the WHO Classification of Tumours of the Breast 2012.

    Science.gov (United States)

    Tan, Puay Hoon; Ellis, Ian O

    2013-06-01

    In the 4th edition of the WHO Classification of Tumours of the Breast, myoepithelial lesions are retitled myoepithelial and epithelial-myoepithelial lesions in order to better reflect the dual participation of luminal and myoepithelial compartments in some key entities. Malignant myoepithelioma, described as a section within the chapter on myoepithelial lesions in the 3rd edition, is recognised in the 4th edition as part of metaplastic carcinoma. Adenomyoepithelioma with malignancy is categorised in terms of the cellular component undergoing malignant transformation. The list of antibodies that can be used for identifying myoepithelial cells is updated. Among mesenchymal lesions, new additions are nodular fasciitis and atypical vascular lesions, while the haemangiopericytoma is removed. The 3rd edition stated that pathological prediction of behaviour of phyllodes tumours is difficult in the individual case. In the 4th edition, some progress has been made in prioritisation and weighting of histological parameters that can potentially estimate probability of recurrence. The WHO Working Group advocates leaning towards a diagnosis of fibroadenoma in cases where there is histological uncertainty in distinction from a benign phyllodes tumour, or adopting the neutral term 'benign fibroepithelial neoplasm', as the clinical behaviour of fibroadenoma overlaps with that of benign phyllodes tumour. The 3rd edition terminology of 'periductal stromal sarcoma' is revised to 'periductal stromal tumour', akin to the widespread consensus to avoid the use of the term 'cystosarcoma' in the context of phyllodes tumours.

  5. A case series discussing the anaesthetic management of pregnant patients with brain tumours [v2; ref status: indexed, http://f1000r.es/2hn

    Directory of Open Access Journals (Sweden)

    Alaa A Abd-Elsayed

    2013-12-01

    Full Text Available Pregnancy may aggravate the natural history of an intracranial tumour, and may even unmask a previously unknown diagnosis. Here we present a series of seven patients who had brain tumours during pregnancy. The aim of this case series is to characterize the current perioperative management and to suggest evidence based guidelines for the anaesthetic management of pregnant females with brain tumours. This is a retrospective study. Information on pregnant patients diagnosed with brain tumours that underwent caesarean section (CS and/or brain tumour resection from May 2003 through June 2008 was obtained from the Department of General Anaesthesia and the Rose Ella Burkhardt Brain Tumour & Neuro-Oncology Centre (BBTC at the Cleveland Clinic, OH, USA. The mean age was 34.5 years (range 29-40 years old. Six patients had glioma, two of whom had concomitant craniotomy and CS. Six cases had the tumour in the frontal lobe. Four cases were operated on under general anaesthesia and three underwent awake craniotomy. The neonatal outcomes of the six patients with elective or emergent delivery were six viable infants with normal Apgar scores. Pregnancy was terminated in the 7th patient. In conclusion, good knowledge of the variable anesthetic agents and their effects on the fetus is very important in managing those patients.

  6. Perioperative thromboprophylaxis in patients with craniotomy for brain tumours: a systematic review.

    Science.gov (United States)

    Salmaggi, Andrea; Simonetti, Giorgia; Trevisan, Elisa; Beecher, Deirdre; Carapella, Carmine Maria; DiMeco, Francesco; Conti, Laura; Pace, Andrea; Filippini, Graziella

    2013-06-01

    Venous thromboembolism (VTE) events are frequent in neurooncological patients in perioperative period thus increasing mortality and morbidity. The role of prophylaxis has not yet been established with certainty, and in various neurosurgery and intensive care units the practice is inconsistent. A better definition of the risk/cost/benefit ratio of the various methods, both mechanical (intermittent pneumatic compression-IPC, graduated compression stockings-GCS) and pharmacological (unfractionated heparin-UFH or low molecular weight heparin-LMWH), is warranted. We aim to define the optimal prophylactic treatment in the perioperative period in neurooncological patients. A systematic review of the literature was performed in Medline, Embase and Cochrane Library. Thirteen randomized controlled trials (RCTs) were identified, in which physical methods (IPC or GCS) and/or drugs (UFH or LMWHs) were evaluated in perioperative prophylaxis of neurological patients, mostly with brain cancer not treated with anticoagulants for other diseases. The analysis was conducted on a total of 1,932 randomized patients of whom 1,558 had brain tumours. Overall data show a trend of reduction of VTE in patients treated with mechanical methods (IPC or GCS) that should be initiated preoperatively and continued until discharge or longer in case of persistence of risk factors. The addition of enoxaparin starting the day after surgery, significantly reduces clinically manifest VTE, despite an increase in major bleeding events. Further studies are needed to delineate the types of patients with an increase of VTE risk and risk/benefits ratio of physical and pharmacological treatments in the perioperative period.

  7. Validating a robust double‐quantum‐filtered 1H MRS lactate measurement method in high‐grade brain tumours

    OpenAIRE

    Payne, G S; Harris, L M; Cairns, G.S.; Messiou, C; deSouza, N M; Macdonald, A.; Saran, F.; Leach, M. O.

    2016-01-01

    1H MRS measurements of lactate are often confounded by overlapping lipid signals. Double‐quantum (DQ) filtering eliminates lipid signals and permits single‐shot measurements, which avoid subtraction artefacts in moving tissues. This study evaluated a single‐voxel‐localized DQ filtering method qualitatively and quantitatively for measuring lactate concentrations in the presence of lipid, using high‐grade brain tumours in which the results could be compared with standard acquisition as a refere...

  8. In-vivo imaging of the morphology and blood perfusion of brain tumours in rats with UHR-OCT (Conference Presentation)

    Science.gov (United States)

    Bizheva, Kostadinka; Tan, Bingyao; Fisher, Carl J.; Mason, Erik; Lilge, Lothar D.

    2017-02-01

    Brain tumors are characterized with morphological changes at cellular level such as enlarged, non-spherical nuclei, microcalcifications, cysts, etc., and are highly vascularized. In this study, two research-grade optical coherence tomography (OCT) systems operating at 800 nm and 1060 nm with axial resolution of 0.95 µm and 3.5 µm in biological tissue respectively, were used to image in vivo and ex vivo the structure of brain tumours in rats. Female Fischer 344 rats were used for this study, which has received ethics clearance by the Animal Research Ethics Committees of the University of Waterloo and the University Health Network, Toronto. Brain tumours were induced by injection of rat brain cancer cell line (RG2 glioma) through a small craniotomy. Presence of brain tumours was verified by MRI imaging on day 7 post tumour cells injection. The in vivo OCT imaging session was conducted on day 14 of the study with the 1060 nm OCT system and both morphological OCT, Doppler OCT and OMAG images were acquired from the brain tumour and the surrounding healthy brain tissue. After completion of the imaging procedure, the brains were harvested, fixed in formalin and reimaged after 2 weeks with the 800 nm OCT system. The in vivo and ex vivo OCT morphological images were correlated with H and E histology. Results from this study demonstrate that UHR-OCT can distinguish between healthy and cancerous brain tissue based on differences in structural and vascular pattern.

  9. Classification of types of stuttering symptoms based on brain activity.

    Science.gov (United States)

    Jiang, Jing; Lu, Chunming; Peng, Danling; Zhu, Chaozhe; Howell, Peter

    2012-01-01

    Among the non-fluencies seen in speech, some are more typical (MT) of stuttering speakers, whereas others are less typical (LT) and are common to both stuttering and fluent speakers. No neuroimaging work has evaluated the neural basis for grouping these symptom types. Another long-debated issue is which type (LT, MT) whole-word repetitions (WWR) should be placed in. In this study, a sentence completion task was performed by twenty stuttering patients who were scanned using an event-related design. This task elicited stuttering in these patients. Each stuttered trial from each patient was sorted into the MT or LT types with WWR put aside. Pattern classification was employed to train a patient-specific single trial model to automatically classify each trial as MT or LT using the corresponding fMRI data. This model was then validated by using test data that were independent of the training data. In a subsequent analysis, the classification model, just established, was used to determine which type the WWR should be placed in. The results showed that the LT and the MT could be separated with high accuracy based on their brain activity. The brain regions that made most contribution to the separation of the types were: the left inferior frontal cortex and bilateral precuneus, both of which showed higher activity in the MT than in the LT; and the left putamen and right cerebellum which showed the opposite activity pattern. The results also showed that the brain activity for WWR was more similar to that of the LT and fluent speech than to that of the MT. These findings provide a neurological basis for separating the MT and the LT types, and support the widely-used MT/LT symptom grouping scheme. In addition, WWR play a similar role as the LT, and thus should be placed in the LT type.

  10. Classification of types of stuttering symptoms based on brain activity.

    Directory of Open Access Journals (Sweden)

    Jing Jiang

    Full Text Available Among the non-fluencies seen in speech, some are more typical (MT of stuttering speakers, whereas others are less typical (LT and are common to both stuttering and fluent speakers. No neuroimaging work has evaluated the neural basis for grouping these symptom types. Another long-debated issue is which type (LT, MT whole-word repetitions (WWR should be placed in. In this study, a sentence completion task was performed by twenty stuttering patients who were scanned using an event-related design. This task elicited stuttering in these patients. Each stuttered trial from each patient was sorted into the MT or LT types with WWR put aside. Pattern classification was employed to train a patient-specific single trial model to automatically classify each trial as MT or LT using the corresponding fMRI data. This model was then validated by using test data that were independent of the training data. In a subsequent analysis, the classification model, just established, was used to determine which type the WWR should be placed in. The results showed that the LT and the MT could be separated with high accuracy based on their brain activity. The brain regions that made most contribution to the separation of the types were: the left inferior frontal cortex and bilateral precuneus, both of which showed higher activity in the MT than in the LT; and the left putamen and right cerebellum which showed the opposite activity pattern. The results also showed that the brain activity for WWR was more similar to that of the LT and fluent speech than to that of the MT. These findings provide a neurological basis for separating the MT and the LT types, and support the widely-used MT/LT symptom grouping scheme. In addition, WWR play a similar role as the LT, and thus should be placed in the LT type.

  11. Classification of Types of Stuttering Symptoms Based on Brain Activity

    Science.gov (United States)

    Jiang, Jing; Lu, Chunming; Peng, Danling; Zhu, Chaozhe; Howell, Peter

    2012-01-01

    Among the non-fluencies seen in speech, some are more typical (MT) of stuttering speakers, whereas others are less typical (LT) and are common to both stuttering and fluent speakers. No neuroimaging work has evaluated the neural basis for grouping these symptom types. Another long-debated issue is which type (LT, MT) whole-word repetitions (WWR) should be placed in. In this study, a sentence completion task was performed by twenty stuttering patients who were scanned using an event-related design. This task elicited stuttering in these patients. Each stuttered trial from each patient was sorted into the MT or LT types with WWR put aside. Pattern classification was employed to train a patient-specific single trial model to automatically classify each trial as MT or LT using the corresponding fMRI data. This model was then validated by using test data that were independent of the training data. In a subsequent analysis, the classification model, just established, was used to determine which type the WWR should be placed in. The results showed that the LT and the MT could be separated with high accuracy based on their brain activity. The brain regions that made most contribution to the separation of the types were: the left inferior frontal cortex and bilateral precuneus, both of which showed higher activity in the MT than in the LT; and the left putamen and right cerebellum which showed the opposite activity pattern. The results also showed that the brain activity for WWR was more similar to that of the LT and fluent speech than to that of the MT. These findings provide a neurological basis for separating the MT and the LT types, and support the widely-used MT/LT symptom grouping scheme. In addition, WWR play a similar role as the LT, and thus should be placed in the LT type. PMID:22761887

  12. Increased levels of deleted in malignant brain tumours 1 (DMBT1) in active bacteria-related appendicitis

    DEFF Research Database (Denmark)

    Kaemmerer, Elke; Schneider, Ursula; Klaus, Christina

    2012-01-01

    Kaemmerer E, Schneider U, Klaus C, Plum P, Reinartz A, Adolf M, Renner M, Wolfs T G A M, Kramer B W, Wagner N, Mollenhauer J & Gassler N (2012) Histopathology Increased levels of deleted in malignant brain tumours 1 (DMBT1) in active bacteria-related appendicitis Aims:  Deleted in malignant brain...... in bacteria-related active intestinal inflammation such as appendicitis. Methods and results:  mRNA and protein levels of DMBT1 were analysed in surgical resections of 50 appendices (active inflammation: n = 25). In non-actively inflamed appendices, inter-individual differences in basal DMBT1 levels...

  13. In vitro growth environment produces lipidomic and electron transport chain abnormalities in mitochondria from non-tumorigenic astrocytes and brain tumours

    Directory of Open Access Journals (Sweden)

    Thomas N Seyfried

    2009-05-01

    Full Text Available The mitochondrial lipidome influences ETC (electron transport chain and cellular bioenergetic efficiency. Brain tumours are largely dependent on glycolysis for energy due to defects in mitochondria and oxidative phosphorylation. In the present study, we used shotgun lipidomics to compare the lipidome in highly purified mitochondria isolated from normal brain, from brain tumour tissue, from cultured tumour cells and from non-tumorigenic astrocytes. The tumours included the CT-2A astrocytoma and an EPEN (ependymoblastoma, both syngeneic with the C57BL/6J (B6 mouse strain. The mitochondrial lipidome in cultured CT-2A and EPEN tumour cells were compared with those in cultured astrocytes and in solid tumours grown in vivo. Major differences were found between normal tissue and tumour tissue and between in vivo and in vitro growth environments for the content or composition of ethanolamine glycerophospholipids, phosphatidylglycerol and cardiolipin. The mitochondrial lipid abnormalities in solid tumours and in cultured cells were associated with reductions in multiple ETC activities, especially Complex I. The in vitro growth environment produced lipid and ETC abnormalities in cultured non-tumorigenic astrocytes that were similar to those associated with tumorigenicity. It appears that the culture environment obscures the boundaries of the Crabtree and the Warburg effects. These results indicate that in vitro growth environments can produce abnormalities in mitochondrial lipids and ETC activities, thus contributing to a dependency on glycolysis for ATP production.

  14. Ncut在颅脑 MRI肿瘤提取中的应用研究%ON APPLYING NORMALISED CUT TO BRAIN MRI TUMOUR EXTRACTION

    Institute of Scientific and Technical Information of China (English)

    宋广军; 赵春兰

    2013-01-01

    颅脑肿瘤自身边缘包含重要的病变信息,提取脑肿瘤区域,对脑部疾病的诊断和治疗具有重要意义。 Ncut ( Normalized Cut)是基于图理的典型分割方法。将Ncut算法应用到颅脑MRI肿瘤图像的分割中,针对不同颅脑MRI肿瘤图像,进行相关参数测试,选择合适的权重及参数,进行颅脑MR肿瘤的提取。通过利用Matlab进行仿真测试可知Ncut方法能够提取出肿瘤所在的基本轮廓,取得较好效果。%The brain tumour edge contains important pathology information itself ;it has the important meaning to extract brain tumour area for brain disease diagnosis and treatment .Ncut algorithm is the typical segmentation method based on graph theory .We apply the Ncut algorithm to MRI brain tumour image segmentation , test the related parameters according to different brain MRI tumour images , and select appropriate weights and parameters to extract MR image brain tumour .Through the use of Matlab in simulation test , we know that the Ncut method can extract the basic outline of tumour and achieve good effect .

  15. Retrieving binary answers using whole-brain activity pattern classification

    Directory of Open Access Journals (Sweden)

    Norberto Eiji Nawa

    2015-12-01

    Full Text Available Multivariate pattern analysis (MVPA has been successfully employed to advance our understanding of where and how information regarding different mental states is represented in the human brain, bringing new insights into how these states come to fruition, and providing a promising complement to the mass-univariate approach. Here, we employed MVPA to classify whole-brain activity patterns occurring in single fMRI scans, in order to retrieve binary answers from experiment participants. Five healthy volunteers performed two types of mental task while in the MRI scanner: counting down numbers and recalling positive autobiographical events. Data from these runs were used to train individual machine learning based classifiers that predicted which mental task was being performed based on the voxel-based brain activity patterns. On a different day, the same volunteers reentered the scanner and listened to six statements (e.g., the month you were born is an odd number, and were told to countdown numbers if the statement was true (yes or recall positive events otherwise (no. The previously trained classifiers were then used to assign labels (yes/no to the scans collected during the 24-second response periods following each one of the statements. Mean classification accuracies at the single scan level were in the range of 73.6% to 80.8%, significantly above chance for all participants. When applying a majority vote on the scans within each response period, i.e., the most frequent label (yes/no in the response period becomes the answer to the previous statement, 5.0 to 5.8 sentences, out of 6, were correctly classified in each one of the runs, on average. These results indicate that binary answers can be retrieved from whole-brain activity patterns, suggesting that MVPA provides an alternative way to establish basic communication with unresponsive patients when other techniques are not successful.

  16. Patterns of exposure to infectious diseases and social contacts in early life and risk of brain tumours in children and adolescents

    DEFF Research Database (Denmark)

    Andersen, T V; Schmidt, L S; Poulsen, A H;

    2013-01-01

    BACKGROUND: Infectious diseases and social contacts in early life have been proposed to modulate brain tumour risk during late childhood and adolescence. METHODS: CEFALO is an interview-based case-control study in Denmark, Norway, Sweden and Switzerland, including children and adolescents aged 7-...... tumour may reflect involvement of immune functions, recall bias or inverse causality and deserve further attention.......BACKGROUND: Infectious diseases and social contacts in early life have been proposed to modulate brain tumour risk during late childhood and adolescence. METHODS: CEFALO is an interview-based case-control study in Denmark, Norway, Sweden and Switzerland, including children and adolescents aged 7......-19 years with primary intracranial brain tumours diagnosed between 2004 and 2008 and matched population controls. RESULTS: The study included 352 cases (participation rate: 83%) and 646 controls (71%). There was no association with various measures of social contacts: daycare attendance, number...

  17. Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

    Science.gov (United States)

    Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

    2017-08-01

    This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

  18. Misdiagnosis of Child Abuse Related to Delay in Diagnosing a Paediatric Brain Tumour

    Directory of Open Access Journals (Sweden)

    Lynne Wrennall Ph.D.

    2008-01-01

    Full Text Available Conflicting opinion regarding the relative weight that should be allocated to the investigation of organic causes of child illness, compared to the pursuit of suspicions of child abuse, has generated considerable public debate. The discourse of Munchausen Syndrome by Proxy/Fabricated and Induced Illness is at the centre of contention. In particular, concern has arisen that children's medical needs are being neglected when their conditions are misdiagnosed as child abuse. This paper documents a case study in which the use of Child Protection procedures was linked to the belief that the child's illness had “no organic cause.” The case study is contextualised in a review of literature relevant to the diagnostic process. The deployment of the Child Protection perspective resulted in significant delay in the diagnosis of the child's brain tumour. The child was ultimately found to be suffering from an optic chasm mass lesion involving the hypothalamus and the medial temporal regions, resulting in Diencephalic Syndrome. The evidence in this case is that erring on the side of suspecting Munchausen Syndrome by Proxy/Fabricated and Induced Illness, was not “erring on the side of the child.” Several lessons need to be learned from the case. The importance of ensuring that the Child Protection perspective does not displace adequate assessment of alternative explanations for the child's condition is emphasised, as is the need for good communication in medical relationships. Strategies involving empathy, mediation, negotiation and conflict resolution may provide a more appropriate and therapeutic alternative to the use of Child Protection procedures in cases where the diagnosis is contentious. The need to re-write relevant policy, protocols and guidance is imperative.

  19. Misdiagnosis of Child Abuse Related to Delay in Diagnosing a Paediatric Brain Tumour

    Directory of Open Access Journals (Sweden)

    Lynne Wrennall

    2008-01-01

    Full Text Available Conflicting opinion regarding the relative weight that should be allocated to the investigation of organic causes of child illness, compared to the pursuit of suspicions of child abuse, has generated considerable public debate. The discourse of Munchausen Syndrome by Proxy/Fabricated and Induced Illness is at the centre of contention. In particular, concern has arisen that children’s medical needs are being neglected when their conditions are misdiagnosed as child abuse. This paper documents a case study in which the use of Child Protection procedures was linked to the belief that the child’s illness had “no organic cause.” The case study is contextualised in a review of literature relevant to the diagnostic process. The deployment of the Child Protection perspective resulted in significant delay in the diagnosis of the child’s brain tumour. The child was ultimately found to be suffering from an optic chasm mass lesion involving the hypothalamus and the medial temporal regions, resulting in Diencephalic Syndrome. The evidence in this case is that erring on the side of suspecting Munchausen Syndrome by Proxy/Fabricated and Induced Illness, was not “erring on the side of the child.” Several lessons need to be learned from the case. The importance of ensuring that the Child Protection perspective does not displace adequate assessment of alternative explanations for the child’s condition is emphasised, as is the need for good communication in medical relationships. Strategies involving empathy, mediation, negotiation and conflict resolution may provide a more appropriate and therapeutic alternative to the use of Child Protection procedures in cases where the diagnosis is contentious. The need to re-write relevant policy, protocols and guidance is imperative.

  20. Spectroscopic magnetic resonance imaging of the brain: voxel localisation and tissue segmentation in the follow up of brain tumour.

    Science.gov (United States)

    Poloni, Guy; Bastianello, S; Vultaggio, Angela; Pozzi, S; Maccabelli, Gloria; Germani, Giancarlo; Chiarati, Patrizia; Pichiecchio, Anna

    2008-01-01

    The field of application of magnetic resonance spectroscopy (MRS) in biomedical research is expanding all the time and providing opportunities to investigate tissue metabolism and function. The data derived can be integrated with the information on tissue structure gained from conventional and non-conventional magnetic resonance imaging (MRI) techniques. Clinical MRS is also strongly expected to play an important role as a diagnostic tool. Essential for the future success of MRS as a clinical and research tool in biomedical sciences, both in vivo and in vitro, is the development of an accurate, biochemically relevant and physically consistent and reliable data analysis standard. Stable and well established analysis algorithms, in both the time and the frequency domain, are already available, as is free commercial software for implementing them. In this study, we propose an automatic algorithm that takes into account anatomical localisation, relative concentrations of white matter, grey matter, cerebrospinal fluid and signal abnormalities and inter-scan patient movement. The endpoint is the collection of a series of covariates that could be implemented in a multivariate analysis of covariance (MANCOVA) of the MRS data, as a tool for dealing with differences that may be ascribed to the anatomical variability of the subjects, to inaccuracies in the localisation of the voxel or slab, or to movement, rather than to the pathology under investigation. The aim was to develop an analysis procedure that can be consistently and reliably applied in the follow up of brain tumour. In this study, we demonstrate that the inclusion of such variables in the data analysis of quantitative MRS is fundamentally important (especially in view of the reduced accuracy typical of MRS measures compared to other MRI techniques), reducing the occurrence of false positives.

  1. Essential problems in the interpretation of epidemiologic evidence for an association between mobile phone use and brain tumours

    Science.gov (United States)

    Kundi, Michael

    2010-11-01

    Due to the close proximity of a mobile phone to the head when placing a call, concerns have been raised that exposure from microwaves during mobile phone use may exert adverse health effects and, in particular, may increase the risk of brain tumours. In response to these concerns epidemiological studies have been conducted, most applying the case-control design. While epidemiology can provide decisive evidence for an association between an exposure and a disease fundamental problems arise if exposure is short compared to the natural history of the disease. For brain tumours latencies of decades have been implicated making special considerations about potential effects of exposures necessary that commence during an already growing tumour. It is shown that measures of disease risk like odds ratios and relative risks can under such circumstances not be interpreted as indicators of a long term effect on incidences in the exposed population. Besides this problem, the issues of a suitable exposure metric and the selection of endpoints are unresolved. It is shown that the solution of these problems affords knowledge about the mechanism of action by which exposure increases the risk of manifest disease.

  2. A case series discussing the anaesthetic management of pregnant patients with brain tumours [v1; ref status: indexed, http://f1000r.es/y7

    Directory of Open Access Journals (Sweden)

    Alaa A Abd-Elsayed

    2013-03-01

    Full Text Available Pregnancy may aggravate the natural history of an intracranial tumour, and may even unmask a previously unknown diagnosis. Here we present a series of seven patients who had brain tumours during pregnancy. The aim of this case series is to characterize the current perioperative management and to suggest evidence based guidelines for the anaesthetic management of pregnant females with brain tumours. This is a retrospective study. Information on pregnant patients diagnosed with brain tumours that underwent caesarean section (CS and/or brain tumour resection from May 2003 through June 2008 was obtained from the Department of General Anaesthesia and the Rose Ella Burkhardt Brain Tumour & Neuro-Oncology Centre (BBTC at the Cleveland Clinic, OH, USA. The mean age was 34.5 years (range 29-40 years old. Six patients had glioma, two of whom had concomitant craniotomy and CS. Six cases had the tumour in the frontal lobe. Four cases were operated on under general anaesthesia and three underwent awake craniotomy. The neonatal outcomes of the six patients with elective or emergent delivery were six viable infants with normal Apgar scores. Pregnancy was terminated in the 7th patient. In conclusion, management of brain tumours in pregnant women is mainly reliant on case reports and the doctor’s personal experience. Therefore, close communication between the neurosurgeon, neuroanaesthetist, obstetrician and the patient is crucial. General anaesthesia, propofol, dexmedetomidine and remifentanil were used in our study and were safe. Although this may not agree with previous studies, desflurane and isoflurane were used in our patients with no detectable complications.

  3. Cellular Telephones, Magnetic Field Exposure, Risk of Brain Tumours and Cancer at Other Sites: A Cohort Study (invited paper)

    Energy Technology Data Exchange (ETDEWEB)

    Johansen, C.; Olsen, J.H

    1999-07-01

    The purpose of the study is to investigate whether exposure to electromagnetic fields from cellular telephones is associated with brain tumours and cancer at other sites. Key information has been obtained on all cellular telephone subscribers in Denmark from 1 January 1982 to 31 December 1995. The overall subscriber cohort will include approximately 500,000 individuals. Collected information includes name of subscriber, address, telephone number, system used (analogue or digital), and annual use of the telephone. The name and address of the subscribers will be linked to the Central Population Register, and the personal identification number will be supplied in addition to information on vital status and migration. Finally, all members of the cohort will be linked to the Danish Cancer Registry, and the observed number of tumours will be compared with those expected on the basis of national cancer incidence rates stratified by sex, age, and calendar time. (author)

  4. A standardized and reproducible protocol for serum-free monolayer culturing of primary paediatric brain tumours to be utilized for therapeutic assays.

    Science.gov (United States)

    Sandén, Emma; Eberstål, Sofia; Visse, Edward; Siesjö, Peter; Darabi, Anna

    2015-01-01

    In vitro cultured brain tumour cells are indispensable tools for drug screening and therapeutic development. Serum-free culture conditions tentatively preserve the features of the original tumour, but commonly comprise neurosphere propagation, which is a technically challenging procedure. Here, we define a simple, non-expensive and reproducible serum-free cell culture protocol for establishment and propagation of primary paediatric brain tumour cultures as adherent monolayers. The success rates for establishment of primary cultures (including medulloblastomas, atypical rhabdoid tumour, ependymomas and astrocytomas) were 65% (11/17) and 78% (14/18) for sphere cultures and monolayers respectively. Monolayer culturing was particularly feasible for less aggressive tumour subsets, where neurosphere cultures could not be generated. We show by immunofluorescent labelling that monolayers display phenotypic similarities with corresponding sphere cultures and primary tumours, and secrete clinically relevant inflammatory factors, including PGE2, VEGF, IL-6, IL-8 and IL-15. Moreover, secretion of PGE2 was considerably reduced by treatment with the COX-2 inhibitor Valdecoxib, demonstrating the functional utility of our newly established monolayer for preclinical therapeutic assays. Our findings suggest that this culture method could increase the availability and comparability of clinically representative in vitro models of paediatric brain tumours, and encourages further molecular evaluation of serum-free monolayer cultures.

  5. Whole brain irradiation with hippocampal sparing and dose escalation on multiple brain metastases. Local tumour control and survival

    Energy Technology Data Exchange (ETDEWEB)

    Oehlke, Oliver; Wucherpfennig, David; Prokic, Vesna [University Medical Center Freiburg, Department of Radiation Oncology, Freiburg (Germany); Fels, Franziska [University Medical Center Freiburg, Department of Radiation Oncology, Freiburg (Germany); St. Josefs Hospital, Department of Radiation Oncology, Offenburg (Germany); Frings, Lars [University Medical Center Freiburg, Department of Radiation Oncology, Freiburg (Germany); University Hospital Freiburg, Department of Geriatrics and Gerontology, Freiburg (Germany); University Medical Center Freiburg, Department of Nuclear Medicine, Freiburg (Germany); Egger, Karl [University Medical Center Freiburg, Department of Neuroradiology, Freiburg (Germany); Weyerbrock, Astrid [University Medical Center Freiburg, Department of Neurosurgery, Freiburg (Germany); Nieder, Carsten [Nordland Hospital, Department of Oncology and Palliative Medicine, Bodoe (Norway); University of Tromsoe, Institute of Clinical Medicine, Faculty of Health Sciences, Tromsoe (Norway); Grosu, Anca-Ligia [University Medical Center Freiburg, Department of Radiation Oncology, Freiburg (Germany); German Cancer Consortium (DKTK), Freiburg (Germany); German Cancer Research Center (DKFZ), Heidelberg (Germany)

    2015-01-16

    Hippocampal-avoidance whole brain radiotherapy (HA-WBRT) for multiple brain metastases may prevent treatment-related cognitive decline, compared to standard WBRT. Additionally, simultaneous integrated boost (SIB) on individual metastases may further improve the outcome. Here, we present initial data concerning local tumour control (LTC), intracranial progression-free survival (PFS), overall survival (OS), toxicity and safety for this new irradiation technique. Twenty patients, enrolled between 2011 and 2013, were treated with HA-WBRT (30 Gy in 12 fractions, D{sub 98} {sub %} to hippocampus ≤ 9 Gy) and a SIB (51 Gy) on multiple (2-13) metastases using a volumetric modulated arc therapy (VMAT) approach based on 2-4 arcs. Metastases were evaluated bidimensionally along the two largest diameters in contrast-enhanced three-dimensional T1-weighed MRI. Median follow-up was 40 weeks. The median time to progression of boosted metastases has not been reached yet, corresponding to a LTC rate of 73 %. Median intracranial PFS was 40 weeks, corresponding to a 1-year PFS of 45.3 %. Median OS was 71.5 weeks, corresponding to a 1-year OS of 60 %. No obvious acute or late toxicities grade > 2 (NCI CTCAE v4.03) were observed. D{sub mean} to the bilateral hippocampi was 6.585 Gy ± 0.847 (α/β = 2 Gy). Two patients developed a new metastasis in the area of hippocampal avoidance. HA-WBRT (simultaneous integrated protection, SIP) with SIB to metastases is a safe and tolerable regime that shows favorable LTC for patients with multiple brain metastases, while it has the potential to minimize the side-effect of cognitive deterioration. (orig.) [German] Die Hippocampus-schonende Ganzhirnbestrahlung (HS-GHB) kann im Vergleich zur Standard-GHB die Verschlechterung der neurokognitiven Funktion verhindern. Zusaetzlich vermag ein simultan integrierter Boost (SIB) auf die Metastasen die Prognose der betroffenen Patienten weiter zu verbessern. In dieser Studie praesentieren wir erste Ergebnisse

  6. Dynamic CT perfusion imaging of intra-axial brain tumours: differentiation of high-grade gliomas from primary CNS lymphomas

    Energy Technology Data Exchange (ETDEWEB)

    Schramm, Peter; Xyda, Argyro; Knauth, Michael [University of Goettingen, Medical Center, Department of Neuroradiology, Goettingen (Germany); Klotz, Ernst [Computed Tomography, SIEMENS Healthcare Sector, Forchheim (Germany); Tronnier, Volker [University Schleswig-Holstein, Department of Neurosurgery, Luebeck (Germany); Hartmann, Marius [University of Heidelberg, Medical Center, Division of Neuroradiology, Department of Neurology, Heidelberg (Germany)

    2010-10-15

    Perfusion computed tomography (PCT) allows to quantitatively assess haemodynamic characteristics of brain tissue. We investigated if different brain tumor types can be distinguished from each other using Patlak analysis of PCT data. PCT data from 43 patients with brain tumours were analysed with a commercial implementation of the Patlak method. Four patients had low-grade glioma (WHO II), 31 patients had glioblastoma (WHO IV) and eight patients had intracerebral lymphoma. Tumour regions of interest (ROIs) were drawn in a morphological image and automatically transferred to maps of cerebral blood flow (CBF), cerebral blood volume (CBV) and permeability (K {sup Trans}). Mean values were calculated, group differences were tested using Wilcoxon and Mann Whitney U-tests. In comparison with normal parenchyma, low-grade gliomas showed no significant difference of perfusion parameters (p > 0.05), whereas high-grade gliomas demonstrated significantly higher values (p < 0.0001 for K {sup Trans}, p < 0.0001 for CBV and p = 0.0002 for CBF). Lymphomas displayed significantly increased mean K{sup Trans} values compared with unaffected cerebral parenchyma (p = 0.0078) but no elevation of CBV. High-grade gliomas show significant higher CBV values than lymphomas (p = 0.0078). PCT allows to reliably classify gliomas and lymphomas based on quantitative measurements of CBV and K {sup Trans}. (orig.)

  7. Advance care planning in patients with primary malignant brain tumours: a systematic review

    Directory of Open Access Journals (Sweden)

    Krystal Song

    2016-10-01

    Full Text Available Advance care planning (ACP is a process of reflection and communication of a person’s future health care preferences, and has been shown to improve end-of-life care for patients. The aim of this systematic review is to present an evidence-based overview of ACP in patients with primary malignant brain tumours (pmBT. A comprehensive literature search was conducted using medical and health science electronic databases (PubMed, Cochrane, Embase, MEDLINE, ProQuest, Social Care Online, Scopus and Web of Science up to July 2016. Manual search of bibliographies of articles and grey literature search were also conducted. Two independent reviewers selected studies, extracted data and assessed the methodologic quality of the studies using the Critical Appraisal Skills Program’s appraisal tools. All studies were included irrespective of the study design. A meta-analysis was not possible due to heterogeneity amongst included studies; therefore, a narrative analysis was performed for best evidence synthesis. Overall, 19 studies were included (1 RCT, 17 cohort studies, 1 qualitative study with 4686 participants. All studies scored low to moderate on the methodological quality assessment, implying high risk of bias. A single RCT evaluating a video decision support tool in facilitating ACP in pmBT patients showed a beneficial effect in promoting comfort care and gaining confidence in decision–making. However, the effect of the intervention on quality of life and care at the end-of-life were unclear. There was a low rate of use of ACP discussions at the end-of-life. Advance Directive completion rates and place of death varied between different studies. Positive effects of ACP included lower hospital readmission rates, and intensive care unit utilization. None of the studies assessed mortality outcomes associated with ACP. In conclusion, this review found some beneficial effects of ACP in pmBT. The literature still remains limited in this area, with lack of

  8. Multimodal imaging utilising integrated MR-PET for human brain tumour assessment

    Energy Technology Data Exchange (ETDEWEB)

    Neuner, Irene [Institute of Neuroscience and Medicine 4, INM 4, Juelich (Germany); RWTH Aachen University, Department of Psychiatry, Psychotherapy and Psychosomatics, Aachen (Germany); JARA-BRAIN-Translational Medicine, Aachen (Germany); Kaffanke, Joachim B. [Institute of Neuroscience and Medicine 4, INM 4, Juelich (Germany); MR-Transfer e.K., Wuppertal (Germany); Langen, Karl-Josef; Kops, Elena Rota; Tellmann, Lutz; Stoffels, Gabriele; Weirich, Christoph; Filss, Christian; Scheins, Juergen; Herzog, Hans [Institute of Neuroscience and Medicine 4, INM 4, Juelich (Germany); Shah, N. Jon [Institute of Neuroscience and Medicine 4, INM 4, Juelich (Germany); RWTH Aachen University, Department of Neurology, Aachen (Germany); JARA-BRAIN-Translational Medicine, Aachen (Germany)

    2012-12-15

    The development of integrated magnetic resonance (MR)-positron emission tomography (PET) hybrid imaging opens up new horizons for imaging in neuro-oncology. In cerebral gliomas the definition of tumour extent may be difficult to ascertain using standard MR imaging (MRI) only. The differentiation of post-therapeutic scar tissue, tumour rests and tumour recurrence is challenging. The relationship to structures such as the pyramidal tract to the tumour mass influences the therapeutic neurosurgical approach. The diagnostic information may be enriched by sophisticated MR techniques such as diffusion tensor imaging (DTI), multiple-volume proton MR spectroscopic imaging (MRSI) and functional MRI (fMRI). Metabolic imaging with PET, especially using amino acid tracers such as {sup 18}F-fluoroethyl-l-tyrosine (FET) or {sup 11}C-l-methionine (MET) will indicate tumour extent and response to treatment. The new technologies comprising MR-PET hybrid systems have the advantage of providing comprehensive answers by a one-stop-job of 40-50 min. The combined approach provides data of different modalities using the same iso-centre, resulting in optimal spatial and temporal realignment. All images are acquired exactly under the same physiological conditions. We describe the imaging protocol in detail and provide patient examples for the different imaging modalities such as FET-PET, standard structural imaging (T1-weighted, T2-weighted, T1-weighted contrast agent enhanced), DTI, MRSI and fMRI. (orig.)

  9. Reducing Dataset Size in Frequency Domain for Brain Computer Interface Motor Imagery Classification

    Directory of Open Access Journals (Sweden)

    Ch.Aparna

    2010-12-01

    Full Text Available Brain computer interface is an emerging area of research where the BCI system is able to detect and interpret the mental activity into computer interpretable signals opening a wide area of applications where activities can be completed without using muscular movement. In Brain Computer Interface research, for classification of EEG signals the raw signals captured has to undergo some preprocessing, to obtain the right attributes for classification. In this paper, we present a system which allows for classification of mental tasks based on a statistical data obtained in frequency domain using Discrete cosine transform and extracting useful frequencies from the same with application of decision tree algorithms for classification.

  10. Mir-34a mimics are potential therapeutic agents for p53-mutated and chemo-resistant brain tumour cells.

    Directory of Open Access Journals (Sweden)

    Yuen Ngan Fan

    Full Text Available Chemotherapeutic drug resistance and relapse remains a major challenge for paediatric (medulloblastoma and adult (glioblastoma brain tumour treatment. Medulloblastoma tumours and cell lines with mutations in the p53 signalling pathway have been shown to be specifically insensitive to DNA damaging agents. The aim of this study was to investigate the potential of triggering cell death in p53 mutated medulloblastoma cells by a direct activation of pro-death signalling downstream of p53 activation. Since non-coding microRNAs (miRNAs have the ability to fine tune the expression of a variety of target genes, orchestrating multiple downstream effects, we hypothesised that triggering the expression of a p53 target miRNA could induce cell death in chemo-resistant cells. Treatment with etoposide, increased miR-34a levels in a p53-dependent fashion and the level of miR-34a transcription was correlated with the cell sensitivity to etoposide. miR-34a activity was validated by measuring the expression levels of one of its well described target: the NADH dependent sirtuin1 (SIRT1. Whilst drugs directly targeting SIRT1, were potent to trigger cell death at high concentrations only, introduction of synthetic miR-34a mimics was able to induce cell death in p53 mutated medulloblastoma and glioblastoma cell lines. Our results show that the need of a functional p53 signaling pathway can be bypassed by direct activation of miR-34a in brain tumour cells.

  11. Cerebellar mutism syndrome in children with brain tumours of the posterior fossa

    DEFF Research Database (Denmark)

    Wibroe, Morten; Cappelen, Johan; Castor, Charlotte

    2017-01-01

    , the clinical course and strategies for prevention and treatment are yet to be determined.Methods: This observational, prospective, multicentre study will include 500 children with posterior fossa tumours. It opened late 2014 with participation from 20 Nordic and Baltic centres. From 2016, five British centres...

  12. Assessing occupational exposure to chemicals in an international epidemiological study of brain tumours.

    Science.gov (United States)

    van Tongeren, Martie; Kincl, Laurel; Richardson, Lesley; Benke, Geza; Figuerola, Jordi; Kauppinen, Timo; Lakhani, Ramzan; Lavoué, Jérôme; McLean, Dave; Plato, Nils; Cardis, Elisabeth

    2013-06-01

    The INTEROCC project is a multi-centre case-control study investigating the risk of developing brain cancer due to occupational chemical and electromagnetic field exposures. To estimate chemical exposures, the Finnish Job Exposure Matrix (FINJEM) was modified to improve its performance in the INTEROCC study and to address some of its limitations, resulting in the development of the INTEROCC JEM. An international team of occupational hygienists developed a crosswalk between the Finnish occupational codes used in FINJEM and the International Standard Classification of Occupations 1968 (ISCO68). For ISCO68 codes linked to multiple Finnish codes, weighted means of the exposure estimates were calculated. Similarly, multiple ISCO68 codes linked to a single Finnish code with evidence of heterogeneous exposure were refined. One of the key time periods in FINJEM (1960-1984) was split into two periods (1960-1974 and 1975-1984). Benzene exposure estimates in early periods were modified upwards. The internal consistency of hydrocarbon exposures and exposures to engine exhaust fumes was improved. Finally, exposure to polycyclic aromatic hydrocarbon and benzo(a)pyrene was modified to include the contribution from second-hand smoke. The crosswalk ensured that the FINJEM exposure estimates could be applied to the INTEROCC study subjects. The modifications generally resulted in an increased prevalence of exposure to chemical agents. This increased prevalence of exposure was not restricted to the lowest categories of cumulative exposure, but was seen across all levels for some agents. Although this work has produced a JEM with important improvements compared to FINJEM, further improvements are possible with the expansion of agents and additional external data.

  13. Diagnostic benefits of presurgical fMRI in patients with brain tumours in the primary sensorimotor cortex

    Energy Technology Data Exchange (ETDEWEB)

    Wengenroth, Martina; Blatow, M.; Guenther, J. [University of Heidelberg Medical School, Department of Neuroradiology, Heidelberg (Germany); Akbar, M. [University of Heidelberg Medical School, Department of Orthopaedics, Heidelberg (Germany); Tronnier, V.M. [University of Schleswig-Holstein, Department of Neurosurgery, Luebeck (Germany); Stippich, C. [University Hospital Basle, Department of Diagnostic and Interventional Neuroradiology, Basle (Switzerland)

    2011-07-15

    Reliable imaging of eloquent tumour-adjacent brain areas is necessary for planning function-preserving neurosurgery. This study evaluates the potential diagnostic benefits of presurgical functional magnetic resonance imaging (fMRI) in comparison to a detailed analysis of morphological MRI data. Standardised preoperative functional and structural neuroimaging was performed on 77 patients with rolandic mass lesions at 1.5 Tesla. The central region of both hemispheres was allocated using six morphological and three functional landmarks. fMRI enabled localisation of the motor hand area in 76/77 patients, which was significantly superior to analysis of structural MRI (confident localisation of motor hand area in 66/77 patients; p < 0.002). FMRI provided additional diagnostic information in 96% (tongue representation) and 97% (foot representation) of patients. FMRI-based presurgical risk assessment correlated in 88% with a positive postoperative clinical outcome. Routine presurgical FMRI allows for superior assessment of the spatial relationship between brain tumour and motor cortex compared with a very detailed analysis of structural 3D MRI, thus significantly facilitating the preoperative risk-benefit assessment and function-preserving surgery. The additional imaging time seems justified. FMRI has the potential to reduce postoperative morbidity and therefore hospitalisation time. (orig.)

  14. Epidemiology, severity classification, and outcome of moderate and severe traumatic brain injury: a prospective multicenter study

    NARCIS (Netherlands)

    Andriessen, T.M.J.C.; Horn, J.; Franschman, G.; Naalt, J. van der; Haitsma, I.; Jacobs, B.; Steyerberg, E.W.; Vos, P.E.

    2011-01-01

    Changes in the demographics, approach, and treatment of traumatic brain injury (TBI) patients require regular evaluation of epidemiological profiles, injury severity classification, and outcomes. This prospective multicenter study provides detailed information on TBI-related variables of 508 moderat

  15. Epidemiology, Severity Classification, and Outcome of Moderate and Severe Traumatic Brain Injury: A Prospective Multicenter Study

    NARCIS (Netherlands)

    T.M.J.C. Andriessen; J. Horn; G. Franschman; J. van der Naalt; I. Haitsma; B. Jacobs; E.W. Steyerberg; P.E. Vos

    2011-01-01

    Changes in the demographics, approach, and treatment of traumatic brain injury (TBI) patients require regular evaluation of epidemiological profiles, injury severity classification, and outcomes. This prospective multicenter study provides detailed information on TBI-related variables of 508 moderat

  16. Epidemiology, Severity Classification, and Outcome of Moderate and Severe Traumatic Brain Injury : A Prospective Multicenter Study

    NARCIS (Netherlands)

    Andriessen, Teuntje M. J. C.; Horn, Janneke; Franschman, Gaby; van der Naalt, Joukje; Haitsma, Iain; Jacobs, Bram; Steyerberg, Ewout W.; Vos, Pieter E.

    2011-01-01

    Changes in the demographics, approach, and treatment of traumatic brain injury (TBI) patients require regular evaluation of epidemiological profiles, injury severity classification, and outcomes. This prospective multicenter study provides detailed information on TBI-related variables of 508 moderat

  17. Cerebellar mutism syndrome in children with brain tumours of the posterior fossa.

    Science.gov (United States)

    Wibroe, Morten; Cappelen, Johan; Castor, Charlotte; Clausen, Niels; Grillner, Pernilla; Gudrunardottir, Thora; Gupta, Ramneek; Gustavsson, Bengt; Heyman, Mats; Holm, Stefan; Karppinen, Atte; Klausen, Camilla; Lönnqvist, Tuula; Mathiasen, René; Nilsson, Pelle; Nysom, Karsten; Persson, Karin; Rask, Olof; Schmiegelow, Kjeld; Sehested, Astrid; Thomassen, Harald; Tonning-Olsson, Ingrid; Zetterqvist, Barbara; Juhler, Marianne

    2017-06-21

    Central nervous system tumours constitute 25% of all childhood cancers; more than half are located in the posterior fossa and surgery is usually part of therapy. One of the most disabling late effects of posterior fossa tumour surgery is the cerebellar mutism syndrome (CMS) which has been reported in up to 39% of the patients but the exact incidence is uncertain since milder cases may be unrecognized. Recovery is usually incomplete. Reported risk factors are tumour type, midline location and brainstem involvement, but the exact aetiology, surgical and other risk factors, the clinical course and strategies for prevention and treatment are yet to be determined. This observational, prospective, multicentre study will include 500 children with posterior fossa tumours. It opened late 2014 with participation from 20 Nordic and Baltic centres. From 2016, five British centres and four Dutch centres will join with a total annual accrual of 130 patients. Three other major European centres are invited to join from 2016/17. Follow-up will run for 12 months after inclusion of the last patient. All patients are treated according to local practice. Clinical data are collected through standardized online registration at pre-determined time points pre- and postoperatively. Neurological status and speech functions are examined pre-operatively and postoperatively at 1-4 weeks, 2 and 12 months. Pre- and postoperative speech samples are recorded and analysed. Imaging will be reviewed centrally. Pathology is classified according to the 2007 WHO system. Germline DNA will be collected from all patients for associations between CMS characteristics and host genome variants including pathway profiles. Through prospective and detailed collection of information on 1) differences in incidence and clinical course of CMS for different patient and tumour characteristics, 2) standardized surgical data and their association with CMS, 3) diversities and results of other therapeutic interventions

  18. In-phantom two-dimensional thermal neutron distribution for intraoperative boron neutron capture therapy of brain tumours

    Science.gov (United States)

    Yamamoto, T.; Matsumura, A.; Yamamoto, K.; Kumada, H.; Shibata, Y.; Nose, T.

    2002-07-01

    The aim of this study was to determine the in-phantom thermal neutron distribution derived from neutron beams for intraoperative boron neutron capture therapy (IOBNCT). Gold activation wires arranged in a cylindrical water phantom with (void-in-phantom) or without (standard phantom) a cylinder styrene form placed inside were irradiated by using the epithermal beam (ENB) and the mixed thermal-epithermal beam (TNB-1) at the Japan Research Reactor No 4. With ENB, we observed a flattened distribution of thermal neutron flux and a significantly enhanced thermal flux delivery at a depth compared with the results of using TNB-1. The thermal neutron distribution derived from both the ENB and TNB-1 was significantly improved in the void-in-phantom, and a double high dose area was formed lateral to the void. The flattened distribution in the circumference of the void was observed with the combination of ENB and the void-in-phantom. The measurement data suggest that the ENB may provide a clinical advantage in the form of an enhanced and flattened dose delivery to the marginal tissue of a post-operative cavity in which a residual and/or microscopically infiltrating tumour often occurs. The combination of the epithermal neutron beam and IOBNCT will improve the clinical results of BNCT for brain tumours.

  19. An improved brain image classification technique with mining and shape prior segmentation procedure.

    Science.gov (United States)

    Rajendran, P; Madheswaran, M

    2012-04-01

    The shape prior segmentation procedure and pruned association rule with ImageApriori algorithm has been used to develop an improved brain image classification system are presented in this paper. The CT scan brain images have been classified into three categories namely normal, benign and malignant, considering the low-level features extracted from the images and high level knowledge from specialists to enhance the accuracy in decision process. The experimental results on pre-diagnosed brain images showed 97% sensitivity, 91% specificity and 98.5% accuracy. The proposed algorithm is expected to assist the physicians for efficient classification with multiple key features per image.

  20. Brain tumours at 7T MRI compared to 3T - contrast effect after half and full standard contrast agent dose: initial results

    Energy Technology Data Exchange (ETDEWEB)

    Noebauer-Huhmann, Iris-Melanie; Weber, M. [Medical University of Vienna, High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Vienna (Austria); Medical University of Vienna, Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Vienna (Austria); Szomolanyi, P.; Juras, V. [Medical University of Vienna, High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Vienna (Austria); Slovak Academy of Sciences, Department of Imaging Methods, Institute of Measurement Science, Bratislava (Slovakia); Kronnerwetter, C. [Medical University of Vienna, High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Vienna (Austria); Widhalm, G. [Medical University of Vienna, Department of Neurosurgery, Vienna (Austria); Nemec, S.; Prayer, D. [Medical University of Vienna, Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Vienna (Austria); Ladd, M.E. [University Duisburg-Essen, Erwin L. Hahn Institute for Magnetic Resonance Imaging, Essen (Germany); German Cancer Research Center (DKFZ), Division of Medical Physics in Radiology, Heidelberg (Germany); Trattnig, S. [Medical University of Vienna, High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Vienna (Austria); Ludwig Boltzmann Institute for Experimental and Clinical Traumatology, Austrian Cluster for Tissue Regeneration, Vienna (Austria)

    2015-01-15

    To compare the contrast agent effect of a full dose and half the dose of gadobenate dimeglumine in brain tumours at 7 Tesla (7T) MR versus 3 Tesla (3T). Ten patients with primary brain tumours or metastases were examined. Signal intensities were assessed in the lesion and normal brain. Tumour-to-brain contrast and lesion enhancement were calculated. Additionally, two independent readers subjectively graded the image quality and artefacts. The enhanced mean tumour-to-brain contrast and lesion enhancement were significantly higher at 7T than at 3T for both half the dose (91.8 ± 45.8 vs. 43.9 ± 25.3 [p = 0.010], 128.1 ± 53.7 vs. 75.5 ± 32.4 [p = 0.004]) and the full dose (129.2 ± 50.9 vs. 66.6 ± 33.1 [p = 0.002], 165.4 ± 54.2 vs. 102.6 ± 45.4 [p = 0.004]). Differences between dosages at each field strength were also significant. Lesion enhancement was higher with half the dose at 7T than with the full dose at 3T (p =.037), while the tumour-to-brain contrast was not significantly different. Subjectively, contrast enhancement, visibility, and lesion delineation were better at 7T and with the full dose. All parameters were rated as good, at the least. Half the routine contrast agent dose at 7T provided higher lesion enhancement than the full dose at 3T which indicates the possibility of dose reduction at 7T. (orig.)

  1. DMBT1, a new member of the SRCR superfamily, on chromosome 10q25.3-26.1 is deleted in malignant brain tumours

    DEFF Research Database (Denmark)

    Mollenhauer, J; Wiemann, S; Scheurlen, W

    1997-01-01

    Loss of sequences from human chromosome 10q has been associated with the progression of human cancer. Medulloblastoma and glioblastoma multiforme are the most common malignant brain tumours in children and adults, respectively. In glioblastoma multiforme, the most aggressive form, 80% of the tumo...

  2. Patient-specific dosimetry for intracavitary 32P-chromic phosphate colloid therapy of cystic brain tumours.

    Science.gov (United States)

    Denis-Bacelar, Ana M; Romanchikova, Marina; Chittenden, Sarah; Saran, Frank H; Mandeville, Henry; Du, Yong; Flux, Glenn D

    2013-10-01

    (32)P-chromic phosphate colloid treatments of astrocytoma and craniopharyngioma cystic brain tumours in paediatric patients are conventionally based on a sphere model under the assumption of uniform uptake. The aims of this study were to determine the distribution of the absorbed dose delivered by (32)P on a patient-specific basis and to evaluate the accuracy with which this can be predicted from a pretherapy administration of (99m)Tc-Sn colloid. Three patients were treated with (32)P-chromic phosphate colloid following (99m)Tc-Sn colloid administrations. Convolution dosimetry was performed using pretherapy and posttherapy sequential SPECT imaging, and verified with EGSnrc Monte Carlo radiation transport simulations. Mean absorbed doses to the cyst wall and dose-volume histograms were also calculated and compared with those obtained by the sphere model approach. Highly nonuniform uptake distributions of both the (99m)Tc and (32)P colloids were observed and characterized by dose-volume histograms to the cyst wall. Mean absorbed doses delivered to the cyst wall, obtained with the convolution method, were on average 21 % (SD 18 %) and 50 % (SD 30 %) lower than those predicted by the (99m)Tc distribution and the uniform assumption of the sphere model, respectively. Absorbed doses delivered to the cyst wall by (32)P are more accurately predicted from image-based patient-specific convolution dosimetry than from simple sphere models. These results indicate the necessity to perform personalized treatment planning and verification for intracavitary irradiation of cystic brain tumours treated with radiocolloids. Patient-specific dosimetry can be used to guide the frequency and levels of repeated administrations and would facilitate data collection and comparison to support the multicentre trials necessary to progress this therapy.

  3. Improvement effect on the depth-dose distribution by CSF drainage and air infusion of a tumour-removed cavity in boron neutron capture therapy for malignant brain tumours

    Science.gov (United States)

    Sakurai, Yoshinori; Ono, Koji; Miyatake, Shin-ichi; Maruhashi, Akira

    2006-03-01

    Boron neutron capture therapy (BNCT) without craniotomy for malignant brain tumours was started using an epi-thermal neutron beam at the Kyoto University Reactor in June 2002. We have tried some techniques to overcome the treatable-depth limit in BNCT. One of the effective techniques is void formation utilizing a tumour-removed cavity. The tumorous part is removed by craniotomy about 1 week before a BNCT treatment in our protocol. Just before the BNCT irradiation, the cerebro-spinal fluid (CSF) in the tumour-removed cavity is drained out, air is infused to the cavity and then the void is made. This void improves the neutron penetration, and the thermal neutron flux at depth increases. The phantom experiments and survey simulations modelling the CSF drainage and air infusion of the tumour-removed cavity were performed for the size and shape of the void. The advantage of the CSF drainage and air infusion is confirmed for the improvement in the depth-dose distribution. From the parametric surveys, it was confirmed that the cavity volume had good correlation with the improvement effect, and the larger effect was expected as the cavity volume was larger.

  4. AN ARTIFICIAL FISH SWARM OPTIMIZED FUZZY MRI IMAGE SEGMENTATION APPROACH FOR IMPROVING IDENTIFICATION OF BRAIN TUMOUR

    OpenAIRE

    Jagadeesan, R; S.N. Sivanandam

    2013-01-01

    In image processing, it is difficult to detect the abnormalities in brain especially in MRI brain images. Also the tumor segmentation from MRI image data is an important; however it is time consumingwhile carried out by medical specialists. A lot of methods have been proposed to solve MR images problems, quite difficult to develop an automated recognition system which could process on a large information of patient and provide a correct estimation. Hence enhanced k-means and fuzzy c-means wit...

  5. Comparison of the prevalence of KRAS-LCS6 polymorphism (rs61764370) within different tumour types (colorectal, breast, non-small cell lung cancer and brain tumours). A study of the Czech population.

    Science.gov (United States)

    Uvirova, Magdalena; Simova, Jarmila; Kubova, Barbora; Dvorackova, Nina; Tomaskova, Hana; Sedivcova, Monika; Dite, Petr

    2015-09-01

    A germline SNP (rs61764370) is located in a let-7 complementary site (LCS6) in the 3'UTR of KRAS oncogene, and it was found to alter the binding capability of the mature let-7 microRNA to the KRAS mRNA. The aim of the study was to evaluate the frequency of the KRAS-LCS6 variant allele in different cancer types that included patients with colorectal cancer (CRC), breast cancer (BC), non-small cell lung cancer (NSCLC) and brain tumour patient subgroups from the Czech Republic. The occurrence of this genetic variant was correlated with the presence of selected somatic mutations representing predictive biomarkers in the respective tumours. DNA of tumour tissues was isolated from 428 colorectal cancer samples, 311 non-small cell lung cancer samples, 195 breast cancer samples and 151 samples with brain tumour. Analysis of SNP (rs61764370) was performed by the PCR+RFLP method and direct sequencing. KRAS, BRAF and EGFR mutation status was assessed using real-time PCR. The status of the HER2 gene was assessed using the FISH method. The KRAS-LCS6 TG genotype has been detected in 16.4% (32/195) of breast cancer cases (in HER2 positive breast cancer 3.3%, in HER2 negative breast cancer 20.1%), in 12.4% (53/428) of CRC cases (KRAS/BRAF wild type CRC in 10.6%, KRAS mutant CRC in 10.1%, BRAF V600E mutant CRC in 18.5%), in 13.2% (41/311) of NSCLC samples, (EGFR mutant NSCLC patients in 8%, EGFR wild type NSCLC in 12.9%), and 17.9% (27/151) of brain tumour cases. The KRAS-LCS6 TG genotype was not significantly different across the studied tumours. In our study, the GG genotype has not been found among the cancer samples. Based on the findings, it is concluded that the occurrence of the KRAS-LCS6 TG genotype was statistically significantly different in association with status of the HER2 gene in breast cancer. Furthermore, significant association between the mutation status of analysed somatic variants in genes of the EGFR signalling pathway (KRAS, BRAF, EGFR) and the KRAS-LCS6

  6. Word pair classification during imagined speech using direct brain recordings

    Science.gov (United States)

    Martin, Stephanie; Brunner, Peter; Iturrate, Iñaki; Millán, José Del R.; Schalk, Gerwin; Knight, Robert T.; Pasley, Brian N.

    2016-05-01

    People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70–150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58% p perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications.

  7. Behavioral state classification in epileptic brain using intracranial electrophysiology

    Science.gov (United States)

    Kremen, Vaclav; Duque, Juliano J.; Brinkmann, Benjamin H.; Berry, Brent M.; Kucewicz, Michal T.; Khadjevand, Fatemeh; Van Gompel, Jamie; Stead, Matt; St. Louis, Erik K.; Worrell, Gregory A.

    2017-04-01

    Objective. Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach. Data from seven patients (age 34+/- 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results. Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  ⩾90% using a single electrode contact and single spectral feature. Significance. Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

  8. Localisation of motor areas in brain tumour patients: a comparison of preoperative [{sup 18}F]FDG-PET and intraoperative cortical electrostimulation

    Energy Technology Data Exchange (ETDEWEB)

    Schreckenberger, M.; Sabri, O.; Meyer, P.T.; Zeggel, T.; Zimny, M.; Buell, U. [Technische Univ. Aachen (Germany). Dept. of Nuclear Medicine; Spetzger, U.; Gilsbach, J. [Dept. of Neurosurgery, Aachen Univ. of Technology (Germany)

    2001-09-01

    Assessment of the exact spatial relation between tumour and adjacent functionally relevant brain areas is a primary tool in the presurgical planning in brain tumour patients. The purpose of this study was to compare a preoperative fluorine-18 fluorodeoxyglucose positron emission tomography ([{sup 18}F]FDG PET) activation protocol in patients with tumours near the central area with the results of intraoperative direct cortical electrostimulation, and to determine whether non-invasive preoperative PET imaging can provide results equivalent to those achieved with the invasive neurosurgical ''gold standard''. In this prospective study, we examined 20 patients with various tumours of the central area, performing two PET scans (each 30 min after i.v. injection of 134-341 MBq [{sup 18}F]FDG) in each patient: (1) a resting baseline scan and (2) an activation scan using a standardised motor task (finger tapping, foot stretching). Following PET/MRI realignment and normalisation to the whole brain counts, parametric images of the activation versus the rest study were calculated and pixels above categorical threshold values were projected to the individual MRI for bimodal assessment of morphology and function (PET/MRI overlay). Intraoperative direct cortical electrostimulation was performed using a Viking IV probe (5 pulses, each of 100 {mu}s) and documented using a dedicated neuro navigation system. Results were compared with the preoperative PET findings. PET revealed significant activation of the contralateral primary motor cortex in 95% (19/20) of the brain tumour patients (hand activation 13/13, foot activation 6/7), showing a mean increase in normalised [{sup 18}F]FDG uptake of 20.5%{+-}5.2% (hand activation task) and 17.2%{+-}2.5% (foot activation task). Additionally detected activation of the ipsilateral primary motor cortex was interpreted as a metabolic indication for interhemispheric compensational processes. Evaluation of the PET findings by

  9. AN IMPROVED TECHNIQUE FOR IDENTIFICATION AND CLASSIFICATION OF BRAIN DISORDER FROM MRI BRAIN IMAGE

    Directory of Open Access Journals (Sweden)

    Finitha Joseph

    2015-11-01

    Full Text Available Medical image processing is developing recently due to its wide applications. An efficient MRI image segmentation is needed at present. In this paper, MRI brain segmentation is done by Semi supervised learning which does not require pathology modelling and, thus, allows high degree of automation. In abnormality detection, a vector is characterized as anomalous if it does not comply with the probability distribution obtained from normal data. The estimation of the probability density function, however, is usually not feasible due to large data dimensionality. In order to overcome this challenge, we treat every image as a network of locally coherent image partitions (overlapping blocks. We formulate and maximize a strictly concave likelihood function estimating abnormality for each partition and fuse the local estimates into a globally optimal estimate that satisfies the consistency constraints, based on a distributed estimation algorithm. After this features are extracted by Gray-Level Co-occurrence Matrices (GLCM algorithm and those features are given to Particle Spam Optimization (PSO and finally classification is done by using Library Support Vector Machine (LIBSVM.Thus results are evaluated and proved its efficiency using accuracy.

  10. Intravenous versus inhalational techniques for rapid emergence from anaesthesia in patients undergoing brain tumour surgery: A Cochrane systematic review

    Directory of Open Access Journals (Sweden)

    Hemanshu Prabhakar

    2017-01-01

    Full Text Available Background: Early and rapid emergence from anaesthesia is desirable for most neurosurgical patients. With the availability of newer intravenous and inhalational anaesthetic agents, all of which have inherent advantages and disadvantages, we remain uncertain as to which technique may result in more rapid early recovery from anaesthesia. The objective of this review was to assess the effects of intravenous versus inhalational techniques for rapid emergence from anaesthesia in patients undergoing brain tumour surgery. Methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL; 2014, Issue 6 in The Cochrane Library, MEDLINE via Ovid SP (1966 to June 2014 and EMBASE via Ovid SP (1980 to June 2014. We also searched specific websites, such as www.indmed.nic.in, www.cochrane-sadcct.org and www.clinicaltrials.gov (October 2014. We included randomised controlled trials (RCTs that compared the use of intravenous anaesthetic agents such as propofol and thiopentone with inhalational anaesthetic agents such as isoflurane and sevoflurane for maintenance of general anaesthesia during brain tumour surgery. Primary outcomes were emergence from anaesthesia (assessed by time to follow verbal commands, in minutes and adverse events during emergence, such as haemodynamic changes, agitation, desaturation, muscle weakness, nausea and vomiting, shivering and pain. Secondary outcomes were time to eye opening, recovery from anaesthesia using the Aldrete or modified Aldrete score (i.e., time to attain score ≥9, in minutes, opioid consumption, brain relaxation (as assessed by the surgeon on a 4- or 5-point scale and complications of anaesthetic techniques, such as intraoperative haemodynamic instability in terms of hypotension or hypertension (mmHg, increased or decreased heart rate (beats/min and brain swelling. We used standardised methods in conducting the systematic review, as described by the Cochrane Handbook for Systematic Reviews of

  11. A role for the malignant brain tumour (MBT domain protein LIN-61 in DNA double-strand break repair by homologous recombination.

    Directory of Open Access Journals (Sweden)

    Nicholas M Johnson

    Full Text Available Malignant brain tumour (MBT domain proteins are transcriptional repressors that function within Polycomb complexes. Some MBT genes are tumour suppressors, but how they prevent tumourigenesis is unknown. The Caenorhabditis elegans MBT protein LIN-61 is a member of the synMuvB chromatin-remodelling proteins that control vulval development. Here we report a new role for LIN-61: it protects the genome by promoting homologous recombination (HR for the repair of DNA double-strand breaks (DSBs. lin-61 mutants manifest numerous problems associated with defective HR in germ and somatic cells but remain proficient in meiotic recombination. They are hypersensitive to ionizing radiation and interstrand crosslinks but not UV light. Using a novel reporter system that monitors repair of a defined DSB in C. elegans somatic cells, we show that LIN-61 contributes to HR. The involvement of this MBT protein in HR raises the possibility that MBT-deficient tumours may also have defective DSB repair.

  12. Motor imagery classification by means of source analysis for brain computer interface applications

    Science.gov (United States)

    Qin, Lei; Ding, Lei; He, Bin

    2004-09-01

    We report a pilot study of performing classification of motor imagery for brain-computer interface applications, by means of source analysis of scalp-recorded EEGs. Independent component analysis (ICA) was used as a spatio-temporal filter extracting signal components relevant to left or right motor imagery (MI) tasks. Source analysis methods including equivalent dipole analysis and cortical current density imaging were applied to reconstruct equivalent neural sources corresponding to MI, and classification was performed based on the inverse solutions. The classification was considered correct if the equivalent source was found over the motor cortex in the corresponding hemisphere. A classification rate of about 80% was achieved in the human subject studied using both the equivalent dipole analysis and the cortical current density imaging analysis. The present promising results suggest that the source analysis approach could manifest a clearer picture on the cortical activity, and thus facilitate the classification of MI tasks from scalp EEGs.

  13. Automated Brain Image classification using Neural Network Approach and Abnormality Analysis

    Directory of Open Access Journals (Sweden)

    P.Muthu Krishnammal

    2015-06-01

    Full Text Available Image segmentation of surgical images plays an important role in diagnosis and analysis the anatomical structure of human body. Magnetic Resonance Imaging (MRI helps in obtaining a structural image of internal parts of the body. This paper aims at developing an automatic support system for stage classification using learning machine and to detect brain Tumor by fuzzy clustering methods to detect the brain Tumor in its early stages and to analyze anatomical structures. The three stages involved are: feature extraction using GLCM and the tumor classification using PNN-RBF network and segmentation using SFCM. Here fast discrete curvelet transformation is used to analyze texture of an image which be used as a base for a Computer Aided Diagnosis (CAD system .The Probabilistic Neural Network with radial basis function is employed to implement an automated Brain Tumor classification. It classifies the stage of Brain Tumor that is benign, malignant or normal automatically. Then the segmentation of the brain abnormality using Spatial FCM and the severity of the tumor is analysed using the number of tumor cells in the detected abnormal region.The proposed method reports promising results in terms of training performance and classification accuracies.

  14. Brain tumor classification and segmentation using sparse coding and dictionary learning.

    Science.gov (United States)

    Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

    2016-08-01

    This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

  15. L-Phenylalanine preloading reduces the (10)B(n, α)(7)Li dose to the normal brain by inhibiting the uptake of boronophenylalanine in boron neutron capture therapy for brain tumours.

    Science.gov (United States)

    Watanabe, Tsubasa; Tanaka, Hiroki; Fukutani, Satoshi; Suzuki, Minoru; Hiraoka, Masahiro; Ono, Koji

    2016-01-01

    Boron neutron capture therapy (BNCT) is a cellular-level particle radiation therapy that combines the selective delivery of boron compounds to tumour tissue with neutron irradiation. Previously, high doses of one of the boron compounds used for BNCT, L-BPA, were found to reduce the boron-derived irradiation dose to the central nervous system. However, injection with a high dose of L-BPA is not feasible in clinical settings. We aimed to find an alternative method to improve the therapeutic efficacy of this therapy. We examined the effects of oral preloading with various analogues of L-BPA in a xenograft tumour model and found that high-dose L-phenylalanine reduced the accumulation of L-BPA in the normal brain relative to tumour tissue. As a result, the maximum irradiation dose in the normal brain was 19.2% lower in the L-phenylalanine group relative to the control group. This study provides a simple strategy to improve the therapeutic efficacy of conventional boron compounds for BNCT for brain tumours and the possibility to widen the indication of BNCT to various kinds of other tumours.

  16. AN ARTIFICIAL FISH SWARM OPTIMIZED FUZZY MRI IMAGE SEGMENTATION APPROACH FOR IMPROVING IDENTIFICATION OF BRAIN TUMOUR

    Directory of Open Access Journals (Sweden)

    R.Jagadeesan

    2013-07-01

    Full Text Available In image processing, it is difficult to detect the abnormalities in brain especially in MRI brain images. Also the tumor segmentation from MRI image data is an important; however it is time consumingwhile carried out by medical specialists. A lot of methods have been proposed to solve MR images problems, quite difficult to develop an automated recognition system which could process on a large information of patient and provide a correct estimation. Hence enhanced k-means and fuzzy c-means with firefly algorithm for a segmentation of brain magnetic resonance images were developed. Thisalgorithm is based on maximum measure of the distance function which is found for cluster center detection process using the Mahalanobis concept. Particularly the firefly algorithm is implemented tooptimize the Fuzzy C-means membership function for better accuracy segmentation process. At the same time the convergence criteria is fixed for the efficient clustering method. The Firefly algorithmparameters are set fixed and they do not adjust by the time. As well Firefly algorithm does not memorize any history of better situation for each firefly and this reasons they travel in any case of it, and they miss their situations. So there is a need of better algorithm that could provide even better solution than the firefly algorithm. To attain this requirement as a proposed work the Artificial Fish Swarm Algorithm to optimize the fuzzy membership function. During surveying of the previous literature, it has been found out that no work has been done in segmentation of brain tumor using AFSA based clustering. In AFSA, artificial fishes for next movement act completely independent from past and next movement is justrelated to current position of artificial fish and its other companions which lead to select best initial centers for the MRI brain tumor segmentation. Experimental results show that presented method has an acceptable performance than the previous method.

  17. Assessing the performance of four different categories of histological criteria in brain tumours grading by means of a computer-aided diagnosis image analysis system.

    Science.gov (United States)

    Kostopoulos, S; Konstandinou, C; Sidiropoulos, K; Ravazoula, P; Kalatzis, I; Asvestas, P; Cavouras, D; Glotsos, D

    2015-10-01

    Brain tumours are considered one of the most lethal and difficult to treat forms of cancer, with unknown aetiology and lack of any realistic screening. In this study, we examine, whether the combination of descriptive criteria, used by expert histopathologists in assessing histologic tissue samples, and quantitative image analysis features may improve the diagnostic accuracy of brain tumour grading. Data comprised 61 cases of brain cancers (astrocytomas, oligodendrogliomas, meningiomas) collected from the archives of the University Hospital of Patras, Greece. Incorporating physician's descriptive criteria and image analysis's quantitative features into a discriminant function, a computer-aided diagnosis system was designed for discriminating low-grade from high-grade brain tumours. Physician's descriptive features, when solely used in the system, proved of high discrimination accuracy (93.4%). When verbal descriptive features were combined with quantitative image analysis features in the system, discrimination accuracy improved to 98.4%. The generalization of the proposed system to unseen data converged to an overall prediction accuracy of 86.7% ± 5.4%. Considering that histological grading affects treatment selection and diagnostic errors may be notable in clinical practice, the utilization of the proposed system may safeguard against diagnostic misinterpretations in every day clinical practice. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

  18. Multiple instance learning for classification of dementia in brain MRI.

    Science.gov (United States)

    Tong, Tong; Wolz, Robin; Gao, Qinquan; Hajnal, Joseph V; Rueckert, Daniel

    2013-01-01

    Machine learning techniques have been widely used to support the diagnosis of neurological diseases such as dementia. Recent approaches utilize local intensity patterns within patches to derive voxelwise grading measures of disease. However, the relationships among these patches are usually ignored. In addition, there is some ambiguity in assigning disease labels to the extracted patches. Not all of the patches extracted from patients with dementia are characteristic of morphology associated with disease. In this paper, we propose to use a multiple instance learning method to address the problem of assigning training labels to the patches. In addition, a graph is built for each image to exploit the relationships among these patches, which aids the classification work. We illustrate the proposed approach in an application for the detection of Alzheimer's disease (AD): Using the baseline MR images of 834 subjects from the ADNI study, the proposed method can achieve a classification accuracy of 88.8% between AD patients and healthy controls, and 69.6% between patients with stable Mild Cognitive Impairment (MCI) and progressive MCI. These results compare favourably with state-of-the-art classification methods.

  19. BRAIN TUMOR CLASSIFICATION BASED ON CLUSTERED DISCRETE COSINE TRANSFORM IN COMPRESSED DOMAIN

    Directory of Open Access Journals (Sweden)

    V. Anitha

    2014-01-01

    Full Text Available This study presents a novel method to classify the brain tumors by means of efficient and integrated methods so as to increase the classification accuracy. In conventional systems, the problem being the same to extract the feature sets from the database and classify tumors based on the features sets. The main idea in plethora of earlier researches related to any classification method is to increase the classification accuracy.The actual need is to achieve a better accuracy in classification, by extracting more relevant feature sets after dimensionality reduction. There exists a trade-off between accuracy and the number of feature sets. Hence the focus in this study is to implement Discrete Cosine Transform (DCT on the brain tumor images for various classes. Using DCT, by itself, it offers a fair dimension reduction in feature sets.Later on, sequentially K-means algorithm is applied on DCT coefficients to cluster the feature sets. These cluster information are considered as refined feature sets and classified using Support Vector Machine (SVM is proposed in this study. This method of using DCT helps to adjust and vary the performance of classification based on the count of the DCT coefficients taken into account. There exists a good demand for an automatic classification of brain tumors which grealtly helps in the process of diagnosis. In this novel work, an average of 97% and a maximum of 100% classification accuracy has been achieved. This research is basically aiming and opening a new way of classification under compressed domain. Hence this study may be highly suitable for diagnosing under mobile computing and internet based medical diagnosis.

  20. A numerical model for the study of photoacoustic imaging of brain tumours

    CERN Document Server

    Firouzi, Kamyar

    2015-01-01

    Photoacoustic imaging has shown great promise for medical imaging, where optical energy absorption by blood haemoglobin is used as the contrast mechanism. A numerical method was developed for the in-silico assessment of the photoacoustic image reconstruction of the brain. Image segmentation techniques were used to prepare a digital phantom from MR images. Light transport through brain tissue was modelled using a Finite Element approach. The resulting acoustic pressure was then estimated by pulsed photoacoustics considerations. The forward acoustic wave propagation was modelled by the linearized coupled first order wave equations and solved by an acoustic k-space method. Since skull bone is an elastic solid and strongly attenuates ultrasound (due to both scattering and absorption), a k-space method was developed for elastic media. To model scattering effects, a new approach was applied based on propagation in random media. In addition, absorption effects were incorporated using a power law. Finally, the acoust...

  1. EVALUATION OF BRAIN TUMOURS BY MRI TECHNIQUES AND THEIR HISTOPATHOLOGICAL CORRELATION

    OpenAIRE

    Mohammad Shamim; Reyaz; Anju; Dinesh Kumar; Paricharak

    2014-01-01

    : This study was conducted on thirty patients of brain tumors diagnosed on CT scan/ Conventional MRI. It was performed in the Department of Radiological and PET Imaging, Institute of Nuclear Medicine and Allied Sciences (INMAS), Brig S. K. Mazumdar Marg , Lucknow road, Delhi. Out of thirty patients, 19 patients (63.33%) were male and 11 patients (36.66%) were female. Their ages ranged from 22 to 63 years. The most common presenting symptom was headache followed by seizures...

  2. Use of the Graded Prognostic Assessment (GPA) score in patients with brain metastases from primary tumours not represented in the diagnosis-specific GPA studies.

    Science.gov (United States)

    Nieder, C; Andratschke, N H; Geinitz, H; Grosu, A L

    2012-08-01

    Assessment of prognostic factors might influence treatment decisions in patients with brain metastases. Based on large studies, the diagnosis-specific graded prognostic assessment (GPA) score is a useful tool. However, patients with unknown or rare primary tumours are not represented in this model. A pragmatic approach might be use of the first GPA version which is not limited to specific primary tumours. This retrospective analysis examines for the first time whether the GPA is a valid score in patients not eligible for the diagnosis-specific GPA. It includes 71 patients with unknown primary tumour, bladder cancer, ovarian cancer, thyroid cancer or other uncommon primaries. Survival was evaluated in uni- and multivariate tests. The GPA significantly predicted survival. Moreover, improved survival was seen in patients treated with surgical resection or radiosurgery (SRS) for brain metastases. The older recursive partitioning analysis (RPA) score was significant in univariate analysis. However, the multivariate model with RPA, GPA and surgery or SRS versus none showed that only GPA and type of treatment were independent predictors of survival. Ideally, cooperative research efforts would lead to development of diagnosis-specific scores also for patients with rare or unknown primary tumours. In the meantime, a pragmatic approach of using the general GPA score appears reasonable.

  3. Use of the Graded Prognostic Assessment (GPA) score in patients with brain metastases from primary tumours not represented in the diagnosis-specific GPA studies

    Energy Technology Data Exchange (ETDEWEB)

    Nieder, C. [Nordland Hospital, Bodoe (Norway). Dept. of Oncology and Palliative Medicine; Tromsoe Univ. (Norway). Inst. of Clinical Medicine; Andratschke, N.H. [University Hospital Rostock (Germany). Dept. of Radiation Oncology; Geinitz, H. [Klinikum rechts der Isar der Technischen Univ. Muenchen (Germany). Dept. of Radiation Oncology; Grosu, A.L. [University Hospital Freiburg (Germany). Dept. of Radiation Oncology

    2012-08-15

    Background and purpose: Assessment of prognostic factors might influence treatment decisions in patients with brain metastases. Based on large studies, the diagnosis-specific graded prognostic assessment (GPA) score is a useful tool. However, patients with unknown or rare primary tumours are not represented in this model. A pragmatic approach might be use of the first GPA version which is not limited to specific primary tumours. Patients and methods: This retrospective analysis examines for the first time whether the GPA is a valid score in patients not eligible for the diagnosis-specific GPA. It includes 71 patients with unknown primary tumour, bladder cancer, ovarian cancer, thyroid cancer or other uncommon primaries. Survival was evaluated in uni- and multivariate tests. Results: The GPA significantly predicted survival. Moreover, improved survival was seen in patients treated with surgical resection or radiosurgery (SRS) for brain metastases. The older recursive partitioning analysis (RPA) score was significant in univariate analysis. However, the multivariate model with RPA, GPA and surgery or SRS versus none showed that only GPA and type of treatment were independent predictors of survival. Conclusion: Ideally, cooperative research efforts would lead to development of diagnosis-specific scores also for patients with rare or unknown primary tumours. In the meantime, a pragmatic approach of using the general GPA score appears reasonable. (orig.)

  4. Model sparsity and brain pattern interpretation of classification models in neuroimaging

    DEFF Research Database (Denmark)

    Rasmussen, Peter Mondrup; Madsen, Kristoffer Hougaard; Churchill, Nathan W

    2012-01-01

    Interest is increasing in applying discriminative multivariate analysis techniques to the analysis of functional neuroimaging data. Model interpretation is of great importance in the neuroimaging context, and is conventionally based on a ‘brain map’ derived from the classification model...

  5. Challenges relating to solid tumour brain metastases in clinical trials, part 1: patient population, response, and progression. A report from the RANO group.

    Science.gov (United States)

    Lin, Nancy U; Lee, Eudocia Q; Aoyama, Hidefumi; Barani, Igor J; Baumert, Brigitta G; Brown, Paul D; Camidge, D Ross; Chang, Susan M; Dancey, Janet; Gaspar, Laurie E; Harris, Gordon J; Hodi, F Stephen; Kalkanis, Steven N; Lamborn, Kathleen R; Linskey, Mark E; Macdonald, David R; Margolin, Kim; Mehta, Minesh P; Schiff, David; Soffietti, Riccardo; Suh, John H; van den Bent, Martin J; Vogelbaum, Michael A; Wefel, Jeffrey S; Wen, Patrick Y

    2013-09-01

    Therapeutic outcomes for patients with brain metastases need to improve. A critical review of trials specifically addressing brain metastases shows key issues that could prevent acceptance of results by regulatory agencies, including enrolment of heterogeneous groups of patients and varying definitions of clinical endpoints. Considerations specific to disease, modality, and treatment are not consistently addressed. Additionally, the schedule of CNS imaging and consequences of detection of new or progressive brain metastases in trials mainly exploring the extra-CNS activity of systemic drugs are highly variable. The Response Assessment in Neuro-Oncology (RANO) working group is an independent, international, collaborative effort to improve the design of trials in patients with brain tumours. In this two-part series, we review the state of clinical trials of brain metastases and suggest a consensus recommendation for the development of criteria for future clinical trials.

  6. Classification of Brain Signals in Normal Subjects and Patients with Epilepsy Using Mixture of Experts

    Directory of Open Access Journals (Sweden)

    S. Amoozegar

    2013-06-01

    Full Text Available EEG is one of the most important and common sources for study of brain function and neurological disorders. Automated systems are under study for many years to detect EEG changes. Because of the importance of making correct decision, we are looking for better classification methods for EEG signals. In this paper a smart compound system is used for classifying EEG signals to different groups. Since in each classification the system accuracy of making decision is very important, in this study we look for some methods to improve the accuracy of EEG signals classification. In this paper the use of Mixture of Experts for improving the EEG signals classification of normal subjects and patients with epilepsy is shown and the classification accuracy is evaluated. Decision making was performed in two stages: 1 feature extractions with different methods of eigenvector and 2 Classification using the classifier trained by extracted features. This smart system inputs are formed from composites features that are selected appropriate with network structure. In this study tree methods based on eigenvectors (Minimum Norm, MUSIC, Pisarenko are chosen for the estimation of Power Spectral Density (PSD. After the implementation of ME and train it on composite features, we propose that this technique can reach high classification accuracy. Hence, EEG signals classification of epilepsy patients in different situations and control subjects is available. In this study, Mixture of Experts structure was used for EEG signals classification. Proper performance of Neural Network depends on the size of train and test data. Combination of multiple Neural Networks even without using the probable structure in obtaining weights in classification problem can produce high accuracy in less time, which is important and valuable in the classification point of view.

  7. Automatic classification of lung tumour heterogeneity according to a visual-based score system in dynamic contrast enhanced CT sequences

    Science.gov (United States)

    Bevilacqua, Alessandro; Baiocco, Serena

    2016-03-01

    Computed tomography (CT) technologies have been considered for a long time as one of the most effective medical imaging tools for morphological analysis of body parts. Contrast Enhanced CT (CE-CT) also allows emphasising details of tissue structures whose heterogeneity, inspected through visual analysis, conveys crucial information regarding diagnosis and prognosis in several clinical pathologies. Recently, Dynamic CE-CT (DCE-CT) has emerged as a promising technique to perform also functional hemodynamic studies, with wide applications in the oncologic field. DCE-CT is based on repeated scans over time performed after intravenous administration of contrast agent, in order to study the temporal evolution of the tracer in 3D tumour tissue. DCE-CT pushes towards an intensive use of computers to provide automatically quantitative information to be used directly in clinical practice. This requires that visual analysis, representing the gold-standard for CT image interpretation, gains objectivity. This work presents the first automatic approach to quantify and classify the lung tumour heterogeneities based on DCE-CT image sequences, so as it is performed through visual analysis by experts. The approach developed relies on the spatio-temporal indices we devised, which also allow exploiting temporal data that enrich the knowledge of the tissue heterogeneity by providing information regarding the lesion status.

  8. Efficient multilevel brain tumor segmentation with integrated bayesian model classification.

    Science.gov (United States)

    Corso, J J; Sharon, E; Dube, S; El-Saden, S; Sinha, U; Yuille, A

    2008-05-01

    We present a new method for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main contribution of the paper is a Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm, and apply the technique to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes. The computationally efficient method runs orders of magnitude faster than current state-of-the-art techniques giving comparable or improved results. Our quantitative results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of glioblastoma multiforme brain tumor.

  9. EVALUATION OF BRAIN TUMOURS BY MRI TECHNIQUES AND THEIR HISTOPATHOLOGICAL CORRELATION

    Directory of Open Access Journals (Sweden)

    Mohammad Shamim

    2014-12-01

    Full Text Available : This study was conducted on thirty patients of brain tumors diagnosed on CT scan/ Conventional MRI. It was performed in the Department of Radiological and PET Imaging, Institute of Nuclear Medicine and Allied Sciences (INMAS, Brig S. K. Mazumdar Marg , Lucknow road, Delhi. Out of thirty patients, 19 patients (63.33% were male and 11 patients (36.66% were female. Their ages ranged from 22 to 63 years. The most common presenting symptom was headache followed by seizures. MRI is a powerful tool for evaluation and characterization of brain tumors because of its superior soft tissue contrast and multiplanar capabilities. All these patients underwent routine MRI sequences, including T1W, T2WI and FLAIR sequences. Histopathological correlation was obtained in all the patients to serve as the gold standard. Out of thirty patients selected for this study, twenty cases were found to be malignant and ten cases were benign on histopathological evaluation. Majority of malignant lesions were glioblastomamultiforme. Amongst benign cases, majorities were meningioma, one was a granulomatous lesion and one was a benign cystic lesion. On conventional MRI sequences, including T1, T2 and FLAIR, there was significant overlap between appearances of benign and malignant lesions in their intensity on various sequences. Moreover, it has got no prognostic value in follow up of patients after therapy.

  10. Influence of metallothioneins on zinc and copper distribution in brain tumours.

    Science.gov (United States)

    Floriańczyk, Bolesław; Osuchowski, Jacek; Kaczmarczyk, Robert; Trojanowski, Tomasz; Stryjecka-Zimmer, Marta

    2003-01-01

    Metallothioneins take part in the homeostasis of the ions of the metals which are necessary for the proper metabolism of the organism (zinc, copper), in biosynthesis regulation of the zinc-containing proteins and also in the detoxication of metals from the tissues. They also protect the tissue from the effects of free radicals, radiation, electrophilic pharmacological agents used in the cancer therapy and from mutagens. The experimental materials were brain astrocytomas, benign gliomas and malignant gliomas. The levels of the metallothionein were determined by cadmium-haemoglobin affinity assay using the cadmium isotope (109Cd). The values of zinc and copper were determined by means of atomic absorption spectrophotometry. In our studies, the level of metallothioneins in the group of malignant neoplasms was slightly higher than the level of these proteins in the group of benign neoplasms. The correlation coefficient of the studied parameters proved an interrelation between the levels of zinc and copper and the content of metallothioneins. In malignant neoplasms, the level of zinc showed a positive relationship with the metallothionein level, whereas the copper content showed an inverse relationship. There was a statistical difference, but no significant difference, in the levels of copper between malignant and benign groups.

  11. Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

    Science.gov (United States)

    Jamal, Wasifa; Das, Saptarshi; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

    2014-08-01

    Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

  12. Simultaneous evaluation of brain tumour metabolism, structure and blood volume using [{sup 18}F]-fluoroethyltyrosine (FET) PET/MRI: feasibility, agreement and initial experience

    Energy Technology Data Exchange (ETDEWEB)

    Henriksen, Otto M.; Hansen, Adam E.; Law, Ian [Copenhagen University Hospital Rigshospitalet Blegdamsvej, Department of Clinical Physiology Nuclear Medicine and PET, Copenhagen (Denmark); Larsen, Vibeke A. [Copenhagen University Hospital Rigshospitalet Blegdamsvej, Department of Radiology, Copenhagen (Denmark); Muhic, Aida; Poulsen, Hans S. [Copenhagen University Hospital Rigshospitalet Blegdamsvej, Department of Oncology, Copenhagen (Denmark); Larsson, Henrik B.W. [Copenhagen University Hospital Rigshospitalet Glostrup, Functional Imaging Unit, Department of Clinical Physiology Nuclear Medicine and PET, Glostrup (Denmark)

    2016-01-15

    imaging of brain tumour metabolism and perfusion using hybrid PET/MR systems may provide complementary information on tumour biology, but the potential clinical value remains to be determined in future trials. (orig.)

  13. An efficient approach of EEG feature extraction and classification for brain computer interface

    Institute of Scientific and Technical Information of China (English)

    Wu Ting; Yan Guozheng; Yang Banghua

    2009-01-01

    In the study of brain-computer interfaces, a method of feature extraction and classification used for two kinds of imaginations is proposed. It considers Euclidean distance between mean traces recorded from the channels with two kinds of imaginations as a feature, and determines imagination classes using threshold value. It analyzed the background of experiment and theoretical foundation referring to the data sets of BCI 2003, and compared the classification precision with the best result of the competition. The result shows that the method has a high precision and is advantageous for being applied to practical systems.

  14. An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

    Directory of Open Access Journals (Sweden)

    Muhammad Faisal Siddiqui

    Full Text Available A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT, principal component analysis (PCA, and least squares support vector machine (LS-SVM are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%. Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities

  15. An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

    Science.gov (United States)

    Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan

    2015-01-01

    A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the

  16. The use of stimulant medication to improve neurocognitive and learning outcomes in children diagnosed with brain tumours: a systematic review.

    Science.gov (United States)

    Smithson, Emilie F; Phillips, Robert; Harvey, David W; Morrall, Matthew C H J

    2013-09-01

    Impaired attention is reported in children following treatment for brain tumours (BT). Attention problems impact on information processing and encoding, ultimately leading to academic under-performance. Having been successfully used to manage attention-deficit/hyperactivity disorder (ADHD), stimulants such as methylphenidate (MPH) have been investigated as a beneficial treatment for survivors of childhood BT. In order to develop appropriate strategies to manage late neurocognitive effects, the results of such trials should be evaluated to identify those children most likely to benefit from stimulants. An advanced search was performed across twelve electronic databases for the selection of relevant randomised controlled trials with at least one active stimulant-treated arm. Abstracts were screened for eligibility and data on study design and results were extracted. Of the 5446 records identified, 11 full text articles were assessed for eligibility and seven included in qualitative synthesis. Of the seven papers there were four original trials. Short term outcomes for MPH on objective direct measures of attention and parent/teacher ratings of behaviour were favourable. Observations of side effects indicate that MPH is generally well tolerated. Heterogeneity of study design and outcome measures precluded meta-analysis. Despite yielding only a small number of trials with limited sample size, studies investigating the use of stimulant medication in survivors of childhood BT have provided promising outcomes. Current evidence indicates males, older age when treated, and higher baseline intelligence quotient (IQ) were predictive of greater responsiveness to MPH. While encouraging, additional research using a standardised protocol of outcome measures would be beneficial in identifying those likely to benefit from stimulant use. Further, the available data have yet to establish clear evidence for the effectiveness of MPH being translated into improvements on standardised

  17. Classification of EEG with structural feature dictionaries in a brain computer interface.

    Science.gov (United States)

    Göksu, Fikri; Ince, Nuri Firat; Tadipatri, Vijay Aditya; Tewfik, Ahmed H

    2008-01-01

    We present a new method for the classification of EEG in a brain computer interface by adapting subject specific features in spectral, temporal and spatial domain. For this particular purpose we extend our previous work on ECoG classification based on structural feature dictionary and apply it to extract the spectro-temporal patterns of multichannel EEG recordings related to a motor imagery task. The construction of the feature dictionary based on undecimated wavelet packet transform is extended to block FFT. We evaluate several subset selection algorithms to select a small number of features for final classification. We tested our proposed approach on five subjects of BCI Competition 2005 dataset- IVa. By adapting the wavelet filter for each subject, the algorithm achieved an average classification accuracy of 91.4% The classification results and characteristic of selected features indicate that the proposed algorithm can jointly adapt to EEG patterns in spectro-spatio-temporal domain and provide classification accuracies as good as existing methods used in the literature.

  18. Non-target adjacent stimuli classification improves performance of classical ERP-based brain computer interface

    Science.gov (United States)

    Ceballos, G. A.; Hernández, L. F.

    2015-04-01

    Objective. The classical ERP-based speller, or P300 Speller, is one of the most commonly used paradigms in the field of Brain Computer Interfaces (BCI). Several alterations to the visual stimuli presentation system have been developed to avoid unfavorable effects elicited by adjacent stimuli. However, there has been little, if any, regard to useful information contained in responses to adjacent stimuli about spatial location of target symbols. This paper aims to demonstrate that combining the classification of non-target adjacent stimuli with standard classification (target versus non-target) significantly improves classical ERP-based speller efficiency. Approach. Four SWLDA classifiers were trained and combined with the standard classifier: the lower row, upper row, right column and left column classifiers. This new feature extraction procedure and the classification method were carried out on three open databases: the UAM P300 database (Universidad Autonoma Metropolitana, Mexico), BCI competition II (dataset IIb) and BCI competition III (dataset II). Main results. The inclusion of the classification of non-target adjacent stimuli improves target classification in the classical row/column paradigm. A gain in mean single trial classification of 9.6% and an overall improvement of 25% in simulated spelling speed was achieved. Significance. We have provided further evidence that the ERPs produced by adjacent stimuli present discriminable features, which could provide additional information about the spatial location of intended symbols. This work promotes the searching of information on the peripheral stimulation responses to improve the performance of emerging visual ERP-based spellers.

  19. Intra-individual, randomised comparison of the MRI contrast agents gadobutrol versus gadoteridol in patients with primary and secondary brain tumours, evaluated in a blinded read

    Energy Technology Data Exchange (ETDEWEB)

    Koenig, M. [Klinikum Luenen St. Marien-Hospital, Department of Diagnostic and Interventional Radiology and Neuroradiology, Luenen (Germany); Schulte-Altedorneburg, G. [Staedtisches Klinikum Muenchen Harlaching, Department of Diagnostic and Interventional Radiology, Neuroradiology and Nuclear Medicine, Muenchen (Germany); Piontek, M.; Heuser, L. [Universitaetsklinikum Knappschaftskrankenhaus GmbH, Department of Diagnostic and Interventional Radiology, Neuroradiology and Nuclear Medicine, Bochum (Germany); Hentsch, A. [Radiologisches Institut Hohenzollernstrasse, Koblenz (Germany); Spangenberg, P. [Universitaetsklinikum Knappschaftskrankenhaus GmbH, Department of Neurosurgery, Bochum (Germany); Schwenke, C. [SCO:SSiS, Berlin (Germany); Harders, A. [Universitaetsklinikum Knappschaftskrankenhaus GmbH, Department of Neurosurgery Knappschaftskrankenhaus, Bochum (Germany)

    2013-12-15

    To prove that 1.0 M gadobutrol provides superior contrast enhancement and MRI image characteristics of primary and secondary brain tumours compared with 0.5 M gadoteridol, thereby providing superior diagnostic information. Brain MRI was performed in two separate examinations in patients scheduled for neurosurgery. Independent injections of 1.0 M gadobutrol and 0.5 M gadoteridol at doses of 0.1 mmol Gd/kg body weight were administered per patient in randomised order. Evaluation was performed in an off-site blinded read. Fifty-one patients in the full analysis set (FAS) were eligible for efficacy analysis and 44 for the per-protocol analysis. For the primary efficacy variable ''preference in contrast enhancement for one contrast agent or the other'', the rate of ''gadobutrol preferred'' was estimated at 0.73 (95 % confidence interval 0.61; 0.83), showing significant superiority of gadobutrol over gadoteridol. Calculated lesion-to-brain contrast and the results of all qualitative secondary efficacy variables were also in favour of gadobutrol. Keeping a sufficient time delay after contrast application proved to be essential to get optimal image quality. Compared with 0.5 M gadoteridol, 1.0 M gadobutrol was proven to have significantly superior contrast enhancement characteristics in a routine MRI protocol of primary and secondary brain tumours. (orig.)

  20. Gene expression profiling of gliomas: merging genomic and histopathological classification for personalised therapy

    OpenAIRE

    Vitucci, M; Hayes, D N; Miller, C R

    2010-01-01

    The development of DNA microarray technologies over the past decade has revolutionised translational cancer research. These technologies were originally hailed as more objective, comprehensive replacements for traditional histopathological cancer classification systems, based on microscopic morphology. Although DNA microarray-based gene expression profiling (GEP) remains unlikely in the near term to completely replace morphological classification of primary brain tumours, specifically the dif...

  1. Feature Extraction from Subband Brain Signals and Its Classification

    Science.gov (United States)

    Mukul, Manoj Kumar; Matsuno, Fumitoshi

    This paper considers both the non-stationarity as well as independence/uncorrelated criteria along with the asymmetry ratio over the electroencephalogram (EEG) signals and proposes a hybrid approach of the signal preprocessing methods before the feature extraction. A filter bank approach of the discrete wavelet transform (DWT) is used to exploit the non-stationary characteristics of the EEG signals and it decomposes the raw EEG signals into the subbands of different center frequencies called as rhythm. A post processing of the selected subband by the AMUSE algorithm (a second order statistics based ICA/BSS algorithm) provides the separating matrix for each class of the movement imagery. In the subband domain the orthogonality as well as orthonormality criteria over the whitening matrix and separating matrix do not come respectively. The human brain has an asymmetrical structure. It has been observed that the ratio between the norms of the left and right class separating matrices should be different for better discrimination between these two classes. The alpha/beta band asymmetry ratio between the separating matrices of the left and right classes will provide the condition to select an appropriate multiplier. So we modify the estimated separating matrix by an appropriate multiplier in order to get the required asymmetry and extend the AMUSE algorithm in the subband domain. The desired subband is further subjected to the updated separating matrix to extract subband sub-components from each class. The extracted subband sub-components sources are further subjected to the feature extraction (power spectral density) step followed by the linear discriminant analysis (LDA).

  2. Classification of Autism Spectrum Disorder Using Supervised Learning of Brain Connectivity Measures Extracted from Synchrostates

    CERN Document Server

    Jamal, Wasifa; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

    2014-01-01

    Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave ...

  3. Classification of normal and pathological aging processes based on brain MRI morphology measures

    Science.gov (United States)

    Perez-Gonzalez, J. L.; Yanez-Suarez, O.; Medina-Bañuelos, V.

    2014-03-01

    Reported studies describing normal and abnormal aging based on anatomical MRI analysis do not consider morphological brain changes, but only volumetric measures to distinguish among these processes. This work presents a classification scheme, based both on size and shape features extracted from brain volumes, to determine different aging stages: healthy control (HC) adults, mild cognitive impairment (MCI), and Alzheimer's disease (AD). Three support vector machines were optimized and validated for the pair-wise separation of these three classes, using selected features from a set of 3D discrete compactness measures and normalized volumes of several global and local anatomical structures. Our analysis show classification rates of up to 98.3% between HC and AD; of 85% between HC and MCI and of 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indexes to classify different aging stages.

  4. Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition.

    Directory of Open Access Journals (Sweden)

    Jun Cheng

    Full Text Available Automatic classification of tissue types of region of interest (ROI plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor in T1-weighted contrast-enhanced MRI (CE-MRI images. Spatial pyramid matching (SPM, which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM, and bag-of-words (BoW model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.

  5. Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition.

    Science.gov (United States)

    Cheng, Jun; Huang, Wei; Cao, Shuangliang; Yang, Ru; Yang, Wei; Yun, Zhaoqiang; Wang, Zhijian; Feng, Qianjin

    2015-01-01

    Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.

  6. Extreme learning machine-based classification of ADHD using brain structural MRI data.

    Directory of Open Access Journals (Sweden)

    Xiaolong Peng

    Full Text Available BACKGROUND: Effective and accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD is currently of significant interest. ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc. The aims of this study were as follows: (1 Propose an ADHD classification model using the extreme learning machine (ELM algorithm for automatic, efficient and objective clinical ADHD diagnosis. (2 Assess the computational efficiency and the effect of sample size on both ELM and support vector machine (SVM methods and analyze which brain segments are involved in ADHD. METHODS: High-resolution three-dimensional MR images were acquired from 55 ADHD subjects and 55 healthy controls. Multiple brain measures (cortical thickness, etc. were calculated using a fully automated procedure in the FreeSurfer software package. In total, 340 cortical features were automatically extracted from 68 brain segments with 5 basic cortical features. F-score and SFS methods were adopted to select the optimal features for ADHD classification. Both ELM and SVM were evaluated for classification accuracy using leave-one-out cross-validation. RESULTS: We achieved ADHD prediction accuracies of 90.18% for ELM using eleven combined features, 84.73% for SVM-Linear and 86.55% for SVM-RBF. Our results show that ELM has better computational efficiency and is more robust as sample size changes than is SVM for ADHD classification. The most pronounced differences between ADHD and healthy subjects were observed in the frontal lobe, temporal lobe, occipital lobe and insular. CONCLUSION: Our ELM-based algorithm for ADHD diagnosis performs considerably better than the traditional SVM algorithm. This result suggests that ELM may be used for the clinical diagnosis of ADHD and the investigation of different brain diseases.

  7. Wireless brain-machine interface using EEG and EOG: brain wave classification and robot control

    Science.gov (United States)

    Oh, Sechang; Kumar, Prashanth S.; Kwon, Hyeokjun; Varadan, Vijay K.

    2012-04-01

    A brain-machine interface (BMI) links a user's brain activity directly to an external device. It enables a person to control devices using only thought. Hence, it has gained significant interest in the design of assistive devices and systems for people with disabilities. In addition, BMI has also been proposed to replace humans with robots in the performance of dangerous tasks like explosives handling/diffusing, hazardous materials handling, fire fighting etc. There are mainly two types of BMI based on the measurement method of brain activity; invasive and non-invasive. Invasive BMI can provide pristine signals but it is expensive and surgery may lead to undesirable side effects. Recent advances in non-invasive BMI have opened the possibility of generating robust control signals from noisy brain activity signals like EEG and EOG. A practical implementation of a non-invasive BMI such as robot control requires: acquisition of brain signals with a robust wearable unit, noise filtering and signal processing, identification and extraction of relevant brain wave features and finally, an algorithm to determine control signals based on the wave features. In this work, we developed a wireless brain-machine interface with a small platform and established a BMI that can be used to control the movement of a robot by using the extracted features of the EEG and EOG signals. The system records and classifies EEG as alpha, beta, delta, and theta waves. The classified brain waves are then used to define the level of attention. The acceleration and deceleration or stopping of the robot is controlled based on the attention level of the wearer. In addition, the left and right movements of eye ball control the direction of the robot.

  8. New KF-PP-SVM classification method for EEG in brain-computer interfaces.

    Science.gov (United States)

    Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

    2014-01-01

    Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

  9. Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database.

    NARCIS (Netherlands)

    Julia-Sape, M.; Acosta, D.M.; Majos, C.; Moreno-Torres, A.; Wesseling, P.; Acebes, J.J.; Griffiths, J.R.; Arus, C.

    2006-01-01

    OBJECT: The aim of this study was to estimate the accuracy of routine magnetic resonance (MR) imaging studies in the classification of brain tumors in terms of both cell type and grade of malignancy. METHODS: The authors retrospectively assessed the correlation between neuroimaging classifications a

  10. Supervised, Multivariate, Whole-brain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia Research

    Directory of Open Access Journals (Sweden)

    Eva Janousova

    2016-08-01

    Full Text Available We examined how penalized linear discriminant analysis with resampling, which is a supervised, multivariate, whole-brain reduction technique, can help schizophrenia diagnostics and research. In an experiment with magnetic resonance brain images of 52 first-episode schizophrenia patients and 52 healthy controls, this method allowed us to select brain areas relevant to schizophrenia, such as the left prefrontal cortex, the anterior cingulum, the right anterior insula, the thalamus and the hippocampus. Nevertheless, the classification performance based on such reduced data was not significantly better than the classification of data reduced by mass univariate selection using a t-test or unsupervised multivariate reduction using principal component analysis. Moreover, we found no important influence of the type of imaging features, namely local deformations or grey matter volumes, and the classification method, specifically linear discriminant analysis or linear support vector machines, on the classification results. However, we ascertained significant effect of a cross-validation setting on classification performance as classification results were overestimated even though the resampling was performed during the selection of brain imaging features. Therefore, it is critically important to perform cross-validation in all steps of the analysis (not only during classification in case there is no external validation set to avoid optimistically biasing the results of classification studies.

  11. Fusing in vivo and ex vivo NMR sources of information for brain tumor classification

    Science.gov (United States)

    Croitor-Sava, A. R.; Martinez-Bisbal, M. C.; Laudadio, T.; Piquer, J.; Celda, B.; Heerschap, A.; Sima, D. M.; Van Huffel, S.

    2011-11-01

    In this study we classify short echo-time brain magnetic resonance spectroscopic imaging (MRSI) data by applying a model-based canonical correlation analyses algorithm and by using, as prior knowledge, multimodal sources of information coming from high-resolution magic angle spinning (HR-MAS), MRSI and magnetic resonance imaging. The potential and limitations of fusing in vivo and ex vivo nuclear magnetic resonance sources to detect brain tumors is investigated. We present various modalities for multimodal data fusion, study the effect and the impact of using multimodal information for classifying MRSI brain glial tumors data and analyze which parameters influence the classification results by means of extensive simulation and in vivo studies. Special attention is drawn to the possibility of considering HR-MAS data as a complementary dataset when dealing with a lack of MRSI data needed to build a classifier. Results show that HR-MAS information can have added value in the process of classifying MRSI data.

  12. Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification.

    Science.gov (United States)

    Vrooman, Henri A; Cocosco, Chris A; van der Lijn, Fedde; Stokking, Rik; Ikram, M Arfan; Vernooij, Meike W; Breteler, Monique M B; Niessen, Wiro J

    2007-08-01

    Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue in MR data, requires training on manually labeled subjects. This manual labeling is a laborious and time-consuming procedure. In this work, a new fully automated brain tissue classification procedure is presented, in which kNN training is automated. This is achieved by non-rigidly registering the MR data with a tissue probability atlas to automatically select training samples, followed by a post-processing step to keep the most reliable samples. The accuracy of the new method was compared to rigid registration-based training and to conventional kNN-based segmentation using training on manually labeled subjects for segmenting gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in 12 data sets. Furthermore, for all classification methods, the performance was assessed when varying the free parameters. Finally, the robustness of the fully automated procedure was evaluated on 59 subjects. The automated training method using non-rigid registration with a tissue probability atlas was significantly more accurate than rigid registration. For both automated training using non-rigid registration and for the manually trained kNN classifier, the difference with the manual labeling by observers was not significantly larger than inter-observer variability for all tissue types. From the robustness study, it was clear that, given an appropriate brain atlas and optimal parameters, our new fully automated, non-rigid registration-based method gives accurate and robust segmentation results. A similarity index was used for comparison with manually trained kNN. The similarity indices were 0.93, 0.92 and 0.92, for CSF, GM and WM, respectively. It can be concluded that our fully automated method using non-rigid registration may replace manual segmentation, and thus that automated brain tissue segmentation without laborious manual training is feasible.

  13. Percutaneous renal tumour biopsy.

    Science.gov (United States)

    Delahunt, Brett; Samaratunga, Hemamali; Martignoni, Guido; Srigley, John R; Evans, Andrew J; Brunelli, Matteo

    2014-09-01

    The use of percutaneous renal tumour biopsy (RTB) as a diagnostic tool for the histological characterization of renal masses has increased dramatically within the last 30 years. This increased utilization has paralleled advances in imaging techniques and an evolving knowledge of the clinical value of nephron sparing surgery. Improved biopsy techniques using image guidance, coupled with the use of smaller gauge needles has led to a decrease in complication rates. Reports from series containing a large number of cases have shown the non-diagnostic rate of RTB to range from 4% to 21%. Re-biopsy has been shown to reduce this rate, while the use of molecular markers further improves diagnostic sensitivity. In parallel with refinements of the biopsy procedure, there has been a rapid expansion in our understanding of the complexity of renal cell neoplasia. The 2013 Vancouver Classification is the current classification for renal tumours, and contains five additional entities recognized as novel forms of renal malignancy. The diagnosis of tumour morphotype on RTB is usually achievable on routine histology; however, immunohistochemical studies may be of assistance in difficult cases. The morphology of the main tumour subtypes, based upon the Vancouver Classification, is described and differentiating features are discussed.

  14. Classification

    Science.gov (United States)

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  15. Predict or classify: The deceptive role of time-locking in brain signal classification

    Science.gov (United States)

    Rusconi, Marco; Valleriani, Angelo

    2016-06-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

  16. Brain Network Activation Analysis Utilizing Spatiotemporal Features for Event Related Potentials Classification

    Directory of Open Access Journals (Sweden)

    Yaki Stern

    2016-12-01

    Full Text Available The purpose of this study was to introduce an improved tool for automated classification of event-related potentials (ERPs using spatiotemporally parcellated events incorporated into a functional brain network activation (BNA analysis. The auditory oddball ERP paradigm was selected to demonstrate and evaluate the improved tool. Methods: The ERPs of each subject were decomposed into major dynamic spatiotemporal events. Then, a set of spatiotemporal events representing the group was generated by aligning and clustering the spatiotemporal events of all individual subjects. The temporal relationship between the common group events generated a network, which is the spatiotemporal reference BNA model. Scores were derived by comparing each subject’s spatiotemporal events to the reference BNA model and were then entered into a support vector machine classifier to classify subjects into relevant subgroups. The reliability of the BNA scores (test-retest repeatability using intraclass correlation and their utility as a classification tool were examined in the context of Target-Novel classification. Results: BNA intraclass correlation values of repeatability ranged between 0.51 and 0.82 for the known ERP components N100, P200 and P300. Classification accuracy was high when the trained data were validated on the same subjects for different visits (AUCs 0.93 and 0.95. The classification accuracy remained high for a test group recorded at a different clinical center with a different recording system (AUCs 0.81, 0.85 for 2 visits. Conclusion: The improved spatiotemporal BNA analysis demonstrates high classification accuracy. The BNA analysis method holds promise as a tool for diagnosis, follow-up and drug development associated with different neurological conditions.

  17. Brain Machine Interface: Analysis of segmented EEG Signal Classification Using Short-Time PCA and Recurrent Neural Networks

    Directory of Open Access Journals (Sweden)

    C. R. Hema

    2008-01-01

    Full Text Available Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI for communication; the BMI uses the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper a BMI design using five mental tasks from two subjects were studied, a combination of two tasks is studied per subject. An Elman recurrent neural network is proposed for classification of EEG signals. Two feature extraction algorithms using overlapped and non overlapped signal segments are analyzed. Principal component analysis is used for extracting features from the EEG signal segments. Classification performance of overlapping EEG signal segments is observed to be better in terms of average classification with a range of 78.5% to 100%, while the non overlapping EEG signal segments show better classification in terms of maximum classifications.

  18. Computational Classification Approach to Profile Neuron Subtypes from Brain Activity Mapping Data.

    Science.gov (United States)

    Li, Meng; Zhao, Fang; Lee, Jason; Wang, Dong; Kuang, Hui; Tsien, Joe Z

    2015-07-27

    The analysis of cell type-specific activity patterns during behaviors is important for better understanding of how neural circuits generate cognition, but has not been well explored from in vivo neurophysiological datasets. Here, we describe a computational approach to uncover distinct cell subpopulations from in vivo neural spike datasets. This method, termed "inter-spike-interval classification-analysis" (ISICA), is comprised of four major steps: spike pattern feature-extraction, pre-clustering analysis, clustering classification, and unbiased classification-dimensionality selection. By using two key features of spike dynamic - namely, gamma distribution shape factors and a coefficient of variation of inter-spike interval - we show that this ISICA method provides invariant classification for dopaminergic neurons or CA1 pyramidal cell subtypes regardless of the brain states from which spike data were collected. Moreover, we show that these ISICA-classified neuron subtypes underlie distinct physiological functions. We demonstrate that the uncovered dopaminergic neuron subtypes encoded distinct aspects of fearful experiences such as valence or value, whereas distinct hippocampal CA1 pyramidal cells responded differentially to ketamine-induced anesthesia. This ISICA method should be useful to better data mining of large-scale in vivo neural datasets, leading to novel insights into circuit dynamics associated with cognitions.

  19. Asynchronous P300 classification in a reactive brain-computer interface during an outlier detection task

    Science.gov (United States)

    Krumpe, Tanja; Walter, Carina; Rosenstiel, Wolfgang; Spüler, Martin

    2016-08-01

    Objective. In this study, the feasibility of detecting a P300 via an asynchronous classification mode in a reactive EEG-based brain-computer interface (BCI) was evaluated. The P300 is one of the most popular BCI control signals and therefore used in many applications, mostly for active communication purposes (e.g. P300 speller). As the majority of all systems work with a stimulus-locked mode of classification (synchronous), the field of applications is limited. A new approach needs to be applied in a setting in which a stimulus-locked classification cannot be used due to the fact that the presented stimuli cannot be controlled or predicted by the system. Approach. A continuous observation task requiring the detection of outliers was implemented to test such an approach. The study was divided into an offline and an online part. Main results. Both parts of the study revealed that an asynchronous detection of the P300 can successfully be used to detect single events with high specificity. It also revealed that no significant difference in performance was found between the synchronous and the asynchronous approach. Significance. The results encourage the use of an asynchronous classification approach in suitable applications without a potential loss in performance.

  20. Classification

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2017-01-01

    This article presents and discusses definitions of the term “classification” and the related concepts “Concept/conceptualization,”“categorization,” “ordering,” “taxonomy” and “typology.” It further presents and discusses theories of classification including the influences of Aristotle...... and Wittgenstein. It presents different views on forming classes, including logical division, numerical taxonomy, historical classification, hermeneutical and pragmatic/critical views. Finally, issues related to artificial versus natural classification and taxonomic monism versus taxonomic pluralism are briefly...

  1. Joint Time-Frequency-Space Classification of EEG in a Brain-Computer Interface Application

    Directory of Open Access Journals (Sweden)

    Molina Gary N Garcia

    2003-01-01

    Full Text Available Brain-computer interface is a growing field of interest in human-computer interaction with diverse applications ranging from medicine to entertainment. In this paper, we present a system which allows for classification of mental tasks based on a joint time-frequency-space decorrelation, in which mental tasks are measured via electroencephalogram (EEG signals. The efficiency of this approach was evaluated by means of real-time experimentations on two subjects performing three different mental tasks. To do so, a number of protocols for visualization, as well as training with and without feedback, were also developed. Obtained results show that it is possible to obtain good classification of simple mental tasks, in view of command and control, after a relatively small amount of training, with accuracies around 80%, and in real time.

  2. Verbal fluency indicators of malingering in traumatic brain injury: classification accuracy in known groups.

    Science.gov (United States)

    Curtis, Kelly L; Thompson, Laura K; Greve, Kevin W; Bianchini, Kevin J

    2008-09-01

    A known-groups design was used to determine the classification accuracy of verbal fluency variables in detecting Malingered Neurocognitive Dysfunction (MND) in traumatic brain injury (TBI). Participants were 204 TBI and 488 general clinical patients. The Slick et al. (1999) criteria were used to classify the TBI patients into non-MND and MND groups. An educationally corrected FAS Total Correct word T-score proved to be the most accurate of the several verbal fluency indicators examined. Classification accuracy of this variable at specific cutoffs is presented in a cumulative frequency table. This variable accurately differentiated non-MND from MND mild TBI patients but its accuracy was unacceptable in moderate/severe TBI. The clinical application of these findings is discussed.

  3. Decrease of deleted in malignant brain tumour-1 (DMBT-1) expression is a crucial late event in intrahepatic cholangiocarcinoma

    DEFF Research Database (Denmark)

    Sasaki, M; Huang, S-F; Chen, M-F

    2003-01-01

    AIMS: To investigate the participation of DMBT-1, a candidate tumour suppressor gene, in the development of intrahepatic cholangiocarcinoma via intraductal papillary neoplasm of the liver (IPN-L) arising in hepatolithiasis. DMBT-1 plays a role in mucosal immune defence. METHODS AND RESULTS: The e...

  4. Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.

    Science.gov (United States)

    Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V; Robles, Montserrat; Aparici, F; Martí-Bonmatí, L; García-Gómez, Juan M

    2015-01-01

    Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

  5. Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.

    Directory of Open Access Journals (Sweden)

    Javier Juan-Albarracín

    Full Text Available Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM, whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF. An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

  6. Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique

    Science.gov (United States)

    Jones, Timothy L.; Byrnes, Tiernan J.; Yang, Guang; Howe, Franklyn A.; Bell, B. Anthony; Barrick, Thomas R.

    2015-01-01

    Background There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. Methods DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. Results Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. Conclusions D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning. PMID:25121771

  7. Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

    Directory of Open Access Journals (Sweden)

    R. Rajesh Sharma

    2015-01-01

    algorithm (RGSA. Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002. The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

  8. Single-trial EEG classification using in-phase average for brain-computer interface

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Communication signals should be estimated by a single trial in a brain-computer interface.Since the relativity of visual evoked potentials from different sites should be stronger than those of the spontaneous electro encephalogram(EEG),this paper adopted the time-lock averaged signals from multi-channels as features.200 trials of EEG recordings evoked by target or non-target stimuli were classified by the support vector machine(SVM).Results show that a classification accuracy of higher than 97% can be obtained by merely using the 250-550 ms time section of the averaged signals with channel Cz and Pz as features.It suggests that a possible approach to boost communication speed and simplify the designation of the brain-computer interface(BCI)system is worthy of an attempt in this way.

  9. Predict or classify: The deceptive role of time-locking in brain signal classification

    CERN Document Server

    Rusconi, Marco

    2016-01-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate...

  10. Real-time fMRI using brain-state classification.

    Science.gov (United States)

    LaConte, Stephen M; Peltier, Scott J; Hu, Xiaoping P

    2007-10-01

    We have implemented a real-time functional magnetic resonance imaging system based on multivariate classification. This approach is distinctly different from spatially localized real-time implementations, since it does not require prior assumptions about functional localization and individual performance strategies, and has the ability to provide feedback based on intuitive translations of brain state rather than localized fluctuations. Thus this approach provides the capability for a new class of experimental designs in which real-time feedback control of the stimulus is possible-rather than using a fixed paradigm, experiments can adaptively evolve as subjects receive brain-state feedback. In this report, we describe our implementation and characterize its performance capabilities. We observed approximately 80% classification accuracy using whole brain, block-design, motor data. Within both left and right motor task conditions, important differences exist between the initial transient period produced by task switching (changing between rapid left or right index finger button presses) and the subsequent stable period during sustained activity. Further analysis revealed that very high accuracy is achievable during stable task periods, and that the responsiveness of the classifier to changes in task condition can be much faster than signal time-to-peak rates. Finally, we demonstrate the versatility of this implementation with respect to behavioral task, suggesting that our results are applicable across a spectrum of cognitive domains. Beyond basic research, this technology can complement electroencephalography-based brain computer interface research, and has potential applications in the areas of biofeedback rehabilitation, lie detection, learning studies, virtual reality-based training, and enhanced conscious awareness. Wiley-Liss, Inc.

  11. Classification of spectra and search for bio-makers in prostate tumours form proton nuclear magnetic resonance spectroscopy; Classification de spectres et recherche de biomarqueurs en spectroscopie par resonnance magnetique nucleaire du proton dans les tumeurs prostatiques

    Energy Technology Data Exchange (ETDEWEB)

    Parfait, S.

    2010-12-06

    Prostate cancer is the most common cancer in men over 50 years. Current detection methods either lack sensitivity or specificity or are unpleasant for the patient. Magnetic resonance spectroscopy allows the study of metabolism in vivo. The use of a high field machine (>3 T) has allowed us to dispense with the use of an endorectal coil, which is particularly uncomfortable for the patient. The objective of this work is to create an automatic method to detect cancer by processing data obtained through magnetic resonance spectroscopy. MRS is a complex phenomenon, very sensitive to acquisition conditions. First, we have studied how to improve and optimise signal acquisition. However, even with a very good quality signal, it must still undergo further post-processing to be analysed automatically by a classification method. Further work was therefore needed to investigate which post-processing steps were required in order to optimize the spectra for classification. We then investigated the optimal classification method for this problem. A particular set of steps (signal acquisition, processing and spectral classification data) allows us to highlight the presence of prostate tumors with an overall error rate of less than 12%. In a second step, we searched for new bio-markers within the spectra. These bio-markers could be a metabolite or a specific frequency range corresponding to several metabolites. We did not find any additional significant attributes other than choline and citrate, however, some frequency bands seem to participate in improving the error rate. Finally, we expanded our investigation by attempting to apply these techniques to the rat. Technical constraints related to acquisition did not allow us to obtain a sufficient number of spectra in the pre-clinical cases. Nonetheless, we have validated the feasibility of MRS in rodents and its relevance in the brain. The technique, however, must be improved in order to be validated in the case of prostate cancer in

  12. An in vivo genetic screen in Drosophila identifies the orthologue of human cancer/testis gene SPO11 among a network of targets to inhibit lethal(3)malignant brain tumour growth.

    Science.gov (United States)

    Rossi, Fabrizio; Molnar, Cristina; Hashiyama, Kazuya; Heinen, Jan P; Pampalona, Judit; Llamazares, Salud; Reina, José; Hashiyama, Tomomi; Rai, Madhulika; Pollarolo, Giulia; Fernández-Hernández, Ismael; Gonzalez, Cayetano

    2017-08-01

    Using transgenic RNAi technology, we have screened over 4000 genes to identify targets to inhibit malignant growth caused by the loss of function of lethal(3)malignant brain tumour in Drosophila in vivo We have identified 131 targets, which belong to a wide range of gene ontologies. Most of these target genes are not significantly overexpressed in mbt tumours hence showing that, rather counterintuitively, tumour-linked overexpression is not a good predictor of functional requirement. Moreover, we have found that most of the genes upregulated in mbt tumours remain overexpressed in tumour-suppressed double-mutant conditions, hence revealing that most of the tumour transcriptome signature is not necessarily correlated with malignant growth. One of the identified target genes is meiotic W68 (mei-W68), the Drosophila orthologue of the human cancer/testis gene Sporulation-specific protein 11 (SPO11), the enzyme that catalyses the formation of meiotic double-strand breaks. We show that Drosophila mei-W68/SPO11 drives oncogenesis by causing DNA damage in a somatic tissue, hence providing the first instance in which a SPO11 orthologue is unequivocally shown to have a pro-tumoural role. Altogether, the results from this screen point to the possibility of investigating the function of human cancer relevant genes in a tractable experimental model organism like Drosophila. © 2017 The Authors.

  13. Threshold selection for classification of MR brain images by clustering method

    Energy Technology Data Exchange (ETDEWEB)

    Moldovanu, Simona [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania); Dumitru Moţoc High School, 15 Milcov St., 800509, Galaţi (Romania); Obreja, Cristian; Moraru, Luminita, E-mail: luminita.moraru@ugal.ro [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania)

    2015-12-07

    Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

  14. Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity.

    Science.gov (United States)

    Rashid, Barnaly; Arbabshirani, Mohammad R; Damaraju, Eswar; Cetin, Mustafa S; Miller, Robyn; Pearlson, Godfrey D; Calhoun, Vince D

    2016-07-01

    Recently, functional network connectivity (FNC, defined as the temporal correlation among spatially distant brain networks) has been used to examine the functional organization of brain networks in various psychiatric illnesses. Dynamic FNC is a recent extension of the conventional FNC analysis that takes into account FNC changes over short periods of time. While such dynamic FNC measures may be more informative about various aspects of connectivity, there has been no detailed head-to-head comparison of the ability of static and dynamic FNC to perform classification in complex mental illnesses. This paper proposes a framework for automatic classification of schizophrenia, bipolar and healthy subjects based on their static and dynamic FNC features. Also, we compare cross-validated classification performance between static and dynamic FNC. Results show that the dynamic FNC significantly outperforms the static FNC in terms of predictive accuracy, indicating that features from dynamic FNC have distinct advantages over static FNC for classification purposes. Moreover, combining static and dynamic FNC features does not significantly improve the classification performance over the dynamic FNC features alone, suggesting that static FNC does not add any significant information when combined with dynamic FNC for classification purposes. A three-way classification methodology based on static and dynamic FNC features discriminates individual subjects into appropriate diagnostic groups with high accuracy. Our proposed classification framework is potentially applicable to additional mental disorders.

  15. Classification of brain disease in magnetic resonance images using two-stage local feature fusion

    Science.gov (United States)

    Li, Tao; Li, Wu; Yang, Yehui

    2017-01-01

    Background Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale. Methods Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to. Results We apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block. Discussion This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls. PMID:28207873

  16. Boosting Brain Connectome Classification Accuracy in Alzheimer’s disease using Higher-Order Singular Value Decomposition

    Directory of Open Access Journals (Sweden)

    Liang eZhan

    2015-07-01

    Full Text Available Alzheimer's disease (AD is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI, are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer’s disease. Here, we focused on anatomical brain networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer’s Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer’s disease.

  17. Impact of brain tumour location on emotion and personality: a voxel-based lesion-symptom mapping study on mentalization processes.

    Science.gov (United States)

    Campanella, Fabio; Shallice, Tim; Ius, Tamara; Fabbro, Franco; Skrap, Miran

    2014-09-01

    Patients affected by brain tumours may show behavioural and emotional regulation deficits, sometimes showing flattened affect and sometimes experiencing a true 'change' in personality. However, little evidence is available to the surgeon as to what changes are likely to occur with damage at specific sites, as previous studies have either relied on single cases or provided only limited anatomical specificity, mostly reporting associations rather than dissociations of symptoms. We investigated these aspects in patients undergoing surgery for the removal of cerebral tumours. We argued that many of the problems described can be ascribed to the onset of difficulties in one or more of the different levels of the process of mentalizing (i.e. abstracting and reflecting upon) emotion and intentions, which impacts on everyday behaviour. These were investigated in terms of (i) emotion recognition; (ii) Theory of Mind; (iii) alexithymia; and (iv) self-maturity (personality disorder). We hypothesized that temporo/limbic areas would be critical for processing emotion and intentions at a more perceptual level, while frontal lobe structures would be more critical when higher levels of mentalization/abstraction are required. We administered four different tasks, Task 1: emotion recognition of Ekman faces; Task 2: the Eyes Test (Theory of Mind); Task 3: Toronto Alexithymia Scale; and Task 4: Temperament and Character Inventory (a personality inventory), both immediately before and few days after the operation for the removal of brain tumours in a series of 71 patients (age range: 18-75 years; 33 female) with lesions located in the left or right frontal, temporal and parietal lobes. Lobe-based and voxel-based analysis confirmed that tasks requiring interpretation of emotions and intentions at more basic (less mentalized) levels (Tasks 1 and 2) were more affected by temporo/insular lesions, with emotion recognition (Task 1) being maximally impaired by anterior temporal and amygdala

  18. Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas

    Science.gov (United States)

    Chestek, Cynthia A.; Gilja, Vikash; Blabe, Christine H.; Foster, Brett L.; Shenoy, Krishna V.; Parvizi, Josef; Henderson, Jaimie M.

    2013-04-01

    Objective. Brain-machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system.Approach. We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed five distinct isometric hand postures, as well as four distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with two participants. Main results. Classification rates were 68%, 84% and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1×). A similar increase in errors (2.6×) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system. Significance. These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training

  19. Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses.

    Science.gov (United States)

    Kocevar, Gabriel; Stamile, Claudio; Hannoun, Salem; Cotton, François; Vukusic, Sandra; Durand-Dubief, Françoise; Sappey-Marinier, Dominique

    2016-01-01

    Purpose: In this work, we introduce a method to classify Multiple Sclerosis (MS) patients into four clinical profiles using structural connectivity information. For the first time, we try to solve this question in a fully automated way using a computer-based method. The main goal is to show how the combination of graph-derived metrics with machine learning techniques constitutes a powerful tool for a better characterization and classification of MS clinical profiles. Materials and Methods: Sixty-four MS patients [12 Clinical Isolated Syndrome (CIS), 24 Relapsing Remitting (RR), 24 Secondary Progressive (SP), and 17 Primary Progressive (PP)] along with 26 healthy controls (HC) underwent MR examination. T1 and diffusion tensor imaging (DTI) were used to obtain structural connectivity matrices for each subject. Global graph metrics, such as density and modularity, were estimated and compared between subjects' groups. These metrics were further used to classify patients using tuned Support Vector Machine (SVM) combined with Radial Basic Function (RBF) kernel. Results: When comparing MS patients to HC subjects, a greater assortativity, transitivity, and characteristic path length as well as a lower global efficiency were found. Using all graph metrics, the best F-Measures (91.8, 91.8, 75.6, and 70.6%) were obtained for binary (HC-CIS, CIS-RR, RR-PP) and multi-class (CIS-RR-SP) classification tasks, respectively. When using only one graph metric, the best F-Measures (83.6, 88.9, and 70.7%) were achieved for modularity with previous binary classification tasks. Conclusion: Based on a simple DTI acquisition associated with structural brain connectivity analysis, this automatic method allowed an accurate classification of different MS patients' clinical profiles.

  20. Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses

    Science.gov (United States)

    Kocevar, Gabriel; Stamile, Claudio; Hannoun, Salem; Cotton, François; Vukusic, Sandra; Durand-Dubief, Françoise; Sappey-Marinier, Dominique

    2016-01-01

    Purpose: In this work, we introduce a method to classify Multiple Sclerosis (MS) patients into four clinical profiles using structural connectivity information. For the first time, we try to solve this question in a fully automated way using a computer-based method. The main goal is to show how the combination of graph-derived metrics with machine learning techniques constitutes a powerful tool for a better characterization and classification of MS clinical profiles. Materials and Methods: Sixty-four MS patients [12 Clinical Isolated Syndrome (CIS), 24 Relapsing Remitting (RR), 24 Secondary Progressive (SP), and 17 Primary Progressive (PP)] along with 26 healthy controls (HC) underwent MR examination. T1 and diffusion tensor imaging (DTI) were used to obtain structural connectivity matrices for each subject. Global graph metrics, such as density and modularity, were estimated and compared between subjects' groups. These metrics were further used to classify patients using tuned Support Vector Machine (SVM) combined with Radial Basic Function (RBF) kernel. Results: When comparing MS patients to HC subjects, a greater assortativity, transitivity, and characteristic path length as well as a lower global efficiency were found. Using all graph metrics, the best F-Measures (91.8, 91.8, 75.6, and 70.6%) were obtained for binary (HC-CIS, CIS-RR, RR-PP) and multi-class (CIS-RR-SP) classification tasks, respectively. When using only one graph metric, the best F-Measures (83.6, 88.9, and 70.7%) were achieved for modularity with previous binary classification tasks. Conclusion: Based on a simple DTI acquisition associated with structural brain connectivity analysis, this automatic method allowed an accurate classification of different MS patients' clinical profiles. PMID:27826224

  1. Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses

    Directory of Open Access Journals (Sweden)

    Gabriel Kocevar

    2016-10-01

    Full Text Available Purpose: In this work, we introduce a method to classify Multiple Sclerosis (MS patients into four clinical profiles using structural connectivity information. For the first time, we try to solve this question in a fully automated way using a computer-based method. The main goal is to show how the combination of graph-derived metrics with machine learning techniques constitutes a powerful tool for a better characterization and classification of MS clinical profiles.Materials and methods: Sixty-four MS patients (12 Clinical Isolated Syndrome (CIS, 24 Relapsing Remitting (RR, 24 Secondary Progressive (SP, and 17 Primary Progressive (PP along with 26 healthy controls (HC underwent MR examination. T1 and diffusion tensor imaging (DTI were used to obtain structural connectivity matrices for each subject. Global graph metrics, such as density and modularity, were estimated and compared between subjects’ groups. These metrics were further used to classify patients using tuned Support Vector Machine (SVM combined with Radial Basic Function (RBF kernel.Results: When comparing MS patients to HC subjects, a greater assortativity, transitivity and characteristic path length as well as a lower global efficiency were found. Using all graph metrics, the best F-Measures (91.8%, 91.8%, 75.6% and 70.6% were obtained for binary (HC-CIS, CIS-RR, RR-PP and multi-class (CIS-RR-SP classification tasks, respectively. When using only one graph metric, the best F-Measures (83.6%, 88.9% and 70.7% were achieved for modularity with previous binary classification tasks.Conclusion: Based on a simple DTI acquisition associated with structural brain connectivity analysis, this automatic method allowed an accurate classification of different MS patients’ clinical profiles.

  2. EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces

    Science.gov (United States)

    Ashari, Rehab Bahaaddin

    Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences. This dissertation studies and examines different ideas using principal angles and subspaces concepts. It introduces a novel mathematical approach for comparing sets of EEG signals for use in new BCI technology. The success of the presented results show that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. In this application, the appearance of a subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Smoothing the signals before using them is the only preprocessing step that was implemented in this study. The smoothing process based on minimizing the second derivative in time is implemented to increase the classification accuracy instead of using the bandpass filter that relies on assumptions on the frequency content of EEG. This study examines four different ways of removing outliers that are based on the principal angles and shows that the outlier removal methods did not help in the presented situations. One of the concepts that this dissertation focused on is the effect of the number of trials on the classification accuracies. The achievement of the good classification results by using a small number of trials starting from two trials only

  3. Extracting salient brain patterns for imaging-based classification of neurodegenerative diseases.

    Science.gov (United States)

    Rueda, Andrea; González, Fabio A; Romero, Eduardo

    2014-06-01

    Neurodegenerative diseases comprise a wide variety of mental symptoms whose evolution is not directly related to the visual analysis made by radiologists, who can hardly quantify systematic differences. Moreover, automatic brain morphometric analyses, that do perform this quantification, contribute very little to the comprehension of the disease, i.e., many of these methods classify but they do not produce useful anatomo-functional correlations. This paper presents a new fully automatic image analysis method that reveals discriminative brain patterns associated to the presence of neurodegenerative diseases, mining systematic differences and therefore grading objectively any neurological disorder. This is accomplished by a fusion strategy that mixes together bottom-up and top-down information flows. Bottom-up information comes from a multiscale analysis of different image features, while the top-down stage includes learning and fusion strategies formulated as a max-margin multiple-kernel optimization problem. The capacity of finding discriminative anatomic patterns was evaluated using the Alzheimer's disease (AD) as the use case. The classification performance was assessed under different configurations of the proposed approach in two public brain magnetic resonance datasets (OASIS-MIRIAD) with patients diagnosed with AD, showing an improvement varying from 6.2% to 13% in the equal error rate measure, with respect to what has been reported by the feature-based morphometry strategy. In terms of the anatomical analysis, discriminant regions found by the proposed approach highly correlates to what has been reported in clinical studies of AD.

  4. Canadian Study of Determinants of Endometabolic Health in ChIlDrEn (CanDECIDE study): a cohort study protocol examining the mechanisms of obesity in survivors of childhood brain tumours.

    Science.gov (United States)

    Samaan, M Constantine; Thabane, Lehana; Burrow, Sarah; Dillenburg, Rejane F; Scheinemann, Katrin

    2013-06-20

    Childhood obesity has reached epidemic proportions and is impacting children's health globally. In adults, obesity is associated with chronic low-grade inflammation that leads to insulin resistance, which is one of the important mechanisms through which dysregulation of metabolism occurs. There is limited information available about the contribution of inflammation to metabolic health in obese children, and how individual and lifestyle factors impact this risk. One of the paediatric groups at risk of higher rates of obesity includes the survivors of childhood brain tumours. The aim of this study was to evaluate the mechanisms that contribute to inflammation in obese survivors of childhood brain tumours. This is a prospective cohort study. We will recruit lean and obese survivors of childhood brain tumours, and a control group composed of lean and obese children with no history of tumours. We will measure circulating and urinary cytokine levels and cytokine gene expression in monocytes. In addition, the methylation patterns of cytokine genes and that of toll-like receptor genes will be evaluated. These will be correlated with individual and lifestyle factors including age, sex, ethnicity, puberty, body mass index, fasting lipid levels, insulin sensitivity, diet, exercise, sleep, stress and built environment. The sample size calculation showed that we need 25 participants per arm This study has received ethics approval from the institutional review board. Once completed, we will publish this work in peer-reviewed journals and share the findings in presentations and posters in meetings. This study will permit the interrogation of inflammation as a contributor to obesity and its complications in obese survivors of childhood brain tumours and compare them with lean survivors and lean and obese controls with no history of tumours, which may help identify therapeutic and preventative interventions to combat the rising tide of obesity.

  5. Simultaneous evaluation of brain tumour metabolism, structure and blood volume using [18F]-fluoroethyltyrosine (FET) PET/MRI

    DEFF Research Database (Denmark)

    Henriksen, Otto M.; Larsen, Vibeke A; Muhic, Aida;

    2016-01-01

    PURPOSE: Both [(18)F]-fluoroethyltyrosine (FET) PET and blood volume (BV) MRI supplement routine T1-weighted contrast-enhanced MRI in gliomas, but whether the two modalities provide identical or complementary information is unresolved. The aims of the study were to investigate the feasibility...... congruence in the tumour volumes determined by FET PET, BV MRI and contrast-enhanced MRI. RESULTS: FET volume and TBRmax were higher in BV-positive than in BV-negative scans, and both VOLBV and rBVmax were higher in FET-positive than in FET-negative scans. TBRmax and rBVmax were positively correlated (R (2...

  6. Automated Outcome Classification of Computed Tomography Imaging Reports for Pediatric Traumatic Brain Injury.

    Science.gov (United States)

    Yadav, Kabir; Sarioglu, Efsun; Choi, Hyeong Ah; Cartwright, Walter B; Hinds, Pamela S; Chamberlain, James M

    2016-02-01

    The authors have previously demonstrated highly reliable automated classification of free-text computed tomography (CT) imaging reports using a hybrid system that pairs linguistic (natural language processing) and statistical (machine learning) techniques. Previously performed for identifying the outcome of orbital fracture in unprocessed radiology reports from a clinical data repository, the performance has not been replicated for more complex outcomes. To validate automated outcome classification performance of a hybrid natural language processing (NLP) and machine learning system for brain CT imaging reports. The hypothesis was that our system has performance characteristics for identifying pediatric traumatic brain injury (TBI). This was a secondary analysis of a subset of 2,121 CT reports from the Pediatric Emergency Care Applied Research Network (PECARN) TBI study. For that project, radiologists dictated CT reports as free text, which were then deidentified and scanned as PDF documents. Trained data abstractors manually coded each report for TBI outcome. Text was extracted from the PDF files using optical character recognition. The data set was randomly split evenly for training and testing. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for negation, certainty, and temporal status. A random subset stratified by site was analyzed using descriptive quantitative content analysis to confirm identification of TBI findings based on the National Institute of Neurological Disorders and Stroke (NINDS) Common Data Elements project. Findings were coded for presence or absence, weighted by frequency of mentions, and past/future/indication modifiers were filtered. After combining with the manual reference standard, a decision tree classifier was created using data mining tools WEKA 3.7.5 and Salford Predictive Miner 7

  7. Effects of an alveolar recruitment maneuver on subdural pressure, brain swelling, and mean arterial pressure in patients undergoing supratentorial tumour resection: a randomized crossover study.

    Science.gov (United States)

    Flexman, Alana M; Gooderham, Peter A; Griesdale, Donald E; Argue, Ruth; Toyota, Brian

    2017-06-01

    Although recruitment maneuvers have been advocated as part of a lung protective ventilation strategy, their effects on cerebral physiology during elective neurosurgery are unknown. Our objectives were to determine the effects of an alveolar recruitment maneuver on subdural pressure (SDP), brain relaxation score (BRS), and cerebral perfusion pressure among patients undergoing supratentorial tumour resection. In this prospective crossover study, patients scheduled for resection of a supratentorial brain tumour were randomized to undergo either a recruitment maneuver (30 cm of water for 30 sec) or a "sham" maneuver (5 cm of water for 30 sec), followed by the alternative intervention after a 90-sec equilibration period. Subdural pressure was measured through a dural perforation following opening of the cranium. Subdural pressure and mean arterial pressure (MAP) were recorded continuously. The blinded neurosurgeon provided a BRS at baseline and at the end of each intervention. During each treatment, the changes in SDP, BRS, and MAP were compared. Twenty-one patients underwent the study procedure. The increase in SDP was higher during the recruitment maneuver than during the sham maneuver (difference, 3.9 mmHg; 95% confidence interval [CI], 2.2 to 5.6; P recruitment maneuver than in the sham maneuver (difference, -9.0 mmHg; 95% CI, -12.5 to -5.6; P recruitment maneuver. The BRS did not change with either maneuver. Our results suggest that recruitment maneuvers increase subdural pressure and reduce cerebral perfusion pressure, although the clinical importance of these findings is thus far unknown. This trial was registered with ClinicalTrials.gov, NCT02093117.

  8. Supervised novelty detection in brain tissue classification with an application to white matter hyperintensities

    Science.gov (United States)

    Kuijf, Hugo J.; Moeskops, Pim; de Vos, Bob D.; Bouvy, Willem H.; de Bresser, Jeroen; Biessels, Geert Jan; Viergever, Max A.; Vincken, Koen L.

    2016-03-01

    Novelty detection is concerned with identifying test data that differs from the training data of a classifier. In the case of brain MR images, pathology or imaging artefacts are examples of untrained data. In this proof-of-principle study, we measure the behaviour of a classifier during the classification of trained labels (i.e. normal brain tissue). Next, we devise a measure that distinguishes normal classifier behaviour from abnormal behavior that occurs in the case of a novelty. This will be evaluated by training a kNN classifier on normal brain tissue, applying it to images with an untrained pathology (white matter hyperintensities (WMH)), and determine if our measure is able to identify abnormal classifier behaviour at WMH locations. For our kNN classifier, behaviour is modelled as the mean, median, or q1 distance to the k nearest points. Healthy tissue was trained on 15 images; classifier behaviour was trained/tested on 5 images with leave-one-out cross-validation. For each trained class, we measure the distribution of mean/median/q1 distances to the k nearest point. Next, for each test voxel, we compute its Z-score with respect to the measured distribution of its predicted label. We consider a Z-score >=4 abnormal behaviour of the classifier, having a probability due to chance of 0.000032. Our measure identified >90% of WMH volume and also highlighted other non-trained findings. The latter being predominantly vessels, cerebral falx, brain mask errors, choroid plexus. This measure is generalizable to other classifiers and might help in detecting unexpected findings or novelties by measuring classifier behaviour.

  9. Constructing prognostic model incorporating the 2004 WHO/ISUP classification for patients with non-muscle-invasive urothelial tumours of the urinary bladder.

    Science.gov (United States)

    Pan, Chin-Chen; Chang, Yen-Hwa; Chen, Kuang-Kuo; Yu, Hui-Jung; Sun, Chih-Hao; Ho, Donald M T

    2010-10-01

    To construct a prognostic model for recurrence-free survival (RFS), progression-free survival (PFS) and cancer-specific survival (CSS) for patients who have undergone transurethral resection of non-muscle-invasive (pTa/pT1) urinary bladder urothelial tumours. 1366 patients who had undergone transurethral resection of primary non-muscle-invasive urothelial tumours (pTa, 891 patients; pT1, 475 patients) confined to the bladder were retrospectively studied. Tumours were classified according to the 2004 WHO/International Society of Urologic Pathology grading system. Kaplan-Meier and stepwise Cox regression models were applied, and 200 bootstrap resamples were used to generate survival estimates and 95% CIs. A nomogram was developed that incorporated significant variables predicting survival. RFS, PFS and CSS probabilities for non-muscle-invasive bladder urothelial tumours were calculated. Incorporating salient prognostic factors (tumour grade, pT stage, patient age, status of intravesical instillation), the model satisfactorily predicted PFS (concordance index=0.79) and CSS (concordance index=0.87). Robust nomograms were created to predict PFS and CSS. These data provide an overall perspective of disease outcomes which may aid in developing individualised follow-up programmes.

  10. Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects

    Directory of Open Access Journals (Sweden)

    Nikolay V. Manyakov

    2011-01-01

    Full Text Available We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI on a group of amyotrophic lateral sclerosis (ALS, middle cerebral artery (MCA stroke, and subarachnoid hemorrhage (SAH patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects.

  11. Pineal anlage tumour - a rare entity with divergent histology.

    Science.gov (United States)

    Ahuja, Arvind; Sharma, Mehar Chand; Suri, Vaishali; Sarkar, Chitra; Sharma, B S; Garg, Ajay

    2011-06-01

    Pineal anlage tumour is a rare tumour of the pineal gland that is not listed in the 2007 World Health Organization classification of tumours of the central nervous system. Pineal anlage has been defined as a primary pineal tumour with both neuroepithelial and ectomesenchymal differentiation but without endodermal differentiation. We report a pineal anlage tumour in a 4-month-old boy, the youngest patient reported with this rare tumour, with a brief review of the literature. Clinicians and neuropathologists should be aware of this entity as it is likely to be misdiagnosed as a teratoma or a melanocytic tumour of the central nervous system.

  12. Brain fingerprinting classification concealed information test detects US Navy military medical information with P300

    Directory of Open Access Journals (Sweden)

    Lawrence A. Farwell

    2014-12-01

    Full Text Available A classification concealed information test (CIT used the brain fingerprinting method of applying P300 event-related potential (ERP in detecting information that is 1 acquired in real life and 2 unique to US Navy experts in military medicine. Military medicine experts and non-experts were asked to push buttons in response to 3 types of text stimuli. Targets contain known information relevant to military medicine, are identified to subjects as relevant, and require pushing one button. Subjects are told to push another button to all other stimuli. Probes contain concealed information relevant to military medicine, and are not identified to subjects. Irrelevants contain equally plausible, but incorrect/irrelevant information. Error rate was 0%. Median and mean statistical confidences for individual determinations were 99.9% with no indeterminates (results lacking sufficiently high statistical confidence to be classified. We compared error rate and statistical confidence for determinations of both information present and information absent produced by classification CIT (Is a probe ERP more similar to a target or to an irrelevant ERP? versus comparison CIT (Does a probe produce a larger ERP than an irrelevant? using P300 plus the late negative component (LNP; together, P300-MERMER. Comparison CIT produced a significantly higher error rate (20% and lower statistical confidences -- mean 67%; information-absent mean was 28.9%, less than chance (50%. We compared analysis using P300 alone with the P300 + LNP. P300 alone produced the same 0% error rate but significantly lower statistical confidences. These findings add to the evidence that the brain fingerprinting methods as described here provide sufficient conditions to produce less than 1% error rate and greater than 95% median statistical confidence in a CIT on information obtained in the course of real life that is characteristic of individuals with specific training, expertise, or organizational

  13. An atlas-based fuzzy connectedness method for automatic tissue classification in brain MRI

    Institute of Scientific and Technical Information of China (English)

    ZHOU Yongxin; BAI Jing

    2006-01-01

    A framework incorporating a subject-registered atlas into the fuzzy connectedness (FC) method is proposed for the automatic tissue classification of 3D images of brain MRI. The pre-labeled atlas is first registered onto the subject to provide an initial approximate segmentation. The initial segmentation is used to estimate the intensity histograms of gray matter and white matter. Based on the estimated intensity histograms, multiple seed voxels are assigned to each tissue automatically. The normalized intensity histograms are utilized in the FC method as the intensity probability density function (PDF) directly. Relative fuzzy connectedness technique is adopted in the final classification of gray matter and white matter. Experimental results based on the 20 data sets from IBSR are included, as well as comparisons of the performance of our method with that of other published methods. This method is fully automatic and operator-independent. Therefore, it is expected to find wide applications, such as 3D visualization, radiation therapy planning, and medical database construction.

  14. Computerized "Learn-As-You-Go" classification of traumatic brain injuries using NEISS narrative data.

    Science.gov (United States)

    Chen, Wei; Wheeler, Krista K; Lin, Simon; Huang, Yungui; Xiang, Huiyun

    2016-04-01

    One important routine task in injury research is to effectively classify injury circumstances into user-defined categories when using narrative text. However, traditional manual processes can be time consuming, and existing batch learning systems can be difficult to utilize by novice users. This study evaluates a "Learn-As-You-Go" machine-learning program. When using this program, the user trains classification models and interactively checks on accuracy until a desired threshold is reached. We examined the narrative text of traumatic brain injuries (TBIs) in the National Electronic Injury Surveillance System (NEISS) and classified TBIs into sport and non-sport categories. Our results suggest that the DUALIST "Learn-As-You-Go" program, which features a user-friendly online interface, is effective in injury narrative classification. In our study, the time frame to classify tens of thousands of narratives was reduced from a few days to minutes after approximately sixty minutes of training. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. A Framework to Support Automated Classification and Labeling of Brain Electromagnetic Patterns

    Directory of Open Access Journals (Sweden)

    Gwen A. Frishkoff

    2007-01-01

    Full Text Available This paper describes a framework for automated classification and labeling of patterns in electroencephalographic (EEG and magnetoencephalographic (MEG data. We describe recent progress on four goals: 1 specification of rules and concepts that capture expert knowledge of event-related potentials (ERP patterns in visual word recognition; 2 implementation of rules in an automated data processing and labeling stream; 3 data mining techniques that lead to refinement of rules; and 4 iterative steps towards system evaluation and optimization. This process combines top-down, or knowledge-driven, methods with bottom-up, or data-driven, methods. As illustrated here, these methods are complementary and can lead to development of tools for pattern classification and labeling that are robust and conceptually transparent to researchers. The present application focuses on patterns in averaged EEG (ERP data. We also describe efforts to extend our methods to represent patterns in MEG data, as well as EM patterns in source (anatomical space. The broader aim of this work is to design an ontology-based system to support cross-laboratory, cross-paradigm, and cross-modal integration of brain functional data. Tools developed for this project are implemented in MATLAB and are freely available on request.

  16. CYSTIC LESIONS OF THE BRAIN - A CLASSIFICATION BASED ON PATHOGENESIS, WITH CONSIDERATION OF HISTOLOGICAL AND RADIOLOGICAL FEATURES

    NARCIS (Netherlands)

    GO, KG; HEW, JM; KAMMAN, RL; MOLENAAR, WM; PRUIM, J; BLAAUW, EH

    1993-01-01

    A classification of the existing multitude of cystic lesions of the brain is proposed, which allows an understanding of their genesis and consequent therapeutic implications, as well as their diagnostic characteristics. Essentially, cerebral cystic lesions may be classified into the following catego

  17. Pattern classification of brain activation during emotional processing in subclinical depression : psychosis proneness as potential confounding factor

    NARCIS (Netherlands)

    Modinos, Gemma; Mechelli, Andrea; Pettersson-Yeo, William; Allen, Paul; McGuire, Philip; Aleman, Andre

    2013-01-01

    We used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II). Two groups w

  18. Pattern classification of brain activation during emotional processing in subclinical depression : psychosis proneness as potential confounding factor

    NARCIS (Netherlands)

    Modinos, Gemma; Mechelli, Andrea; Pettersson-Yeo, William; Allen, Paul; McGuire, Philip; Aleman, Andre

    2013-01-01

    We used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II). Two groups

  19. Learning and combining image neighborhoods using random forests for neonatal brain disease classification.

    Science.gov (United States)

    Zimmer, Veronika A; Glocker, Ben; Hahner, Nadine; Eixarch, Elisenda; Sanroma, Gerard; Gratacós, Eduard; Rueckert, Daniel; González Ballester, Miguel Ángel; Piella, Gemma

    2017-08-09

    It is challenging to characterize and classify normal and abnormal brain development during early childhood. To reduce the complexity of heterogeneous data population, manifold learning techniques are increasingly applied, which find a low-dimensional representation of the data, while preserving all relevant information. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure and it is highly application dependent. The recently proposed neighborhood approximation forests learn a neighborhood structure in a dataset based on a user-defined distance. We propose a framework to learn multiple pairwise distances in a population of brain images and to combine them in an unsupervised manner optimally in a manifold learning step. Unlike other methods that only use a univariate distance measure, our method allows for a natural combination of multiple distances from heterogeneous sources. As a result, it yields a representation of the population that preserves the multiple distances. Furthermore, our method also selects the most predictive features associated with the distances. We evaluate our method in neonatal magnetic resonance images of three groups (term controls, patients affected by intrauterine growth restriction and mild isolated ventriculomegaly). We show that combining multiple distances related to the condition improves the overall characterization and classification of the three clinical groups compared to the use of single distances and classical unsupervised manifold learning. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. The impact of image dynamic range on texture classification of brain white matter

    Directory of Open Access Journals (Sweden)

    de Certaines Jacques D

    2008-12-01

    Full Text Available Abstract Background The Greylevel Cooccurrence Matrix method (COM is one of the most promising methods used in Texture Analysis of Magnetic Resonance Images. This method provides statistical information about the spatial distribution of greylevels in the image which can be used for classification of different tissue regions. Optimizing the size and complexity of the COM has the potential to enhance the reliability of Texture Analysis results. In this paper we investigate the effect of matrix size and calculation approach on the ability of COM to discriminate between peritumoral white matter and other white matter regions. Method MR images were obtained from patients with histologically confirmed brain glioblastoma using MRI at 3-T giving isotropic resolution of 1 mm3. Three Regions of Interest (ROI were outlined in visually normal white matter on three image slices based on relative distance from the tumor: one peritumoral white matter region and two distant white matter regions on both hemispheres. Volumes of Interest (VOI were composed from the three slices. Two different calculation approaches for COM were used: i Classical approach (CCOM on each individual ROI, and ii Three Dimensional approach (3DCOM calculated on VOIs. For, each calculation approach five dynamic ranges (number of greylevels N were investigated (N = 16, 32, 64, 128, and 256. Results Classification showed that peritumoral white matter always represents a homogenous class, separate from other white matter, regardless of the value of N or the calculation approach used. The best test measures (sensitivity and specificity for average CCOM were obtained for N = 128. These measures were also optimal for 3DCOM with N = 128, which additionally showed a balanced tradeoff between the measures. Conclusion We conclude that the dynamic range used for COM calculation significantly influences the classification results for identical samples. In order to obtain more reliable classification

  1. An automated technique for potential differentiation of ovarian mature teratomas from other benign tumours using neural networks classification of 2D ultrasound static images: a pilot study

    Science.gov (United States)

    Al-karawi, Dhurgham; Sayasneh, A.; Al-Assam, Hisham; Jassim, Sabah; Page, N.; Timmerman, D.; Bourne, T.; Du, Hongbo

    2017-05-01

    Ovarian cysts are a common pathology in women of all age groups. It is estimated that 5-10% of women have a surgical intervention to remove an ovarian cyst in their lifetime. Given this frequency rate, characterization of ovarian masses is essential for optimal management of patients. Patients with benign ovarian masses can be managed conservatively if they are asymptomatic. Mature teratomas are common benign ovarian cysts that occur, in most cases, in premenopausal women. These ovarian cysts can contain different types of human tissue including bone, cartilage, fat, hair, or other tissue. If they are causing no symptoms, they can be harmless and may not require surgery. Subjective assessment by ultrasound examiners has a high diagnostic accuracy when characterising mature teratomas from other types of tumours. The aim of this study is to develop a computerised technique with the potential to characterise mature teratomas and distinguish them from other types of benign ovarian tumours. Local Binary Pattern (LBP) was applied to extract texture features that are specific in distinguishing teratomas. Neural Networks (NN) was then used as a classifier for recognising mature teratomas. A pilot sample set of 130 B-mode static ovarian ultrasound images (41 mature teratomas tumours and 89 other types of benign tumours) was used to test the effectiveness of the proposed technique. Test results show an average accuracy rate of 99.4% with a sensitivity of 100%, specificity of 98.8% and positive predictive value of 98.9%. This study demonstrates that the NN and LBP techniques can accurately classify static 2D B-mode ultrasound images of benign ovarian masses into mature teratomas and other types of benign tumours.

  2. Subdural Pressure and Brain Condition During Propofol Vs Isoflurane - Nitrous Oxide Anaesthesia in Patients Undergoing Elective Supratentorial Tumour Surgery

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    Sankari Santra

    2009-01-01

    No differences were found between the groups with regards to demographics, neuroradiologic diagnosis, posi-tion of head and time of ICP measurement. Before hyperventilation, both ICP and dural tension were significantly lower in Group I compared with Group-II (P< 0.05. But after hyperventilation there was no significant difference of ICP and dural tension in between groups. The degree of brain swelling after opening of dura was similar in both groups. There was a positive correlation between measured ICP and brain swelling score.

  3. Radiosensitivity in vitro of clonogenic and non-clonogenic glioblastoma cells obtained from a human brain tumour

    Energy Technology Data Exchange (ETDEWEB)

    Buronfosse, A.; Thomas, C.P.; Ginestet, C.; Dore, J.F. [Centre de Lutte Contre le Cancer Leon-Berard, 69 - Lyon (France)

    1994-11-01

    Cells obtained from a human glioblastoma (G5) were characterized and used to develop an assay measuring their radiosensitivity in vitro. Surviving fractions were estimated 12 days after irradiation by image analysis of the total surface occupied by the cells. This report evaluates 4 experimental factors which may influence the radiosensitivity in vitro of G5 cells: passage number, delay between plating and irradiation, cell density and clonal heterogeneity. The radiosensitivity of the G5 cell line was found to be passage-independent at least between passages 12 and 75. Experimental conditions influence the radiosensitivity as surviving fraction at 2 Gy (SF2) range from 90% (5 000 cells/well, irradiation 72 h after seeding) to 49% (2 500 cells per well, irradiation 24 h after seeding). The heterogeneity of the radiosensitivity is large at the clonal level as SF2 of six clones isolated from the G5 line were 45%, 50%, 72%, 74%, 79% and 84%. Finally, when G5 cells were irradiated at low cell density and at the beginning of the growth phase, the radiosensitivity measured with this assay is comparable to that obtained with a standard colony assay. We propose that this assay may be useful to determine the intrinsic radiosensitivity of cells obtained from human tumours. (authors). 24 refs., 8 figs., 2 tabs.

  4. A toolbox for real-time subject-independent and subject-dependent classification of brain states from fMRI signals.

    Directory of Open Access Journals (Sweden)

    Mohit eRana

    2013-10-01

    Full Text Available There is a recent increase in the use of multivariate analysis and pattern classification in prediction and real-time feedback of brain states from functional imaging signals and mapping of spatio-temporal patterns of brain activity. Here we present MANAS, a generalized software toolbox for performing online and offline classification of fMRI signals. MANAS has been developed using MATLAB, LIBSVM and SVMlight packages to achieve a cross-platform environment. MANAS is targeted for neuroscience investigations and brain rehabilitation applications, based on neurofeedback and brain-computer interface (BCI paradigms. MANAS provides two different approaches for real-time classification: subject dependent and subject independent classification. In this article, we present the methodology of real-time subject dependent and subject independent pattern classification of fMRI signals; the MANAS software architecture and subsystems; and finally demonstrate the use of the system with experimental results.* M. Rana and N. Gupta are equally contributing authors.

  5. Diagnostic performance of whole brain volume perfusion CT in intra-axial brain tumors: Preoperative classification accuracy and histopathologic correlation

    Energy Technology Data Exchange (ETDEWEB)

    Xyda, Argyro, E-mail: argyro.xyda@med.uni-goettingen.de [Department of Neuroradiology, Georg-August University, University Hospital of Goettingen, Robert-Koch Strasse 40, 37075 Goettingen (Germany); Department of Radialogy, University Hospital of Heraklion, Voutes, 71110 Heraklion, Crete (Greece); Haberland, Ulrike, E-mail: ulrike.haberland@siemens.com [Siemens AG Healthcare Sector, Computed Tomography, Siemensstr. 1, 91301 Forchheim (Germany); Klotz, Ernst, E-mail: ernst.klotz@siemens.com [Siemens AG Healthcare Sector, Computed Tomography, Siemensstr. 1, 91301 Forchheim (Germany); Jung, Klaus, E-mail: kjung1@uni-goettingen.de [Department of Medical Statistics, Georg-August University, Humboldtallee 32, 37073 Goettingen (Germany); Bock, Hans Christoph, E-mail: cbock@gmx.de [Department of Neurosurgery, Johannes Gutenberg University Hospital of Mainz, Langenbeckstraße 1, 55101 Mainz (Germany); Schramm, Ramona, E-mail: ramona.schramm@med.uni-goettingen.de [Department of Neuroradiology, Georg-August University, University Hospital of Goettingen, Robert-Koch Strasse 40, 37075 Goettingen (Germany); Knauth, Michael, E-mail: michael.knauth@med.uni-goettingen.de [Department of Neuroradiology, Georg-August University, University Hospital of Goettingen, Robert-Koch Strasse 40, 37075 Goettingen (Germany); Schramm, Peter, E-mail: p.schramm@med.uni-goettingen.de [Department of Neuroradiology, Georg-August University, University Hospital of Goettingen, Robert-Koch Strasse 40, 37075 Goettingen (Germany)

    2012-12-15

    Background: To evaluate the preoperative diagnostic power and classification accuracy of perfusion parameters derived from whole brain volume perfusion CT (VPCT) in patients with cerebral tumors. Methods: Sixty-three patients (31 male, 32 female; mean age 55.6 ± 13.9 years), with MRI findings suspected of cerebral lesions, underwent VPCT. Two readers independently evaluated VPCT data. Volumes of interest (VOIs) were marked circumscript around the tumor according to maximum intensity projection volumes, and then mapped automatically onto the cerebral blood volume (CBV), flow (CBF) and permeability Ktrans perfusion datasets. A second VOI was placed in the contra lateral cortex, as control. Correlations among perfusion values, tumor grade, cerebral hemisphere and VOIs were evaluated. Moreover, the diagnostic power of VPCT parameters, by means of positive and negative predictive value, was analyzed. Results: Our cohort included 32 high-grade gliomas WHO III/IV, 18 low-grade I/II, 6 primary cerebral lymphomas, 4 metastases and 3 tumor-like lesions. Ktrans demonstrated the highest sensitivity, specificity and positive predictive value, with a cut-off point of 2.21 mL/100 mL/min, for both the comparisons between high-grade versus low-grade and low-grade versus primary cerebral lymphomas. However, for the differentiation between high-grade and primary cerebral lymphomas, CBF and CBV proved to have 100% specificity and 100% positive predictive value, identifying preoperatively all the histopathologically proven high-grade gliomas. Conclusion: Volumetric perfusion data enable the hemodynamic assessment of the entire tumor extent and provide a method of preoperative differentiation among intra-axial cerebral tumors with promising diagnostic accuracy.

  6. Neural network classification of autoregressive features from electroencephalogram signals for brain computer interface design

    Science.gov (United States)

    Huan, Nai-Jen; Palaniappan, Ramaswamy

    2004-09-01

    In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.

  7. New Zealand adolescents’ cellphone and cordless phone user-habits: are they at increased risk of brain tumours already? A cross-sectional study

    Directory of Open Access Journals (Sweden)

    Redmayne Mary

    2013-01-01

    Full Text Available Abstract Background Cellphone and cordless phone use is very prevalent among early adolescents, but the extent and types of use is not well documented. This paper explores how, and to what extent, New Zealand adolescents are typically using and exposed to active cellphones and cordless phones, and considers implications of this in relation to brain tumour risk, with reference to current research findings. Methods This cross-sectional study recruited 373 Year 7 and 8 school students with a mean age of 12.3 years (range 10.3-13.7 years from the Wellington region of New Zealand. Participants completed a questionnaire and measured their normal body-to-phone texting distances. Main exposure-metrics included self-reported time spent with an active cellphone close to the body, estimated time and number of calls on both phone types, estimated and actual extent of SMS text-messaging, cellphone functions used and people texted. Statistical analyses used Pearson Chi2 tests and Pearson’s correlation coefficient (r. Analyses were undertaken using SPSS version 19.0. Results Both cellphones and cordless phones were used by approximately 90% of students. A third of participants had already used a cordless phone for ≥ 7 years. In 4 years from the survey to mid-2013, the cordless phone use of 6% of participants would equal that of the highest Interphone decile (≥ 1640 hours, at the surveyed rate of use. High cellphone use was related to cellphone location at night, being woken regularly, and being tired at school. More than a third of parents thought cellphones carried a moderate-to-high health risk for their child. Conclusions While cellphones were very popular for entertainment and social interaction via texting, cordless phones were most popular for calls. If their use continued at the reported rate, many would be at increased risk of specific brain tumours by their mid-teens, based on findings of the Interphone and Hardell-group studies.

  8. Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI

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    N. Sauwen

    2016-01-01

    Full Text Available Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs, as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI, diffusion-weighted imaging (DWI and magnetic resonance spectroscopic imaging (MRSI have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

  9. Discriminative analysis of brain functional connectivity patterns for mental fatigue classification.

    Science.gov (United States)

    Sun, Yu; Lim, Julian; Meng, Jianjun; Kwok, Kenneth; Thakor, Nitish; Bezerianos, Anastasios

    2014-10-01

    Mental fatigue is a commonly experienced state that can be induced by placing heavy demands on cognitive systems. This often leads to lowered productivity and increased safety risks. In this study, we developed a functional-connectivity based mental fatigue monitoring method. Twenty-six subjects underwent a 20-min mentally demanding test of sustained attention with high-resolution EEG monitoring. Functional connectivity patterns were obtained on the cortical surface via source localization of cortical activities in the first and last 5-min quartiles of the experiment. Multivariate pattern analysis was then adopted to extract the highly discriminative functional connectivity information. The algorithm used in the present study demonstrated an overall accuracy of 81.5% (p fatigue classification through leave-one-out cross validation. Moreover, we found that the most discriminative connectivity features were located in or across middle frontal gyrus and several motor areas, in agreement with the important role that these cortical regions play in the maintenance of sustained attention. This work therefore demonstrates the feasibility of a functional-connectivity-based mental fatigue assessment method, opening up a new avenue for modeling natural brain dynamics under different mental states. Our method has potential applications in several domains, including traffic and industrial safety.

  10. Brain MR Image Segmentation for Tumor Detection using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Monica Subashini.M

    2013-04-01

    Full Text Available Detection, diagnosis and evaluation of Brain tumour is an important task in recent days. MRI is the current technology which enables the detection, diagnosis and evaluation. The medical problems are severe if tumour is detected at the later stage. Hence diagnosis is necessary at the earliest. In this work, pulse coupled neural network is applied for enhancing the MR Images. The enhanced images aresegmented and classified using back propagation networks. The Classification involves labelling the images into normal and abnormal (tumor detected. If the input MRI brain images are more in number,the physician could seek the help of this model and the network would help the physician to save time for further analysis. PCNN and BPN are less complex in nature and hence the processing of MRI brainimages is very simple. The term ‘abnormal’ indicates the presence of tumour. The tumour may be benign or malignant and it needs medical support for further classification.

  11. Estimating associations of mobile phone use and brain tumours taking into account laterality: a comparison and theoretical evaluation of applied methods.

    Science.gov (United States)

    Frederiksen, Kirsten; Deltour, Isabelle; Schüz, Joachim

    2012-12-10

    Estimating exposure-outcome associations using laterality information on exposure and on outcome is an issue, when estimating associations of mobile phone use and brain tumour risk. The exposure is localized; therefore, a potential risk is expected to exist primarily on the side of the head, where the phone is usually held (ipsilateral exposure), and to a lesser extent at the opposite side of the head (contralateral exposure). Several measures of the associations with ipsilateral and contralateral exposure, dealing with different sampling designs, have been presented in the literature. This paper presents a general framework for the analysis of such studies using a likelihood-based approach in a competing risks model setting. The approach clarifies the implicit assumptions required for the validity of the presented estimators, particularly that in some approaches the risk with contralateral exposure is assumed to be zero. The performance of the estimators is illustrated in a simulation study showing for instance that while in some scenarios there is a loss of statistical power, others - in case of a positive ipsilateral exposure-outcome association - would result in a negatively biased estimate of the contralateral exposure parameter, irrespective of any additional recall bias. In conclusion, our theoretical evaluations and results from the simulation study emphasize the importance of setting up a formal model, which furthermore allows for estimation in more complicated and perhaps more realistic exposure settings, such as taking into account exposure to both sides of the head.

  12. Interactions between pre-processing and classification methods for event-related-potential classification: best-practice guidelines for brain-computer interfacing.

    Science.gov (United States)

    Farquhar, J; Hill, N J

    2013-04-01

    Detecting event related potentials (ERPs) from single trials is critical to the operation of many stimulus-driven brain computer interface (BCI) systems. The low strength of the ERP signal compared to the noise (due to artifacts and BCI irrelevant brain processes) makes this a challenging signal detection problem. Previous work has tended to focus on how best to detect a single ERP type (such as the visual oddball response). However, the underlying ERP detection problem is essentially the same regardless of stimulus modality (e.g., visual or tactile), ERP component (e.g., P300 oddball response, or the error-potential), measurement system or electrode layout. To investigate whether a single ERP detection method might work for a wider range of ERP BCIs we compare detection performance over a large corpus of more than 50 ERP BCI datasets whilst systematically varying the electrode montage, spectral filter, spatial filter and classifier training methods. We identify an interesting interaction between spatial whitening and regularised classification which made detection performance independent of the choice of spectral filter low-pass frequency. Our results show that pipeline consisting of spectral filtering, spatial whitening, and regularised classification gives near maximal performance in all cases. Importantly, this pipeline is simple to implement and completely automatic with no expert feature selection or parameter tuning required. Thus, we recommend this combination as a "best-practice" method for ERP detection problems.

  13. Multimodal optical imaging database from tumour brain human tissue: endogenous fluorescence from glioma, metastasis and control tissues

    Science.gov (United States)

    Poulon, Fanny; Ibrahim, Ali; Zanello, Marc; Pallud, Johan; Varlet, Pascale; Malouki, Fatima; Abi Lahoud, Georges; Devaux, Bertrand; Abi Haidar, Darine

    2017-02-01

    Eliminating time-consuming process of conventional biopsy is a practical improvement, as well as increasing the accuracy of tissue diagnoses and patient comfort. We addressed these needs by developing a multimodal nonlinear endomicroscope that allows real-time optical biopsies during surgical procedure. It will provide immediate information for diagnostic use without removal of tissue and will assist the choice of the optimal surgical strategy. This instrument will combine several means of contrast: non-linear fluorescence, second harmonic generation signal, reflectance, fluorescence lifetime and spectral analysis. Multimodality is crucial for reliable and comprehensive analysis of tissue. Parallel to the instrumental development, we currently improve our understanding of the endogeneous fluorescence signal with the different modalities that will be implemented in the stated. This endeavor will allow to create a database on the optical signature of the diseased and control brain tissues. This proceeding will present the preliminary results of this database on three types of tissues: cortex, metastasis and glioblastoma.

  14. Preoperative shunts in thalamic tumours.

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    Goel A

    2000-10-01

    Full Text Available Thirty one patients with thalamic glioma underwent a pre-tumour resection shunt surgery. The procedure was uneventful in 23 patients with relief from symptoms of increased intracranial pressure. Eight patients worsened after the procedure. The level of sensorium worsened from excessively drowsy state to unconsciousness in seven patients. Three patients developed hemiparesis, 4 developed paresis of extra-ocular muscles and altered pupillary reflexes, and 1 developed incontinence of urine and persistent vomiting. Alteration in the delicately balanced intracranial pressure and movements in the tumour and vital adjacent brain areas could be the probable cause of the worsening in the neurological state in these 8 patients. On the basis of these observations and on review of literature, it is postulated that the ventricular dilatation following an obstruction in the path of the cerebrospinal fluid flow by a tumour could be a natural defense phenomenon of the brain.

  15. Translation and pilot validation of Hindi translation of assessing quality of life in patients with primary brain tumours using EORTC brain module (BN-20

    Directory of Open Access Journals (Sweden)

    Budrukkar Ashwini

    2006-01-01

    Full Text Available Aim: To translate and validate the European Organisation for Research and Treatment for Cancer (EORTC brain cancer module (BN-20 into Hindi to make it available for patients and scientific community. Methods and Results: The EORTC BN-20 was translated into Hindi using standard guidelines by EORTC. The process included forward translation by two translators, discussion with the translators in case of discrepancies and formation of first intermediate questionnaire. This questionnaire was then given to two more translators who translated this questionnaire back into English. These 2 questionnaires were then compared with the original EORTC questionnaire and the second intermediate questionnaire was formed. The second intermediate questionnaire was subsequently administered in 10 patients with brain tumors who had never seen the questionnaire before, for pilot-testing. Each of these 10 patients after filling up the questionnaire themselves was then interviewed for any difficulty encountered during the filling up of the questionnaire. These were in the form of specific modules including difficulty in answering, confusion while answering and difficulty to understand, whether the questions were upsetting and if patients would have asked the question in any different way. There were major suggestions in three questions, which were incorporated into the second intermediate questionnaire to form the final Hindi BN-20 questionnaire. Conclusion: The final Hindi BN-20 has been approved by EORTC and can be used in clinical practice and studies for patients with brain tumors.

  16. Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

    Science.gov (United States)

    Cuadra, Meritxell Bach; Cammoun, Leila; Butz, Torsten; Cuisenaire, Olivier; Thiran, Jean-Philippe

    2005-12-01

    This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

  17. Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Noman Naseer

    2016-01-01

    Full Text Available We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest using functional near-infrared spectroscopy (fNIRS signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA, quadratic discriminant analysis (QDA, k-nearest neighbour (kNN, the Naïve Bayes approach, support vector machine (SVM, and artificial neural networks (ANN, were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that the p values were statistically significant relative to all of the other classifiers (p < 0.005 using HbO signals.

  18. Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

    Science.gov (United States)

    Qureshi, Nauman Khalid; Noori, Farzan Majeed; Hong, Keum-Shik

    2016-01-01

    We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that the p values were statistically significant relative to all of the other classifiers (p < 0.005) using HbO signals.

  19. Matched signal detection on graphs: Theory and application to brain imaging data classification.

    Science.gov (United States)

    Hu, Chenhui; Sepulcre, Jorge; Johnson, Keith A; Fakhri, Georges E; Lu, Yue M; Li, Quanzheng

    2016-01-15

    Motivated by recent progress in signal processing on graphs, we have developed a matched signal detection (MSD) theory for signals with intrinsic structures described by weighted graphs. First, we regard graph Laplacian eigenvalues as frequencies of graph-signals and assume that the signal is in a subspace spanned by the first few graph Laplacian eigenvectors associated with lower eigenvalues. The conventional matched subspace detector can be applied to this case. Furthermore, we study signals that may not merely live in a subspace. Concretely, we consider signals with bounded variation on graphs and more general signals that are randomly drawn from a prior distribution. For bounded variation signals, the test is a weighted energy detector. For the random signals, the test statistic is the difference of signal variations on associated graphs, if a degenerate Gaussian distribution specified by the graph Laplacian is adopted. We evaluate the effectiveness of the MSD on graphs both with simulated and real data sets. Specifically, we apply MSD to the brain imaging data classification problem of Alzheimer's disease (AD) based on two independent data sets: 1) positron emission tomography data with Pittsburgh compound-B tracer of 30 AD and 40 normal control (NC) subjects, and 2) resting-state functional magnetic resonance imaging (R-fMRI) data of 30 early mild cognitive impairment and 20 NC subjects. Our results demonstrate that the MSD approach is able to outperform the traditional methods and help detect AD at an early stage, probably due to the success of exploiting the manifold structure of the data.

  20. Gastric Calcifying Fibrous Tumour

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    Tan Attila

    2006-01-01

    Full Text Available Intramucosal gastric tumours are most commonly found to be gastrointestinal stromal tumours or leiomyomas (smooth muscle tumours; however, a variety of other uncommon mesenchymal tumours can occur in the stomach wall. A rare benign calcifying fibrous tumour is reported and the endoscopic appearance, ultrasound findings and morphology are documented. A review of the literature found only two similar cases.

  1. Factors related to pregnancy and birth and the risk of childhood brain tumours: The ESTELLE and ESCALE studies (SFCE, France).

    Science.gov (United States)

    Bailey, Helen D; Rios, Paula; Lacour, Brigitte; Guerrini-Rousseau, Léa; Bertozzi, Anne-Isabelle; Leblond, Pierre; Faure-Conter, Cécile; Pellier, Isabelle; Freycon, Claire; Michon, Jean; Puget, Stéphanie; Ducassou, Stéphane; Orsi, Laurent; Clavel, Jacqueline

    2017-04-15

    Little is known of the causes of childhood brain tumors (CBT). The aims of this study were to investigate whether extremes of birth weight were associated with increased risk of CBT and whether maternal preconceptional folic acid supplementation or breastfeeding reduced the risk. In addition, other maternal characteristics and birth related factors were also investigated. We pooled data from two French national population-based case-control studies with similar designs conducted in 2003-2004 and 2010-2011. The mothers of 510 CBT cases (directly recruited from the national childhood cancer register) and 3,102 controls aged under 15 years, frequency matched by age and gender did a telephone interview, which focussed on demographic and perinatal characteristics, and maternal life style habits and reproductive history. Odds ratios (OR) and 95% confidence intervals (CI) were estimated using unconditional logistic regression, adjusted for age, sex, study of origin and relevant confounders. No association was found between CBT and birth weight or fetal growth. The use of preconceptional folic acid supplementation was rare (5.3% in cases and 7.8% in controls) and the OR was 0.8 (95% CI 0.5, 1.4). There was no association with breastfeeding, even prolonged (six months or more; OR 1.0, 95% CI 0.8, 1.4). Neither was there any association between CBT and other investigated factors (maternal body mass index, gestational weight gain, congenital abnormality, maternal reproductive history or use of fertility treatments. Although large, this study was underpowered for subtype analyses. Pooling data with other population-based studies may provide further insight into findings by CBT subtypes.

  2. O6-Methylguanine-DNA methyltransferase protein expression by immunohistochemistry in brain and non-brain systemic tumours: systematic review and meta-analysis of correlation with methylation-specific polymerase chain reaction

    Directory of Open Access Journals (Sweden)

    Ibáñez Javier

    2011-01-01

    Full Text Available Abstract Background The DNA repair protein O6-Methylguanine-DNA methyltransferase (MGMT confers resistance to alkylating agents. Several methods have been applied to its analysis, with methylation-specific polymerase chain reaction (MSP the most commonly used for promoter methylation study, while immunohistochemistry (IHC has become the most frequently used for the detection of MGMT protein expression. Agreement on the best and most reliable technique for evaluating MGMT status remains unsettled. The aim of this study was to perform a systematic review and meta-analysis of the correlation between IHC and MSP. Methods A computer-aided search of MEDLINE (1950-October 2009, EBSCO (1966-October 2009 and EMBASE (1974-October 2009 was performed for relevant publications. Studies meeting inclusion criteria were those comparing MGMT protein expression by IHC with MGMT promoter methylation by MSP in the same cohort of patients. Methodological quality was assessed by using the QUADAS and STARD instruments. Previously published guidelines were followed for meta-analysis performance. Results Of 254 studies identified as eligible for full-text review, 52 (20.5% met the inclusion criteria. The review showed that results of MGMT protein expression by IHC are not in close agreement with those obtained with MSP. Moreover, type of tumour (primary brain tumour vs others was an independent covariate of accuracy estimates in the meta-regression analysis beyond the cut-off value. Conclusions Protein expression assessed by IHC alone fails to reflect the promoter methylation status of MGMT. Thus, in attempts at clinical diagnosis the two methods seem to select different groups of patients and should not be used interchangeably.

  3. Tumours of histiocytes and accessory dendritic cells : an immunohistochemical approach to classification from the International Lymphoma Study Group based on 61 cases

    NARCIS (Netherlands)

    Pileri, SA; Grogan, TM; Harris, NL; Banks, P; Campo, E; Chan, JKC; Favera, RD; Delsol, G; De Wolf-Peeters, C; Falini, B; Gascoyne, RD; Gaulard, P; Gatter, KC; Isaacson, PG; Jaffe, ES; Kluin, P; Knowles, DM; Mason, DY; Mori, S; Muller-Hermelink, HK; Piris, MA; Ralfkiaer, E; Stein, H; Su, IJ; Warnke, RA; Weiss, LM

    2002-01-01

    Neoplasms of histiocytes and dendritic cells are rare, and their phenotypic and biological definition is incomplete. Seeking to identify antigens detectable in paraffin-embedded sections that might allow a more complete, rational immunophenotypic classification of histiocytic/dendritic cell neoplasm

  4. TNM-O: ontology support for staging of malignant tumours.

    Science.gov (United States)

    Boeker, Martin; França, Fábio; Bronsert, Peter; Schulz, Stefan

    2016-11-14

    Objectives of this work are to (1) present an ontological framework for the TNM classification system, (2) exemplify this framework by an ontology for colon and rectum tumours, and (3) evaluate this ontology by assigning TNM classes to real world pathology data. The TNM ontology uses the Foundational Model of Anatomy for anatomical entities and BioTopLite 2 as a domain top-level ontology. General rules for the TNM classification system and the specific TNM classification for colorectal tumours were axiomatised in description logic. Case-based information was collected from tumour documentation practice in the Comprehensive Cancer Centre of a large university hospital. Based on the ontology, a module was developed that classifies pathology data. TNM was represented as an information artefact, which consists of single representational units. Corresponding to every representational unit, tumours and tumour aggregates were defined. Tumour aggregates consist of the primary tumour and, if existing, of infiltrated regional lymph nodes and distant metastases. TNM codes depend on the location and certain qualities of the primary tumour (T), the infiltrated regional lymph nodes (N) and the existence of distant metastases (M). Tumour data from clinical and pathological documentation were successfully classified with the ontology. A first version of the TNM Ontology represents the TNM system for the description of the anatomical extent of malignant tumours. The present work demonstrates its representational power and completeness as well as its applicability for classification of instance data.

  5. Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor

    Directory of Open Access Journals (Sweden)

    Gemma Modinos

    2013-02-01

    Full Text Available We used Support Vector Machine (SVM to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II. Two groups were subsequently formed: (i subclinical (mild mood disturbance (n = 17 and (ii no mood disturbance (n = 17. Participants also completed a self-report questionnaire on subclinical psychotic symptoms, the Community Assessment of Psychic Experiences Questionnaire (CAPE positive subscale. The functional magnetic resonance imaging (fMRI paradigm entailed passive viewing of negative emotional and neutral scenes. The pattern of brain activity during emotional processing allowed correct group classification with an overall accuracy of 77% (p = 0.002, within a network of regions including the amygdala, insula, anterior cingulate cortex and medial prefrontal cortex. However, further analysis suggested that the classification accuracy could also be explained by subclinical psychotic symptom scores (correlation with SVM weights r = 0.459, p = 0.006. Psychosis proneness may thus be a confounding factor for neuroimaging studies in subclinical depression.

  6. Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor.

    Science.gov (United States)

    Modinos, Gemma; Mechelli, Andrea; Pettersson-Yeo, William; Allen, Paul; McGuire, Philip; Aleman, Andre

    2013-01-01

    We used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II). Two groups were subsequently formed: (i) subclinical (mild) mood disturbance (n = 17) and (ii) no mood disturbance (n = 17). Participants also completed a self-report questionnaire on subclinical psychotic symptoms, the Community Assessment of Psychic Experiences Questionnaire (CAPE) positive subscale. The functional magnetic resonance imaging (fMRI) paradigm entailed passive viewing of negative emotional and neutral scenes. The pattern of brain activity during emotional processing allowed correct group classification with an overall accuracy of 77% (p = 0.002), within a network of regions including the amygdala, insula, anterior cingulate cortex and medial prefrontal cortex. However, further analysis suggested that the classification accuracy could also be explained by subclinical psychotic symptom scores (correlation with SVM weights r = 0.459, p = 0.006). Psychosis proneness may thus be a confounding factor for neuroimaging studies in subclinical depression.

  7. Wilms' tumour (nephroblastoma)

    African Journals Online (AJOL)

    surgeon who first described this type of tumour in 1899. Wilms' tumour .... Open biopsy should be avoided at all costs, as it. 'upstages' the tumour. Survival ... surgeon. No laparoscopic surgery should be done, as the whole abdomen has to be.

  8. Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

    Science.gov (United States)

    Formisano, Elia; De Martino, Federico; Valente, Giancarlo

    2008-09-01

    Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

  9. Classification of Parkinsonian Syndromes from FDG-PET Brain Data Using Decision Trees with SSM/PCA Features

    Directory of Open Access Journals (Sweden)

    D. Mudali

    2015-01-01

    Full Text Available Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (fMRI data.

  10. Abnormality Segmentation and Classification of Brain MR Images using Combined Edge, Texture Region Features and Radial basics Function

    Directory of Open Access Journals (Sweden)

    B. Balakumar

    2013-09-01

    Full Text Available Magnetic Resonance Images (MRI are widely used in the diagnosis of Brain tumor. In this study we have developed a new approach for automatic classification of the normal and abnormal non-enhanced MRI images. The proposed method consists of four stages namely Preprocessing, feature extraction, feature reduction and classification. In the first stage anisotropic filter is applied for noise reduction and to make the image suitable for extracting the features. In the second stage, Region growing base segmentation is used for partitioning the image into meaningful regions. In the third stage, combined edge and Texture based features are extracted using Histogram and Gray Level Co-occurrence Matrix (GLCM from the segmented image. In the next stage PCA is used to reduce the dimensionality of the Feature space which results in a more efficient and accurate classification. Finally, in the classification stage, a supervised Radial Basics Function (RBF classifier is used to classify the experimental images into normal and abnormal. The obtained experimental are evaluated using the metrics sensitivity, specificity and accuracy. For comparison, the performance of the proposed technique has significantly improved the tumor detection accuracy with other neural network based classifier SVM, FFNN and FSVM.

  11. A wavelet-based time frequency analysis approach for classification of motor imagery for brain computer interface applications

    Science.gov (United States)

    Qin, Lei; He, Bin

    2005-12-01

    Electroencephalogram (EEG) recordings during motor imagery tasks are often used as input signals for brain-computer interfaces (BCIs). The translation of these EEG signals to control signals of a device is based on a good classification of various kinds of imagination. We have developed a wavelet-based time-frequency analysis approach for classifying motor imagery tasks. Time-frequency distributions (TFDs) were constructed based on wavelet decomposition and event-related (de)synchronization patterns were extracted from symmetric electrode pairs. The weighted energy difference of the electrode pairs was then compared to classify the imaginary movement. The present method has been tested in nine human subjects and reached an averaged classification rate of 78%. The simplicity of the present technique suggests that it may provide an alternative method for EEG-based BCI applications.

  12. 2015版犠犎犗肺肿瘤组织学分类解读%Fourth edition of WHO classification of tumours of the lung,published in 2015

    Institute of Scientific and Technical Information of China (English)

    陈真伟; 滕晓东

    2016-01-01

    To compare 4th edition of WHO Classification of Tumours of the Lung (published in 2015)and 3rd edi-tion (published in 2004)and review relevant literature.The 4th edition not only incorporated a great number of changes but also newly added 6 tumors entities,ie:NUT carcinoma,myoepithelial tumours,pulmonary myxoid sarcoma with EWSR1-CREB1 translocation,intravascular large B-cell lymphoma,Erdheim-Chester disease,Meningioma NOS,as well as updated the contents of molecular pathology and immunohistochemistry of lung cancer,emphasizing detection of lung cancer relate genes and targeted therapies,renamed some of the tumours,for example,sclerosing haemangioma replaced by sclerosing pneumocytoma,using the term lepidic adenocarcinoma for formerly bronchioloalveolar carcinoma.%对第4版(2015年)WHO 肺肿瘤分类与第3版(2004年)肿瘤分类进行比较,并复习相关文献。2015版肿瘤分类作了大量的改动,对肺肿瘤的分类做了一些修改,新增了6个病种,即 NUT 中线癌、肌上皮肿瘤、伴有 EWSR1-CREB1基因易位的肺黏液样肉瘤、血管内大 B 细胞淋巴瘤、Erdheim-Chester 病和脑膜瘤非特异型;更新了肺癌的分子病理和免疫组织化学等内容,强调了肺癌相关基因的检测与靶向治疗。对一些肿瘤作了重新命名,如硬化性血管瘤命名为硬化性肺泡细胞瘤,使用贴壁型腺癌代替原先的细支气管肺泡癌。

  13. Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG).

    Science.gov (United States)

    Lehmann, Christoph; Koenig, Thomas; Jelic, Vesna; Prichep, Leslie; John, Roy E; Wahlund, Lars-Olof; Dodge, Yadolah; Dierks, Thomas

    2007-04-15

    The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

  14. Toward FRP-Based Brain-Machine Interfaces-Single-Trial Classification of Fixation-Related Potentials.

    Directory of Open Access Journals (Sweden)

    Andrea Finke

    Full Text Available The co-registration of eye tracking and electroencephalography provides a holistic measure of ongoing cognitive processes. Recently, fixation-related potentials have been introduced to quantify the neural activity in such bi-modal recordings. Fixation-related potentials are time-locked to fixation onsets, just like event-related potentials are locked to stimulus onsets. Compared to existing electroencephalography-based brain-machine interfaces that depend on visual stimuli, fixation-related potentials have the advantages that they can be used in free, unconstrained viewing conditions and can also be classified on a single-trial level. Thus, fixation-related potentials have the potential to allow for conceptually different brain-machine interfaces that directly interpret cortical activity related to the visual processing of specific objects. However, existing research has investigated fixation-related potentials only with very restricted and highly unnatural stimuli in simple search tasks while participant's body movements were restricted. We present a study where we relieved many of these restrictions while retaining some control by using a gaze-contingent visual search task. In our study, participants had to find a target object out of 12 complex and everyday objects presented on a screen while the electrical activity of the brain and eye movements were recorded simultaneously. Our results show that our proposed method for the classification of fixation-related potentials can clearly discriminate between fixations on relevant, non-relevant and background areas. Furthermore, we show that our classification approach generalizes not only to different test sets from the same participant, but also across participants. These results promise to open novel avenues for exploiting fixation-related potentials in electroencephalography-based brain-machine interfaces and thus providing a novel means for intuitive human-machine interaction.

  15. The study of brain tumour during pregnancy%妊娠合并颅内肿瘤的临床分析

    Institute of Scientific and Technical Information of China (English)

    于文; 黄春玉; 高婉丽; 梁辉; 侯晓慧; 梁竹巍; 李文君

    2015-01-01

    Objective:To study the clinical characteristics and the pregnancy outcomes of pregnancy complicated with brain tumour. Methods:A retrospective analysis of clinical data of 16 cases of pregnancy in patients with brain tumor from Oct. 1986 to Sep. 2013 in our hospital were ana—lyzed. Results:Among the 16 cases,there were 4 maternal deaths occurred and 12 cases of neonatal survived:1 case of medical abortion,1 case of spontaneous abortion,6 cases of artifical abortion and mid-term abortion,6 cases of neonatal survical,2cases of neonata death. 14 cases of intracranial tumors with surgical operation,1 case die for lung and intracranial infection before craniotomy,an—other case whose intracranial glioma tumor recurred after surgery,who had cerebral hernia,and in a critical condition,the pregnant die after family give up surgical treatment. Postoperative pathological indicated that 6 patients with malignant tumor:1 case of small sticks of glioma. 1 case of metastatic cancer. 1 case of astrocytoma glioma;1 case of between the deformation of astrocytoma,local rubber mother variable. 1 case of neurocytoma. 1 case of mixed neuromal cell glioma. 8 cases with benign tumor:3 cases of meningiomas;3 cases of schwannoma;2 cases of neurofibromatosis. Conclusions:Pregnancy complicated with brain tumour often occurs in the second and third trimester gestation. Early pregnancy combined intracranial tumor,we suggest to terminate pregnancy and then to treat intracranial diseases. The patient who 34 weeks of gestation,we do surgical operation after cesarean section. Any benign tumors progress slowly,response well to corticosteroids may continue pregnancy, Malignant tumor,which progress rapidly or in a critical condition,we should treat intracranial dis—ease actively. The patients who had history of intracranial malignant tumor surgery should be termi—nate gestation actively in early pregnancy. Cesarean section under general anesthesia is advisable. Neonatal rescue should be

  16. Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

    Directory of Open Access Journals (Sweden)

    A.V. Faria

    2011-02-01

    Full Text Available High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

  17. Toward a brain-computer interface for Alzheimer's disease patients by combining classical conditioning and brain state classification.

    Science.gov (United States)

    Liberati, Giulia; Dalboni da Rocha, Josué Luiz; van der Heiden, Linda; Raffone, Antonino; Birbaumer, Niels; Olivetti Belardinelli, Marta; Sitaram, Ranganatha

    2012-01-01

    Brain-computer interfaces (BCIs) provide alternative methods for communicating and acting on the world, since messages or commands are conveyed from the brain to an external device without using the normal output pathways of peripheral nerves and muscles. Alzheimer's disease (AD) patients in the most advanced stages, who have lost the ability to communicate verbally, could benefit from a BCI that may allow them to convey basic thoughts (e.g., "yes" and "no") and emotions. There is currently no report of such research, mostly because the cognitive deficits in AD patients pose serious limitations to the use of traditional BCIs, which are normally based on instrumental learning and require users to self-regulate their brain activation. Recent studies suggest that not only self-regulated brain signals, but also involuntary signals, for instance related to emotional states, may provide useful information about the user, opening up the path for so-called "affective BCIs". These interfaces do not necessarily require users to actively perform a cognitive task, and may therefore be used with patients who are cognitively challenged. In the present hypothesis paper, we propose a paradigm shift from instrumental learning to classical conditioning, with the aim of discriminating "yes" and "no" thoughts after associating them to positive and negative emotional stimuli respectively. This would represent a first step in the development of a BCI that could be used by AD patients, lending a new direction not only for communication, but also for rehabilitation and diagnosis.

  18. The role of rehabilitation measures in reintegration of children with brain tumours or leukaemia and their families after completion of cancer treatment: a study protocol.

    Science.gov (United States)

    Peikert, Mona Leandra; Inhestern, Laura; Bergelt, Corinna

    2017-08-11

    For ill children as well as for their parents and siblings, childhood cancer poses a major challenge. Little is known about the reintegration into daily life of childhood cancer survivors and their families. The aim of this prospective observational study is to further the understanding of the role of rehabilitation measures in the reintegration process of childhood leukaemia or brain tumour survivors and their family members after the end of cancer treatment. This prospective observational study consists of three study arms: a quantitative study in cooperation with three German paediatric oncological study registries (study arm 1), a quantitative study in cooperation with a rehabilitation clinic that offers a family-oriented paediatric oncological rehabilitation programme (study arm 2) and a qualitative study at 12-month follow-up including families from the study arms 1 and 2 (study arm 3). In study arm 1, children, parents and siblings are surveyed after treatment (baseline), 4-6 months after baseline measurement and at 12-month follow-up. In study arm 2, data are collected at the beginning and at the end of the rehabilitation measure and at 12-month follow-up. Families are assessed with standardised questionnaires on quality of life, emotional and behavioural symptoms, depression, anxiety, fear of progression, coping and family functioning. Furthermore, self-developed items on rehabilitation aims and reintegration into daily life are used. Where applicable, users and non-users of rehabilitation measures will be compared regarding the outcome parameters. Longitudinal data will be analysed by means of multivariate analysis strategies. Reference values will be used for comparisons if applicable. Qualitative data will be analysed using thematic analysis. This study has been approved by the medical ethics committee of the Medical Chamber of Hamburg. Data will be published in peer-reviewed journals and presented at conferences. © Article author(s) (or their

  19. Reconstructive options in pelvic tumours

    Directory of Open Access Journals (Sweden)

    Mayilvahanan N

    2005-01-01

    Full Text Available Background: Pelvic tumours present a complex problem. It is difficult to choose between limb salvage and hemipelvectomy. Method: Forty three patients of tumours of pelvis underwent limb salvage resection with reconstruction in 32 patients. The majority were chondrosarcomas (20 cases followed by Ewing sarcoma. Stage II B was the most common stage in malignant lesions and all the seven benign lesions were aggressive (B3. Surgical margins achieved were wide in 31 and marginal in 12 cases. Ilium was involved in 51% of cases and periacetabular involvement was seen in 12 patients. The resections done were mostly of types I &II of Enneking′s classification of pelvic resection. Arthrodesis was attempted in 24 patients. Customized Saddle prosthesis was used in seven patients and no reconstruction in 12 patients. Adjuvant chemotherapy was given to all high-grade malignant tumours, combined with radiotherapy in 7 patients. Results: With a mean follow up of 48.5 months and one patient lost to follow up, the recurrence rate among the evaluated cases was 16.6%. Oncologically, 30 patients were continuously disease free with 7 local recurrences and 4 deaths due to disseminated disease and 2 patients died of other causes. During the initial years, satisfactory functional results were achieved with prosthetic replacement. Long-term functional result of 36 patients who were alive at the time of latest follow up was satisfactory in 75% who underwent arthrodesis and in those where no reconstruction was used. We also describe a method of new classification of pelvic resections that clarifies certain shortcomings of the previous systems of classification. Conclusion: Selection of a procedure depends largely on the patient factors, the tumour grade, the resultant defect and the tissue factors. Resection with proper margins gives better functional and oncological results

  20. Evaluation of the seventh edition of the tumour, node, metastasis (TNM) classification for colon cancer in two nationwide registries of the United States and Japan.

    Science.gov (United States)

    Hashiguchi, Y; Hase, K; Kotake, K; Ueno, H; Shinto, E; Mochizuki, H; Yamamoto, J; Sugihara, K

    2012-09-01

    The new TNM classification is currently being implemented. We evaluated the TNM-7 staging system based on the two nationwide colon cancer registries in the United States and Japan to clarify whether this system better stratifies patients' prognoses than the TNM-6 did and to determine whether stratification can be effectively simplified. The Surveillance, Epidemiology, and End Results population-based data from 1988 to 2001 for 50139 colon cancer patients and the multi-institutional registry data from the Japanese Society for Cancer of the Colon and Rectum from 1984 to 1994 for 10754 patients were analysed. We devised a modified version of the TNM-7 staging system to allow simpler classification of the TN categories and compared the TNM-6, TNM-7, modified TNM-7, and the Dukes staging system based on survival curves and objective statistical tests such as likelihood ratio χ(2) tests, Akaike's information criterion, and Harrell's c-index. The TNM-7 was superior to the TNM-6 in all objective statistical tests in the United States (c-index; 0.700 vs 0.696, PTNM-7 is much simpler, but it nevertheless showed similar values to those of the original TNM-7 (c-index; the United States 0.702, Japan 0.733).   The new TNM-7 is complicated but better at stratifying patients than the TNM-6 in the United States and Japan, and could be effectively simplified. © 2011 The Authors. Colorectal Disease © 2011 The Association of Coloproctology of Great Britain and Ireland.

  1. Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra.

    Science.gov (United States)

    Adebileje, Sikiru Afolabi; Ghasemi, Keyvan; Aiyelabegan, Hammed Tanimowo; Saligheh Rad, Hamidreza

    2017-04-01

    Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd.

  2. Brain classification reveals the right cerebellum as the best biomarker of dyslexia

    Directory of Open Access Journals (Sweden)

    Demonet Jean

    2009-06-01

    Full Text Available Abstract Background Developmental dyslexia is a specific cognitive disorder in reading acquisition that has genetic and neurological origins. Despite histological evidence for brain differences in dyslexia, we recently demonstrated that in large cohort of subjects, no differences between control and dyslexic readers can be found at the macroscopic level (MRI voxel, because of large variances in brain local volumes. In the present study, we aimed at finding brain areas that most discriminate dyslexic from control normal readers despite the large variance across subjects. After segmenting brain grey matter, normalizing brain size and shape and modulating the voxels' content, normal readers' brains were used to build a 'typical' brain via bootstrapped confidence intervals. Each dyslexic reader's brain was then classified independently at each voxel as being within or outside the normal range. We used this simple strategy to build a brain map showing regional percentages of differences between groups. The significance of this map was then assessed using a randomization technique. Results The right cerebellar declive and the right lentiform nucleus were the two areas that significantly differed the most between groups with 100% of the dyslexic subjects (N = 38 falling outside of the control group (N = 39 95% confidence interval boundaries. The clinical relevance of this result was assessed by inquiring cognitive brain-based differences among dyslexic brain subgroups in comparison to normal readers' performances. The strongest difference between dyslexic subgroups was observed between subjects with lower cerebellar declive (LCD grey matter volumes than controls and subjects with higher cerebellar declive (HCD grey matter volumes than controls. Dyslexic subjects with LCD volumes performed worse than subjects with HCD volumes in phonologically and lexicon related tasks. Furthermore, cerebellar and lentiform grey matter volumes interacted in dyslexic

  3. Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective.

    Science.gov (United States)

    Kim, Yong-Ku; Na, Kyoung-Sae

    2018-01-03

    Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future. Copyright

  4. Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review.

    Science.gov (United States)

    Tohka, Jussi

    2014-11-28

    Quantitative analysis of magnetic resonance (MR) brain images are facilitated by the development of automated segmentation algorithms. A single image voxel may contain of several types of tissues due to the finite spatial resolution of the imaging device. This phenomenon, termed partial volume effect (PVE), complicates the segmentation process, and, due to the complexity of human brain anatomy, the PVE is an important factor for accurate brain structure quantification. Partial volume estimation refers to a generalized segmentation task where the amount of each tissue type within each voxel is solved. This review aims to provide a systematic, tutorial-like overview and categorization of methods for partial volume estimation in brain MRI. The review concentrates on the statistically based approaches for partial volume estimation and also explains differences to other, similar image segmentation approaches.

  5. Tumours and tumourous diseases; Tumoren, tumoraehnliche Erkrankungen

    Energy Technology Data Exchange (ETDEWEB)

    Winkelmann, W. (ed.)

    2005-07-01

    This book on tumours and tumourous diseases comprises two parts: 1. Bone tumours and tumourous lesions. 2. Soft tissue tumours and tumourous lesions. Details are presented on pathology, diagnosis, conservative and perioperative therapy, surgical therapy, complications after resection, indicators for amputation, recommendations for follow-up treatment, radiotherapy, radionuclide therapy, alternative therapies, therapy concepts in case of metastases, tissue engineering and plastic surgery. (uke) [German] Der vorliegende Band der Reihe Orthopaedie und orthopaedische Chirurgie behandelt das Thema Tumoren und tumoraehnliche Erkrankungen. Der Band teilt sich in zwei Kapitel: 1. Knochentumoren und tumorartige Laesionen und 2. Weichteiltumoren und tumorartige Laesionen. Dargestellt werden Pathologie, Diagnostik, konservative und perioperative Therapie, chirurgische Therapie, Komplikationen nach Resektion, Indikatoren zur Amputation, Nachsorgeempfehlung, Strahlentherapie, Radionuklidtherapie, alternative Therapieverfahren, Therapiekonzepte bei Metastasen, Tissue Engineering und plastisch-chirurgische Massnahmen. (uke)

  6. Progressive Graph-Based Transductive Learning for Multi-modal Classification of Brain Disorder Disease.

    Science.gov (United States)

    Wang, Zhengxia; Zhu, Xiaofeng; Adeli, Ehsan; Zhu, Yingying; Zu, Chen; Nie, Feiping; Shen, Dinggang; Wu, Guorong

    2016-10-01

    Graph-based Transductive Learning (GTL) is a powerful tool in computer-assisted diagnosis, especially when the training data is not sufficient to build reliable classifiers. Conventional GTL approaches first construct a fixed subject-wise graph based on the similarities of observed features (i.e., extracted from imaging data) in the feature domain, and then follow the established graph to propagate the existing labels from training to testing data in the label domain. However, such a graph is exclusively learned in the feature domain and may not be necessarily optimal in the label domain. This may eventually undermine the classification accuracy. To address this issue, we propose a progressive GTL (pGTL) method to progressively find an intrinsic data representation. To achieve this, our pGTL method iteratively (1) refines the subject-wise relationships observed in the feature domain using the learned intrinsic data representation in the label domain, (2) updates the intrinsic data representation from the refined subject-wise relationships, and (3) verifies the intrinsic data representation on the training data, in order to guarantee an optimal classification on the new testing data. Furthermore, we extend our pGTL to incorporate multi-modal imaging data, to improve the classification accuracy and robustness as multi-modal imaging data can provide complementary information. Promising classification results in identifying Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and Normal Control (NC) subjects are achieved using MRI and PET data.

  7. Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards

    Directory of Open Access Journals (Sweden)

    Mark Plitt

    2015-01-01

    Conclusions: While individuals can be classified as having ASD with statistically significant accuracy from their rs-fMRI scans alone, this method falls short of biomarker standards. Classification methods provided further evidence that ASD functional connectivity is characterized by dysfunction of large-scale functional networks, particularly those involved in social information processing.

  8. Magnetic resonance imaging in classification of congenital muscular dystrophies with brain abnormalities

    NARCIS (Netherlands)

    vanderKnaap, MS; Smit, LME; Barth, PG; CatsmanBerrevoets, CE; Brouwer, OF; Begeer, JH; deCoo, IFM; Valk, J.

    A survey was performed of magnetic resonance imaging (MRI) findings in 21 patients with congenital muscular dystrophy (QID) with cerebral abnormalities to evaluate the contribution of MRI to the classification of CMD patients. In 5 patients with Walker-Warburg syndrome (WWS), MRI showed

  9. Magnetic resonance imaging in classification of congenital muscular dystrophies with brain abnormalities

    NARCIS (Netherlands)

    vanderKnaap, MS; Smit, LME; Barth, PG; CatsmanBerrevoets, CE; Brouwer, OF; Begeer, JH; deCoo, IFM; Valk, J.

    1997-01-01

    A survey was performed of magnetic resonance imaging (MRI) findings in 21 patients with congenital muscular dystrophy (QID) with cerebral abnormalities to evaluate the contribution of MRI to the classification of CMD patients. In 5 patients with Walker-Warburg syndrome (WWS), MRI showed hydrocephalu

  10. Computerized “Learn-As-You-Go” Classification of Traumatic Brain Injuries Using NEISS Narrative Data

    OpenAIRE

    Chen, Wei; Wheeler, Krista K.; Lin, Simon; Huang, Yungui; Xiang, Huiyun

    2016-01-01

    One important routine task in injury research is to effectively classify injury circumstances into user-defined categories when using narrative text. However, traditional manual processes can be time consuming, and existing batch learning systems can be difficult to utilize by novice users. This study evaluates a “learn-as-you-go” machine-learning program. When using this program, the user trains classification models and interactively checks on accuracy until a desired threshold is reached. ...

  11. Computational Classification Approach to Profile Neuron Subtypes from Brain Activity Mapping Data

    OpenAIRE

    Meng Li; Fang Zhao; Jason Lee; Dong Wang; Hui Kuang; Joe Z Tsien

    2015-01-01

    The analysis of cell type-specific activity patterns during behaviors is important for better understanding of how neural circuits generate cognition, but has not been well explored from in vivo neurophysiological datasets. Here, we describe a computational approach to uncover distinct cell subpopulations from in vivo neural spike datasets. This method, termed “inter-spike-interval classification-analysis” (ISICA), is comprised of four major steps: spike pattern feature-extraction, pre-cluste...

  12. Tissue Classification

    DEFF Research Database (Denmark)

    Van Leemput, Koen; Puonti, Oula

    2015-01-01

    Computational methods for automatically segmenting magnetic resonance images of the brain have seen tremendous advances in recent years. So-called tissue classification techniques, aimed at extracting the three main brain tissue classes (white matter, gray matter, and cerebrospinal fluid), are now...... well established. In their simplest form, these methods classify voxels independently based on their intensity alone, although much more sophisticated models are typically used in practice. This article aims to give an overview of often-used computational techniques for brain tissue classification...

  13. Classification of traumatic brain injury severity using informed data reduction in a series of binary classifier algorithms.

    Science.gov (United States)

    Prichep, Leslie S; Jacquin, Arnaud; Filipenko, Julie; Dastidar, Samanwoy Ghosh; Zabele, Stephen; Vodencarević, Asmir; Rothman, Neil S

    2012-11-01

    Assessment of medical disorders is often aided by objective diagnostic tests which can lead to early intervention and appropriate treatment. In the case of brain dysfunction caused by head injury, there is an urgent need for quantitative evaluation methods to aid in acute triage of those subjects who have sustained traumatic brain injury (TBI). Current clinical tools to detect mild TBI (mTBI/concussion) are limited to subjective reports of symptoms and short neurocognitive batteries, offering little objective evidence for clinical decisions; or computed tomography (CT) scans, with radiation-risk, that are most often negative in mTBI. This paper describes a novel methodology for the development of algorithms to provide multi-class classification in a substantial population of brain injured subjects, across a broad age range and representative subpopulations. The method is based on age-regressed quantitative features (linear and nonlinear) extracted from brain electrical activity recorded from a limited montage of scalp electrodes. These features are used as input to a unique "informed data reduction" method, maximizing confidence of prospective validation and minimizing over-fitting. A training set for supervised learning was used, including: "normal control," "concussed," and "structural injury/CT positive (CT+)." The classifier function separating CT+ from the other groups demonstrated a sensitivity of 96% and specificity of 78%; the classifier separating "normal controls" from the other groups demonstrated a sensitivity of 81% and specificity of 74%, suggesting high utility of such classifiers in acute clinical settings. The use of a sequence of classifiers where the desired risk can be stratified further supports clinical utility.

  14. Targeting tumour Cell Plasticity

    Institute of Scientific and Technical Information of China (English)

    Elizabeth D. WILLIAMS

    2009-01-01

    @@ Her research is focused on understanding the mechanisms of tumour progression and metastasis, particularly in uro-logical carcinomas (bladder and prostate). Tumour cell plasticity, including epithelial-mesenchymal transition, is a cen-tral theme in Dr Williams' work.

  15. A Framework to Support Automated Classification and Labeling of Brain Electromagnetic Patterns

    Science.gov (United States)

    2007-10-01

    Spatial analysis of evoked po- tentials in man—a review,” Progress in Neurobiology, vol. 23, no. 3, pp. 227–250, 1984. [38] “ Cartool software...Functional Brain Mapping Laboratory, Geneva, Switzerland, http://brainmapping.unige.ch/ Cartool .htm. [39] T. Koenig, K. Kochi, and D. Lehmann, “Event

  16. Classification effects of real and imaginary movement selective attention tasks on a P300-based brain-computer interface

    Science.gov (United States)

    Salvaris, Mathew; Sepulveda, Francisco

    2010-10-01

    Brain-computer interfaces (BCIs) rely on various electroencephalography methodologies that allow the user to convey their desired control to the machine. Common approaches include the use of event-related potentials (ERPs) such as the P300 and modulation of the beta and mu rhythms. All of these methods have their benefits and drawbacks. In this paper, three different selective attention tasks were tested in conjunction with a P300-based protocol (i.e. the standard counting of target stimuli as well as the conduction of real and imaginary movements in sync with the target stimuli). The three tasks were performed by a total of 10 participants, with the majority (7 out of 10) of the participants having never before participated in imaginary movement BCI experiments. Channels and methods used were optimized for the P300 ERP and no sensory-motor rhythms were explicitly used. The classifier used was a simple Fisher's linear discriminant. Results were encouraging, showing that on average the imaginary movement achieved a P300 versus No-P300 classification accuracy of 84.53%. In comparison, mental counting, the standard selective attention task used in previous studies, achieved 78.9% and real movement 90.3%. Furthermore, multiple trial classification results were recorded and compared, with real movement reaching 99.5% accuracy after four trials (12.8 s), imaginary movement reaching 99.5% accuracy after five trials (16 s) and counting reaching 98.2% accuracy after ten trials (32 s).

  17. Radiation therapy for brain metastases from breast cancer by histological classification

    Energy Technology Data Exchange (ETDEWEB)

    Mizutani, Yoshihide [Showa Univ., Tokyo (Japan). School of Medicine; Yamashita, Takashi; Sakamoto, Goi

    2001-02-01

    One hundred thirteen patients with metastatic brain tumor from breast cancer who were treated with external irradiation between 1989 and 1997 at Cancer Institute Hospital were studied. The patients were all histopathologically proven to have invasive ductal carcinoma (scirrhous type 54 cases, papillotubular type 18, solid-tubular type 41). The patients were evaluated for efficacy and histopathological subtypes. The time interval between the diagnosis of breast cancer and brain metastases was 53.6 months for the scirrhous type, 75.0 months for the papillotubular type, and 35.5 months for the solid-tubular type. The time interval between the diagnosis of initial distant metastases and brain metastases was 14.3 months for the scirrhous type, 22.5 months for the papillotubular type, and 12.5 months for the solid-tubular type. Efficacy rates (CR+PR) for external irradiation of the brain metastases were 40.0% for the scirrhous type, 66.7% for the papillotubular type, and 36.6% for the solid-tubular type. The papillotubular type had a favorable efficacy rate compared with the other two types. Median survival time (MST) from the start of treatment for brain metastases and one-year survival rate were 5 months and 11.1% for the scirrhous type, 7 months and 41.5% for the papillotubular type, and 4 months and 28.3% for the solid-tubular type, respectively. No statistically significant difference between survival rates was observed among the histopathological types. Univariate analysis showed performance status, number of metastatic tumors, and existence of extracranial metastases without bony metastasis to be significantly related to prognosis. Multivariate analysis showed only extracranial metastases without bony metastases to be related to prognosis. (author)

  18. Cardiac tumours in children

    Directory of Open Access Journals (Sweden)

    Parsons Jonathan M

    2007-03-01

    Full Text Available Abstract Cardiac tumours are benign or malignant neoplasms arising primarily in the inner lining, muscle layer, or the surrounding pericardium of the heart. They can be primary or metastatic. Primary cardiac tumours are rare in paediatric practice with a prevalence of 0.0017 to 0.28 in autopsy series. In contrast, the incidence of cardiac tumours during foetal life has been reported to be approximately 0.14%. The vast majority of primary cardiac tumours in children are benign, whilst approximately 10% are malignant. Secondary malignant tumours are 10–20 times more prevalent than primary malignant tumours. Rhabdomyoma is the most common cardiac tumour during foetal life and childhood. It accounts for more than 60% of all primary cardiac tumours. The frequency and type of cardiac tumours in adults differ from those in children with 75% being benign and 25% being malignant. Myxomas are the most common primary tumours in adults constituting 40% of benign tumours. Sarcomas make up 75% of malignant cardiac masses. Echocardiography, Computing Tomography (CT and Magnetic Resonance Imaging (MRI of the heart are the main non-invasive diagnostic tools. Cardiac catheterisation is seldom necessary. Tumour biopsy with histological assessment remains the gold standard for confirmation of the diagnosis. Surgical resection of primary cardiac tumours should be considered to relieve symptoms and mechanical obstruction to blood flow. The outcome of surgical resection in symptomatic, non-myxomatous benign cardiac tumours is favourable. Patients with primary cardiac malignancies may benefit from palliative surgery but this approach should not be recommended for patients with metastatic cardiac tumours. Surgery, chemotherapy and radiotherapy may prolong survival. The prognosis for malignant primary cardiac tumours is generally extremely poor.

  19. Extracellular vesicles in the biology of brain tumour stem cells--Implications for inter-cellular communication, therapy and biomarker development.

    Science.gov (United States)

    Nakano, Ichiro; Garnier, Delphine; Minata, Mutsuko; Rak, Janusz

    2015-04-01

    Extracellular vesicles (EVs) act as carriers of molecular and oncogenic signatures present in subsets of tumour cells and tumour-associated stroma, and as mediators of intercellular communication. These processes likely involve cancer stem cells (CSCs). EVs represent a unique pathway of cellular export and cell-to-cell transfer of insoluble molecular regulators such as membrane receptors, signalling proteins and metabolites, thereby influencing the functional integration of cancer cell populations. While mechanisms that control biogenesis, cargo and uptake of different classes of EVs (exosomes, microvesicles, ectosomes, large oncosomes) are poorly understood, they likely remain under the influence of stress-responses, microenvironment and oncogenic processes that define the biology and heterogeneity of human cancers. In glioblastoma (GBM), recent molecular profiling approaches distinguished several disease subtypes driven by distinct molecular, epigenetic and mutational mechanisms, leading to formation of proneural, neural, classical and mesenchymal tumours. Moreover, molecularly distinct clonal cellular lineages co-exist within individual GBM lesions, where they differentiate according to distinct stem cell hierarchies resulting in several facets of tumour heterogeneity and the related potential for intercellular interactions. Glioma stem cells (GSCs) may carry signatures of either proneural or mesenchymal GBM subtypes and differ in several biological characteristics that are, at least in part, represented by the output and repertoire of EV production (vesiculome). We report that vesiculomes differ between known GBM subtypes. EVs may also reflect and influence the equilibrium of the stem cell hierarchy, contain oncogenic drivers and modulate the microenvironment (vascular niche). The GBM/GSC subtype-specific differentials in EV cargo of proteins, transcripts, microRNA and DNA may enable detection of the dynamics of the stem cell compartment and result in

  20. Mathematical Modelling of a Brain Tumour Initiation and Early Development: A Coupled Model of Glioblastoma Growth, Pre-Existing Vessel Co-Option, Angiogenesis and Blood Perfusion.

    Directory of Open Access Journals (Sweden)

    Yan Cai

    Full Text Available We propose a coupled mathematical modelling system to investigate glioblastoma growth in response to dynamic changes in chemical and haemodynamic microenvironments caused by pre-existing vessel co-option, remodelling, collapse and angiogenesis. A typical tree-like architecture network with different orders for vessel diameter is designed to model pre-existing vasculature in host tissue. The chemical substances including oxygen, vascular endothelial growth factor, extra-cellular matrix and matrix degradation enzymes are calculated based on the haemodynamic environment which is obtained by coupled modelling of intravascular blood flow with interstitial fluid flow. The haemodynamic changes, including vessel diameter and permeability, are introduced to reflect a series of pathological characteristics of abnormal tumour vessels including vessel dilation, leakage, angiogenesis, regression and collapse. Migrating cells are included as a new phenotype to describe the migration behaviour of malignant tumour cells. The simulation focuses on the avascular phase of tumour development and stops at an early phase of angiogenesis. The model is able to demonstrate the main features of glioblastoma growth in this phase such as the formation of pseudopalisades, cell migration along the host vessels, the pre-existing vasculature co-option, angiogenesis and remodelling. The model also enables us to examine the influence of initial conditions and local environment on the early phase of glioblastoma growth.

  1. Mathematical Modelling of a Brain Tumour Initiation and Early Development: A Coupled Model of Glioblastoma Growth, Pre-Existing Vessel Co-Option, Angiogenesis and Blood Perfusion.

    Science.gov (United States)

    Cai, Yan; Wu, Jie; Li, Zhiyong; Long, Quan

    2016-01-01

    We propose a coupled mathematical modelling system to investigate glioblastoma growth in response to dynamic changes in chemical and haemodynamic microenvironments caused by pre-existing vessel co-option, remodelling, collapse and angiogenesis. A typical tree-like architecture network with different orders for vessel diameter is designed to model pre-existing vasculature in host tissue. The chemical substances including oxygen, vascular endothelial growth factor, extra-cellular matrix and matrix degradation enzymes are calculated based on the haemodynamic environment which is obtained by coupled modelling of intravascular blood flow with interstitial fluid flow. The haemodynamic changes, including vessel diameter and permeability, are introduced to reflect a series of pathological characteristics of abnormal tumour vessels including vessel dilation, leakage, angiogenesis, regression and collapse. Migrating cells are included as a new phenotype to describe the migration behaviour of malignant tumour cells. The simulation focuses on the avascular phase of tumour development and stops at an early phase of angiogenesis. The model is able to demonstrate the main features of glioblastoma growth in this phase such as the formation of pseudopalisades, cell migration along the host vessels, the pre-existing vasculature co-option, angiogenesis and remodelling. The model also enables us to examine the influence of initial conditions and local environment on the early phase of glioblastoma growth.

  2. Distributed effects of methylphenidate on the network structure of the resting brain: a connectomic pattern classification analysis.

    Science.gov (United States)

    Sripada, Chandra Sekhar; Kessler, Daniel; Welsh, Robert; Angstadt, Michael; Liberzon, Israel; Phan, K Luan; Scott, Clayton

    2013-11-01

    Methylphenidate is a psychostimulant medication that produces improvements in functions associated with multiple neurocognitive systems. To investigate the potentially distributed effects of methylphenidate on the brain's intrinsic network architecture, we coupled resting state imaging with multivariate pattern classification. In a within-subject, double-blind, placebo-controlled, randomized, counterbalanced, cross-over design, 32 healthy human volunteers received either methylphenidate or placebo prior to two fMRI resting state scans separated by approximately one week. Resting state connectomes were generated by placing regions of interest at regular intervals throughout the brain, and these connectomes were submitted for support vector machine analysis. We found that methylphenidate produces a distributed, reliably detected, multivariate neural signature. Methylphenidate effects were evident across multiple resting state networks, especially visual, somatomotor, and default networks. Methylphenidate reduced coupling within visual and somatomotor networks. In addition, default network exhibited decoupling with several task positive networks, consistent with methylphenidate modulation of the competitive relationship between these networks. These results suggest that connectivity changes within and between large-scale networks are potentially involved in the mechanisms by which methylphenidate improves attention functioning.

  3. Brain Tumor Classification Using AFM in Combination with Data Mining Techniques

    Directory of Open Access Journals (Sweden)

    Marlene Huml

    2013-01-01

    Full Text Available Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.

  4. Brain tumor classification using AFM in combination with data mining techniques.

    Science.gov (United States)

    Huml, Marlene; Silye, René; Zauner, Gerald; Hutterer, Stephan; Schilcher, Kurt

    2013-01-01

    Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.

  5. Multilevel Segmentation and Integrated Bayesian Model Classification with an Application to Brain Tumor Segmentation

    OpenAIRE

    Corso, Jason J.; Eitan Sharon; Alan Yuille

    2006-01-01

    We present a new method for automatic segmentation of heterogeneous image data, which is very common in medical image analysis. The main contribution of the paper is a mathematical formulation for incorporating soft model assignments into the calculation of affinities, which are traditionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm. We apply the technique to the task of detecting and segmenting brain tumo...

  6. Binary Color Classification For Brain Computer Interface Using Neural Networks And Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Charmi Sunil Mehta

    2014-04-01

    Full Text Available As the power of modern computers grows alongside our understanding of the human brain, we move a step closer in transforming some pretty spectacular science fiction into reality. The advent of Brain Computer Interface (BCI is indeed leading us to a burgeoning era of complete automation empowering our interaction with computer not only with robustness but with also a gift of intelligence. For the fraction of our society suffering from severe motor disabilities BCI has offered a novel solution of overcoming the problems faced in communicating and environment control. Thus the purpose of our current research is to harness the brain‟s ability to generate Visually Evoked Potentials (VEPs by capturing the response of the brain to the transitions of color from grey to green and grey to red. Our prime focus is to explore EEG-based signal processing techniques in order to classify two colors; which can be further deployed in future by coupling the actuators so as to perform few basic tasks. The extracted EEG features are classified using Support Vector Machines (SVM and Artificial Neural Networks (ANN. We recorded 100% accuracy on testing the model after training and validation process. Moreover, we obtained 90% accuracy on re-testing the model with all samples acquired for the task using Quadratic SVM classifier.

  7. Automatic brain caudate nuclei segmentation and classification in diagnostic of Attention-Deficit/Hyperactivity Disorder.

    Science.gov (United States)

    Igual, Laura; Soliva, Joan Carles; Escalera, Sergio; Gimeno, Roger; Vilarroya, Oscar; Radeva, Petia

    2012-12-01

    We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods. Copyright © 2012 Elsevier Ltd. All rights reserved.

  8. Randomized pilot study and qualitative evaluation of a clinical decision support system for brain tumour diagnosis based on SV ¹H MRS: evaluation as an additional information procedure for novice radiologists.

    Science.gov (United States)

    Sáez, Carlos; Martí-Bonmatí, Luis; Alberich-Bayarri, Angel; Robles, Montserrat; García-Gómez, Juan M

    2014-02-01

    The results of a randomized pilot study and qualitative evaluation of the clinical decision support system Curiam BT are reported. We evaluated the system's feasibility and potential value as a radiological information procedure complementary to magnetic resonance (MR) imaging to assist novice radiologists in diagnosing brain tumours using MR spectroscopy (1.5 and 3.0T). Fifty-five cases were analysed at three hospitals according to four non-exclusive diagnostic questions. Our results show that Curiam BT improved the diagnostic accuracy in all the four questions. Additionally, we discuss the findings of the users' feedback about the system, and the further work to optimize it for real environments and to conduct a large clinical trial.

  9. A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction

    Energy Technology Data Exchange (ETDEWEB)

    Wels, Michael; Hornegger, Joachim [Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nuremberg, Martensstr. 3, 91058 Erlangen (Germany); Zheng Yefeng; Comaniciu, Dorin [Corporate Research and Technologies, Siemens Corporate Technology, 755 College Road East, Princeton, NJ 08540 (United States); Huber, Martin, E-mail: michael.wels@informatik.uni-erlangen.de [Corporate Research and Technologies, Siemens Corporate Technology, Guenther-Scharowsky-Str. 1, 91058 Erlangen (Germany)

    2011-06-07

    We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average

  10. Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.

    Science.gov (United States)

    Barker, Jocelyn; Hoogi, Assaf; Depeursinge, Adrien; Rubin, Daniel L

    2016-05-01

    Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1% (p Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p < 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes.

  11. Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy.

    Science.gov (United States)

    Combrisson, Etienne; Jerbi, Karim

    2015-07-30

    Machine learning techniques are increasingly used in neuroscience to classify brain signals. Decoding performance is reflected by how much the classification results depart from the rate achieved by purely random classification. In a 2-class or 4-class classification problem, the chance levels are thus 50% or 25% respectively. However, such thresholds hold for an infinite number of data samples but not for small data sets. While this limitation is widely recognized in the machine learning field, it is unfortunately sometimes still overlooked or ignored in the emerging field of brain signal classification. Incidentally, this field is often faced with the difficulty of low sample size. In this study we demonstrate how applying signal classification to Gaussian random signals can yield decoding accuracies of up to 70% or higher in two-class decoding with small sample sets. Most importantly, we provide a thorough quantification of the severity and the parameters affecting this limitation using simulations in which we manipulate sample size, class number, cross-validation parameters (k-fold, leave-one-out and repetition number) and classifier type (Linear-Discriminant Analysis, Naïve Bayesian and Support Vector Machine). In addition to raising a red flag of caution, we illustrate the use of analytical and empirical solutions (binomial formula and permutation tests) that tackle the problem by providing statistical significance levels (p-values) for the decoding accuracy, taking sample size into account. Finally, we illustrate the relevance of our simulations and statistical tests on real brain data by assessing noise-level classifications in Magnetoencephalography (MEG) and intracranial EEG (iEEG) baseline recordings. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. Novel Discrete Compactness-Based Training for Vector Quantization Networks: Enhancing Automatic Brain Tissue Classification

    Directory of Open Access Journals (Sweden)

    Ricardo Pérez-Aguila

    2013-01-01

    Full Text Available An approach for nonsupervised segmentation of Computed Tomography (CT brain slices which is based on the use of Vector Quantization Networks (VQNs is described. Images are segmented via a VQN in such way that tissue is characterized according to its geometrical and topological neighborhood. The main contribution rises from the proposal of a similarity metric which is based on the application of Discrete Compactness (DC which is a factor that provides information about the shape of an object. One of its main strengths lies in the sense of its low sensitivity to variations, due to noise or capture defects, in the shape of an object. We will present, compare, and discuss some examples of segmentation networks trained under Kohonen’s original algorithm and also under our similarity metric. Some experiments are established in order to measure the effectiveness and robustness, under our application of interest, of the proposed networks and similarity metric.

  13. Imaging of sacral tumours

    Energy Technology Data Exchange (ETDEWEB)

    Gerber, S.; Ollivier, L.; Brisse, H.; Neuenschwander, S. [Institut Curie, Department of Radiology, Paris (France); Leclere, J. [Institut Gustave Roussy, Department of Radiology, Villejuif (France); Vanel, D. [The Rizzoli Institute, Department of Radiology, Bologna (Italy); Missenard, G. [Institut Gustave Roussy, Comite de pathologie tumorale de l' appareil locomoteur, Villejuif (France); Pinieux, G. de [CHRU de Tours, Department of Pathology, Hopital Trousseau, Tours (France)

    2008-04-15

    All components of the sacrum (bone, cartilage, bone marrow, meninges, nerves, notochord remnants, etc.) can give rise to benign or malignant tumours. Bone metastases and intraosseous sites of haematological malignancies, lymphoma and multiple myeloma are the most frequent aetiologies, while primary bone tumours and meningeal or nerve tumours are less common. Some histological types have a predilection for the sacrum, especially chordoma and giant cell tumour. Clinical signs are usually minor, and sacral tumours are often discovered in the context of nerve root or pelvic organ compression. The roles of conventional radiology, CT and MRI are described and compared with the histological features of the main tumours. The impact of imaging on treatment decisions and follow-up is also reviewed. (orig.)

  14. MR diffusion imaging of human intracranial tumours

    DEFF Research Database (Denmark)

    Krabbe, K; Gideon, P; Wagn, P;

    1997-01-01

    We used MRI for in vivo measurement of brain water self-diffusion in patients with intracranial tumours. The study included 28 patients (12 with high-grade and 3 with low-grade gliomas, 7 with metastases, 5 with meningiomas and 1 with a cerebral abscess). Apparent diffusion coefficients (ADC) wer...

  15. Molecular and metabolic pattern classification for detection of brain glioma progression

    Energy Technology Data Exchange (ETDEWEB)

    Imani, Farzin, E-mail: imanif@upmc.edu [Department of Radiology, University of Pittsburgh Medical Center, PA (United States); Boada, Fernando E. [Department of Radiology, University of Pittsburgh Medical Center, PA (United States); Lieberman, Frank S. [Department of Neurology, University of Pittsburgh Medical Center, PA (United States); Davis, Denise K.; Mountz, James M. [Department of Radiology, University of Pittsburgh Medical Center, PA (United States)

    2014-02-15

    Objectives: The ability to differentiate between brain tumor progression and radiation therapy induced necrosis is critical for appropriate patient management. In order to improve the differential diagnosis, we combined fluorine-18 2-fluoro-deoxyglucose positron emission tomography ({sup 18}F-FDG PET), proton magnetic resonance spectroscopy ({sup 1}H MRS) and histological data to develop a multi-parametric machine-learning model. Methods: We enrolled twelve post-therapy patients with grade 2 and 3 gliomas that were suspicious of tumor progression. All patients underwent {sup 18}F-FDG PET and {sup 1}H MRS. Maximal standardized uptake value (SUVmax) of the tumors and reference regions were obtained. Multiple 2D maps of choline (Cho), creatine (Cr), and N-acetylaspartate (NAA) of the tumors were generated. A support vector machine (SVM) learning model was established to take imaging biomarkers and histological data as input vectors. A combination of clinical follow-up and multiple sequential MRI studies served as the basis for assessing the clinical outcome. All vector combinations were evaluated for diagnostic accuracy and cross validation. The optimal cutoff value of individual parameters was calculated using Receiver operating characteristic (ROC) plots. Results: The SVM and ROC analyses both demonstrated that SUVmax of the lesion was the most significant single diagnostic parameter (75% accuracy) followed by Cho concentration (67% accuracy). SVM analysis of all paired parameters showed SUVmax and Cho concentration in combination could achieve 83% accuracy. SUVmax of the lesion paired with SUVmax of the white matter as well as the tumor Cho paired with the tumor Cr both showed 83% accuracy. These were the most significant paired diagnostic parameters of either modality. Combining all four parameters did not improve the results. However, addition of two more parameters, Cho and Cr of brain parenchyma contralateral to the tumor, increased the accuracy to 92

  16. Image Data Mining for Pattern Classification and Visualization of Morphological Changes in Brain MR Images.

    Science.gov (United States)

    Murakawa, Saki; Ikuta, Rie; Uchiyama, Yoshikazu; Shiraishi, Junji

    2016-02-01

    Hospital information systems (HISs) and picture archiving and communication systems (PACSs) are archiving large amounts of data (i.e., "big data") that are not being used. Therefore, many research projects in progress are trying to use "big data" for the development of early diagnosis, prediction of disease onset, and personalized therapies. In this study, we propose a new method for image data mining to identify regularities and abnormalities in the large image data sets. We used 70 archived magnetic resonance (MR) images that were acquired using three-dimensional magnetization-prepared rapid acquisition with gradient echo (3D MP-RAGE). These images were obtained from the Alzheimer's disease neuroimaging initiative (ADNI) database. For anatomical standardization of the data, we used the statistical parametric mapping (SPM) software. Using a similarity matrix based on cross-correlation coefficients (CCs) calculated from an anatomical region and a hierarchical clustering technique, we classified all the abnormal cases into five groups. The Z score map identified the difference between a standard normal brain and each of those from the Alzheimer's groups. In addition, the scatter plot obtained from two similarity matrixes visualized the regularities and abnormalities in the image data sets. Image features identified using our method could be useful for understanding of image findings associated with Alzheimer's disease.

  17. 18F-FDG PET brain images as features for Alzheimer classification

    Science.gov (United States)

    Azmi, M. H.; Saripan, M. I.; Nordin, A. J.; Ahmad Saad, F. F.; Abdul Aziz, S. A.; Wan Adnan, W. A.

    2017-08-01

    2-Deoxy-2-[fluorine-18] fluoro-D-glucose (18F-FDG) Positron Emission Tomography (PET) imaging offers meaningful information for various types of diseases diagnosis. In Alzheimer's disease (AD), the hypometabolism of glucose which observed on the low intensity voxel in PET image may relate to the onset of the disease. The importance of early detection of AD is inevitable because the resultant brain damage is irreversible. Several statistical analysis and machine learning algorithm have been proposed to investigate the rate and the pattern of the hypometabolism. This study focus on the same aim with further investigation was performed on several hypometabolism pattern. Some pre-processing steps were implemented to standardize the data in order to minimize the effect of resolution and anatomical differences. The features used are the mean voxel intensity within the AD pattern mask, which derived from several z-score and FDR threshold values. The global mean voxel (GMV) and slice-based mean voxel (SbMV) intensity were observed and used as input to the neural network. Several neural network architectures were tested and compared to the nearest neighbour method. The highest accuracy equals to 0.9 and recorded at z-score ≤-1.3 with 1 node neural network architecture (sensitivity=0.81 and specificity=0.95) and at z-score ≤-0.7 with 10 nodes neural network (sensitivity=0.83 and specificity=0.94).

  18. Fine-tuning convolutional deep features for MRI based brain tumor classification

    Science.gov (United States)

    Ahmed, Kaoutar B.; Hall, Lawrence O.; Goldgof, Dmitry B.; Liu, Renhao; Gatenby, Robert A.

    2017-03-01

    Prediction of survival time from brain tumor magnetic resonance images (MRI) is not commonly performed and would ordinarily be a time consuming process. However, current cross-sectional imaging techniques, particularly MRI, can be used to generate many features that may provide information on the patient's prognosis, including survival. This information can potentially be used to identify individuals who would benefit from more aggressive therapy. Rather than using pre-defined and hand-engineered features as with current radiomics methods, we investigated the use of deep features extracted from pre-trained convolutional neural networks (CNNs) in predicting survival time. We also provide evidence for the power of domain specific fine-tuning in improving the performance of a pre-trained CNN's, even though our data set is small. We fine-tuned a CNN initially trained on a large natural image recognition dataset (Imagenet ILSVRC) and transferred the learned feature representations to the survival time prediction task, obtaining over 81% accuracy in a leave one out cross validation.

  19. Reliability of a novel, semi-quantitative scale for classification of structural brain magnetic resonance imaging in children with cerebral palsy.

    Science.gov (United States)

    Fiori, Simona; Cioni, Giovanni; Klingels, Katrjin; Ortibus, Els; Van Gestel, Leen; Rose, Stephen; Boyd, Roslyn N; Feys, Hilde; Guzzetta, Andrea

    2014-09-01

    To describe the development of a novel rating scale for classification of brain structural magnetic resonance imaging (MRI) in children with cerebral palsy (CP) and to assess its interrater and intrarater reliability. The scale consists of three sections. Section 1 contains descriptive information about the patient and MRI. Section 2 contains the graphical template of brain hemispheres onto which the lesion is transposed. Section 3 contains the scoring system for the quantitative analysis of the lesion characteristics, grouped into different global scores and subscores that assess separately side, regions, and depth. A larger interrater and intrarater reliability study was performed in 34 children with CP (22 males, 12 females; mean age at scan of 9 y 5 mo [SD 3 y 3 mo], range 4 y-16 y 11 mo; Gross Motor Function Classification System level I, [n=22], II [n=10], and level III [n=2]). Very high interrater and intrarater reliability of the total score was found with indices above 0.87. Reliability coefficients of the lobar and hemispheric subscores ranged between 0.53 and 0.95. Global scores for hemispheres, basal ganglia, brain stem, and corpus callosum showed reliability coefficients above 0.65. This study presents the first visual, semi-quantitative scale for classification of brain structural MRI in children with CP. The high degree of reliability of the scale supports its potential application for investigating the relationship between brain structure and function and examining treatment response according to brain lesion severity in children with CP. © 2014 Mac Keith Press.

  20. Parapharyngeal space primary tumours.

    Science.gov (United States)

    Grilli, Gianluigi; Suarez, Vanessa; Muñoz, María Gabriela; Costales, María; Llorente, José Luis

    The aim of this study is to present our experience with the diagnostic and therapeutic approaches for parapharyngeal space tumours. This study is a retrospective review of 90 patients diagnosed with tumours of the parapharyngeal space and treated surgically between 1984 and 2015. Patients whose tumours were not primary but invaded the parapharyngeal space expanding from another region, tumours originating in the deep lobe of the parotid gland and head and neck metastasis were excluded from this study. 74% percent of the parapharyngeal space neoplasms were benign and 26% were malignant. Pleomorphic adenoma was the most common neoplasm (27%), followed by paragangliomas (25%), miscellaneous malignant tumours (16%), neurogenic tumours (12%), miscellaneous benign tumours (10%), and malignant salivary gland tumours (10%). The transcervical approach was used in 56 cases, cervical-transparotid approach in 15 cases, type A infratemporal fossa approach in 13 cases, transmandibular approach in 4 cases and transoral approach in 2 cases. The most common complications were those deriving from nervous injuries. Most parapharyngeal space tumours can be removed surgically with a low rate of complications and recurrence. The transcervical approach is the most frequently used. Copyright © 2016 Elsevier España, S.L.U. and Sociedad Española de Otorrinolaringología y Cirugía de Cabeza y Cuello. All rights reserved.

  1. Extracting multiscale pattern information of fMRI based functional brain connectivity with application on classification of autism spectrum disorders.

    Directory of Open Access Journals (Sweden)

    Hui Wang

    Full Text Available We employed a multi-scale clustering methodology known as "data cloud geometry" to extract functional connectivity patterns derived from functional magnetic resonance imaging (fMRI protocol. The method was applied to correlation matrices of 106 regions of interest (ROIs in 29 individuals with autism spectrum disorders (ASD, and 29 individuals with typical development (TD while they completed a cognitive control task. Connectivity clustering geometry was examined at both "fine" and "coarse" scales. At the coarse scale, the connectivity clustering geometry produced 10 valid clusters with a coherent relationship to neural anatomy. A supervised learning algorithm employed fine scale information about clustering motif configurations and prevalence, and coarse scale information about intra- and inter-regional connectivity; the algorithm correctly classified ASD and TD participants with sensitivity of 82.8% and specificity of 82.8%. Most of the predictive power of the logistic regression model resided at the level of the fine-scale clustering geometry, suggesting that cellular versus systems level disturbances are more prominent in individuals with ASD. This article provides validation for this multi-scale geometric approach to extracting brain functional connectivity pattern information and for its use in classification of ASD.

  2. Extracting multiscale pattern information of fMRI based functional brain connectivity with application on classification of autism spectrum disorders.

    Science.gov (United States)

    Wang, Hui; Chen, Chen; Fushing, Hsieh

    2012-01-01

    We employed a multi-scale clustering methodology known as "data cloud geometry" to extract functional connectivity patterns derived from functional magnetic resonance imaging (fMRI) protocol. The method was applied to correlation matrices of 106 regions of interest (ROIs) in 29 individuals with autism spectrum disorders (ASD), and 29 individuals with typical development (TD) while they completed a cognitive control task. Connectivity clustering geometry was examined at both "fine" and "coarse" scales. At the coarse scale, the connectivity clustering geometry produced 10 valid clusters with a coherent relationship to neural anatomy. A supervised learning algorithm employed fine scale information about clustering motif configurations and prevalence, and coarse scale information about intra- and inter-regional connectivity; the algorithm correctly classified ASD and TD participants with sensitivity of 82.8% and specificity of 82.8%. Most of the predictive power of the logistic regression model resided at the level of the fine-scale clustering geometry, suggesting that cellular versus systems level disturbances are more prominent in individuals with ASD. This article provides validation for this multi-scale geometric approach to extracting brain functional connectivity pattern information and for its use in classification of ASD.

  3. Investigation of the trade-off between time window length, classifier update rate and classification accuracy for restorative brain-computer interfaces.

    Science.gov (United States)

    Darvishi, Sam; Ridding, Michael C; Abbott, Derek; Baumert, Mathias

    2013-01-01

    Recently, the application of restorative brain-computer interfaces (BCIs) has received significant interest in many BCI labs. However, there are a number of challenges, that need to be tackled to achieve efficient performance of such systems. For instance, any restorative BCI needs an optimum trade-off between time window length, classification accuracy and classifier update rate. In this study, we have investigated possible solutions to these problems by using a dataset provided by the University of Graz, Austria. We have used a continuous wavelet transform and the Student t-test for feature extraction and a support vector machine (SVM) for classification. We find that improved results, for restorative BCIs for rehabilitation, may be achieved by using a 750 milliseconds time window with an average classification accuracy of 67% that updates every 32 milliseconds.

  4. Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment

    OpenAIRE

    Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; Lu, Chengbiao

    2016-01-01

    At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accura...

  5. Biochemistry of neuroendocrine tumours.

    Science.gov (United States)

    de Herder, Wouter W

    2007-03-01

    Several circulating or urinary tumour markers can be used for the diagnosis and follow-up of functioning and clinically non-functioning neuroendocrine tumours of the pancreatic islet cells and intestinal tract. Among the specific tumour markers are serotonin and its metabolites--e.g. 5-hydroxyindoleacetic acid (5-HIAA)--in carcinoid tumours and the carcinoid syndrome, insulin and its precursors or breakdown products in insulinoma, and gastrin in gastrinoma. Plasma vasointestinal polypeptide (VIP) determinations have been used in the diagnosis of VIPoma, plasma glucagon for glucagonoma, and serum somatostatin for somatostatinoma. Among the tumour-non-specific markers are: chromogranins, neuron-specific enolase (NSE), alpha-subunits of the glycoprotein hormones, catecholamines, pancreatic polypeptide (PP), ghrelin and adrenomedullin.

  6. Towards a multimodal brain-computer interface: combining fNIRS and fTCD measurements to enable higher classification accuracy.

    Science.gov (United States)

    Faress, Ahmed; Chau, Tom

    2013-08-15

    Previous brain-computer interface (BCI) research has largely focused on single neuroimaging modalities such as near-infrared spectroscopy (NIRS) or transcranial Doppler ultrasonography (TCD). However, multimodal brain-computer interfaces, which combine signals from different brain modalities, have been suggested as a potential means of improving the accuracy of BCI systems. In this paper, we compare the classification accuracies attainable using NIRS signals alone, TCD signals alone, and a combination of NIRS and TCD signals. Nine able-bodied subjects (mean age=25.7) were recruited and simultaneous measurements were made with NIRS and TCD instruments while participants were prompted to perform a verbal fluency task or to remain at rest, within the context of a block-stimulus paradigm. Using Linear Discriminant Analysis, the verbal fluency task was classified at mean accuracies of 76.1±9.9%, 79.4±10.3%, and 86.5±6.0% using NIRS, TCD, and NIRS-TCD systems respectively. In five of nine participants, classification accuracies with the NIRS-TCD system were significantly higher (paccuracy of future brain-computer interfaces. Copyright © 2013 Elsevier Inc. All rights reserved.

  7. Is the shock index based classification of hypovolemic shock applicable in multiple injured patients with severe traumatic brain injury?—an analysis of the TraumaRegister DGU®

    OpenAIRE

    Fröhlich, Matthias; Driessen, Arne; Böhmer, Andreas; Nienaber, Ulrike; Igressa, Alhadi; Probst, Christian; Bouillon, Bertil; Maegele, Marc; Mutschler, Manuel; ,

    2016-01-01

    Background A new classification of hypovolemic shock based on the shock index (SI) was proposed in 2013. This classification contains four classes of shock and shows good correlation with acidosis, blood product need and mortality. Since their applicability was questioned, the aim of this study was to verify the validity of the new classification in multiple injured patients with traumatic brain injury. Methods Between 2002 and 2013, data from 40 888 patients from the TraumaRegister DGU® were...

  8. [Brain tumor and headache.].

    Science.gov (United States)

    Kiss, I; Franz, M; Kilian, M

    1994-09-01

    The possible association of brain tumour with headache was investigated in 100 patients seen for brain surgery. Preoperatively, 43 patients suffered from headache. These patients were thoroughly questioned about the nature of their pain. Investigation included the McGill Pain Questionnaire. In only 11 of the patients was headache the primary symptom of a brain tumour. Pain intensity was found to be lower in patients with brain tumour then in those with extracranial tumours or headache of other origins. Female subjects, patients under 50 years of age and those with elevated intracranial pressure experienced more intensive pain. Diurnal variation in pain intensity was observed in 60% of patients with headache. There was no evidence, however, of an association with elevated intracranial pressure. Our investigations yielded new information concerning the epidemology of headache accompanying brain tumours. Headache is not an early cardinal symptom of brain tumours, as was generally believed earlier. With the help of the McGill Pain Questionnaire a fine quantitative and qualitative characterization of headache of different origins could be made. The connection between tumour localization and pain lateralization, as well as the possible mechanisms of intracranial pain projection was extensively analysed. The interpretations of the results are at best hypotheses and they do not help determine why more than half of the patients with brain tumour did not experience headache.

  9. Brain Machine Interface: Analysis of segmented EEG Signal Classification Using Short-Time PCA and Recurrent Neural Networks

    OpenAIRE

    C. R. Hema; Paulraj, M.P.; Nagarajan, R.; Sazali Yaacob; Abdul Hamid Adom

    2008-01-01

    Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI) for communication; the BMI uses the electrical activity of the brain detected by scalp ...

  10. A pulmonary mucinous cystic tumour of borderline malignancy.

    Science.gov (United States)

    Bacha, D; Ayadi-Kaddour, A; Smati, B; Kilani, T; El Mezni, F

    2008-06-01

    We report a well-documented case of pulmonary mucinous cystic tumour of borderline malignancy involving the left lower lobe. The lesion was found incidentally by chest radiograph and CT scan with a provisional diagnosis of bronchioloalveolar carcinoma. The tumour was 4 cm in its greatest dimension, cystic and filled with gelatinous mucus. Microscopically, the neoplastic mucinous epithelium was composed of cuboidal cells with focally nuclear stratification and mild to moderate nuclear atypia. The patient has remained free from recurrence or metastases for 6 years. Pulmonary mucinous cystic tumour of borderline malignancy is a rare, recently described neoplasm, which spans a spectrum of tumours with malignant potential. The recent World Health Organization classification of lung tumours does not recognize this entity, which has a very good prognosis, and as such should be distinguished from classic pulmonary adenocarcinoma. Histological diagnosis can be difficult to distinguish from cystic bronchioloalveolar carcinoma or metastatic mucinous adenocarcinoma.

  11. Clasificación de las anomalías vasculares (tumores y malformaciones: Características clínicas e historia natural Classification of vascular anomalies (tumours and malformations: Clinical characteristics and natural history

    Directory of Open Access Journals (Sweden)

    P. Redondo

    2004-01-01

    Full Text Available Las anomalías vasculares se dividen en tumores y malformaciones. Dentro de los primeros, los más frecuentes son los hemangiomas, habitualmente no presentes, aunque sí de forma premonitoria en el nacimiento, que durante 10-12 meses crecen por hiperplasia, para posteriormente involucionar de forma progresiva durante un período que puede llegar a durar entre 10 y 12 años. Su incidencia es de hasta un 12% de los recién nacidos, ocurre más en las niñas y se dividen en superficiales, profundos y compuestos. Los hemangiomas congénitos y aquéllos que no involucionan, se consideran entidades raras. Por otra parte, están las malformaciones vasculares con una incidencia menor que los hemangiomas, siempre presentes en el nacimiento, que característicamente crecen por hipertrofia y nunca involucionan. Según la clasificación de la ISSVA, las malformaciones vasculares se dividen en función del vaso afectado en capilares o venulares (mancha en vino de Oporto, venosas, linfáticas, arteriovenosas y combinadas o complejas. Cada una de ellas, con unas peculiaridades clínicas y hemodinámicas definitorias. Dentro del último grupo, se incluyen algunas de bajo flujo, como el síndrome de Klippel-Trenaunay (malformación vascular venular linfática y venosa asociada a hipertrofia músculo-esquelética de una extremidad y otras de alto flujo como el síndrome de Parkes-Weber.Vascular anomalies are divided into tumours and malformations. Haemangiomas are the most frequent amongst the former. Not normally present at birth, except in a premonitory form, they grow for 10-12 months due to hyperplasia, to subsequently undergo a progressive involution for a period that might last from ten to twelve years. They have an incidence of up to 12% in newborns; they are more common amongst girls; and are divided into superficial, deep and compound. Congenital haemangiomas and those that do not undergo involution are considered to be rare entities. Vascular

  12. Automated voxel classification used with atlas-guided diffuse optical tomography for assessment of functional brain networks in young and older adults.

    Science.gov (United States)

    Li, Lin; Cazzell, Mary; Babawale, Olajide; Liu, Hanli

    2016-10-01

    Atlas-guided diffuse optical tomography (atlas-DOT) is a computational means to image changes in cortical hemodynamic signals during human brain activities. Graph theory analysis (GTA) is a network analysis tool commonly used in functional neuroimaging to study brain networks. Atlas-DOT has not been analyzed with GTA to derive large-scale brain connectivity/networks based on near-infrared spectroscopy (NIRS) measurements. We introduced an automated voxel classification (AVC) method that facilitated the use of GTA with atlas-DOT images by grouping unequal-sized finite element voxels into anatomically meaningful regions of interest within the human brain. The overall approach included volume segmentation, AVC, and cross-correlation. To demonstrate the usefulness of AVC, we applied reproducibility analysis to resting-state functional connectivity measurements conducted from 15 young adults in a two-week period. We also quantified and compared changes in several brain network metrics between young and older adults, which were in agreement with those reported by a previous positron emission tomography study. Overall, this study demonstrated that AVC is a useful means for facilitating integration or combination of atlas-DOT with GTA and thus for quantifying NIRS-based, voxel-wise resting-state functional brain networks.

  13. Phyllodes tumours of the breast: a consensus review

    Science.gov (United States)

    Tan, Benjamin Y; Acs, Geza; Apple, Sophia K; Badve, Sunil; Bleiweiss, Ira J; Brogi, Edi; Calvo, José P; Dabbs, David J; Ellis, Ian O; Eusebi, Vincenzo; Farshid, Gelareh; Fox, Stephen B; Ichihara, Shu; Lakhani, Sunil R; Rakha, Emad A; Reis-Filho, Jorge S; Richardson, Andrea L; Sahin, Aysegul; Schmitt, Fernando C; Schnitt, Stuart J; Siziopikou, Kalliopi P; Soares, Fernando A; Tse, Gary M; Vincent-Salomon, Anne; Tan, Puay Hoon

    2016-01-01

    Phyllodes tumours constitute an uncommon but complex group of mammary fibroepithelial lesions. Accurate and reproducible grading of these tumours has long been challenging, owing to the need to assess multiple stratified histological parameters, which may be weighted differently by individual pathologists. Distinction of benign phyllodes tumours from cellular fibroadenomas is fraught with difficulty, due to overlapping microscopic features. Similarly, separation of the malignant phyllodes tumour from spindle cell metaplastic carcinoma and primary breast sarcoma can be problematic. Phyllodes tumours are treated by surgical excision. However, there is no consensus on the definition of an appropriate surgical margin to ensure completeness of excision and reduction of recurrence risk. Interpretive subjectivity, overlapping histological diagnostic criteria, suboptimal correlation between histological classification and clinical behaviour and the lack of robust molecular predictors of outcome make further investigation of the pathogenesis of these fascinating tumours a matter of active research. This review consolidates the current understanding of their pathobiology and clinical behaviour, and includes proposals for a rational approach to the classification and management of phyllodes tumours. PMID:26768026

  14. The TNM 8 M1b and M1c classification for non-small cell lung cancer in a cohort of patients with brain metastases.

    Science.gov (United States)

    Nieder, C; Hintz, M; Oehlke, O; Bilger, A; Grosu, A L

    2017-09-01

    According to the recent TNM 8 classification, patients with metastatic non-small cell lung cancer (NSCLC) and single extrathoracic metastasis should be classified as stage M1b, while those with 2 or more metastases comprise stage M1c. The purpose of this study was to analyze the impact of this classification in patients with brain metastases. This retrospective study included 172 patients treated with individualized approaches. Actuarial survival was calculated. Uni- and multivariate analyses were performed. Thirty patients (17%) were staged as M1b. Those with squamous cell cancer were more likely to harbor M1b disease (29%, adenocarcinoma 14%, other histology 17%, p = 0.16). Median survival was 5.4 months (8.0 months in case of M1b disease and 4.5 months in case of M1c disease, p = 0.001). Multivariate analysis confirmed the role of M1b stage. M1b patients managed with upfront surgery or radiosurgery had significantly longer median survival than those who received whole-brain irradiation (21.0 vs. 3.5 months, p = 0.0001) and the potential to survive beyond 5 years. We found the M1b classification to provide clinically relevant information. The multivariate analysis suggested that patients with M1b disease, better performance status and younger age have better survival.

  15. [Gastric mesenchymal tumours (GIST)].

    Science.gov (United States)

    Spivach, Arrigo; Fezzi, Margherita; Sartori, Alberto; Belgrano, Manuel; Rimondini, Alessandra; Cuttin-Zernich, Roberto; Covab, Maria Assunta; Bonifacio, Daniela; Buri, Luigi; Pagani, Carlo; Zanconati, Fabrizio

    2008-01-01

    The incidence of gastrointestinal stromal tumours (GIST) has increased in recent years. A number of authors have attempted to define the actual nature of these tumours. Immunohistochemistry highlighting the positivity of tyrosine-kinase (CD117/c-Kit) has revealed the difference between gastrointestinal stromal tumours and other mesenchymal tumours and, therefore, the possibility of medical rather than surgical therapy. We retrospectively reviewed 19 patients affected by primary gastric GIST, who underwent surgery in recent years with subsequent follow-up. Gastroscopy and gastrointestinal tract radiography were used not only to obtain the diagnosis but also to establish the size, density, contours, ulceration, regional lymphadenopathy, mesenteric infiltration and the presence of metastases. The aim of this study was to evaluate the roles of endoscopy and radiology in this pathology and the advantages and limitations of each individual technique.

  16. Bilateral Malignant Brenner Tumour

    Directory of Open Access Journals (Sweden)

    Nasser D Choudhary, S.Manzoor Kadri, Ruby Reshi, S. Besina, Mansoor A. Laharwal, Reyaz tasleem, Qurrat A. Chowdhary

    2002-10-01

    Full Text Available Bilateral malignant Brenner tumour ofovary is extremely rate. A case ofmalignant Brenner tumourinvolving both the ovaries with mctastasis to mesentery in a 48 year femalc is presented. Grosslyo'arian masses were firm with soft areas, encapsulated and having bosselated external surfaces.Cut sections showed yellowish white surface with peripheral cysts (in both tumours. Microscopyrevealed transitional cell carcinoma with squamoid differentiation at places. Metastatic deposits werefound in the mesentery. Endometrium showed cystic glandular hyperplasia.

  17. Elucidating a magnetic resonance imaging-based neuroanatomic biomarker for psychosis: classification analysis using probabilistic brain atlas and machine learning algorithms.

    Science.gov (United States)

    Sun, Daqiang; van Erp, Theo G M; Thompson, Paul M; Bearden, Carrie E; Daley, Melita; Kushan, Leila; Hardt, Molly E; Nuechterlein, Keith H; Toga, Arthur W; Cannon, Tyrone D

    2009-12-01

    No objective diagnostic biomarkers or laboratory tests have yet been developed for psychotic illness. Magnetic resonance imaging (MRI) studies consistently find significant abnormalities in multiple brain structures in psychotic patients relative to healthy control subjects, but these abnormalities show substantial overlap with anatomic variation that is in the normal range and therefore nondiagnostic. Recently, efforts have been made to discriminate psychotic patients from healthy individuals using machine-learning-based pattern classification methods on MRI data. Three-dimensional cortical gray matter density (GMD) maps were generated for 36 patients with recent-onset psychosis and 36 sex- and age-matched control subjects using a cortical pattern matching method. Between-group differences in GMD were evaluated. Second, the sparse multinomial logistic regression classifier included in the Multivariate Pattern Analysis in Python machine-learning package was applied to the cortical GMD maps to discriminate psychotic patients from control subjects. Patients showed significantly lower GMD, particularly in prefrontal, cingulate, and lateral temporal brain regions. Pattern classification analysis achieved 86.1% accuracy in discriminating patients from controls using leave-one-out cross-validation. These results suggest that even at the early stage of illness, psychotic patients present distinct patterns of regional cortical gray matter changes that can be discriminated from the normal pattern. These findings indicate that we can detect complex patterns of brain abnormality in early stages of psychotic illness, which has critical implications for early identification and intervention in individuals at ultra-high risk for developing psychosis/schizophrenia.

  18. Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application.

    Science.gov (United States)

    Naseer, Noman; Noori, Farzan M; Qureshi, Nauman K; Hong, Keum-Shik

    2016-01-01

    In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features-mean, slope, variance, peak, skewness and kurtosis-are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic vs. rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05) classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic vs. rest for a two-class BCI.

  19. Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application

    Directory of Open Access Journals (Sweden)

    Noman eNaseer

    2016-05-01

    Full Text Available In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS signals with the best accuracies for development of a two-class brain-computer interface (BCI. Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR features — mean, slope, variance, peak, skewness and kurtosis — are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic versus rest using linear discriminant analysis (LDA. It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05 classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic versus rest for a two-class BCI.

  20. Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

    Science.gov (United States)

    Xie, Jianwen; Douglas, Pamela K; Wu, Ying Nian; Brody, Arthur L; Anderson, Ariana E

    2017-04-15

    Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (pcoding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (pcoding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Occurrence studies of intracranial tumours

    Energy Technology Data Exchange (ETDEWEB)

    Larjavaara, S.

    2011-07-01

    Intracranial tumours are a histopathologically heterogeneous group of tumours. This thesis focused on three types of intracranial tumours; gliomas, meningiomas and vestibular schwannomas (VS). The main objectives of the dissertation were to estimate the occurrence of intracranial tumours by different subtypes, and to assess the validity and completeness of the cancer registry data. The specific aims of the publications were to evaluate the validity of reported incidence rates of meningioma cases, to describe the trends of VS incidence in four Nordic countries, and to define the anatomic distribution of gliomas and to investigate their location in relation to mobile phone use. Completeness of meningioma registration was examined by comparing five separate sources of information, and by defining the frequencies of cases reported to the Finnish Cancer Registry (FCR). Incidence trends of VS were assessed in the four Nordic countries over a twenty-one-year period (1987 - 2007) using cancer registry data. The anatomic site of gliomas was evaluated using both crude locations in the cerebral lobes and, in more detail, a three-dimensional (3D) distribution in the brain. In addition, a study on specific locations of gliomas in relation to the typical position of mobile phones was conducted using two separate approaches: a case-case and a case-specular analysis. The thesis was based on four sets of materials. Data from the international Interphone study were used for the studies on gliomas, while the two other studies were register-based. The dataset for meningiomas included meningioma cases from the FCR and four clinical data sources in Tampere University Hospital (neurosurgical clinic, pathology database, hospital discharge register and autopsy register). The data on VS were obtained from the national cancer registries of Denmark, Finland, Norway and Sweden. The coverage of meningiomas was not comprehensive in any of the data sources. The completeness of FCR was

  2. Childhood tumours with a high probability of being part of a tumour predisposition syndrome; reason for referral for genetic consultation.

    Science.gov (United States)

    Postema, Floor A M; Hopman, Saskia M J; Aalfs, Cora M; Berger, Lieke P V; Bleeker, Fonnet E; Dommering, Charlotte J; Jongmans, Marjolijn C J; Letteboer, Tom G W; Olderode-Berends, Maran J W; Wagner, Anja; Hennekam, Raoul C; Merks, Johannes H M

    2017-07-01

    Recognising a tumour predisposition syndrome (TPS) in childhood cancer patients is of major clinical relevance. The presence of a TPS may be suggested by the type of tumour in the child. We present an overview of 23 childhood tumours that in themselves should be a reason to refer a child for genetic consultation. We performed a PubMed search to review the incidence of TPSs in children for 85 tumour types listed in the International Classification of Childhood Cancer third edition (ICCC-3). The results were discussed during a national consensus meeting with representative clinical geneticists from all six academic paediatric oncology centres in The Netherlands. A TPS incidence of 5% or more was considered a high probability and therefore in itself a reason for referral to a clinical geneticist. The literature search resulted in data on the incidence of a TPS in 26 tumours. For 23/26 tumour types, a TPS incidence of 5% or higher was reported. In addition, during the consensus meeting the experts agreed that children with any carcinoma should always be referred for clinical genetic consultation as well, as it may point to a TPS. We present an overview of 23 paediatric tumours with a high probability of a TPS; this will facilitate paediatric oncologists to decide which patients should be referred for genetic consultation merely based on type of tumour. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

    OpenAIRE

    Javier Juan-Albarracín; Elies Fuster-Garcia; Manjón, José V.; Montserrat Robles; Aparici, F.; L Martí-Bonmatí; García-Gómez, Juan M.

    2015-01-01

    Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised appro...

  4. Proton MR spectroscopy of cerebral gliomas at 3 T: spatial heterogeneity, and tumour grade and extent

    Energy Technology Data Exchange (ETDEWEB)

    Di Costanzo, Alfonso; Catapano, Domenico; D' Angelo, Vincenzo A. [Department of Health Sciences, University of Molise, Campobasso (Italy); Scarabino, Tommaso; Popolizio, Teresa; Giannatempo, Giuseppe M. [Department of Neuroradiology, Scientific Institute, Foggia (Italy); Trojsi, Francesca; Bonavita, Simona; Tedeschi, Gioacchino [Department of Neurological Sciences, 2. University of Naples (Italy); Portaluri, Maurizio [Department of Radiotherapy, Perrino Hospital, Brindisi (Italy); Tosetti, Michela [Department of Magnetic Resonance, Scientific Institute Stella Maris, Pisa (Italy); Salvolini, Ugo [Department of Radiology, Polytechnic University of Marches, Ancona (Italy)

    2008-08-15

    This study aimed to evaluate the usefulness of proton MR spectroscopic imaging ({sup 1}H-MRSI) at 3 T in differentiating high- from low-grade gliomas, and tumour from necrosis, oedema or normal tissue. Forty-four patients with brain gliomas and four with meningiomas were retrospectively reviewed. The normalised metabolites choline (nCho), N-acetylaspartate (nNAA), creatine (nCr) and lactate/lipids (nLL), and the metabolite ratios Cho/NAA, NAA/Cr and Cho/Cr were calculated. Necrotic-appearing areas showed two spectroscopic patterns: 'necrosis' with variable nCho and high nLL, and 'cystic necrosis' with variable nLL or nonevident peaks. Peri-enhancing oedematous-appearing areas showed three spectroscopic patterns ('tumour' with abnormal Cho/NAA, 'oedema' with normal Cho/NAA and 'tumour/oedema' with normal nCho and abnormal Cho/NAA) in gliomas, and one ('oedema') in meningiomas. Peri-enhancing or peri-tumour normal-appearing areas showed two patterns ('infiltrated' with abnormal nCho and/or Cho/NAA and 'normal' with normal spectra) in gliomas and one ('normal') in meningiomas. Discriminant analysis showed that classification accuracy between high- and low-grade glioma masses was better with normalised metabolites or all parameters together than metabolite ratios and that among peri-enhancing areas was much better with normalised metabolites. The analysis of spatial distribution of normalised metabolites by 3-T {sup 1}H-MRSI helps to discriminate among different tissues, offering information not available with conventional MRI. (orig.)

  5. Atypical teratoid/rhabdoid tumour in sella turcica in an adult.

    Science.gov (United States)

    Arita, K; Sugiyama, K; Sano, T; Oka, H

    2008-05-01

    Although atypical teratoid rhabdoid tumours preferentially arise in the posterior fossa of infants, we encountered a 56 year old woman with an atypical teratoid rhabdoid tumour located in the sella. She presented with right abducent and oculomotor nerve paresis. Magnetic resonance imaging demonstrated an intrasellar tumour impinging on the right cavernous sinus. Microscopically, the tumour was composed of cells with rhabdoid features; we observed atypia, eccentric nuclei, and intracytoplasmic inclusion bodies. The Ki-67 labeling index was around 30%. The tumour cells were positive for vimentin, epithelial membrane antigen, and neurofilament, but negative for INI1. Despite extended local brain and whole-spine irradiation she died of neural axis dissemination.

  6. Investigating the use of support vector machine classification on structural brain images of preterm-born teenagers as a biological marker.

    Directory of Open Access Journals (Sweden)

    Carlton Chu

    Full Text Available Preterm birth has been shown to induce an altered developmental trajectory of brain structure and function. With the aid support vector machine (SVM classification methods we aimed to investigate whether MRI data, collected in adolescence, could be used to predict whether an individual had been born preterm or at term. To this end we collected T1-weighted anatomical MRI data from 143 individuals (69 controls, mean age 14.6y. The inclusion criteria for those born preterm were birth weight ≤ 1500g and gestational age < 37w. A linear SVM was trained on the grey matter segment of MR images in two different ways. First, all the individuals were used for training and classification was performed by the leave-one-out method, yielding 93% correct classification (sensitivity = 0.905, specificity = 0.942. Separately, a random half of the available data were used for training twice and each time the other, unseen, half of the data was classified, resulting 86% and 91% accurate classifications. Both gestational age (R = -0.24, p<0.04 and birth weight (R = -0.51, p < 0.001 correlated with the distance to decision boundary within the group of individuals born preterm. Statistically significant correlations were also found between IQ (R = -0.30, p < 0.001 and the distance to decision boundary. Those born small for gestational age did not form a separate subgroup in these analyses. The high rate of correct classification by the SVM motivates further investigation. The long-term goal is to automatically and non-invasively predict the outcome of preterm-born individuals on an individual basis using as early a scan as possible.

  7. Classification of Parkinsonian Syndromes from FDG-PET Brain Data Using Decision Trees with SSM/PCA Features

    NARCIS (Netherlands)

    Mudali, D.; Teune, L. K.; Renken, R. J.; Leenders, K. L.; Roerdink, J. B. T. M.

    2015-01-01

    Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's dise

  8. Classification of EEG-P300 Signals Extracted from Brain Activities in BCI Systems Using ν-SVM and BLDA Algorithms

    Directory of Open Access Journals (Sweden)

    Ali MOMENNEZHAD

    2014-06-01

    Full Text Available In this paper, a linear predictive coding (LPC model is used to improve classification accuracy, convergent speed to maximum accuracy, and maximum bitrates in brain computer interface (BCI system based on extracting EEG-P300 signals. First, EEG signal is filtered in order to eliminate high frequency noise. Then, the parameters of filtered EEG signal are extracted using LPC model. Finally, the samples are reconstructed by LPC coefficients and two classifiers, a Bayesian Linear discriminant analysis (BLDA, and b the υ-support vector machine (υ-SVM are applied in order to classify. The proposed algorithm performance is compared with fisher linear discriminant analysis (FLDA. Results show that the efficiency of our algorithm in improving classification accuracy and convergent speed to maximum accuracy are much better. As example at the proposed algorithms, respectively BLDA with LPC model and υ-SVM with LPC model with8 electrode configuration for subject S1 the total classification accuracy is improved as 9.4% and 1.7%. And also, subject 7 at BLDA and υ-SVM with LPC model algorithms (LPC+BLDA and LPC+ υ-SVM after block 11th converged to maximum accuracy but Fisher Linear Discriminant Analysis (FLDA algorithm did not converge to maximum accuracy (with the same configuration. So, it can be used as a promising tool in designing BCI systems.

  9. [An ovarian mucinous borderline tumour with mixed mural nodules].

    Science.gov (United States)

    Dhouibi, A; Denoux, Y; Touil, N; Devouassoux Shisheboran, M; Carbonnel, M; Baglin, A C

    2011-09-01

    The occurrence of mural nodules in serous or mucinous ovarian tumours is not frequent. Mural nodule can be developed in benign, borderline or malignant tumours. They can be benign, malignant or mixed type. Thus the prognosis of the ovarian tumour can be dramatically modified by the presence if these nodules. Eighty-two cases of mural nodules were reported in the literature, among which we account four cases of mixed nodules type. We report an additional case of mixed type mural nodules of anaplastic carcinoma and sarcoma-like developed in an ovarian mucinous borderline tumour at a 60-year-old woman.We give details about the classification, the differential diagnosis and prognosis of theses nodules.

  10. Parallel evolution of tumour subclones mimics diversity between tumours.

    Science.gov (United States)

    Martinez, Pierre; Birkbak, Nicolai Juul; Gerlinger, Marco; McGranahan, Nicholas; Burrell, Rebecca A; Rowan, Andrew J; Joshi, Tejal; Fisher, Rosalie; Larkin, James; Szallasi, Zoltan; Swanton, Charles

    2013-08-01

    Intratumour heterogeneity (ITH) may foster tumour adaptation and compromise the efficacy of personalized medicine approaches. The scale of heterogeneity within a tumour (intratumour heterogeneity) relative to genetic differences between tumours (intertumour heterogeneity) is unknown. To address this, we obtained 48 biopsies from eight stage III and IV clear cell renal cell carcinomas (ccRCCs) and used DNA copy-number analyses to compare biopsies from the same tumour with 440 single tumour biopsies from the Cancer Genome Atlas (TCGA). Unsupervised hierarchical clustering of TCGA and multi-region ccRCC samples revealed segregation of samples from the same tumour into unrelated clusters; 25% of multi-region samples appeared more similar to unrelated samples than to any other sample originating from the same tumour. We found that the majority of recurrent DNA copy number driver aberrations in single biopsies were not present ubiquitously in late-stage ccRCCs and were likely to represent subclonal events acquired during tumour progression. Such heterogeneous subclonal genetic alterations within individual tumours may impair the identification of robust ccRCC molecular subtypes classified by distinct copy number alterations and clinical outcomes. The co-existence of distinct subclonal copy number events in different regions of individual tumours reflects the diversification of individual ccRCCs through multiple evolutionary routes and may contribute to tumour sampling bias and impact upon tumour progression and clinical outcome.

  11. cell tumours of childhood

    African Journals Online (AJOL)

    neuron-specific-enolase, vimentin and neurofilament us- .... ated on a 4-point scale based on the number of positive cells: Negative staining (—) = no tumour cell stained. Minimal .... the same laboratory, have been shown previously to be.

  12. HELICoiD project: a new use of hyperspectral imaging for brain cancer detection in real-time during neurosurgical operations

    Science.gov (United States)

    Fabelo, Himar; Ortega, Samuel; Kabwama, Silvester; Callico, Gustavo M.; Bulters, Diederik; Szolna, Adam; Pineiro, Juan F.; Sarmiento, Roberto

    2016-05-01

    Hyperspectral images allow obtaining large amounts of information about the surface of the scene that is captured by the sensor. Using this information and a set of complex classification algorithms is possible to determine which material or substance is located in each pixel. The HELICoiD (HypErspectraL Imaging Cancer Detection) project is a European FET project that has the goal to develop a demonstrator capable to discriminate, with high precision, between normal and tumour tissues, operating in real-time, during neurosurgical operations. This demonstrator could help the neurosurgeons in the process of brain tumour resection, avoiding the excessive extraction of normal tissue and unintentionally leaving small remnants of tumour. Such precise delimitation of the tumour boundaries will improve the results of the surgery. The HELICoiD demonstrator is composed of two hyperspectral cameras obtained from Headwall. The first one in the spectral range from 400 to 1000 nm (visible and near infrared) and the second one in the spectral range from 900 to 1700 nm (near infrared). The demonstrator also includes an illumination system that covers the spectral range from 400 nm to 2200 nm. A data processing unit is in charge of managing all the parts of the demonstrator, and a high performance platform aims to accelerate the hyperspectral image classification process. Each one of these elements is installed in a customized structure specially designed for surgical environments. Preliminary results of the classification algorithms offer high accuracy (over 95%) in the discrimination between normal and tumour tissues.

  13. Extracellular matrix in tumours as a source of additional neoplastic lesions - a review

    Directory of Open Access Journals (Sweden)

    Madej Janusz A.

    2014-03-01

    Full Text Available The review describes the role of cells of extracellular matrix (ECM as a source of neoplastic outgrowths additional to the original tumour. The cells undergo a spontaneous transformation or stimulation by the original tumour through intercellular signals, e.g. through Shh protein (sonic hedgehog. Additionally, cells of an inflammatory infiltrate, which frequently accompany malignant tumours and particularly carcinomas, may regulate tumour cell behaviour. This is either by restricting tumour proliferation or, inversely, by induction and stimulation of the proliferation of another tumour cell type, e.g. mesenchymal cells. The latter type of tumour may involve formation of histologically differentiated stromal tumours (GIST, which probably originate from interstitial cells of Cajal in the alimentary tract. Occasionally, e.g. in gastric carcinoma, proliferation involves lymphoid follicles and lymphocytes of GALT (gut-associated lymphoid tissue, which gives rise to lymphoma. The process is preceded by the earlier stage of intestinal metaplasia, or is induced by gastritis alone. This is an example of primary involvement of inflammatory infiltrate cells in neoplastic progression. Despite the numerous histogenetic classifications of tumours (zygotoma benignum et zygotoma malignum, or mesenchymomata maligna et mesenchymomata benigna, currently in oncological diagnosis the view prevails that the direction of tumour differentiation and its degree of histologic malignancy (grading are more important factors than the histogenesis of the tumour.

  14. Tumour exosome integrins determine organotropic metastasis.

    Science.gov (United States)

    Hoshino, Ayuko; Costa-Silva, Bruno; Shen, Tang-Long; Rodrigues, Goncalo; Hashimoto, Ayako; Tesic Mark, Milica; Molina, Henrik; Kohsaka, Shinji; Di Giannatale, Angela; Ceder, Sophia; Singh, Swarnima; Williams, Caitlin; Soplop, Nadine; Uryu, Kunihiro; Pharmer, Lindsay; King, Tari; Bojmar, Linda; Davies, Alexander E; Ararso, Yonathan; Zhang, Tuo; Zhang, Haiying; Hernandez, Jonathan; Weiss, Joshua M; Dumont-Cole, Vanessa D; Kramer, Kimberly; Wexler, Leonard H; Narendran, Aru; Schwartz, Gary K; Healey, John H; Sandstrom, Per; Labori, Knut Jørgen; Kure, Elin H; Grandgenett, Paul M; Hollingsworth, Michael A; de Sousa, Maria; Kaur, Sukhwinder; Jain, Maneesh; Mallya, Kavita; Batra, Surinder K; Jarnagin, William R; Brady, Mary S; Fodstad, Oystein; Muller, Volkmar; Pantel, Klaus; Minn, Andy J; Bissell, Mina J; Garcia, Benjamin A; Kang, Yibin; Rajasekhar, Vinagolu K; Ghajar, Cyrus M; Matei, Irina; Peinado, Hector; Bromberg, Jacqueline; Lyden, David

    2015-11-19

    Ever since Stephen Paget's 1889 hypothesis, metastatic organotropism has remained one of cancer's greatest mysteries. Here we demonstrate that exosomes from mouse and human lung-, liver- and brain-tropic tumour cells fuse preferentially with resident cells at their predicted destination, namely lung fibroblasts and epithelial cells, liver Kupffer cells and brain endothelial cells. We show that tumour-derived exosomes uptaken by organ-specific cells prepare the pre-metastatic niche. Treatment with exosomes from lung-tropic models redirected the metastasis of bone-tropic tumour cells. Exosome proteomics revealed distinct integrin expression patterns, in which the exosomal integrins α6β4 and α6β1 were associated with lung metastasis, while exosomal integrin αvβ5 was linked to liver metastasis. Targeting the integrins α6β4 and αvβ5 decreased exosome uptake, as well as lung and liver metastasis, respectively. We demonstrate that exosome integrin uptake by resident cells activates Src phosphorylation and pro-inflammatory S100 gene expression. Finally, our clinical data indicate that exosomal integrins could be used to predict organ-specific metastasis.

  15. Survival of patients with nonseminomatous germ cell cancer: A review of the IGCC classification by Cox regression and recursive partitioning

    NARCIS (Netherlands)

    M.R. van Dijk (Merel); E.W. Steyerberg (Ewout); S.P. Stenning; E. Dusseldorp (Elise); J.D.F. Habbema (Dik)

    2004-01-01

    textabstractThe International Germ Cell Consensus (IGCC) classification identifies good, intermediate and poor prognosis groups among patients with metastatic nonseminomatous germ cell tumours (NSGCT). It uses the risk factors primary site, presence of nonpulmonary visceral metastases and tumour mar

  16. Malignant salivary gland tumours

    Energy Technology Data Exchange (ETDEWEB)

    Thompson, S.H. (University of the Witwatersrand, Johannesburg (South Africa). Dept. of Oral Pathology)

    1982-08-01

    The most frequent malignant salivary gland tumours are the mucoepidermoid tumour, adenoid cystic carcinoma and adenocarcinoma. The major salivary glands and the minor glands of the mouth and upper respiratory tract may potentially develop any of these malignant lesions. Malignant lesions most frequently present as a palpable mass and tend to enlarge more rapidly than benign neoplasms. Pain, paresthesia, muscle paralysis and fixation to surrounding tissue are all ominous signs and symptoms. The only reliable means of differential diagnosis of these lesions is biopsy and histologic analysis. Therapy involves surgery or a combination of surgery and radiation therapy. The ultimate prognosis is governed by the intrinsic biologic behaviour of the neoplasms, the extent of disease and adequate clinical therapy.

  17. Skull base tumours

    Energy Technology Data Exchange (ETDEWEB)

    Borges, Alexandra [Instituto Portugues de Oncologia Francisco Gentil, Servico de Radiologia, Rua Professor Lima Basto, 1093 Lisboa Codex (Portugal)], E-mail: borgesalexandra@clix.pt

    2008-06-15

    With the advances of cross-sectional imaging radiologists gained an increasing responsibility in the management of patients with skull base pathology. As this anatomic area is hidden to clinical exam, surgeons and radiation oncologists have to rely on imaging studies to plan the most adequate treatment. To fulfil these endeavour radiologists need to be knowledgeable about skull base anatomy, about the main treatment options available, their indications and contra-indications and needs to be aware of the wide gamut of pathologies seen in this anatomic region. This article will provide a radiologists' friendly approach to the central skull base and will review the most common central skull base tumours and tumours intrinsic to the bony skull base.

  18. Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment

    Science.gov (United States)

    Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; Lu, Chengbiao

    2016-01-01

    At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals. PMID:27458376

  19. Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment.

    Science.gov (United States)

    Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; Lu, Chengbiao

    2016-01-01

    At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals.

  20. Enhancing the classification accuracy of steady-state visual evoked potential-based brain-computer interfaces using phase constrained canonical correlation analysis

    Science.gov (United States)

    Pan, Jie; Gao, Xiaorong; Duan, Fang; Yan, Zheng; Gao, Shangkai

    2011-06-01

    In this study, a novel method of phase constrained canonical correlation analysis (p-CCA) is presented for classifying steady-state visual evoked potentials (SSVEPs) using multichannel electroencephalography (EEG) signals. p-CCA is employed to improve the performance of the SSVEP-based brain-computer interface (BCI) system using standard CCA. SSVEP response phases are estimated based on the physiologically meaningful apparent latency and are added as a reliable constraint into standard CCA. The results of EEG experiments involving 10 subjects demonstrate that p-CCA consistently outperforms standard CCA in classification accuracy. The improvement is up to 6.8% using 1-4 s data segments. The results indicate that the reliable measurement of phase information is of importance in SSVEP-based BCIs.

  1. Distribution, characterization and clinical significance of microglia in glioneuronal tumours from patients with chronic intractable epilepsy

    NARCIS (Netherlands)

    Aronica, E.; Gorter, J.A.; Redeker, S; Ramkema, M.; Spliets, W.G.M.; van Rijen, P.C.; Leenstra, S.; Troost, D.

    2005-01-01

    Cells of the microglia/macrophage lineage represent an important component of different brain tumours. However, there is little information about the microglia/macrophage cell system in glioneuronal tumours and its possible contribution to the high epileptogenecity of these lesions. In the present

  2. The feasibility of a brain tumour website

    DEFF Research Database (Denmark)

    Piil, K; Jakobsen, J; Juhler, M

    2015-01-01

    one overarching theme 'challenges and barriers'. Being newly diagnosed, patients described a chaotic and overwhelming life situation and had difficulties in identifying with their new and changed role. When using the BTW, some patients and caregivers experienced technological challenges, while...

  3. A Subset of Cerebrospinal Fluid Proteins from a Multi-Analyte Panel Associated with Brain Atrophy, Disease Classification and Prediction in Alzheimer's Disease.

    Science.gov (United States)

    Khan, Wasim; Aguilar, Carlos; Kiddle, Steven J; Doyle, Orla; Thambisetty, Madhav; Muehlboeck, Sebastian; Sattlecker, Martina; Newhouse, Stephen; Lovestone, Simon; Dobson, Richard; Giampietro, Vincent; Westman, Eric; Simmons, Andrew

    2015-01-01

    In this exploratory neuroimaging-proteomic study, we aimed to identify CSF proteins associated with AD and test their prognostic ability for disease classification and MCI to AD conversion prediction. Our study sample consisted of 295 subjects with CSF multi-analyte panel data and MRI at baseline downloaded from ADNI. Firstly, we tested the statistical effects of CSF proteins (n = 83) to measures of brain atrophy, CSF biomarkers, ApoE genotype and cognitive decline. We found that several proteins (primarily CgA and FABP) were related to either brain atrophy or CSF biomarkers. In relation to ApoE genotype, a unique biochemical profile characterised by low CSF levels of Apo E was evident in ε4 carriers compared to ε3 carriers. In an exploratory analysis, 3/83 proteins (SGOT, MCP-1, IL6r) were also found to be mildly associated with cognitive decline in MCI subjects over a 4-year period. Future studies are warranted to establish the validity of these proteins as prognostic factors for cognitive decline. For disease classification, a subset of proteins (n = 24) combined with MRI measurements and CSF biomarkers achieved an accuracy of 95.1% (Sensitivity 87.7%; Specificity 94.3%; AUC 0.95) and accurately detected 94.1% of MCI subjects progressing to AD at 12 months. The subset of proteins included FABP, CgA, MMP-2, and PPP as strong predictors in the model. Our findings suggest that the marker of panel of proteins identified here may be important candidates for improving the earlier detection of AD. Further targeted proteomic and longitudinal studies would be required to validate these findings with more generalisability.

  4. Classification accuracy of the Millon Clinical Multiaxial Inventory-III modifier indices in the detection of malingering in traumatic brain injury.

    Science.gov (United States)

    Aguerrevere, Luis E; Greve, Kevin W; Bianchini, Kevin J; Ord, Jonathan S

    2011-06-01

    The present study used criterion groups validation to determine the ability of the Millon Clinical Multiaxial Inventory-III (MCMI-III) modifier indices to detect malingering in traumatic brain injury (TBI). Patients with TBI who met criteria for malingered neurocognitive dysfunction (MND) were compared to those who showed no indications of malingering. Data were collected from 108 TBI patients referred for neuropsychological evaluation. Base rate (BR) scores were used for MCMI-III modifier indices: Disclosure, Desirability, and Debasement. Malingering classification was based on the Slick, Sherman, and Iverson (1999) criteria for MND. TBI patients were placed in one of three groups: MND (n = 55), not-MND (n = 26), or Indeterminate (n = 26).The not-MND group had lower modifier index scores than the MND group. At scores associated with a 4% false-positive (FP) error rate, sensitivity was 47% for Disclosure, 51% for Desirability, and 55% for Debasement. Examination of joint classification analysis demonstrated 54% sensitivity at cutoffs associated with 0% FP error rate. Results suggested that scores from all MCMI-III modifier indices are useful for identifying intentional symptom exaggeration in TBI. Debasement was the most sensitive of the three indices. Clinical implications are discussed.

  5. Fibre intake and incident colorectal cancer depending on fibre source, sex, tumour location and Tumour, Node, Metastasis stage.

    Science.gov (United States)

    Vulcan, Alexandra; Brändstedt, Jenny; Manjer, Jonas; Jirström, Karin; Ohlsson, Bodil; Ericson, Ulrika

    2015-09-28

    Studies on fibre intake and incident colorectal cancer (CRC) indicate inverse associations. Differences by tumour stage have not been examined. We examined associations between fibre intake and its sources, and incidental CRC. Separate analyses were carried out on the basis of sex, tumour location and the Tumour, Node, Metastasis (TNM) classification. The Malmö Diet and Cancer Study is a population-based cohort study, including individuals aged 45-74 years. Dietary data were collected through a modified diet history method. The TNM classification was obtained from pathology/clinical records and re-evaluated. Among 27 931 individuals (60% women), we found 728 incident CRC cases during 428 924 person-years of follow-up. Fibre intake was inversely associated with CRC risk (P(trend) = 0.026). Concerning colon cancer, we observed borderline interaction between fibre intake and sex (P = 0.052) and significant protective association restricted to women (P(trend) = 0.013). Intake of fruits and berries was inversely associated with colon cancer in women (P(trend) = 0.022). We also observed significant interactions between intakes of fibre (P = 0.048) and vegetables (P = 0.039) and sex on rectal cancer, but no significant associations were seen between intake of fibre, or its sources, in either of the sexes. Except for inverse associations between intake of fibre-rich cereal products and N0- and M0-tumours, we did not observe significant associations with different TNM stages. Our findings suggest different associations between fibre intake and CRC depending on sex, tumour site and fibre source. High fibre intake, especially from fruits and berries, may, above all, prevent tumour development in the colon in women. No clear differences by TNM classification were detected.

  6. Research of signal classification for brain-computer interface%模拟阅读型脑-机接口信号分类研究

    Institute of Scientific and Technical Information of China (English)

    朱学才; 李梅; 邹思轩

    2011-01-01

    脑-机接口(BCI)研究中的一个关键问题是如何正确地对EEG信号进行模式分类,以输出控制命令.本文在对“模拟自然阅读”模式下非靶刺激和靶刺激诱发的EEG进行去均值、低通滤波、下采样和归一化等处理后,结合对视觉诱发事件相关电位时域特征分析,提取出最佳特征量,分别利用BP神经网络和线性感知器算法对这些特征模式进行了分类.最终平均识别正确率分别达到87%和84%以上.对比研究表明,BP神经网络算法的分类效果较好,推测这是由于大部分EEG模式线性可分,只有10%左右线性不可分但非线性可分造成的.为提高分类正确率,简化BCI设计,详细研究了信号时程、时段的选择以及通道的选取对模式分类精度的影响.结果表明,信号时程越长分类精度越高;信号时段的选择对分类精度亦有较大的影响.通过实验发现:32个通道中,选取第14(PO3)通道的EEG进行模式分类的精度最高.%In order to produce the output of command, a key issue in brain-computer interface (BCD is to classify EEG signals correctly. EEG signal preprocessing were discussed in this paper, which include the lowpass filtering, baseline removing, down-sampling and normalization, et al. The EEG signals were recorded in an "Imitating Nature Reading" modality. The optimal feature patterns were extracted basing on the analyses in temporal and frequency for the Visual Evoked Event Related Potentials, and then BP Neural Networks and the perceptron approach were used separately to classify these patterns. The best classification results on testing set revealed an accuracy of more than 87% and 84 % respectively. The effects of classification by BP Neural Networks were better than those by perceptron approach, which revealed that about 90% EEG patterns are linearly separable while other 10% are linearly inseparable but maybe non-linearly separable. In order to get a better accuracy of

  7. Comparison of Pre-Processing and Classification Techniques for Single-Trial and Multi-Trial P300-Based Brain Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Chanan S. Syan

    2010-01-01

    Full Text Available The P300 component of Event Related Brain Potentials (ERP is commonly used in Brain Computer Interfaces (BCI to translate the intentions of an individual into commands for external devices. The P300 response, however, resides in a signal environment of high background noise. Consequently, the main problem in developing a P300-based BCI lies in identifying the P300 response in the presence of this noise. Traditionally, attenuating the background activity of P300 data is done by averaging multiple trials of recorded signals. This method, though effective, suffers two drawbacks. First, collecting multiple trials of data is time consuming and delays the BCI response. Second, latency distortions may appear in the averaged result due to variable time-locking of the P300 in the individual trials. Problem statement: The use of single-trial P300 data overcomes both these shortcomings. However, single-trial data must be properly denoised to allow for reliable BCI operation. Single-trial P300-based BCIs have been implemented using a variety of signal processing techniques and classification methodologies. However, comparing the accuracies of these systems to other multi-trial systems is likely to include the comparison of more than just the trial format (single-trial/multi-trial as the data quality and recording circumstances are likely to be dissimilar. Approach: This issue was directly addressed by comparing the performance comparison of three different preprocessing agents and three classification methodologies on the same data set over both the single-trial and multi-trial settings. The P300 data set of BCI Competition II was used to facilitate this comparison. Results: The LDA classifier exhibited the best performance in classifying unseen P300 spatiotemporal features in both the single-trial (74.19% and multi-trial format (100%. It is also very efficient in terms of computational and memory requirements. Conclusion: This study can serve as a general

  8. EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer Interface

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    In this study, we propose a method of classifying a spontaneous electroencephalogram (EEG) approach to a brain-computer interface. Ten subjects, aged 21-32 years, volunteered to imagine left-and right- hand movements. An independent component analysis based on a fixed-point algorithm is used to eliminate the activities found in the EEG signals. We use a fractal dimension value to reveal the embedded potential responses in the human brain. The different fractal dimension values between the relaxing and imaging periods are computed. Featured data is classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Two conventional methods, namely, the use of the autoregressive (AR) model and the band power estimation (BPE) as features, and the linear discriminant analysis (LDA) as a classifier, are selected for comparison in this study. Experimental results show that the proposed method is more effective than the conventional methods.

  9. Low grade epithelial stromal tumour of the seminal vesicle

    Directory of Open Access Journals (Sweden)

    Pozzoli Gianluigi

    2008-09-01

    Full Text Available Abstract Background The mixed epithelial stromal tumour is morphologically characterised by a mixture of solid and cystic areas consisting of a biphasic proliferation of glands admixed with solid areas of spindle cells with variable cellularity and growth patterns. In previous reports the seminal vesicle cystoadenoma was either considered a synonym of or misdiagnosed as mixed epithelial stromal tumour. The recent World Health Organisation Classification of Tumours considered the two lesions as two distinct neoplasms. This work is aimed to present the low-grade epithelial stromal tumour case and the review of the literature to the extent of establishing the true frequency of the neoplasm. Case presentation We describe a low-grade epithelial stromal tumour of the seminal vesicle in a 50-year-old man. Computed tomography showed a 9 × 4.5 cm pelvic mass in the side of the seminal vesicle displacing the prostate and the urinary bladder. Magnetic resonance was able to define tissue planes between the lesion and the adjacent structures and provided useful information for an accurate conservative laparotomic surgical approach. The histology revealed biphasic proliferation of benign glands admixed with stromal cellularity, with focal atypia. After 26 months after the excision the patient is still alive with no evidence of disease. Conclusion Cystoadenoma and mixed epithelial stromal tumour of seminal vesicle are two distinct pathological entities with different histological features and clinical outcome. Due to the unavailability of accurate prognostic parameters, the prediction of the potential biological evolution of mixed epithelial stromal tumour is still difficult. In our case magnetic resonance imaging was able to avoid an exploratory laparotomy and to establish an accurate conservative surgical treatment of the tumour.

  10. Vaginal haemangioendothelioma: an unusual tumour.

    LENUS (Irish Health Repository)

    Mohan, H

    2012-02-01

    Vaginal tumours are uncommon and this is a particularly rare case of a vaginal haemangioendothelioma in a 38-year-old woman. Initial presentation consisted of symptoms similar to uterovaginal prolapse with "something coming down". Examination under anaesthesia demonstrated a necrotic anterior vaginal wall tumour. Histology of the lesion revealed a haemangioendothelioma which had some features of haemangiopericytoma. While the natural history of vaginal haemangioendothelioma is uncertain, as a group, they have a propensity for local recurrence. To our knowledge this is the third reported case of a vaginal haemangioendothelioma. Management of this tumour is challenging given the paucity of literature on this tumour. There is a need to add rare tumours to our "knowledge bank" to guide management of these unusual tumours.

  11. Primary bone tumours in infants

    Energy Technology Data Exchange (ETDEWEB)

    Kozlowski, K.; Beluffi, G.; Cohen, D.H.; Padovani, J.; Tamaela, L.; Azouz, M.; Bale, P.; Martin, H.C.; Nayanar, V.V.; Arico, M.

    1985-09-01

    Ten cases of primary bone tumours in infants (1 osteosarcoma, 3 Ewing's sarcoma, 1 chondroblastoma and 5 angiomastosis) are reported. All cases of angiomatosis showed characteristic radiographic findings. In all the other tumours the X-ray appearances were different from those usually seen in older children and adolescents. In the auhtors' opinion the precise diagnosis of malignant bone tumours in infancy is very difficult as no characteristic X-ray features are present in this age period.

  12. Simulating tumour removal in neurosurgery.

    Science.gov (United States)

    Radetzky, A; Rudolph, M

    2001-12-01

    In this article the software system ROBO-SIM is described. ROBO-SIM is a planning and simulation tool for minimally invasive neurosurgery. Different to the most other simulation tools, ROBO-SIM is able to use actual patient's datasets for simulation. Same as in real neurosurgery a planning step, which provides more functionality as up-to-date planning systems on the market, is performed before undergoing the simulated operation. The planning steps include the definition of the trepanation point for entry into the skull and the target point within the depth of the brain, checking the surgical track and doing virtual trepanations (virtual craniotomy). For use with an intra-operative active manipulator, which is guided by the surgeon during real surgery (robotic surgery), go- and non-go-areas can be defined. During operation, the robot restricts the surgeon from leaving these go-areas. After planning, an additional simulation system, which is understood as an extension to the planning step, is used to simulate whole surgical interventions directly on the patient's anatomy basing on the planning data and by using the same instruments as for the real intervention. First tests with ROBO-SIM are performed on a phantom developed for this purpose and on actual patient's datasets with ventricular tumours.

  13. LET-painting increases tumour control probability in hypoxic tumours.

    Science.gov (United States)

    Bassler, Niels; Toftegaard, Jakob; Lühr, Armin; Sørensen, Brita Singers; Scifoni, Emanuele; Krämer, Michael; Jäkel, Oliver; Mortensen, Lise Saksø; Overgaard, Jens; Petersen, Jørgen B

    2014-01-01

    LET-painting was suggested as a method to overcome tumour hypoxia. In vitro experiments have demonstrated a well-established relationship between the oxygen enhancement ratio (OER) and linear energy transfer (LET), where OER approaches unity for high-LET values. However, high-LET radiation also increases the risk for side effects in normal tissue. LET-painting attempts to restrict high-LET radiation to compartments that are found to be hypoxic, while applying lower LET radiation to normoxic tissues. Methods. Carbon-12 and oxygen-16 ion treatment plans with four fields and with homogeneous dose in the target volume, are applied on an oropharyngeal cancer case with an identified hypoxic entity within the tumour. The target dose is optimised to achieve a tumour control probability (TCP) of 95% when assuming a fully normoxic tissue. Using the same primary particle energy fluence needed for this plan, TCP is recalculated for three cases assuming hypoxia: first, redistributing LET to match the hypoxic structure (LET-painting). Second, plans are recalculated for varying hypoxic tumour volume in order to investigate the threshold volume where TCP can be established. Finally, a slight dose boost (5-20%) is additionally allowed in the hypoxic subvolume to assess its impact on TCP. Results. LET-painting with carbon-12 ions can only achieve tumour control for hypoxic subvolumes smaller than 0.5 cm(3). Using oxygen-16 ions, tumour control can be achieved for tumours with hypoxic subvolumes of up to 1 or 2 cm(3). Tumour control can be achieved for tumours with even larger hypoxic subvolumes, if a slight dose boost is allowed in combination with LET-painting. Conclusion. Our findings clearly indicate that a substantial increase in tumour control can be achieved when applying the LET-painting concept using oxygen-16 ions on hypoxic tumours, ideally with a slight dose boost.

  14. Overexpression of Eag1 potassium channels in clinical tumours

    Directory of Open Access Journals (Sweden)

    Schliephacke Tessa

    2006-10-01

    Full Text Available Abstract Background Certain types of potassium channels (known as Eag1, KCNH1, Kv10.1 are associated with the production of tumours in patients and in animals. We have now studied the expression pattern of the Eag1 channel in a large range of normal and tumour tissues from different collections utilising molecular biological and immunohistochemical techniques. Results The use of reverse transcription real-time PCR and specifically generated monoclonal anti-Eag1 antibodies showed that expression of the channel is normally limited to specific areas of the brain and to restricted cell populations throughout the body. Tumour samples, however, showed a significant overexpression of the channel with high frequency (up to 80% depending on the tissue source regardless of the detection method (staining with either one of the antibodies, or detection of Eag1 RNA. Conclusion Inhibition of Eag1 expression in tumour cell lines reduced cell proliferation. Eag1 may therefore represent a promising target for the tailored treatment of human tumours. Furthermore, as normal cells expressing Eag1 are either protected by the blood-brain barrier or represent the terminal stage of normal differentiation, Eag1 based therapies could produce only minor side effects.

  15. A short history of neuroendocrine tumours and their peptide hormones.

    Science.gov (United States)

    de Herder, Wouter W; Rehfeld, Jens F; Kidd, Mark; Modlin, Irvin M

    2016-01-01

    The discovery of neuroendocrine tumours of the gastrointestinal tract and pancreas started in 1870, when Rudolf Heidenhain discovered the neuroendocrine cells, which can lead to the development of these tumours. Siegfried Oberndorfer was the first to introduce the term carcinoid in 1907. The pancreatic islet cells were first described in 1869 by Paul Langerhans. In 1924, Seale Harris was the first to describe endogenous hyperinsulinism/insulinoma. In 1942 William Becker and colleagues were the first to describe the glucagonoma syndrome. The first description of gastrinoma by Robert Zollinger and Edwin Ellison dates from 1955. The first description of the VIPoma syndrome by John Verner and Ashton Morrison dates from 1958. In 1977, the groups of Lars-Inge Larsson and Jens Rehfeld, and of Om Ganda reported the first cases of somatostatinoma. But only in 2013, Jens Rehfeld and colleagues described the CCK-oma syndrome. The most recently updated WHO classification for gastrointestinal neuroendocrine tumours dates from 2010.

  16. Toward more intuitive brain-computer interfacing: classification of binary covert intentions using functional near-infrared spectroscopy

    Science.gov (United States)

    Hwang, Han-Jeong; Choi, Han; Kim, Jeong-Youn; Chang, Won-Du; Kim, Do-Won; Kim, Kiwoong; Jo, Sungho; Im, Chang-Hwan

    2016-09-01

    In traditional brain-computer interface (BCI) studies, binary communication systems have generally been implemented using two mental tasks arbitrarily assigned to "yes" or "no" intentions (e.g., mental arithmetic calculation for "yes"). A recent pilot study performed with one paralyzed patient showed the possibility of a more intuitive paradigm for binary BCI communications, in which the patient's internal yes/no intentions were directly decoded from functional near-infrared spectroscopy (fNIRS). We investigated whether such an "fNIRS-based direct intention decoding" paradigm can be reliably used for practical BCI communications. Eight healthy subjects participated in this study, and each participant was administered 70 disjunctive questions. Brain hemodynamic responses were recorded using a multichannel fNIRS device, while the participants were internally expressing "yes" or "no" intentions to each question. Different feature types, feature numbers, and time window sizes were tested to investigate optimal conditions for classifying the internal binary intentions. About 75% of the answers were correctly classified when the individual best feature set was employed (75.89% ±1.39 and 74.08% ±2.87 for oxygenated and deoxygenated hemoglobin responses, respectively), which was significantly higher than a random chance level (68.57% for pcommunication systems for patients with motor disabilities.

  17. Identification of genes involved in the biology of atypical teratoid/rhabdoid tumours using Drosophila melanogaster

    Science.gov (United States)

    Jeibmann, Astrid; Eikmeier, Kristin; Linge, Anna; Kool, Marcel; Koos, Björn; Schulz, Jacqueline; Albrecht, Stefanie; Bartelheim, Kerstin; Frühwald, Michael C.; Pfister, Stefan M.; Paulus, Werner; Hasselblatt, Martin

    2014-06-01

    Atypical teratoid/rhabdoid tumours (AT/RT) are malignant brain tumours. Unlike most other human brain tumours, AT/RT are characterized by inactivation of one single gene, SMARCB1. SMARCB1 is a member of the evolutionarily conserved SWI/SNF chromatin remodelling complex, which has an important role in the control of cell differentiation and proliferation. Little is known, however, about the pathways involved in the oncogenic effects of SMARCB1 inactivation, which might also represent targets for treatment. Here we report a comprehensive genetic screen in the fruit fly that revealed several genes not yet associated with loss of snr1, the Drosophila homologue of SMARCB1. We confirm the functional role of identified genes (including merlin, kibra and expanded, known to regulate hippo signalling pathway activity) in human rhabdoid tumour cell lines and AT/RT tumour samples. These results demonstrate that fly models can be employed for the identification of clinically relevant pathways in human cancer.

  18. Radiotherapy by particle beams (hadrontherapy) of intracranial tumours: a survey on pathology.

    Science.gov (United States)

    Schiffer, D

    2005-04-01

    A review of the principal contributions of radio-therapy of brain tumours by beam particles is carried out. Neutrons, protons and light ions are considered along with their pros and cons in relation to types and locations of brain tumours. A particular emphasis is given to the pathologic studies of their effects directly o n tumours and on the normal nervous tissue, considering mainly the relevant action mechanisms of the radiation types and the requirements of the clinical therapeutic strategies. For comparison the main features of the pathologic effects of radiotherapy by photons are described. From the review it emerges that the new modality of radiation by protons and light ions, because of their peculiar physical characteristics, may represent a new way of destroying the tumour and sparing normal nervous tissue, especially when deeply located and irregularly shaped tumours are concerned. More neuropathological studies are needed in order to better understand the potentiality of the new treatment of modalities.

  19. A pathological and clinical study of 706 primary tumours of the ovary in the largest tertiary hospital in Ghana.

    Science.gov (United States)

    Akakpo, Patrick Kafui; Derkyi-Kwarteng, Leonard; Gyasi, Richard Kwasi; Quayson, Solomom Edward; Naporo, Simon; Anim, Jehoram Tei

    2017-04-17

    Ovarian tumours are a leading cause of death in Ghana. Even though geographical and racial differences exist in the frequency, types and age distribution of primary ovarian tumours, information about the clinical and pathological characteristics of ovarian tumours in Ghana and its neighboring countries is scanty. We determined the frequency, age distribution, histopathological types and clinical features of primary ovarian tumours diagnosed at the Korle-Bu Teaching Hospital in Ghana to aid in the management of patients. All pathology records of ovarian tumours diagnosed from January 2001 to December 2010 were reviewed. Histopathologically, tumours were classified according to the then World Health Organization 1999 classification. Biographical and clinical data of patients were also collected and entered into Epi-info to determine the frequency, age distribution and other clinical features of the types of ovarian tumour. Seven hundred and six ovarian tumours were studied. Germ cell tumours were the most common (41.9%), with mean age of occurrence being 30.7 years (SD 12.7), they were dominated by mature teratomas (39.2%). Surface epithelial tumours were second, and commonly occurred in women aged 35-44years, 77 (26.8%). Sex cord stromal tumours followed with mean age of occurrence of 40.2 years (SD 17.9). The most common malignant tumours were surface epithelial (52.1%) dominated by serous carcinomas with mean age 50.1 years. Most patients (47.7%) presented within 1 month of onset of symptoms, feeling a lower abdominal mass (38.5%). The most common primary ovarian tumours in this study are Germ cell tumours, dominated by mature teratomas. Adenocarcinomas are mostly serous and occur in younger women compared to findings of other Western studies. The single most common malignant ovarian tumour in children and adolescents is Burkitt lymphoma. Patients who develop ovarian tumours have no specific symptoms or signs at presentation, to aid early diagnosis.

  20. Brain source localization: a new method based on MUltiple SIgnal Classification algorithm and spatial sparsity of the field signal for electroencephalogram measurements.

    Science.gov (United States)

    Vergallo, P; Lay-Ekuakille, A

    2013-08-01

    Brain activity can be recorded by means of EEG (Electroencephalogram) electrodes placed on the scalp of the patient. The EEG reflects the activity of groups of neurons located in the head, and the fundamental problem in neurophysiology is the identification of the sources responsible of brain activity, especially if a seizure occurs and in this case it is important to identify it. The studies conducted in order to formalize the relationship between the electromagnetic activity in the head and the recording of the generated external field allow to know pattern of brain activity. The inverse problem, that is given the sampling field at different electrodes the underlying asset must be determined, is more difficult because the problem may not have a unique solution, or the search for the solution is made difficult by a low spatial resolution which may not allow to distinguish between activities involving sources close to each other. Thus, sources of interest may be obscured or not detected and known method in source localization problem as MUSIC (MUltiple SIgnal Classification) could fail. Many advanced source localization techniques achieve a best resolution by exploiting sparsity: if the number of sources is small as a result, the neural power vs. location is sparse. In this work a solution based on the spatial sparsity of the field signal is presented and analyzed to improve MUSIC method. For this purpose, it is necessary to set a priori information of the sparsity in the signal. The problem is formulated and solved using a regularization method as Tikhonov, which calculates a solution that is the better compromise between two cost functions to minimize, one related to the fitting of the data, and another concerning the maintenance of the sparsity of the signal. At the first, the method is tested on simulated EEG signals obtained by the solution of the forward problem. Relatively to the model considered for the head and brain sources, the result obtained allows to

  1. Integrated multi-omics analysis of oligodendroglial tumours identifies three subgroups of 1p/19q co-deleted gliomas

    OpenAIRE

    Kamoun, Aurélie; Idbaih, Ahmed; Dehais, Caroline; Elarouci, Nabila; Carpentier, Catherine; Letouzé, Eric; Colin, Carole; Mokhtari, Karima; Jouvet, Anne; Uro-Coste, Emmanuelle; Martin-Duverneuil, Nadine; Sanson, Marc; Delattre, Jean-Yves; Figarella-Branger, Dominique; de Reyniès, Aurélien

    2016-01-01

    POLA Network; International audience; Oligodendroglial tumours (OT) are a heterogeneous group of gliomas. Three molecular subgroups are currently distinguished on the basis of the IDH mutation and 1p/19q co-deletion. Here we present an integrated analysis of the transcriptome, genome and methylome of 156 OT. Not only does our multi-omics classification match the current classification but also reveals three subgroups within 1p/19q co-deleted tumours, associated with specific expression patter...

  2. Stem cell research points the way to the cell of origin for intracranial germ cell tumours.

    Science.gov (United States)

    Tan, Chris; Scotting, Paul J

    2013-01-01

    Germ cell tumours found in the brain (intracranial GCTs) are a very unusual class of tumour for two reasons. First, they include a very diverse range of histological subtypes classified together due to their proposed common cell of origin. Second, this proposed cell of origin, the germ cell progenitor, would not normally be found in the tissue where these tumours arise. This is in contrast to all other primary brain tumours, in which the cell of origin is believed to be a brain cell. Indeed, no other class of primary cancer arises from a cell from a distant organ. This theory for the origins of intracranial GCTs has been in place for many decades, but recent data arising from studies of induced pluripotency for regenerative medicine raise serious questions about this dogma. Here we review the cellular origins of intracranial GCTs in the light of these new data and reanalyse the existing data on the biology of this unusual class of tumours. Together, these considerations lead us to conclude that the evidence now falls in favour of a model in which these tumours arise from the transformation of endogenous brain cells. This theory should inform future studies of the aetiology of these tumours and so lead the way to animal models in which to study their development and potential biological therapeutics. Copyright © 2012 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

  3. Testicular germ cell tumours in dogs are predominantly of spermatocytic seminoma type and are frequently associated with somatic cell tumours

    DEFF Research Database (Denmark)

    Bush, J M; Gardiner, D W; Palmer, J S

    2011-01-01

    Unlike seminomas in humans, seminomas in animals are not typically sub-classified as classical or spermatocytic types. To compare testicular germ cell tumours (TGCT) in dogs with those of men, archived tissues from 347 cases of canine testicular tumours were morphologically evaluated...... and characterized using human classification criteria. Histopathological and immunohistological analysis of PLAP, KIT, DAZ and DMRT1 expression revealed that canine seminomas closely resemble human spermatocytic seminomas. In addition, a relatively frequent concomitant presence of somatic cell tumours was noted...... in canine TGCT. None of the canine TGCT evaluated demonstrated the presence of carcinoma in situ cells, a standard feature of human classical seminomas, suggesting that classical seminomas either do not occur in dogs or are rare in occurrence. Canine spermatocytic seminomas may provide a useful model...

  4. A novel Brain Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study.

    Science.gov (United States)

    Amaral, Carlos P; Simões, Marco A; Mouga, Susana; Andrade, João; Castelo-Branco, Miguel

    2017-10-01

    We present a novel virtual-reality P300-based Brain Computer Interface (BCI) paradigm using social cues to direct the focus of attention. We combined interactive immersive virtual-reality (VR) technology with the properties of P300 signals in a training tool which can be used in social attention disorders such as autism spectrum disorder (ASD). We tested the novel social attention training paradigm (P300-based BCI paradigm for rehabilitation of joint-attention skills) in 13 healthy participants, in 3 EEG systems. The more suitable setup was tested online with 4 ASD subjects. Statistical accuracy was assessed based on the detection of P300, using spatial filtering and a Naïve-Bayes classifier. We compared: 1 - g.Mobilab+ (active dry-electrodes, wireless transmission); 2 - g.Nautilus (active electrodes, wireless transmission); 3 - V-Amp with actiCAP Xpress dry-electrodes. Significant statistical classification was achieved in all systems. g.Nautilus proved to be the best performing system in terms of accuracy in the detection of P300, preparation time, speed and reported comfort. Proof of concept tests in ASD participants proved that this setup is feasible for training joint attention skills in ASD. This work provides a unique combination of 'easy-to-use' BCI systems with new technologies such as VR to train joint-attention skills in autism. Our P300 BCI paradigm is feasible for future Phase I/II clinical trials to train joint-attention skills, with successful classification within few trials, online in ASD participants. The g.Nautilus system is the best performing one to use with the developed BCI setup. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Tumours in the Small Bowel

    Directory of Open Access Journals (Sweden)

    N. Kurniawan

    2014-01-01

    Full Text Available Small bowel tumours are rare and originate from a wide variety of benign and malignant entities. Adenocarcinomas are the most frequent primary malignant small bowel tumours. Submucosal tumours like gastrointestinal stromal tumours (GIST or neuroendocrine tumours (NET may show a central umbilication, pathologic vessels, bridging folds or an ulceration of the overlying mucosa. These signs help to differentiate them from harmless bulges caused by impression from outside, e.g. from other intestinal loops. Sarcomas of the small bowel are rare neoplasias with mesenchymal origin, sometimes presenting as protruding masses. Benign tumours like lipoma, fibrolipoma, fibroma, myoma, and heterotopias typically present as submucosal masses. They cannot be differentiated endoscopically from those with malignant potential as GIST or NET. Neuroendocrine carcinomas may present with diffuse infiltration, which may resemble other malignant tumours. The endoscopic appearance of small bowel lymphomas has a great variation from mass lesions to diffuse infiltrative changes. Melanoma metastases are the most frequent metastases to the small bowel. They may be hard to distinguish from other tumours when originating from an amelanotic melanoma.

  6. Unusual tumours of the lung.

    Science.gov (United States)

    Wright, E S; Pike, E; Couves, C M

    1983-09-01

    Unusual lung tumors are not simply pathological curiosities. They demonstrate features of major significance in diagnosis, treatment, and prognosis. Six of these tumours are discussed: (1) Carcinosarcoma is rarely found in the lung. The histogenis of the lesion is unclear and the prognosis is poor. (2) Only three cases of pleomorphic adenoma have previously been described. Differentiation from other "mixed tumours" of the lung is essential. (3) A rare case of bronchial adenoma producing ectopic ACTH is described. Early recognition of these polypeptide hormone-secreting tumours is stressed. (4) Oat cell carcinoma with the myasthenic (Eaton-Lambert) syndrome shows the clinical features which should permit early tumour diagnosis. The hazards of muscle relaxants must be recognized. (5) Prostatic carcinoma with endobronchial metastases is is discussed. The importance of localization of the primary tumour is emphasized. (6) An example of double primary carcinoma is presented. The rarity of this finding may be related to the poor prognosis of patients with bronchogenesis carcinoma.

  7. Intraspinal tumours in the Kenya African.

    Science.gov (United States)

    Ruberti, R F; Carmagnani, A L

    1976-06-01

    Thirty-one cases of intraspinal tumours in the African have been described, with age, sex incidence, frequency, site and histopathology shown. Intraspinal tumours in this series are compared with the larger series. Extradural and intramedullary tumours together with cervical spine tumours appear to be more frequent in this series. There is a high incidence of dumbell tumours in the neurinomas. Sarcomas are the most common type of tumours and mainly affect the thoracic spine.

  8. Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface

    Science.gov (United States)

    Sachs, Nicholas A.; Ruiz-Torres, Ricardo; Perreault, Eric J.; Miller, Lee E.

    2016-02-01

    Objective. It is quite remarkable that brain machine interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed. Approach. We have developed a system that uses two independent Wiener filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The decoder combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state. Main results. We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor’s proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder. Significance. We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the

  9. Staging of Klatskin tumours (hilar cholangiocarcinomas): comparison of MR cholangiography, MR imaging, and endoscopic retrograde cholangiography.

    Science.gov (United States)

    Vogl, Thomas J; Schwarz, Wolfram O; Heller, Matthias; Herzog, Christopher; Zangos, Stephan; Hintze, Rainer E; Neuhaus, Peter; Hammerstingl, Renate M

    2006-10-01

    The aim of the study was to compare prospectively magnetic resonance cholangiography (MRC) and magnetic resonance imaging (MRI) with endoscopic retrograde cholangiography (ERC) in the diagnosis and staging of Klatskin tumours of the biliary tree (hilar cholangiocarcinomas). Forty-six patients with suspected Klatskin tumours of the biliary tract underwent MRI and heavily T2-weighted, non-breathhold, respiratory-triggered fast spin-echo MRC. Forty-two patients underwent ERC within 24 h; in four patients, ERC was not feasible, and percutaneous trans-hepatic cholangiography (PTC) was carried out instead. Two independent investigators evaluated imaging results for the presence of tumour, bile duct dilatation, and stenosis. Clinical and histopathological correlation revealed Klatskin tumours in 33 patients. MRI revealed a slightly hyperintense signal of infiltrated bile ducts in T2-weighted fast spin-echo sequences. The malignant lesion was regularly visualized as a hypointense area in T1-weighted gradient-echo sequences with substantial contrast enhancement along the involved bile duct walls. MRC revealed the location and extension of the tumour in 31 of 33 cases correctly (sensitivity 94%, specificity 100%, diagnostic accuracy 95%). In 27 of 31 cases, ERC enabled accurate staging and diagnosis of Klatskin tumours with a sensitivity of 87%. ERC and PTC combined yielded a sensitivity of 84% and a specificity of 97%. Tumours were grouped according to the Bismuth classification, with MRC allowing correct identification of type I tumour in seven patients, type II tumour in four patients, type III tumour in 12 patients, and type IV tumour in ten patients. MRC provided superior visualization of completely obstructed peripheral systems. MRC in combination with MRI is a reliable non-invasive diagnostic method for the pre-therapeutic staging of Klatskin tumours.

  10. Staging of Klatskin tumours (hilar cholangiocarcinomas): comparison of MR cholangiography, MR imaging, and endoscopic retrograde cholangiography

    Energy Technology Data Exchange (ETDEWEB)

    Vogl, Thomas J.; Schwarz, Wolfram O.; Heller, Matthias; Herzog, Christopher; Zangos, Stephan; Hammerstingl, Renate M. [Johann Wolfgang Goethe University of Frankfurt am Main, Department of Diagnostic and Interventional Radiology, Frankfurt am Main (Germany); Hintze, Rainer E. [Humboldt University of Berlin, Department of Gastroenterology, Berlin (Germany); Neuhaus, Peter [Humboldt University of Berlin, Department of Surgery, Berlin (Germany)

    2006-10-15

    The aim of the study was to compare prospectively magnetic resonance cholangiography (MRC) and magnetic resonance imaging (MRI) with endoscopic retrograde cholangiography (ERC) in the diagnosis and staging of Klatskin tumours of the biliary tree (hilar cholangiocarcinomas). Forty-six patients with suspected Klatskin tumours of the biliary tract underwent MRI and heavily T2-weighted, non-breathhold, respiratory-triggered fast spin-echo MRC. Forty-two patients underwent ERC within 24 h; in four patients, ERC was not feasible, and percutaneous trans-hepatic cholangiography (PTC) was carried out instead. Two independent investigators evaluated imaging results for the presence of tumour, bile duct dilatation, and stenosis. Clinical and histopathological correlation revealed Klatskin tumours in 33 patients. MRI revealed a slightly hyperintense signal of infiltrated bile ducts in T2-weighted fast spin-echo sequences. The malignant lesion was regularly visualized as a hypointense area in T1-weighted gradient-echo sequences with substantial contrast enhancement along the involved bile duct walls. MRC revealed the location and extension of the tumour in 31 of 33 cases correctly (sensitivity 94%, specificity 100%, diagnostic accuracy 95%). In 27 of 31 cases, ERC enabled accurate staging and diagnosis of Klatskin tumours with a sensitivity of 87%. ERC and PTC combined yielded a sensitivity of 84% and a specificity of 97%. Tumours were grouped according to the Bismuth classification, with MRC allowing correct identification of type I tumour in seven patients, type II tumour in four patients, type III tumour in 12 patients, and type IV tumour in ten patients. MRC provided superior visualization of completely obstructed peripheral systems. MRC in combination with MRI is a reliable non-invasive diagnostic method for the pre-therapeutic staging of Klatskin tumours. (orig.)

  11. PINEAL GLAND TUMOURS - OUR EXPERIENCE AT TERTIARY CARE CENTRE: A 3-YEAR STUDY

    Directory of Open Access Journals (Sweden)

    Parsa Mani Mekhala

    2016-09-01

    Full Text Available BACKGROUND Pineal gland tumours are slow growing tumours arising in the pineal gland that resemble normal pineal gland. In WHO classification, they are considered as WHO Grade 1 tumours. Incidence of these tumours is very low. The germ cell tumours in this region are much more common arising from the pluripotent germ cells mistakenly lodged in this region during embryogenesis. Tumours which arise from the stroma are Gliomas and Atypical Teratoid Rhabdoid Tumours (ATRT. Commonest clinical presentation is hydrocephalus due to compression of tectum and upward gaze due to compression of superior colliculi. Radiology aids in giving a provisional diagnosis on CT and MRI. Confirmation with histopathology is important for proper treatment and prognosis. MATERIALS AND METHODS We present a retrospective study done over 3 years at a tertiary care centre. Radiologically diagnosed cases as pineal gland tumours, which were well demarcated within the region of pineal gland were included in the study. The surgically resected specimen were examined by squash cytology and then processed routinely for histopathology and immunohistochemistry. RESULTS AND CONCLUSION A total of 18 cases were studied and analysed. The lesions encountered were germ cell tumours, pineal parenchymal tumours and gliomas. On intraoperative squash, one case, which was diagnosed as glioma was later reported as Pineocytoma on histopathology and immunohistochemistry. Histological subtypes of pineal gland tumours on comparison with SEER database was correlated with our study. CONCLUSION Though pineal gland is a very small organ also considered as vestigial organ in lower primates the tumours present aggressively and are proved to be fatal except pineocytomas, which have a good prognosis with 5 year survival rate of 60 to 75% and hence exact diagnosis is critical to choose correct therapy.

  12. Multifocal Dysembryoplastic Neuroepithelial Tumour with Intradural Spinal Cord Lipomas: Report of a Case

    Directory of Open Access Journals (Sweden)

    Richard D. White

    2011-01-01

    Full Text Available We report a case of temporal lobe epilepsy and incomplete Brown-Sequard syndrome of the thoracic cord. Computed tomography and magnetic resonance (MR imaging showed multiple supratentorial masses with the classical radiological appearances of multifocal dysembryoplastic neuroepithelial tumour (DNET. Spinal MR imaging revealed intradural lipomas, not previously reported in association with multifocal DNET. Presentation and imaging findings are discussed along with classification and natural history of the tumour.

  13. EEG-Based Classification of New Imagery Tasks Using Three-Layer Feedforward Neural Network Classifier for Brain-Computer Interface

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    2006-10-01

    In this paper proposes the classification method of new imagery tasks for simple binary commands approach to a brain-computer interface (BCI). An analysis of imaginary tasks as “yes/no” have been proposed. Since BCI is very helpful technology for the patients who are suffering from severe motor disabilities. The BCI applications can be realized by using an electroencephalogram (EEG) signals recording at the scalp surface through the electrodes. Six healthy subjects (three males and three females), aged 23-30 years, were volunteered to participate in the experiment. During the experiment, 10-questions were used to be stimuli. The feature extraction of the event-related synchronization and event-related desynchronization (ERD/ERS) responses can be determined by the slope coefficient and Euclidian distance (SCED) method. The method uses the three-layer feedforward neural network based on a simple backpropagation algorithm to classify the two feature vectors. The experimental results of the proposed method show the average accuracy rates of 81.5 and 78.8% when the subjects imagine to “yes” and “no”, respectively.

  14. Primary vertebral tumours in children

    Energy Technology Data Exchange (ETDEWEB)

    Kozlowski, K.; Beluffi, G.; Masel, J.; Diard, F.; Ferrari-Ciboldi, F.; Le Dosseur, P.; Labatut, J.

    1984-03-01

    20 cases of primary benign and malignant bone tumours in children were reported. The most common tumours were Ewing's sarcoma, aneurismal bone cyst, benign osteoblastoma and osteoid osteoma. Some rare primary bone tumours in children (osteochondroma, chondroblastoma 6F, primary lymphoma of bone and neurofibromatosis with unusual cervical spinal changes) were also reported. The authors believe that radiographic findings together with clinical history and clinical examination may yield a high percentage of accurate diagnoses. Although microscopy is essential in the final diagnosis, the microscopic report should be also accepted with caution.

  15. Possible involvement of interferon beta in post-operative vasculitis restricted to the tumour bed: a case report.

    Science.gov (United States)

    Abe, Tatsuya; Sugita, Kenji; Morishige, Masaki; Ohnishi, Kouhei; Ishii, Keisuke; Kamida, Tohru; Hikawa, Takamitsu; Fujiki, Minoru; Kobayashi, Hidenori; Kashima, Kenji; Yokoyama, Shigeo

    2008-10-01

    Cerebral vasculitis is a very rare complication after brain tumour surgery. We herein report a case and discuss the origins of this complication. A 52-year-old female was admitted because of motor aphasia due to a left frontal lobe brain tumour. The magnetic resonance imaging (MRI) study revealed a non-enhanced tumour. A partial resection of the tumour and the placement of an Ommaya's reservoir were performed. The pathological diagnosis was an oligoastrocytoma. The patient recovered well without any neurological deficits. Post-operative radiotherapy and the intravenous injection of interferon beta were performed. During these treatments, the patient showed a continued high fever. An MRI scan revealed multiple enhanced lesions in the residual tumour, thus raising suspicions about a post-operative infection. We therefore performed a tumour biopsy and the removal of the exogenous materials. The histopathological diagnosis was vasculitis in the residual tumour. The patient's consciousness and neurological symptoms recovered quickly with the steroid treatment. Following the radiotherapy (50 Gy total), complete remission of the tumour was rapidly obtained and no recurrence was observed. Cerebral vasculitis confined to the tumour bed is an unusual complication; however, this special condition was of critical importance for a successful tumour regression in this patient.

  16. Soft tissue tumours: imaging strategy

    Energy Technology Data Exchange (ETDEWEB)

    Brisse, Herve J. [Institute Curie, Department of Radiology, Paris (France); Orbach, Daniel [Institute Curie, Department of Paediatric Oncology, Paris (France); Klijanienko, Jerzy [Institute Curie, Department of Pathology, Paris (France)

    2010-06-15

    Vascular tumours and malformations, fibrous and fibrohistiocytic tumours and pseudotumours are the most common benign soft-tissue masses observed in children, and can be treated conservatively. Rhabdomyosarcomas are the most frequent malignant tumours, accounting for about half of soft tissue sarcomas. A child referred for a soft-tissue mass should ideally be managed by a multidisciplinary team and primary excision should be proscribed until a definite diagnosis has been established. Clinical examination, conventional radiography and US with Doppler represent the first-line examinations and are sometimes sufficient to make a diagnosis. In all other situations, MRI is mandatory to establish the aggressiveness and extension of the tumour. This technique provides the relevant data to guide the decision regarding tissue sampling. (orig.)

  17. Targeting the erythropoietin receptor on glioma cells reduces tumour growth

    Energy Technology Data Exchange (ETDEWEB)

    Peres, Elodie A.; Valable, Samuel [CERVOxy team ' Hypoxia and cerebrovascular pathophysiology' , UMR 6232 CI-NAPS, Universite de Caen Basse-Normandie, Universite Paris-Descartes, CNRS, CEA. G.I.P. CYCERON, Caen (France); Guillamo, Jean-Sebastien [CERVOxy team ' Hypoxia and cerebrovascular pathophysiology' , UMR 6232 CI-NAPS, Universite de Caen Basse-Normandie, Universite Paris-Descartes, CNRS, CEA. G.I.P. CYCERON, Caen (France); Departement de Neurologie, CHU de Caen (France); Marteau, Lena [CERVOxy team ' Hypoxia and cerebrovascular pathophysiology' , UMR 6232 CI-NAPS, Universite de Caen Basse-Normandie, Universite Paris-Descartes, CNRS, CEA. G.I.P. CYCERON, Caen (France); Bernaudin, Jean-Francois [Service d' Histologie-Biologie Tumorale, ER2UPMC, Universite Paris 6, Hopital Tenon, Paris (France); Roussel, Simon [CERVOxy team ' Hypoxia and cerebrovascular pathophysiology' , UMR 6232 CI-NAPS, Universite de Caen Basse-Normandie, Universite Paris-Descartes, CNRS, CEA. G.I.P. CYCERON, Caen (France); Lechapt-Zalcman, Emmanuele [CERVOxy team ' Hypoxia and cerebrovascular pathophysiology' , UMR 6232 CI-NAPS, Universite de Caen Basse-Normandie, Universite Paris-Descartes, CNRS, CEA. G.I.P. CYCERON, Caen (France); Service d' Anatomie Pathologique, CHU de Caen (France); Bernaudin, Myriam [CERVOxy team ' Hypoxia and cerebrovascular pathophysiology' , UMR 6232 CI-NAPS, Universite de Caen Basse-Normandie, Universite Paris-Descartes, CNRS, CEA. G.I.P. CYCERON, Caen (France); Petit, Edwige, E-mail: epetit@cyceron.fr [CERVOxy team ' Hypoxia and cerebrovascular pathophysiology' , UMR 6232 CI-NAPS, Universite de Caen Basse-Normandie, Universite Paris-Descartes, CNRS, CEA. G.I.P. CYCERON, Caen (France)

    2011-10-01

    Hypoxia has been shown to be one of the major events involved in EPO expression. Accordingly, EPO might be expressed by cerebral neoplastic cells, especially in glioblastoma, known to be highly hypoxic tumours. The expression of EPOR has been described in glioma cells. However, data from the literature remain descriptive and controversial. On the basis of an endogenous source of EPO in the brain, we have focused on a potential role of EPOR in brain tumour growth. In the present study, with complementary approaches to target EPO/EPOR signalling, we demonstrate the presence of a functional EPO/EPOR system on glioma cells leading to the activation of the ERK pathway. This EPO/EPOR system is involved in glioma cell proliferation in vitro. In vivo, we show that the down-regulation of EPOR expression on glioma cells reduces tumour growth and enhances animal survival. Our results support the hypothesis that EPOR signalling in tumour cells is involved in the control of glioma growth.

  18. Tumour markers in gastrointestinal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Lamerz, R.

    1988-02-01

    For non-endocrine gastrointestinal tumours the following tumour markers are of clinical interest: For esophageal cancer CEA (sensitivity, s: 40-60%) and SCC (squamous cell carcinoma antigen, x: 20-50%); for gastric cancer CEA (s: 30-40%) as well as CA 19-9 (s: 30-40%) because of complementary results (additive s: 50-60); for hepatocellular cancer AFP (first choice, s: 70-90%; second choice CA 19-9, s: 50-70%); for cholangiocellular cancer CA 19-9 (s: 40-70%); for secondary liver cancer in general CEA; for biliary tract cancer CA 19-9 (s: 40-70%) as well as for excretory pancreatic cancer (s: 70-90%); for colorectal cancer CEA (s: 40-70%) as a first choice marker, and CA 19-9 (s: 20-60%) as a second choice marker, and for anal cancer SCC. The frequency of tumour marker determinations depends on follow-up care recommendations for different tumour diseases (e.g. 1-3 monthly during the 1st and 2nd postoperative year, following chemotherapy courses, on change of therapy, on restaging and at unclear alteration of the clinical state). Tumour markers are only valuable adjuncts to the medical care of tumour patients and therefore useless as solitary findings or on missing therapeutic consequence.

  19. MRI characteristics of midbrain tumours

    Energy Technology Data Exchange (ETDEWEB)

    Sun, B. [Chinese Academy of Medical Science, Beijing (China). Neurosurgical Inst.]|[Department of Neuroradiology, Beijing Tiantan Hospital (China); Wang, C.C.; Wang, J. [Chinese Academy of Medical Science, Beijing (China). Neurosurgical Inst.

    1999-03-01

    We diagnosed 60 cases of midbrain tumours by MRI between 1993 to 1997. There were 39 males and 21 females, aged 2-64 years, mean 25.6 years. We found 38 patients with true intramedullary midbrain tumours, 11 predominantly in the tectum, 20 in the tegmentum and 7 with a downward extension to the pons; there were 7 within the cerebral aqueduct. There were 22 patients with infiltrating midbrain tumours extending from adjacent structures, 11 cases each from the thalamus and pineal region. All patients received surgical treatment. Gross total resection was achieved in 42 cases, subtotal (> 75 %) resection in 18. Pathological diagnoses included 16 low-grade and 15 high-grade astrocytomas; 5 oligodendroastrocytomas; 2 ependymomas; 11 glioblastomas; and 11 pineal parenchymal or germ-cell tumours. Midbrain tumours are a heterogeneous group of neoplasms, with wide variation in clinical and MRI features, related to the site and type of tumour. MRI not only allows precise analysis of their growth pattern, but also can lead to a correct preoperative diagnosis in the majority of cases. (orig.) (orig.) With 3 figs., 3 tabs., 19 refs.

  20. Brain CT image classification based on least squares support vector machine opti-mized by improved harmony search algorithm%改进和声搜索算法优化LSSVM的脑CT图像分类

    Institute of Scientific and Technical Information of China (English)

    郭正红; 赵丙辰

    2013-01-01

    In order to improve the brain CT image classification accuracy, this paper proposes brain CT mage classification mod-el(IHS-LSSVM)based on the least squares support vector machine and harmony search algorithm. Firstly, the LSSVM parame-ters are taken as different musical tone combination, and then the harmony search algorithm is used to find the optimal parame-ters, and the optimal position adjustment strategy is introduced to enhance the ability of jumping out of local minima, the brain CT image classification model is established according to the optimal parameters, and the performance of the model is tested. The simulation results show that, compared with the other models, IHS-LSSVM not only improves the image classification accu-racy, but also accelerates the classification speed, so it is an effective brain CT image classification model.%为了提高脑CT图像的分类正确率,针对分类器中的最小二乘支持向量机(LSSVM)参数优化问题,提出一种改进和声搜索算法优化LSSVM的脑CT图像分类模型(IHS-LSSVM)。将LSSVM参数看作不同乐器的声调组合,通过和声搜索算法的“调音”找到最优参数,并在寻优过程中引入粒子群算法的最优位置更新策略,增强了算法跳出局部极小值的能力,根据最优参数建立脑CT图像分类模型,并对模型的性能进行仿真测试。仿真结果表明,相对于对比模型,IHS-LSSVM不仅提高了脑CT图像分类正确率,而且加快分类速度,是一种有效的脑CT图像分类模型。

  1. Adenomyoepithelial tumours and myoepithelial carcinomas of the breast – a spectrum of monophasic and biphasic tumours dominated by immature myoepithelial cells

    Directory of Open Access Journals (Sweden)

    Herbst Hermann

    2005-07-01

    Full Text Available Abstract Background Adenomyoepithelial tumours and myoepithelial carcinomas of the breast are primarily defined by the presence of neoplastic cells with a myoepithelial immunophenotype. Current classification schemes are based on purely descriptive features and an assessment of individual prognosis is still problematic. Methods A series of 27 adenomyoepithelial tumours of the breast was analysed immunohistochemically with antibodies directed against various cytokeratins, p63, smooth muscle alpha-actin (SMA and vimentin. Additionally, double immunofluorescence and comparative genomic hybridisation (CGH was performed. Results Immunohistochemically, all the tumours showed a constant expression of high molecular weight cytokeratins (Ck Ck5 and Ck14, p63, SMA and vimentin. With exception of one case diagnosed as myoepithelial carcinoma, all tested tumours expressed low molecular weight cytokeratin Ck18 in variable proportions of cells. Even in monophasic tumours lacking obvious glandular differentiation in conventional staining, a number of neoplastic cells still expressed those cytokeratins. Double immunofluorescence revealed tumour cells exclusively staining for Ck5/Ck14 in the presence of other cell populations that co-expressed high molecular weight Ck5/Ck14 as well as either low molecular weight Ck8/18 or SMA. Based on morphology, we assigned the series to three categories, benign, borderline and malignant. This classification was supported by a stepwise increase in cytogenetic alterations on CGH. Conclusion Adenomyoepithelial tumours comprise a spectrum of neoplasms consisting of an admixture of glandular and myoepithelial differentiation patterns. As a key component SMA-positive cells co-expressing cytokeratins could be identified. Although categorisation of adenomyoepithelial tumours in benign, borderline and malignant was supported by results of CGH, any assessment of prognosis requires to be firmly based on morphological grounds. At present

  2. Classification of binary intentions for individuals with impaired oculomotor function: ‘eyes-closed’ SSVEP-based brain-computer interface (BCI)

    Science.gov (United States)

    Lim, Jeong-Hwan; Hwang, Han-Jeong; Han, Chang-Hee; Jung, Ki-Young; Im, Chang-Hwan

    2013-04-01

    Objective. Some patients suffering from severe neuromuscular diseases have difficulty controlling not only their bodies but also their eyes. Since these patients have difficulty gazing at specific visual stimuli or keeping their eyes open for a long time, they are unable to use the typical steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. In this study, we introduce a new paradigm for SSVEP-based BCI, which can be potentially suitable for disabled individuals with impaired oculomotor function. Approach. The proposed electroencephalography (EEG)-based BCI system allows users to express their binary intentions without needing to open their eyes. A pair of glasses with two light emitting diodes flickering at different frequencies was used to present visual stimuli to participants with their eyes closed, and we classified the recorded EEG patterns in the online experiments conducted with five healthy participants and one patient with severe amyotrophic lateral sclerosis (ALS). Main results. Through offline experiments performed with 11 participants, we confirmed that human SSVEP could be modulated by visual selective attention to a specific light stimulus penetrating through the eyelids. Furthermore, the recorded EEG patterns could be classified with accuracy high enough for use in a practical BCI system. After customizing the parameters of the proposed SSVEP-based BCI paradigm based on the offline analysis results, binary intentions of five healthy participants were classified in real time. The average information transfer rate of our online experiments reached 10.83 bits min-1. A preliminary online experiment conducted with an ALS patient showed a classification accuracy of 80%. Significance. The results of our offline and online experiments demonstrated the feasibility of our proposed SSVEP-based BCI paradigm. It is expected that our ‘eyes-closed’ SSVEP-based BCI system can be potentially used for communication of

  3. WILMS’ TUMOUR IN YOUNG ADULT

    Directory of Open Access Journals (Sweden)

    Senthilvel Arumugam

    2016-08-01

    Full Text Available Wilms’ tumour also called as nephroblastoma is a malignant renal neoplasm of childhood that arises from remnant of immature kidney. About 80% of Wilms’ tumour cases occur before age 5 with a median age of 3.5 years. But adult Wilms’ tumour can occur at any age from 16 to 70 years, the median age in young adult is around 24. CASE REPORT A 16-year-old girl came with history of mass right abdomen, which she noticed for 1 week duration; no urinary symptoms. Her recent blood pressure was 140/90 mmHg. Per abdomen a 10 x 9 cm mass palpable in the right lumbar region, surface smooth, firmto-hard in consistency, non-tender, well defined, no bruit. Urine routine examination was normal; urine culture was sterile; renal and liver function tests were within normal limits; Sr. calcium 9.5 mg/dL. CT abdomen plain and contrast showed a 10 x 9 cm heterodense lesion equivocal with renal cell carcinoma and angiomyolipoma. MR angiogram was done. It showed well-defined encapsulated heterointense mass of size 12 x 8 x 7cm, IVC and bilateral renal vein normal. Since findings were inconclusive, we did a CT-guided biopsy and report came as feature positive for small round cell tumour. Hence, proceeded with right radical nephrectomy. The final histopathology report came as Wilms’ tumour spindle cell variant. Margins clear and ureter not involved. She was then started on adjuvant chemotherapy Inj. Vincristine 2 mg weekly for 27 weeks. She is on regular followup now. CONCLUSION Wilms’ tumour should be considered in a patient who presents with a renal mass with or without loin pain, haematuria especially in young adults. Every attempt should be made to differentiate it from renal cell carcinoma. The outcome for adult Wilms’ tumour is steadily improving with current multimodality treatment approach.

  4. Congruency of tumour volume delineated by FET PET and MRSI

    Energy Technology Data Exchange (ETDEWEB)

    Mauler, Jörg; Langen, Karl-Josef [Institute of Neuroscience and Medicine, Forschungszentrum Jülich (Germany); Maudsley, Andrew A [Miller School of Medicine, University of Miami (United States); Nikoubashman, Omid [Department of Neuroradiology, Faculty of Medicine, RWTH Aachen University (Germany); Filss, Christian; Stoffels, Gabriele; Shah, N Jon [Institute of Neuroscience and Medicine, Forschungszentrum Jülich (Germany)

    2015-05-18

    In addition to MR imaging, PET imaging of O-(2-[18F]Fluorethyl)-L-Tyrosine (FET) uptake provides information on brain tumour extent and metabolic activity. Similarly, MRS has been shown to be of value for distinguishing high- from low-grade gliomas. Based on 2D spatially resolved MRSI, an overlap between 18FET uptake and the choline/N-acetyl-aspartate (Cho/NAA) ratio of more than 75 % has been reported.

  5. Anaesthetic management for combined emergency caesarean section and craniotomy tumour removal

    Directory of Open Access Journals (Sweden)

    Dewi Y Bisri

    2017-01-01

    Full Text Available Presentation of primary intracranial tumour during pregnancy is extremely rare. Symptoms of brain tumour include nausea, vomiting, headache and seizures which mimic symptoms of pregnancy-related hyperemesis or eclampsia. In very few cases, craniotomy tumour removal is performed earlier or even simultaneously with foetal delivery. A 40-year-old woman at 32 weeks of gestation in foetal distress presented to the emergency room with decreased level of consciousness Glasgow Coma Scale 6 (E2M2V2. Computed tomographic scan revealed a mass lesion over the left temporoparietal region with midline shift and intratumoural bleeding. In view of high risk of herniation and foetal distress, she underwent emergency caesarean section followed by craniotomy tumour removal. In parturient with brain tumour, combined surgery of tumour removal and caesarean section is decided based on clinical symptoms, type of tumour and foetal viability. Successful anaesthetic management requires a comprehensive knowledge of physiology and pharmacology, individually tailored to control intracranial pressure while ensuring the safety of mother and foetus.

  6. Tumour banking: the Spanish design.

    Science.gov (United States)

    Morente, M M; de Alava, E; Fernandez, P L

    2007-01-01

    In the last decade the technical advances in high throughput techniques to analyze DNA, RNA and proteins have had a potential major impact on prevention, diagnosis, prognosis and treatment of many human diseases. Key pieces in this process, mainly thinking about the future, are tumour banks and tumour bank networks. To face these challenges, diverse suitable models and designs can be developed. The current article presents the development of a nationwide design of tumour banks in Spain based on a network of networks, specially focusing on its harmonization efforts mainly regarding technical procedures, ethical requirements, unified quality control policy and unique sample identification. We also describe our most important goals for the next years. This model does not correspond to a central tumour bank, but to a cooperative and coordinated network of national and regional networks. Independently from the network in which it is included, sample collections reside in their original institution, where it can be used for further clinical diagnosis, teaching and research activities of each independent hospital. The herein described 'network of networks' functional model could be useful for other countries and/or international tumour bank activities.

  7. Pitfalls in colour photography of choroidal tumours.

    Science.gov (United States)

    Schalenbourg, A; Zografos, L

    2013-02-01

    Colour imaging of fundus tumours has been transformed by the development of digital and confocal scanning laser photography. These advances provide numerous benefits, such as panoramic images, increased contrast, non-contact wide-angle imaging, non-mydriatic photography, and simultaneous angiography. False tumour colour representation can, however, cause serious diagnostic errors. Large choroidal tumours can be totally invisible on angiography. Pseudogrowth can occur because of artefacts caused by different methods of fundus illumination, movement of reference blood vessels, and flattening of Bruch's membrane and sclera when tumour regression occurs. Awareness of these pitfalls should prevent the clinician from misdiagnosing tumours and wrongfully concluding that a tumour has grown.

  8. Pitfalls in colour photography of choroidal tumours

    Science.gov (United States)

    Schalenbourg, A; Zografos, L

    2013-01-01

    Colour imaging of fundus tumours has been transformed by the development of digital and confocal scanning laser photography. These advances provide numerous benefits, such as panoramic images, increased contrast, non-contact wide-angle imaging, non-mydriatic photography, and simultaneous angiography. False tumour colour representation can, however, cause serious diagnostic errors. Large choroidal tumours can be totally invisible on angiography. Pseudogrowth can occur because of artefacts caused by different methods of fundus illumination, movement of reference blood vessels, and flattening of Bruch's membrane and sclera when tumour regression occurs. Awareness of these pitfalls should prevent the clinician from misdiagnosing tumours and wrongfully concluding that a tumour has grown. PMID:23238442

  9. A THREE YEAR RETROSPECTIVE STUDY OF OVARIAN NEOPLASMS WITH SPECIAL EMPHASIS ON SURFACE EPITHELIAL TUMOURS

    Directory of Open Access Journals (Sweden)

    Krishna Bharathi Yarlagadda

    2016-07-01

    Full Text Available BACKGROUND Ovarian tumours being second most common gynaecological cancer in India account for 30% of all cancers of female genital tract. Study conducted to determine relative frequencies of various histological types based on WHO classification and their age distribution with particular emphasis on surface epithelial tumours. This study is undertaken to find out the frequency of incidence of different histopathological subtypes with particular emphasis on surface epithelial tumours and age distribution of ovarian tumours in our institute located in coastal Andhra Pradesh. METHODS This is a retrospective study of 100 cases of ovarian neoplasms collected during a period of 3 years from June 2013 to May 2016 from the Department of Pathology, Katuri Medical College and Hospital, Chinakondrupadu, Guntur, A. P, India. The patients attending our hospital are mostly from rural areas around. Paraffin blocks of all 100 ovarian neoplasms retrieved. Complete clinical and radiological findings analysed from our records. RESULTS The tumours are grouped according to the nature of tumour whether benign or borderline or malignant according to cell of origin, histological subtyping, and age group. Surface epithelial tumours are the most common. Benign tumours outnumber the malignant tumours. Benign ovarian tumours showed a peak in 21-40 Yrs. age group and malignant in the age group of 41- 60 Yrs. Results of our study compared with other studies. CONCLUSION Because of the geographic location, poverty, and illiteracy, patients seek medical advice late. So, awareness among public by health education, passive surveillance, and community screening facility will be helpful in early detection of ovarian neoplasms.

  10. Tumour endothelial cells in high metastatic tumours promote metastasis via epigenetic dysregulation of biglycan

    Science.gov (United States)

    Maishi, Nako; Ohba, Yusuke; Akiyama, Kosuke; Ohga, Noritaka; Hamada, Jun-ichi; Nagao-Kitamoto, Hiroko; Alam, Mohammad Towfik; Yamamoto, Kazuyuki; Kawamoto, Taisuke; Inoue, Nobuo; Taketomi, Akinobu; Shindoh, Masanobu; Hida, Yasuhiro; Hida, Kyoko

    2016-01-01

    Tumour blood vessels are gateways for distant metastasis. Recent studies have revealed that tumour endothelial cells (TECs) demonstrate distinct phenotypes from their normal counterparts. We have demonstrated that features of TECs are different depending on tumour malignancy, suggesting that TECs communicate with surrounding tumour cells. However, the contribution of TECs to metastasis has not been elucidated. Here, we show that TECs actively promote tumour metastasis through a bidirectional interaction between tumour cells and TECs. Co-implantation of TECs isolated from highly metastatic tumours accelerated lung metastases of low metastatic tumours. Biglycan, a small leucine-rich repeat proteoglycan secreted from TECs, activated tumour cell migration via nuclear factor-κB and extracellular signal–regulated kinase 1/2. Biglycan expression was upregulated by DNA demethylation in TECs. Collectively, our results demonstrate that TECs are altered in their microenvironment and, in turn, instigate tumour cells to metastasize, which is a novel mechanism for tumour metastasis. PMID:27295191

  11. Is the shock index based classification of hypovolemic shock applicable in multiple injured patients with severe traumatic brain injury?-an analysis of the TraumaRegister DGU(®).

    Science.gov (United States)

    Fröhlich, Matthias; Driessen, Arne; Böhmer, Andreas; Nienaber, Ulrike; Igressa, Alhadi; Probst, Christian; Bouillon, Bertil; Maegele, Marc; Mutschler, Manuel

    2016-12-12

    A new classification of hypovolemic shock based on the shock index (SI) was proposed in 2013. This classification contains four classes of shock and shows good correlation with acidosis, blood product need and mortality. Since their applicability was questioned, the aim of this study was to verify the validity of the new classification in multiple injured patients with traumatic brain injury. Between 2002 and 2013, data from 40 888 patients from the TraumaRegister DGU(®) were analysed. Patients were classified according to their initial SI at hospital admission (Class I: SI shock based on universally available parameters. Although the pathophysiology in TBI and Non TBI patients and early treatment methods such as the use of vasopressors differ, both groups showed an identical probability of recieving blood products within the respective SI class. Regardless of the presence of TBI, the classification of hypovolemic shock based on the SI enables a fast and reliable assessment of hypovolemic shock in the emergency department. Therefore, the presented study supports the SI as a feasible tool to assess patients at risk for blood product transfusions, even in the presence of severe TBI.

  12. Brain metastases from colorectal cancer

    DEFF Research Database (Denmark)

    Vagn-Hansen, Chris Aksel; Rafaelsen, Søren Rafael

    2001-01-01

    Brain metastases from colorectal cancer are rare. The prognosis for patients with even a single resectable brain metastasis is poor. A case of surgically treated cerebral metastasis from a rectal carcinoma is reported. The brain tumour was radically resected. However, cerebral, as well...... as extracerebral, disease recurred 12 months after diagnosis. Surgical removal of colorectal metastatic brain lesions in selected cases results in a longer survival time....

  13. Cardiac tumours in intrauterine life.

    OpenAIRE

    Groves, A.M.; Fagg, N. L.; Cook, A C; Allan, L. D.

    1992-01-01

    Since 1980, 11 examples of cardiac tumour have been detected in the fetus out of a total of 794 congenital cardiac malformations. Patients were referred because of fetal hydrops in two, a family history of tuberous sclerosis in two, and because of the detection of a tumour mass during a scan at the local hospital in seven. The gestational age range at presentation was from 20-34 weeks. Of eight fetuses where death occurred, the histological type was rhabdomyoma in seven and teratoma in one. I...

  14. Urothelial neoplasia of the urinary bladder--comparison of interobserver variability for WHO Classification 1972 with WHO/ISUP Consensus Classification 1998.

    Science.gov (United States)

    Mamoon, Nadira; Iqbal, Muhammad Ashraf; Jamal, Shahid; Luqman, Muhammad

    2006-01-01

    Classification of urothelial bladder tumours is an important factor in the treatment and prognosis of these lesions. Over the years many classifications have been proposed for this purpose. The objective of this study was to classify urothelial neoplasms of the urinary bladder using the latest WHO/ISUP Consensus Classification 1998 and WHO Classification 1972 and compare the two regarding interobserver variability. This study included 100 consecutive biopsy specimens of urothelial neoplasms of the urinary bladder diagnosed at the department of Histopathology, Armed Forces Institute of Pathology, Rawalpindi. These were classified according to WHO Classification 1972 and WHO/ISUP Consensus Classification 1998 by 2 groups of pathologists independently. The tumour categories for WHO classification 1972; papilloma, and transitional cell carcinoma (TCC) grades I, II and III were compared with the WHO/ISUP Consensus Classification entities of papilloma, papillary neoplasm of low malignant potential, low grade and high grade papillary carcinomas. Kappa statistics were used to evaluate interobserver variability. Chi square test was used to calculate significance. There was agreement on 80 tumours between the two groups of histopathologists when using WHO classification 1972 while there was agreement on 95 tumours using WHO/ISUP consensus classification. The value of Kappa for WHO Classification was 0.68 (good agreement) whereas for WHO/ISUP Consensus Classification it was 0.91 (excellent agreement). The difference between the two systems was statistically significant (pISUP Consensus Classification 1998 showed less interobserver variability than WHO Classification 1972 in the evaluation of bladder tumours. It was found easier to apply by both groups. There was less agreement on the benign and borderline lesions using both the classifications.

  15. Unbiased and automated identification of a circulating tumour cell definition that associates with overall survival

    NARCIS (Netherlands)

    Ligthart, S.T.; Coumans, F.A.W.; Attard, G.; Mukick Cassidy, A.; Bono, de J.S.; Terstappen, L.W.M.M.

    2011-01-01

    Circulating tumour cells (CTC) in patients with metastatic carcinomas are associated with poor survival and can be used to guide therapy. Classification of CTC however remains subjective, as they are morphologically heterogeneous. We acquired digital images, using the CellSearch™ system, from blood

  16. [Prevalence of central nervous system tumours and histological identification in the operated patient: 20 years of experience].

    Science.gov (United States)

    Anaya-Delgadillo, Gustavo; de Juambelz-Cisneros, Pedro Pablo; Fernández-Alvarado, Basilio; Pazos-Gómez, Fernando; Velasco-Torre, Andrea; Revuelta-Gutiérrez, Rogelio

    Central nervous system tumours comprise a heterogeneous group of neoplasms with great histological diversity. Despite the rising prevalence of these tumours in developing countries, some places like Mexico and Latin America have no representative studies that show the real impact of these tumours in our population. To describe the characteristics of the primary and secondary tumours of the central nervous system in the last 20 years in a Mexican institution. Patients with histopathological diagnosis from 1993 to 2013 in our institution, grouping them according to WHO classification 2007, characterising them by age group, gender, and anatomical location. There were a total of 511 tumours of the central nervous system. Of those, 292 were women and 219 men, with a ratio 1.3: 1, and a mean age of 49.3 years. Tumours with higher prevalence were: Meningeal tumours, 171 (33%), followed by neuroepithelial, 121 (24%). Astrocytoma had the highest prevalence in paediatric patients, whereas in those older than 20 years it was the meningioma. The supratentorial location was the most involved. This is the first study of a series of cases in Mexico that is performed by taking into account benign and malignant tumours of the central nervous system, with patients of all age groups with a range of 20 years. While this work only represents a retrospective analysis of an institution, it can be a strong indication of the epidemiology of these tumours in our environment. Copyright © 2016. Publicado por Masson Doyma México S.A.

  17. Follicular infundibulum tumour presenting as cutaneous horn

    Directory of Open Access Journals (Sweden)

    Jayaraman M

    1996-01-01

    Full Text Available Tumour of follicular infundibulum is an organoid tumour with a plate like growth attached to the epidermis with connection from the follicular epithelium. We are reporting such a case unusually presenting as cutaneous horn.

  18. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

    Science.gov (United States)

    Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

    2016-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

  19. Computer-aided hepatic tumour ablation

    CERN Document Server

    Voirin, D; Amavizca, M; Leroy, A; Letoublon, C; Troccaz, J; Voirin, David; Payan, Yohan; Amavizca, Miriam; Leroy, Antoine; Letoublon, Christian; Troccaz, Jocelyne

    2001-01-01

    Surgical resection of hepatic tumours is not always possible. Alternative techniques consist in locally using chemical or physical agents to destroy the tumour and this may be performed percutaneously. It requires a precise localisation of the tumour placement during ablation. Computer-assisted surgery tools may be used in conjunction to these new ablation techniques to improve the therapeutic efficiency whilst benefiting from minimal invasiveness. This communication introduces the principles of a system for computer-assisted hepatic tumour ablation.

  20. Quantification of tumour {sup 18}F-FDG uptake: Normalise to blood glucose or scale to liver uptake?

    Energy Technology Data Exchange (ETDEWEB)

    Keramida, Georgia [Brighton and Sussex Medical School, Clinical Imaging Sciences Centre, Brighton (United Kingdom); Brighton and Sussex University Hospitals NHS Trust, Department of Nuclear Medicine, Brighton (United Kingdom); University of Sussex, Clinical Imaging Sciences Centre, Brighton (United Kingdom); Dizdarevic, Sabina; Peters, A.M. [Brighton and Sussex Medical School, Clinical Imaging Sciences Centre, Brighton (United Kingdom); Brighton and Sussex University Hospitals NHS Trust, Department of Nuclear Medicine, Brighton (United Kingdom); Bush, Janice [Brighton and Sussex Medical School, Clinical Imaging Sciences Centre, Brighton (United Kingdom)

    2015-09-15

    To compare normalisation to blood glucose (BG) with scaling to hepatic uptake for quantification of tumour {sup 18}F-FDG uptake using the brain as a surrogate for tumours. Standardised uptake value (SUV) was measured over the liver, cerebellum, basal ganglia, and frontal cortex in 304 patients undergoing {sup 18}F-FDG PET/CT. The relationship between brain FDG clearance and SUV was theoretically defined. Brain SUV decreased exponentially with BG, with similar constants between cerebellum, basal ganglia, and frontal cortex (0.099-0.119 mmol/l{sup -1}) and similar to values for tumours estimated from the literature. Liver SUV, however, correlated positively with BG. Brain-to-liver SUV ratio therefore showed an inverse correlation with BG, well-fitted with a hyperbolic function (R = 0.83), as theoretically predicted. Brain SUV normalised to BG (nSUV) displayed a nonlinear correlation with BG (R = 0.55); however, as theoretically predicted, brain nSUV/liver SUV showed almost no correlation with BG. Correction of brain SUV using BG raised to an exponential power of 0.099 mmol/l{sup -1} also eliminated the correlation between brain SUV and BG. Brain SUV continues to correlate with BG after normalisation to BG. Likewise, liver SUV is unsuitable as a reference for tumour FDG uptake. Brain SUV divided by liver SUV, however, shows minimal dependence on BG. (orig.)

  1. Intraoral myxoid nerve sheath tumour

    NARCIS (Netherlands)

    Schortinghuis, J; Hille, JJ; Singh, S

    2001-01-01

    A case of an intraoral myxoid nerve sheath tumour of the dorsum of the tongue in a 73-year-old Caucasian male is reported. This case describes the oldest patient with this pathology to date. Immunoperoxidase staining for neuronspecific enolase (NSE) and epithelial membrane antigen (EMA) expression d

  2. Intraoral myxoid nerve sheath tumour

    NARCIS (Netherlands)

    Schortinghuis, J; Hille, JJ; Singh, S

    2001-01-01

    A case of an intraoral myxoid nerve sheath tumour of the dorsum of the tongue in a 73-year-old Caucasian male is reported. This case describes the oldest patient with this pathology to date. Immunoperoxidase staining for neuronspecific enolase (NSE) and epithelial membrane antigen (EMA) expression d

  3. PET imaging in endocrine tumours.

    Science.gov (United States)

    Khan, S; Lloyd, C; Szyszko, T; Win, Z; Rubello, D; Al-Nahhas, A

    2008-06-01

    The role of PET in the assessment of endocrine tumours has been, until recently, restricted to the use of (18)F-fluoro-deoxy-D-glucose ((18)F-FDG). Being a marker of metabolically active lesions that show high grading and low differentiation, FDG is not ideal for this purpose since the majority of endocrine tumours are slow growing and highly differentiated. It is however useful when dedifferentiation takes place and provides excellent prognostic information. A number of hormone precursors and amino acids are labelled with (11)C and used successfully in the management of parathyroid, adrenal and pituitary tumours. However, the short half-life of (11)C radiopharmaceuticals restricts their use to centres with access to an on-site cyclotron, while the high cost of production may limit their use to research purposes. A promising new positron-emission tomography (PET) tracer is Gallium-68 obtained by elution from a long shelf-life generator that makes it economic and cyclotron-independent. Its short half-life and flexible labelling ability to a wide range of peptides and antibodies makes it ideal for PET imaging. In addition to imaging GEP-NETs and phaeochromocytoma, it has the potential to be used in a wider range of endocrine tumours.

  4. Melanotic neuroectodermal tumour of the pineal region

    Energy Technology Data Exchange (ETDEWEB)

    Gorhan, C.; Soto-Ares, G.; Pruvo, J.P. [Dept. of Neuroradiology, Hopital Roger Salengro, CHRU Lille, Lille (France); Ruchoux, M.M. [Dept. of Neuropathology, Hopital Roger Salengro, CHRU Lille (France); Blond, S. [Dept. of Neurosurgery, Hopital Roger Salengro, CHRU Lille (France)

    2001-11-01

    We describe CT and MR findings in a 23-month-old infant with a melanotic neuroectodermal tumour of the pineal gland. The tumour has been stereotactically biopsied and surgically resected. The pathological diagnosis was made on the resected piece. Embryology of the pineal gland and the histology of melanotic neuroectodermal tumour of infancy are discussed. (orig.)

  5. FDG uptake, a surrogate of tumour hypoxia?

    NARCIS (Netherlands)

    Dierckx, Rudi Andre; de Wiele, Christophe Van

    2008-01-01

    Introduction Tumour hyperglycolysis is driven by activation of hypoxia-inducible factor-1 (HIF-1) through tumour hypoxia. Accordingly, the degree of 2-fluro-2-deoxy-D-glucose (FDG) uptake by tumours might indirectly reflect the level of hypoxia, obviating the need for more specific radiopharmaceutic

  6. Radiofrequency for the treatment of liver tumours.

    NARCIS (Netherlands)

    Ruers, T.J.M.; Jong, K.P. de; Ijzermans, J.N.M.

    2005-01-01

    Resection should still be considered the gold standard for many liver tumours. There is, however, growing interest in the use of radiofrequency (RFA) for the treatment of liver tumours. By RFA, tumour tissue can be destructed selectively without significant damage to vascular structures in the

  7. Radiofrequency for the treatment of liver tumours

    NARCIS (Netherlands)

    Ruers, TJM; de Jong, KP; Ijzermans, JNM

    2005-01-01

    Resection should still be considered the gold standard for many liver tumours. There is, however, growing interest in the use of radiofrequency (RFA) for the treatment of liver tumours. By RFA, tumour tissue can be destructed selectively without significant damage to vascular structures in the

  8. The Novel Graph Kernel for Brain Networks with Application to MCI Classification%面向脑网络的新型图核及其在 MCI 分类上的应用

    Institute of Scientific and Technical Information of China (English)

    接标; 张道强

    2016-01-01

    Graph kernel,as a similarity measure of graphs,has been proposed for computing the similarity of a pair of brain networks and applied for classification of brain diseases,such as Alzheimer’s disease (AD)as well as its early stage,i.e.,mild cognitive impairment (MCI). However,existing graph kernels are mainly constructed on general graphs and thus ignore the intrinsic property of brain networks,such as the uniqueness of each node,i.e.,each node corresponds to a unique brain regions,which may affect the performance of brain network analysis (classification).To address this problem,in this paper,a novel graph kernel is proposed for measuring the similarity of brain networks.Specifically,a group of sub-networks are first constructed on each node to reflect the local and multi-level topological properties of brain network.Then, according the uniqueness of each node,a function is defined to measure the similarity of a pair of sub-network groups across different subjects.Finally,the graph kernel on brain network can be defined through computing the similarity of all pairs of sub-network groups.Different from existing graph kernels,our proposed graph kernel not only considers the specific property of brain networks,but also preserves the local connectivity properties of brain networks.The experimental results on both real MCI datasets show that our proposed graph kernel can significantly improve the classification performance in comparison with state-of-the-art graph kernels.%作为一种图的相似性度量,图核已经被提出用于计算脑网络的相似性,并用于分类一些脑疾病,如阿尔茨海默病(Alzheimer’s Disease,AD)以及它的早期阶段,即轻度认知功能障碍(Mild Cognitive Impairment,MCI)。然而,已有图核主要面向一般图而构建,从而忽略了脑网络自身特有的特性,如节点的唯一性(即每个节点对应着唯一的脑区),这可能影响到脑网络分析

  9. Imaging of salivary gland tumours

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Y.Y.P.; Wong, K.T.; King, A.D. [Department of Diagnostic Radiology and Organ Imaging, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin NT, Hong Kong (Hong Kong); Ahuja, A.T. [Department of Diagnostic Radiology and Organ Imaging, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin NT, Hong Kong (Hong Kong)], E-mail: aniltahuja@cuhk.edu.hk

    2008-06-15

    Salivary gland neoplasms account for <3% of all tumors. Most of them are benign and parotid gland is the commonest site. As a general rule, the smaller the involved salivary gland, the higher is the possibility of the tumor being malignant. The role of imaging in assessment of salivary gland tumour is to define intra-glandular vs. extra-glandular location, detect malignant features, assess local extension and invasion, detect nodal metastases and systemic involvement. Image guided fine needle aspiration cytology provides a safe means to obtain cytological confirmation. For lesions in the superficial parotid and submandibular gland, ultrasound is an ideal tool for initial assessment. These are superficial structures accessible by high resolution ultrasound and FNAC which provides excellent resolution and tissue characterization without a radiation hazard. Nodal involvement can also be assessed. If deep tissue extension is suspected or malignancy confirmed on cytology, an MRI or CT is mandatory to evaluate tumour extent, local invasion and perineural spread. For all tumours in the sublingual gland, MRI should be performed as the risk of malignancy is high. For lesions of the deep lobe of parotid gland and the minor salivary glands, MRI and CT are the modalities of choice. Ultrasound has limited visualization of the deep lobe of parotid gland which is obscured by the mandible. Minor salivary gland lesions in the mucosa of oral cavity, pharynx and tracheo-bronchial tree, are also not accessible by conventional ultrasound. Recent study suggests that MR spectroscopy may differentiate malignant and benign salivary gland tumours as well as distinguishing Warthin's tumor from pleomorphic adenoma. However, its role in clinical practice is not well established. Similarly, the role of nuclear medicine and PET scan, in imaging of parotid masses is limited. Sialography is used to delineate the salivary ductal system and has limited role in assessment of tumour extent.

  10. Classificação dos tumores da mama: atualização baseada na nova classificação da Organização Mundial da Saúde de 2012 Classification of tumours of the breast: an update based on the new 2012 World Health Organization Classification

    Directory of Open Access Journals (Sweden)

    Helenice Gobbi

    2012-12-01

    the Classification of Breast Tumors in July 2012. This review summarizes the principal changes that were introduced in the new classification with emphasis on diagnostic and therapeutic implications. The major changes were: (i the new edition is entirely dedicated to breast tumors; (ii the epithelial tumors were sorted differently, recognizing nine special types and variants, and eleven rare types of breast tumors apart from invasive ductal carcinoma of no special type. New codes were included for the lobular, medullary, and metaplastic subtypes; (iii new scores were suggested for the immunohistochemical evaluation of hormone receptor (> 1% positive cells and human epidermal growth factor receptor 2 (HER2 (> 30% highly positive cells surrounding the whole membrane; (iv a new approach to molecular and genomic classification of breast cancer was presented including predictive and prognostic tests using gene expression profile; (v the traditional terminology of intraductal proliferative lesions was maintained and the terminology ductal intraepithelial neoplasia was not recommended; (vi the prognostic importance of distinguishing atypical lobular hyperplasia and lobular carcinoma in situ (LCIS within the spectrum of lobular neoplasia was acknowledged; (vii the columnar cell lesions (columnar cell change and columnar cell hyperplasia without atypia were excluded from the flat epithelial atypia group, whose biological behavior is still unknown. It is expected that the widespread use of the new classification by pathologists and oncologists will benefit patients by improving diagnostic and therapeutic decisions.

  11. 阿片受体类型和功能及其在猪脑中的个体发育特点%Classification and Functions of Opioid Receptors and Their Ontogenic Characterization in Pig Brain

    Institute of Scientific and Technical Information of China (English)

    李定健

    2016-01-01

    The research progress on classification of opioid receptors and introduced functions of four main types of receptors were summarized. In animal brains, changes in the opioid receptor number and their afifnity to opioid ligands can affect opioidergic control of brain functions or central nervous control of endocrine system. In order to provide the references for changes of opioid receptors in pig brain, the ontogenic characterization of opioid receptors in pig brains was elaborated.%总结阿片受体类型的研究进展,介绍4种主要类型受体的功能。动物脑中阿片受体的数量以及它们与阿片配体亲和力的改变能够对脑功能的阿片控制或内分泌系统的中枢神经控制产生影响。为明确猪脑中阿片受体的变化,阐述了阿片受体在猪脑中的个体发育特点。

  12. Antigens in human glioblastomas and meningiomas: Search for tumour and onco-foetal antigens. Estimation of S-100 and GFA protein

    DEFF Research Database (Denmark)

    Dittmann, L; Axelsen, N H; Norgaard-Pedersen, B

    1977-01-01

    Extracts of glioblastomas and meningiomas were analysed by quantitative immunoelectrophoresis for the presence of foetal brain antigens and tumour-associated antigens, and levels of 2 normal brain-specific proteins were also determined. The following antibodies were used: monospecific anti-S-100......-alpha-foetoprotein; and monospecific anti-ferritin. Using the antibodies raised against the tumours, several antigens not present in foetal or adult normal brain were found in the glioblastomas and the meningiomas. These antigens cross-reacted with antigens present in normal liver and were therefore not tumour-associated. S-100...