WorldWideScience

Sample records for human cancer classification

  1. Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices.

    Science.gov (United States)

    Munteanu, Cristian Robert; Magalhães, Alexandre L; Uriarte, Eugenio; González-Díaz, Humberto

    2009-03-21

    The cancer diagnostic is a complex process and, sometimes, the specific markers can interfere or produce negative results. Thus, new simple and fast theoretical models are required. One option is the complex network graphs theory that permits us to describe any real system, from the small molecules to the complex genetic, neural or social networks by transforming real properties in topological indices. This work converts the protein primary structure data in specific Randic's star networks topological indices using the new sequence to star networks (S2SNet) application. A set of 1054 proteins were selected from previous works and contains proteins related or not with two types of cancer, human breast cancer (HBC) and human colon cancer (HCC). The general discriminant analysis method generates an input-coded multi-target classification model with the training/predicting set accuracies of 90.0% for the forward stepwise model type. In addition, a protein subset was modified by single amino acid mutations with higher log-odds PAM250 values and tested with the new classification if can be related with HBC or HCC. In conclusion, we shown that, using simple input data such is the primary protein sequence and the simples linear analysis, it is possible to obtain accurate classification models that can predict if a new protein related with two types of cancer. These results promote the use of the S2SNet in clinical proteomics.

  2. 3D texture analysis for classification of second harmonic generation images of human ovarian cancer

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    Wen, Bruce; Campbell, Kirby R.; Tilbury, Karissa; Nadiarnykh, Oleg; Brewer, Molly A.; Patankar, Manish; Singh, Vikas; Eliceiri, Kevin. W.; Campagnola, Paul J.

    2016-10-01

    Remodeling of the collagen architecture in the extracellular matrix (ECM) has been implicated in ovarian cancer. To quantify these alterations we implemented a form of 3D texture analysis to delineate the fibrillar morphology observed in 3D Second Harmonic Generation (SHG) microscopy image data of normal (1) and high risk (2) ovarian stroma, benign ovarian tumors (3), low grade (4) and high grade (5) serous tumors, and endometrioid tumors (6). We developed a tailored set of 3D filters which extract textural features in the 3D image sets to build (or learn) statistical models of each tissue class. By applying k-nearest neighbor classification using these learned models, we achieved 83-91% accuracies for the six classes. The 3D method outperformed the analogous 2D classification on the same tissues, where we suggest this is due the increased information content. This classification based on ECM structural changes will complement conventional classification based on genetic profiles and can serve as an additional biomarker. Moreover, the texture analysis algorithm is quite general, as it does not rely on single morphological metrics such as fiber alignment, length, and width but their combined convolution with a customizable basis set.

  3. Non-gaussian distributions affect identification of expression patterns, functional annotation, and prospective classification in human cancer genomes.

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    Nicholas F Marko

    Full Text Available INTRODUCTION: Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research. METHODS: We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome. RESULTS: Central moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect. CONCLUSIONS: Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that "small" departures from normality in the expression data distributions are analytically-insignificant and that "robust" gene-calling algorithms can fully compensate for these effects.

  4. Study on image feature extraction and classification for human colorectal cancer using optical coherence tomography

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    Huang, Shu-Wei; Yang, Shan-Yi; Huang, Wei-Cheng; Chiu, Han-Mo; Lu, Chih-Wei

    2011-06-01

    Most of the colorectal cancer has grown from the adenomatous polyp. Adenomatous lesions have a well-documented relationship to colorectal cancer in previous studies. Thus, to detect the morphological changes between polyp and tumor can allow early diagnosis of colorectal cancer and simultaneous removal of lesions. OCT (Optical coherence tomography) has been several advantages including high resolution and non-invasive cross-sectional image in vivo. In this study, we investigated the relationship between the B-scan OCT image features and histology of malignant human colorectal tissues, also en-face OCT image and the endoscopic image pattern. The in-vitro experiments were performed by a swept-source optical coherence tomography (SS-OCT) system; the swept source has a center wavelength at 1310 nm and 160nm in wavelength scanning range which produced 6 um axial resolution. In the study, the en-face images were reconstructed by integrating the axial values in 3D OCT images. The reconstructed en-face images show the same roundish or gyrus-like pattern with endoscopy images. The pattern of en-face images relate to the stages of colon cancer. Endoscopic OCT technique would provide three-dimensional imaging and rapidly reconstruct en-face images which can increase the speed of colon cancer diagnosis. Our results indicate a great potential for early detection of colorectal adenomas by using the OCT imaging.

  5. Clinical classification of cancer cachexia: phenotypic correlates in human skeletal muscle.

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    Neil Johns

    Full Text Available BACKGROUND: Cachexia affects the majority of patients with advanced cancer and is associated with a reduction in treatment tolerance, response to therapy, and duration of survival. One impediment towards the effective treatment of cachexia is a validated classification system. METHODS: 41 patients with resectable upper gastrointestinal (GI or pancreatic cancer underwent characterisation for cachexia based on weight-loss (WL and/or low muscularity (LM. Four diagnostic criteria were used >5%WL, >10%WL, LM, and LM+>2%WL. All patients underwent biopsy of the rectus muscle. Analysis included immunohistochemistry for fibre size and type, protein and nucleic acid concentration, Western blots for markers of autophagy, SMAD signalling, and inflammation. FINDINGS: Compared with non-cachectic cancer patients, patients with LM or LM+>2%WL, mean muscle fibre diameter was reduced by about 25% (p = 0.02 and p = 0.001 respectively. No significant difference in fibre diameter was observed if patients had WL alone. Regardless of classification, there was no difference in fibre number or proportion of fibre type across all myosin heavy chain isoforms. Mean muscle protein content was reduced and the ratio of RNA/DNA decreased in patients with either >5%WL or LM+>2%WL. Compared with non-cachectic patients, SMAD3 protein levels were increased in patients with >5%WL (p = 0.022 and with >10%WL, beclin (p = 0.05 and ATG5 (p = 0.01 protein levels were increased. There were no differences in phospho-NFkB or phospho-STAT3 levels across any of the groups. CONCLUSION: Muscle fibre size, biochemical composition and pathway phenotype can vary according to whether the diagnostic criteria for cachexia are based on weight loss alone, a measure of low muscularity alone or a combination of the two. For intervention trials where the primary end-point is a change in muscle mass or function, use of combined diagnostic criteria may allow identification of a more

  6. Proteomic classification of breast cancer.

    LENUS (Irish Health Repository)

    Kamel, Dalia

    2012-11-01

    Being a significant health problem that affects patients in various age groups, breast cancer has been extensively studied to date. Recently, molecular breast cancer classification has advanced significantly with the availability of genomic profiling technologies. Proteomic technologies have also advanced from traditional protein assays including enzyme-linked immunosorbent assay, immunoblotting and immunohistochemistry to more comprehensive approaches including mass spectrometry and reverse phase protein lysate arrays (RPPA). The purpose of this manuscript is to review the current protein markers that influence breast cancer prediction and prognosis and to focus on novel advances in proteomic classification of breast cancer.

  7. Classification of human leukocyte antigen (HLA) supertypes

    DEFF Research Database (Denmark)

    Wang, Mingjun; Claesson, Mogens H

    2014-01-01

    Identification of new antigenic peptides, derived from infectious agents or cancer cells, which bind to human leukocyte antigen (HLA) class I and II molecules, is of importance for the development of new effective vaccines capable of activating the cellular arm of the immune response. However...... this complexity is to group thousands of different HLA molecules into several so-called HLA supertypes: a classification that refers to a group of HLA alleles with largely overlapping peptide binding specificities. In this chapter, we focus on the state-of-the-art classification of HLA supertypes including HLA...

  8. Naïve Bayes QSDR classification based on spiral-graph Shannon entropies for protein biomarkers in human colon cancer.

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    Aguiar-Pulido, Vanessa; Munteanu, Cristian R; Seoane, José A; Fernández-Blanco, Enrique; Pérez-Montoto, Lázaro G; González-Díaz, Humberto; Dorado, Julián

    2012-06-01

    Fast cancer diagnosis represents a real necessity in applied medicine due to the importance of this disease. Thus, theoretical models can help as prediction tools. Graph theory representation is one option because it permits us to numerically describe any real system such as the protein macromolecules by transforming real properties into molecular graph topological indices. This study proposes a new classification model for proteins linked with human colon cancer by using spiral graph topological indices of protein amino acid sequences. The best quantitative structure-disease relationship model is based on eleven Shannon entropy indices. It was obtained with the Naïve Bayes method and shows excellent predictive ability (90.92%) for new proteins linked with this type of cancer. The statistical analysis confirms that this model allows diagnosing the absence of human colon cancer obtaining an area under receiver operating characteristic of 0.91. The methodology presented can be used for any type of sequential information such as any protein and nucleic acid sequence.

  9. Novelty detection for breast cancer image classification

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    Cichosz, Pawel; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał; Oleszkiewicz, Witold

    2016-09-01

    Using classification learning algorithms for medical applications may require not only refined model creation techniques and careful unbiased model evaluation, but also detecting the risk of misclassification at the time of model application. This is addressed by novelty detection, which identifies instances for which the training set is not sufficiently representative and for which it may be safer to restrain from classification and request a human expert diagnosis. The paper investigates two techniques for isolated instance identification, based on clustering and one-class support vector machines, which represent two different approaches to multidimensional outlier detection. The prediction quality for isolated instances in breast cancer image data is evaluated using the random forest algorithm and found to be substantially inferior to the prediction quality for non-isolated instances. Each of the two techniques is then used to create a novelty detection model which can be combined with a classification model and used at the time of prediction to detect instances for which the latter cannot be reliably applied. Novelty detection is demonstrated to improve random forest prediction quality and argued to deserve further investigation in medical applications.

  10. IDM-PhyChm-Ens: intelligent decision-making ensemble methodology for classification of human breast cancer using physicochemical properties of amino acids.

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    Ali, Safdar; Majid, Abdul; Khan, Asifullah

    2014-04-01

    Development of an accurate and reliable intelligent decision-making method for the construction of cancer diagnosis system is one of the fast growing research areas of health sciences. Such decision-making system can provide adequate information for cancer diagnosis and drug discovery. Descriptors derived from physicochemical properties of protein sequences are very useful for classifying cancerous proteins. Recently, several interesting research studies have been reported on breast cancer classification. To this end, we propose the exploitation of the physicochemical properties of amino acids in protein primary sequences such as hydrophobicity (Hd) and hydrophilicity (Hb) for breast cancer classification. Hd and Hb properties of amino acids, in recent literature, are reported to be quite effective in characterizing the constituent amino acids and are used to study protein foldings, interactions, structures, and sequence-order effects. Especially, using these physicochemical properties, we observed that proline, serine, tyrosine, cysteine, arginine, and asparagine amino acids offer high discrimination between cancerous and healthy proteins. In addition, unlike traditional ensemble classification approaches, the proposed 'IDM-PhyChm-Ens' method was developed by combining the decision spaces of a specific classifier trained on different feature spaces. The different feature spaces used were amino acid composition, split amino acid composition, and pseudo amino acid composition. Consequently, we have exploited different feature spaces using Hd and Hb properties of amino acids to develop an accurate method for classification of cancerous protein sequences. We developed ensemble classifiers using diverse learning algorithms such as random forest (RF), support vector machines (SVM), and K-nearest neighbor (KNN) trained on different feature spaces. We observed that ensemble-RF, in case of cancer classification, performed better than ensemble-SVM and ensemble-KNN. Our

  11. The classification of secondary colorectal liver cancer in human biopsy samples using angular dispersive x-ray diffraction and multivariate analysis

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    Theodorakou, Chrysoula; Farquharson, Michael J [Department of Radiography, City Community and Health Sciences, City University, Northampton Square, EC1V 0HB, London (United Kingdom)], E-mail: christie.theodorakou@physics.cr.man.ac.uk

    2009-08-21

    The motivation behind this study is to assess whether angular dispersive x-ray diffraction (ADXRD) data, processed using multivariate analysis techniques, can be used for classifying secondary colorectal liver cancer tissue and normal surrounding liver tissue in human liver biopsy samples. The ADXRD profiles from a total of 60 samples of normal liver tissue and colorectal liver metastases were measured using a synchrotron radiation source. The data were analysed for 56 samples using nonlinear peak-fitting software. Four peaks were fitted to all of the ADXRD profiles, and the amplitude, area, amplitude and area ratios for three of the four peaks were calculated and used for the statistical and multivariate analysis. The statistical analysis showed that there are significant differences between all the peak-fitting parameters and ratios between the normal and the diseased tissue groups. The technique of soft independent modelling of class analogy (SIMCA) was used to classify normal liver tissue and colorectal liver metastases resulting in 67% of the normal tissue samples and 60% of the secondary colorectal liver tissue samples being classified correctly. This study has shown that the ADXRD data of normal and secondary colorectal liver cancer are statistically different and x-ray diffraction data analysed using multivariate analysis have the potential to be used as a method of tissue classification.

  12. Cancer classification using the Immunoscore: a worldwide task force.

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    Galon, Jérôme; Pagès, Franck; Marincola, Francesco M; Angell, Helen K; Thurin, Magdalena; Lugli, Alessandro; Zlobec, Inti; Berger, Anne; Bifulco, Carlo; Botti, Gerardo; Tatangelo, Fabiana; Britten, Cedrik M; Kreiter, Sebastian; Chouchane, Lotfi; Delrio, Paolo; Arndt, Hartmann; Asslaber, Martin; Maio, Michele; Masucci, Giuseppe V; Mihm, Martin; Vidal-Vanaclocha, Fernando; Allison, James P; Gnjatic, Sacha; Hakansson, Leif; Huber, Christoph; Singh-Jasuja, Harpreet; Ottensmeier, Christian; Zwierzina, Heinz; Laghi, Luigi; Grizzi, Fabio; Ohashi, Pamela S; Shaw, Patricia A; Clarke, Blaise A; Wouters, Bradly G; Kawakami, Yutaka; Hazama, Shoichi; Okuno, Kiyotaka; Wang, Ena; O'Donnell-Tormey, Jill; Lagorce, Christine; Pawelec, Graham; Nishimura, Michael I; Hawkins, Robert; Lapointe, Réjean; Lundqvist, Andreas; Khleif, Samir N; Ogino, Shuji; Gibbs, Peter; Waring, Paul; Sato, Noriyuki; Torigoe, Toshihiko; Itoh, Kyogo; Patel, Prabhu S; Shukla, Shilin N; Palmqvist, Richard; Nagtegaal, Iris D; Wang, Yili; D'Arrigo, Corrado; Kopetz, Scott; Sinicrope, Frank A; Trinchieri, Giorgio; Gajewski, Thomas F; Ascierto, Paolo A; Fox, Bernard A

    2012-10-03

    Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the 'Immunoscore' into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J

  13. Cancer Therapy (Preclinical and Clinical): A Decimal Classification, (Categories 51.1, 51.2, and 51.3).

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    Schneider, John H.

    This hierarchical decimal classification of information related to cancer therapy in humans and animals (preceeded by a few general categories) is a working draft of categories taken from an extensive classification of biomedical information. Because the classification identifies very small areas of cancer information, it can be used for precise…

  14. Pathological Bases for a Robust Application of Cancer Molecular Classification

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    Salvador J. Diaz-Cano

    2015-04-01

    Full Text Available Any robust classification system depends on its purpose and must refer to accepted standards, its strength relying on predictive values and a careful consideration of known factors that can affect its reliability. In this context, a molecular classification of human cancer must refer to the current gold standard (histological classification and try to improve it with key prognosticators for metastatic potential, staging and grading. Although organ-specific examples have been published based on proteomics, transcriptomics and genomics evaluations, the most popular approach uses gene expression analysis as a direct correlate of cellular differentiation, which represents the key feature of the histological classification. RNA is a labile molecule that varies significantly according with the preservation protocol, its transcription reflect the adaptation of the tumor cells to the microenvironment, it can be passed through mechanisms of intercellular transference of genetic information (exosomes, and it is exposed to epigenetic modifications. More robust classifications should be based on stable molecules, at the genetic level represented by DNA to improve reliability, and its analysis must deal with the concept of intratumoral heterogeneity, which is at the origin of tumor progression and is the byproduct of the selection process during the clonal expansion and progression of neoplasms. The simultaneous analysis of multiple DNA targets and next generation sequencing offer the best practical approach for an analytical genomic classification of tumors.

  15. Clinicopathological classification and individualized treatment of breast cancer

    Institute of Scientific and Technical Information of China (English)

    HU Hui; LIU Yin-hua; XU Ling; ZHAO Jian-xin; DUAN Xue-ning; YE Jing-ming; LI Ting

    2013-01-01

    Background The clinicopathological classification was proposed in the St.Gallen Consensus Report 2011.We conducted a retrospective analysis of breast cancer subtypes,tumor-nodal-metastatic (TNM) staging,and histopathological grade to investigate the value of these parameters in the treatment strategies of invasive breast cancer.Methods A retrospective analysis of breast cancer subtypes,TNM staging,and histopathological grading of 213 cases has been performed by the methods recommended in the St.Gallen International Expert Consensus Report 2011.The estrogen receptor (ER),progesterone receptor (PR),human epidermal growth factor receptor-2 (HER2),and Ki-67 of 213 tumor samples have been investigated by immunohistochemistry according to methods for classifying breast cancer subtypes proposed in the St.Gallen Consensus Report 2011.Results The luminal A subtype was found in 53 patients (24.9%),the luminal B subtype was found in 112 patients (52.6%),the HER2-positive subtype was found in 22 patients (10.3%),and the triple-negative subtype was found in 26 patients (12%).Histopathological grade and TNM staging differed significantly among the four subtypes of breast cancer (P<0.001).Conclusion It is important to consider TNM staging and histopathological grading in the treatment strategies of breast cancer based on the current clinicopathological classification methods.

  16. EPA`s program for risk assessment guidelines: Cancer classification issues

    Energy Technology Data Exchange (ETDEWEB)

    Wiltse, J. [Environmental Protection Agency, Washington, DC (United States)

    1990-12-31

    Issues presented are related to classification of weight of evidence in cancer risk assessments. The focus in this paper is on lines of evidence used in constructing a conclusion about potential human carcinogenicity. The paper also discusses issues that are mistakenly addressed as classification issues but are really part of the risk assessment process. 2 figs.

  17. Cancer classification using the Immunoscore: a worldwide task force

    Directory of Open Access Journals (Sweden)

    Galon Jérôme

    2012-10-01

    Full Text Available Abstract Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification summarizes data on tumor burden (T, presence of cancer cells in draining and regional lymph nodes (N and evidence for metastases (M. However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the ‘Immunoscore’ into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of

  18. Nominated Texture Based Cervical Cancer Classification

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    Edwin Jayasingh Mariarputham

    2015-01-01

    Full Text Available Accurate classification of Pap smear images becomes the challenging task in medical image processing. This can be improved in two ways. One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. This paper presents a nominated texture based cervical cancer (NTCC classification system which classifies the Pap smear images into any one of the seven classes. This can be achieved by extracting well defined texture features and selecting best classifier. Seven sets of texture features (24 features are extracted which include relative size of nucleus and cytoplasm, dynamic range and first four moments of intensities of nucleus and cytoplasm, relative displacement of nucleus within the cytoplasm, gray level cooccurrence matrix, local binary pattern histogram, tamura features, and edge orientation histogram. Few types of support vector machine (SVM and neural network (NN classifiers are used for the classification. The performance of the NTCC algorithm is tested and compared to other algorithms on public image database of Herlev University Hospital, Denmark, with 917 Pap smear images. The output of SVM is found to be best for the most of the classes and better results for the remaining classes.

  19. Preprocessing for classification of thermograms in breast cancer detection

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    Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał; Oleszkiewicz, Witold; Cichosz, Paweł; Jagodziński, Dariusz; Matysiewicz, Mateusz

    2016-09-01

    Performance of binary classification of breast cancer suffers from high imbalance between classes. In this article we present the preprocessing module designed to negate the discrepancy in training examples. Preprocessing module is based on standardization, Synthetic Minority Oversampling Technique and undersampling. We show how each algorithm influences classification accuracy. Results indicate that described module improves overall Area Under Curve up to 10% on the tested dataset. Furthermore we propose other methods of dealing with imbalanced datasets in breast cancer classification.

  20. Accurate molecular classification of cancer using simple rules

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    Gotoh Osamu

    2009-10-01

    Full Text Available Abstract Background One intractable problem with using microarray data analysis for cancer classification is how to reduce the extremely high-dimensionality gene feature data to remove the effects of noise. Feature selection is often used to address this problem by selecting informative genes from among thousands or tens of thousands of genes. However, most of the existing methods of microarray-based cancer classification utilize too many genes to achieve accurate classification, which often hampers the interpretability of the models. For a better understanding of the classification results, it is desirable to develop simpler rule-based models with as few marker genes as possible. Methods We screened a small number of informative single genes and gene pairs on the basis of their depended degrees proposed in rough sets. Applying the decision rules induced by the selected genes or gene pairs, we constructed cancer classifiers. We tested the efficacy of the classifiers by leave-one-out cross-validation (LOOCV of training sets and classification of independent test sets. Results We applied our methods to five cancerous gene expression datasets: leukemia (acute lymphoblastic leukemia [ALL] vs. acute myeloid leukemia [AML], lung cancer, prostate cancer, breast cancer, and leukemia (ALL vs. mixed-lineage leukemia [MLL] vs. AML. Accurate classification outcomes were obtained by utilizing just one or two genes. Some genes that correlated closely with the pathogenesis of relevant cancers were identified. In terms of both classification performance and algorithm simplicity, our approach outperformed or at least matched existing methods. Conclusion In cancerous gene expression datasets, a small number of genes, even one or two if selected correctly, is capable of achieving an ideal cancer classification effect. This finding also means that very simple rules may perform well for cancerous class prediction.

  1. Classification of human carcinoma cells using multispectral imagery

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    Ćinar, Umut; Y. Ćetin, Yasemin; Ćetin-Atalay, Rengul; Ćetin, Enis

    2016-03-01

    In this paper, we present a technique for automatically classifying human carcinoma cell images using textural features. An image dataset containing microscopy biopsy images from different patients for 14 distinct cancer cell line type is studied. The images are captured using a RGB camera attached to an inverted microscopy device. Texture based Gabor features are extracted from multispectral input images. SVM classifier is used to generate a descriptive model for the purpose of cell line classification. The experimental results depict satisfactory performance, and the proposed method is versatile for various microscopy magnification options.

  2. Computers vs. Humans in Galaxy Classification

    Science.gov (United States)

    Kohler, Susanna

    2016-04-01

    In this age of large astronomical surveys, one major scientific bottleneck is the analysis of enormous data sets. Traditionally, this task requires human input but could computers eventually take over? A pair of scientists explore this question by testing whether computers can classify galaxies as well as humans.Examples of disagreement: galaxies that Galaxy-Zoo humans classified as spirals with 95% agreement, but the computer algorithm classified as ellipticals with 70% certainty. Most are cases where the computer got it wrong but not all of them. [Adapted from Kuminski et al. 2016]Limits of Citizen ScienceGalaxy Zoo is an internet-based citizen science project that uses non-astronomer volunteers to classify galaxy images. This is an innovative way to provide more manpower, but its still only practical for limited catalog sizes. How do we handle the data from upcoming surveys like the Large Synoptic Survey Telescope (LSST), which will produce billions of galaxy images when it comes online?In a recent study by Evan Kuminski and Lior Shamir, two computer scientists at Lawrence Technological University in Michigan, a machine learning algorithm known as Wndchrm was used to classify a dataset of Sloan Digital Sky Survey (SDSS) galaxies into ellipticals and spirals. The authors goal is to determine whether their algorithm can classify galaxies as accurately as the human volunteers for Galaxy Zoo.Automatic ClassificationAfter training their classifier on a small set of spiral and elliptical galaxies, Kuminski and Shamir set it loose on a catalog of ~3 million SDSS galaxies. The classifier first computes a set of 2,885 numerical descriptors (like textures, edges, and shapes) for each galaxy image, and then uses these descriptors to categorize the galaxy as spiral or elliptical.Rate of agreement of the computer classification with human classification (for the Galaxy Zoo superclean subset) for different ranges of computed classification certainties. For certainties above

  3. Mass spectrometry cancer data classification using wavelets and genetic algorithm.

    Science.gov (United States)

    Nguyen, Thanh; Nahavandi, Saeid; Creighton, Douglas; Khosravi, Abbas

    2015-12-21

    This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.

  4. NIM: a node influence based method for cancer classification.

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    Wang, Yiwen; Yao, Min; Yang, Jianhua

    2014-01-01

    The classification of different cancer types owns great significance in the medical field. However, the great majority of existing cancer classification methods are clinical-based and have relatively weak diagnostic ability. With the rapid development of gene expression technology, it is able to classify different kinds of cancers using DNA microarray. Our main idea is to confront the problem of cancer classification using gene expression data from a graph-based view. Based on a new node influence model we proposed, this paper presents a novel high accuracy method for cancer classification, which is composed of four parts: the first is to calculate the similarity matrix of all samples, the second is to compute the node influence of training samples, the third is to obtain the similarity between every test sample and each class using weighted sum of node influence and similarity matrix, and the last is to classify each test sample based on its similarity between every class. The data sets used in our experiments are breast cancer, central nervous system, colon tumor, prostate cancer, acute lymphoblastic leukemia, and lung cancer. experimental results showed that our node influence based method (NIM) is more efficient and robust than the support vector machine, K-nearest neighbor, C4.5, naive Bayes, and CART.

  5. NIM: A Node Influence Based Method for Cancer Classification

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    Yiwen Wang

    2014-01-01

    Full Text Available The classification of different cancer types owns great significance in the medical field. However, the great majority of existing cancer classification methods are clinical-based and have relatively weak diagnostic ability. With the rapid development of gene expression technology, it is able to classify different kinds of cancers using DNA microarray. Our main idea is to confront the problem of cancer classification using gene expression data from a graph-based view. Based on a new node influence model we proposed, this paper presents a novel high accuracy method for cancer classification, which is composed of four parts: the first is to calculate the similarity matrix of all samples, the second is to compute the node influence of training samples, the third is to obtain the similarity between every test sample and each class using weighted sum of node influence and similarity matrix, and the last is to classify each test sample based on its similarity between every class. The data sets used in our experiments are breast cancer, central nervous system, colon tumor, prostate cancer, acute lymphoblastic leukemia, and lung cancer. experimental results showed that our node influence based method (NIM is more efficient and robust than the support vector machine, K-nearest neighbor, C4.5, naive Bayes, and CART.

  6. Has the new TNM classification for colorectal cancer improved care?

    NARCIS (Netherlands)

    Nagtegaal, I.D.; Quirke, P.; Schmoll, H.J.

    2012-01-01

    In 2009, the Union for International Cancer Control issued the seventh edition of the well-used T (tumor), N (node), and M (metastasis) classification guidelines. There has been a continual refinement of the staging for colorectal cancer since this system for assessing tumor stage was initially adop

  7. Sparse discriminant analysis for breast cancer biomarker identification and classification

    Institute of Scientific and Technical Information of China (English)

    Yu Shi; Daoqing Dai; Chaochun Liu; Hong Yan

    2009-01-01

    Biomarker identification and cancer classification are two important procedures in microarray data analysis. We propose a novel uni-fied method to carry out both tasks. We first preselect biomarker candidates by eliminating unrelated genes through the BSS/WSS ratio filter to reduce computational cost, and then use a sparse discriminant analysis method for simultaneous biomarker identification and cancer classification. Moreover, we give a mathematical justification about automatic biomarker identification. Experimental results show that the proposed method can identify key genes that have been verified in biochemical or biomedical research and classify the breast cancer type correctly.

  8. Multi-label classification for colon cancer using histopathological images.

    Science.gov (United States)

    Xu, Yan; Jiao, Liping; Wang, Siyu; Wei, Junsheng; Fan, Yubo; Lai, Maode; Chang, Eric I-Chao

    2013-12-01

    Colon cancer classification has a significant guidance value in clinical diagnoses and medical prognoses. The classification of colon cancers with high accuracy is the premise of efficient treatment. Our task is to build a system for colon cancer detection and classification based on slide histopathological images. Some former researches focus on single label classification. Through analyzing large amount of colon cancer images, we found that one image may contain cancer regions of multiple types. Therefore, we reformulated the task as multi-label problem. Four kinds of features (Color Histogram, Gray-Level Co-occurrence Matrix, Histogram of Oriented Gradients and Euler number) were introduced to compose our discriminative feature set, extracted from our dataset that includes six single categories and four multi-label categories. In order to evaluate the performance and make comparison with our multi-label model, three commonly used multi-classification methods were designed in our experiment including one-against-all SVM (OAA), one-against-one SVM (OAO) and multi-structure SVM. Four indicators (Precision, Recall, F-measure, and Accuracy) under 3-fold cross-validation were used to validate the performance of our approach. Experiment results show that the precision, recall and F-measure of multi-label method as 73.7%, 68.2%, and 70.8% with all features, which are higher than the other three classifiers. These results demonstrate the effectiveness and efficiency of our method on colon histopathological images analysis.

  9. Magnetic resonance imaging texture analysis classification of primary breast cancer

    Energy Technology Data Exchange (ETDEWEB)

    Waugh, S.A.; Lerski, R.A. [Ninewells Hospital and Medical School, Department of Medical Physics, Dundee (United Kingdom); Purdie, C.A.; Jordan, L.B. [Ninewells Hospital and Medical School, Department of Pathology, Dundee (United Kingdom); Vinnicombe, S. [University of Dundee, Division of Imaging and Technology, Ninewells Hospital and Medical School, Dundee (United Kingdom); Martin, P. [Ninewells Hospital and Medical School, Department of Clinical Radiology, Dundee (United Kingdom); Thompson, A.M. [University of Texas MD Anderson Cancer Center, Department of Surgical Oncology, Houston, TX (United States)

    2016-02-15

    Patient-tailored treatments for breast cancer are based on histological and immunohistochemical (IHC) subtypes. Magnetic Resonance Imaging (MRI) texture analysis (TA) may be useful in non-invasive lesion subtype classification. Women with newly diagnosed primary breast cancer underwent pre-treatment dynamic contrast-enhanced breast MRI. TA was performed using co-occurrence matrix (COM) features, by creating a model on retrospective training data, then prospectively applying to a test set. Analyses were blinded to breast pathology. Subtype classifications were performed using a cross-validated k-nearest-neighbour (k = 3) technique, with accuracy relative to pathology assessed and receiver operator curve (AUROC) calculated. Mann-Whitney U and Kruskal-Wallis tests were used to assess raw entropy feature values. Histological subtype classifications were similar across training (n = 148 cancers) and test sets (n = 73 lesions) using all COM features (training: 75 %, AUROC = 0.816; test: 72.5 %, AUROC = 0.823). Entropy features were significantly different between lobular and ductal cancers (p < 0.001; Mann-Whitney U). IHC classifications using COM features were also similar for training and test data (training: 57.2 %, AUROC = 0.754; test: 57.0 %, AUROC = 0.750). Hormone receptor positive and negative cancers demonstrated significantly different entropy features. Entropy features alone were unable to create a robust classification model. Textural differences on contrast-enhanced MR images may reflect underlying lesion subtypes, which merits testing against treatment response. (orig.)

  10. Contour classification in thermographic images for detection of breast cancer

    Science.gov (United States)

    Okuniewski, Rafał; Nowak, Robert M.; Cichosz, Paweł; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Oleszkiewicz, Witold

    2016-09-01

    Thermographic images of breast taken by the Braster device are uploaded into web application which uses different classification algorithms to automatically decide whether a patient should be more thoroughly examined. This article presents the approach to the task of classifying contours visible on thermographic images of breast taken by the Braster device in order to make the decision about the existence of cancerous tumors in breast. It presents the results of the researches conducted on the different classification algorithms.

  11. Histotype-based prognostic classification of gastric cancer

    Institute of Scientific and Technical Information of China (English)

    Anna Maria Chiaravalli; Catherine Klersy; Alessandro Vanoli; Andrea Ferretti; Carlo Capella; Enrico Solcia

    2012-01-01

    AIM:To test the efficiency of a recently proposed histotype-based grading system in a consecutive series of gastric cancers.METHOIS:Two hundred advanced gastric cancers operated upon in 1980-1987 and followed for a median 159 mo were investigated on hematoxylin-eosinstained sections to identify low-grade [muconodular,well differentiated tubular,diffuse desmoplastic and high lymphoid response (HLR)],high-grade (anaplastic and mucinous invasive) and intermediate-grade (ordinarycohesive,diffuse and mucinous) cancers,in parallel with a previously investigated series of 292 cases.In addition,immunohistochemical analyses for CD8,CD11 and HLA-DR antigens,pancytokeratin and podoplanin,as well as immunohistochemical and molecular tests for microsatellite DNA instability and in situ hybridization for the Epstein-Barr virus (EBV) EBER1 gene were performed.Patient survival was assessed with death rates per 100 person-years and with Kaplan-Meier or Cox model estimates.RESULTS:Collectively,the four low-grade histotypes accounted for 22% and the two high-grade histotypes for 7% of the consecutive cancers investigated,while the remaining 71% of cases were intermediate-grade cancers,with highly significant,stage-independent,survival differences among the three tumor grades (P =0.004 for grade 1 vs 2 and P =0.0019 for grade 2 vs grade 3),thus confirming the results in the original series.A combined analysis of 492 cases showed an improved prognostic value of histotype-based grading compared with the Lauren classification.In addition,it allowed better characterization of rare histotypes,particularly the three subsets of prognostically different mucinous neoplasms,of which 10 ordinary mucinous cancers showed stage-inclusive survival worse than that of 20 muconodular (P =0.037) and better than that of 21 high-grade (P < 0.001) cases.Tumors with high-level microsatellite DNA instability(MSI-H) or EBV infection,together with a third subset negative for both conditions,formed the

  12. Classification of oral cancers using Raman spectroscopy of serum

    Science.gov (United States)

    Sahu, Aditi; Talathi, Sneha; Sawant, Sharada; Krishna, C. Murali

    2014-03-01

    Oral cancers are the sixth most common malignancy worldwide, with low 5-year disease free survival rates, attributable to late detection due to lack of reliable screening modalities. Our in vivo Raman spectroscopy studies have demonstrated classification of normal and tumor as well as cancer field effects (CFE), the earliest events in oral cancers. In view of limitations such as requirement of on-site instrumentation and stringent experimental conditions of this approach, feasibility of classification of normal and cancer using serum was explored using 532 nm excitation. In this study, strong resonance features of β-carotenes, present differentially in normal and pathological conditions, were observed. In the present study, Raman spectra of sera of 36 buccal mucosa, 33 tongue cancers and 17 healthy subjects were recorded using Raman microprobe coupled with 40X objective using 785 nm excitation, a known source of excitation for biomedical applications. To eliminate heterogeneity, average of 3 spectra recorded from each sample was subjected to PC-LDA followed by leave-one-out-cross-validation. Findings indicate average classification efficiency of ~70% for normal and cancer. Buccal mucosa and tongue cancer serum could also be classified with an efficiency of ~68%. Of the two cancers, buccal mucosa cancer and normal could be classified with a higher efficiency. Findings of the study are quite comparable to that of our earlier study, which suggest that there exist significant differences, other than β- carotenes, between normal and cancerous samples which can be exploited for the classification. Prospectively, extensive validation studies will be undertaken to confirm the findings.

  13. Cancer classification based on gene expression using neural networks.

    Science.gov (United States)

    Hu, H P; Niu, Z J; Bai, Y P; Tan, X H

    2015-12-21

    Based on gene expression, we have classified 53 colon cancer patients with UICC II into two groups: relapse and no relapse. Samples were taken from each patient, and gene information was extracted. Of the 53 samples examined, 500 genes were considered proper through analyses by S-Kohonen, BP, and SVM neural networks. Classification accuracy obtained by S-Kohonen neural network reaches 91%, which was more accurate than classification by BP and SVM neural networks. The results show that S-Kohonen neural network is more plausible for classification and has a certain feasibility and validity as compared with BP and SVM neural networks.

  14. Human Viruses and Cancer

    Directory of Open Access Journals (Sweden)

    Abigail Morales-Sánchez

    2014-10-01

    Full Text Available The first human tumor virus was discovered in the middle of the last century by Anthony Epstein, Bert Achong and Yvonne Barr in African pediatric patients with Burkitt’s lymphoma. To date, seven viruses -EBV, KSHV, high-risk HPV, MCPV, HBV, HCV and HTLV1- have been consistently linked to different types of human cancer, and infections are estimated to account for up to 20% of all cancer cases worldwide. Viral oncogenic mechanisms generally include: generation of genomic instability, increase in the rate of cell proliferation, resistance to apoptosis, alterations in DNA repair mechanisms and cell polarity changes, which often coexist with evasion mechanisms of the antiviral immune response. Viral agents also indirectly contribute to the development of cancer mainly through immunosuppression or chronic inflammation, but also through chronic antigenic stimulation. There is also evidence that viruses can modulate the malignant properties of an established tumor. In the present work, causation criteria for viruses and cancer will be described, as well as the viral agents that comply with these criteria in human tumors, their epidemiological and biological characteristics, the molecular mechanisms by which they induce cellular transformation and their associated cancers.

  15. Multiclass cancer classification based on gene expression comparison

    Science.gov (United States)

    Yang, Sitan; Naiman, Daniel Q.

    2016-01-01

    As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analyses, microarray-based cancer classification comprising multiple discriminatory molecular markers is an emerging trend. Such multiclass classification problems pose new methodological and computational challenges for developing novel and effective statistical approaches. In this paper, we introduce a new approach for classifying multiple disease states associated with cancer based on gene expression profiles. Our method focuses on detecting small sets of genes in which the relative comparison of their expression values leads to class discrimination. For an m-class problem, the classification rule typically depends on a small number of m-gene sets, which provide transparent decision boundaries and allow for potential biological interpretations. We first test our approach on seven common gene expression datasets and compare it with popular classification methods including support vector machines and random forests. We then consider an extremely large cohort of leukemia cancer to further assess its effectiveness. In both experiments, our method yields comparable or even better results to benchmark classifiers. In addition, we demonstrate that our approach can integrate pathway analysis of gene expression to provide accurate and biological meaningful classification. PMID:24918456

  16. Human papillomaviruses and cancer.

    Science.gov (United States)

    Haedicke, Juliane; Iftner, Thomas

    2013-09-01

    Human papillomaviruses (HPV) are small oncogenic DNA viruses of which more than 200 types have been identified to date. A small subset of these is etiologically linked to the development of anogenital malignancies such as cervical cancer. In addition, recent studies established a causative relationship between these high-risk HPV types and tonsillar and oropharyngeal cancer. Clinical management of cervical cancer and head and neck squamous cell carcinomas (HNSCCs) is largely standardized and involves surgical removal of the tumor tissue as well as adjuvant chemoradiation therapy. Notably, the response to therapeutic intervention of HPV-positive HNSCCs has been found to be better as compared to HPV-negative tumors. Although the existing HPV vaccine is solely licensed for the prevention of cervical cancer, it might also have prophylactic potential for the development of high-risk HPV-associated HNSCCs. Another group of viruses, which belongs to the beta-HPV subgroup, has been implicated in nonmelanoma skin cancer, however, the etiology remains to be established. Treatment of HPV-induced nonmelanoma skin cancer is based on local excision. However, topically applied immune-modulating substances represent non-surgical alternatives for the management of smaller cutaneous tumors. In this review we present the current knowledge of the role of HPV in cancer development and discuss clinical management options as well as targets for the development of future intervention therapies. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  17. Influence of nuclei segmentation on breast cancer malignancy classification

    Science.gov (United States)

    Jelen, Lukasz; Fevens, Thomas; Krzyzak, Adam

    2009-02-01

    Breast Cancer is one of the most deadly cancers affecting middle-aged women. Accurate diagnosis and prognosis are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important. In this work we compare three powerful segmentation approaches and test their impact on the classification of breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c-means segmentation and textural segmentation based on co-occurrence matrix. Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes four different classifiers were trained and tested with previously extracted features. The compared classifiers are Multilayer Perceptron (MLP), Self-Organizing Maps (SOM), Principal Component-based Neural Network (PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the best results over the three compared approaches and leads to a good feature extraction with a lowest average error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron with an error rate of 3.07% using fuzzy c-means segmentation.

  18. Novel approaches for the molecular classification of prostate cancer

    Institute of Scientific and Technical Information of China (English)

    Robert H. Getzenberg

    2010-01-01

    @@ Among the urologic cancers, prostate cancer is by far the most common, and it appears to have the potential to affect almost all men throughout the world as they age. A number of studies have shown that many men with prostate cancer will not die from their disease, but rather with the disease but from other causes. These men have a form of prostate cancer that is de-scribed as "very low risk" and has often been called indolent. There are however a group of men that have a form of prostate cancer that is much more aggressive and life threatening. Unlike other cancer types, we have few tools to provide for the molecular classification of prostate cancer.

  19. MORPHOLOGICAL CLASSIFICATION OF RENAL-CANCER

    NARCIS (Netherlands)

    STORKEL, S; VANDENBERG, E

    1995-01-01

    The current classification of renal-cell adenomas (RCAs) and carcinomas (RCCs) is based on eight basic cell and tumor types (entities) with characteristic morphologic features: (1) RCCs of clear-cell type, (2) RCAs/RCCs of chromophilic-cell type, (3) RCAs/RCCs of chromophobic-cell type, (4) RCCs of

  20. Detection of skin cancer by classification of Raman spectra.

    Science.gov (United States)

    Sigurdsson, Sigurdur; Philipsen, Peter Alshede; Hansen, Lars Kai; Larsen, Jan; Gniadecka, Monika; Wulf, Hans Christian

    2004-10-01

    Skin lesion classification based on in vitro Raman spectroscopy is approached using a nonlinear neural network classifier. The classification framework is probabilistic and highly automated. The framework includes a feature extraction for Raman spectra and a fully adaptive and robust feedforward neural network classifier. Moreover, classification rules learned by the neural network may be extracted and evaluated for reproducibility, making it possible to explain the class assignment. The classification performance for the present data set, involving 222 cases and five lesion types, was 80.5%+/-5.3% correct classification of malignant melanoma, which is similar to that of trained dermatologists based on visual inspection. The skin cancer basal cell carcinoma has a classification rate of 95.8%+/-2.7%, which is excellent. The overall classification rate of skin lesions is 94.8%+/-3.0%. Spectral regions, which are important for network classification, are demonstrated to reproduce. Small distinctive bands in the spectrum, corresponding to specific lipids and proteins, are shown to hold the discriminating information which the network uses to diagnose skin lesions.

  1. Weakly supervised histopathology cancer image segmentation and classification.

    Science.gov (United States)

    Xu, Yan; Zhu, Jun-Yan; Chang, Eric I-Chao; Lai, Maode; Tu, Zhuowen

    2014-04-01

    Labeling a histopathology image as having cancerous regions or not is a critical task in cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster them into various classes. Existing supervised approaches for image classification and segmentation require detailed manual annotations for the cancer pixels, which are time-consuming to obtain. In this paper, we propose a new learning method, multiple clustered instance learning (MCIL) (along the line of weakly supervised learning) for histopathology image segmentation. The proposed MCIL method simultaneously performs image-level classification (cancer vs. non-cancer image), medical image segmentation (cancer vs. non-cancer tissue), and patch-level clustering (different classes). We embed the clustering concept into the multiple instance learning (MIL) setting and derive a principled solution to performing the above three tasks in an integrated framework. In addition, we introduce contextual constraints as a prior for MCIL, which further reduces the ambiguity in MIL. Experimental results on histopathology colon cancer images and cytology images demonstrate the great advantage of MCIL over the competing methods.

  2. Classification of neuropathic pain in cancer patients

    DEFF Research Database (Denmark)

    Brunelli, Cinzia; Bennett, Michael I; Kaasa, Stein

    2014-01-01

    Neuropathic pain (NP) in cancer patients lacks standards for diagnosis. This study is aimed at reaching consensus on the application of the International Association for the Study of Pain (IASP) special interest group for neuropathic pain (NeuPSIG) criteria to the diagnosis of NP in cancer patients...... was found on the statement "the pathophysiology of NP due to cancer can be different from non-cancer NP" (MED=9, IQR=2). Satisfactory consensus was reached for the first 3 NeuPSIG criteria (pain distribution, history, and sensory findings; MEDs⩾8, IQRs⩽3), but not for the fourth one (diagnostic test....../imaging; MED=6, IQR=3). Agreement was also reached on clinical examination by soft brush or pin stimulation (MEDs⩾7 and IQRs⩽3) and on the use of PRO descriptors for NP screening (MED=8, IQR=3). Based on the study results, a clinical algorithm for NP diagnostic criteria in cancer patients with pain...

  3. Human sapovirus classification based on complete capsid nucleotide sequences.

    Science.gov (United States)

    Oka, Tomoichiro; Mori, Kohji; Iritani, Nobuhiro; Harada, Seiya; Ueki, You; Iizuka, Setsuko; Mise, Keiji; Murakami, Kosuke; Wakita, Takaji; Katayama, Kazuhiko

    2012-02-01

    The genetically diverse sapoviruses (SaVs) are a significant cause of acute human gastroenteritis. Human SaV surveillance is becoming more critical, and a better understanding of the diversity and distribution of the viral genotypes is needed. In this study, we analyzed 106 complete human SaV capsid nucleotide sequences to provide a better understanding of their diversity. Based on those results, we propose a novel standardized classification scheme that meets the requirements of the International Calicivirus Scientific Committee. We believe the classification scheme and strains described here will be of value for the molecular characterization and classification of newly detected SaV genotypes and for comparing data worldwide.

  4. Serglycin in human cancers

    Institute of Scientific and Technical Information of China (English)

    Xin-Jian Li; Chao-Nan Qian

    2011-01-01

    Serglycin belongs to a family of small proteoglycans with Ser-Gly dipeptide repeats,and it is modified with different types of glycosaminoglycan side chains.Intracellular serglycin affects the retention and secretion of proteases,chemokines,or other cytokines by physically binding to these factors in secretory granules.Extracellular serglycin has been found to be released by several types of human cancer cells,and it is able to promote the metastasis of nasopharyngeal carcinoma cells.Serglycin can bind to CD44,which is another glycoprotein located in cellular membrane.Serglycin's function of promoting cancer cell metastasis depends on glycosylation of its core protein,which can be achieved by autocrine as well as paracrine secretion mechanisms.Further investigations are warranted to elucidate serglycin signaling mechanisms with the goal of targeting them to prevent cancer cell metastasis.

  5. Appraisal of progenitor markers in the context of molecular classification of breast cancers.

    Science.gov (United States)

    Haviv, Izhak

    2011-01-25

    Clinical management of breast cancer relies on case stratification, which increasingly employs molecular markers. The motivation behind delineating breast epithelial differentiation is to better target cancer cases through innate sensitivities bequeathed to the cancer from its normal progenitor state. A combination of histopathological and molecular classification of breast cancer cases suggests a role for progenitors in particular breast cancer cases. Although a remarkable fraction of the real tissue repertoire is maintained within a population of independent cell line cultures, some steps that are closer to the terminal differentiation state and that form a majority of primary human breast tissues are missing in the cell line cultures. This raises concerns about current breast cancer models.

  6. Classification of treatment-related mortality in children with cancer

    DEFF Research Database (Denmark)

    Alexander, Sarah; Pole, Jason D; Gibson, Paul

    2015-01-01

    Treatment-related mortality is an important outcome in paediatric cancer clinical trials. An international group of experts in supportive care in paediatric cancer developed a consensus-based definition of treatment-related mortality and a cause-of-death attribution system. The reliability...... and validity of the system was tested in 30 deaths, which were independently assessed by two clinical research associates and two paediatric oncologists. We defined treatment-related mortality as death occurring in the absence of progressive cancer. Of the 30 reviewed deaths, the reliability of classification...

  7. Definition and classification of cancer cachexia: an international consensus.

    Science.gov (United States)

    Fearon, Kenneth; Strasser, Florian; Anker, Stefan D; Bosaeus, Ingvar; Bruera, Eduardo; Fainsinger, Robin L; Jatoi, Aminah; Loprinzi, Charles; MacDonald, Neil; Mantovani, Giovanni; Davis, Mellar; Muscaritoli, Maurizio; Ottery, Faith; Radbruch, Lukas; Ravasco, Paula; Walsh, Declan; Wilcock, Andrew; Kaasa, Stein; Baracos, Vickie E

    2011-05-01

    To develop a framework for the definition and classification of cancer cachexia a panel of experts participated in a formal consensus process, including focus groups and two Delphi rounds. Cancer cachexia was defined as a multifactorial syndrome defined by an ongoing loss of skeletal muscle mass (with or without loss of fat mass) that cannot be fully reversed by conventional nutritional support and leads to progressive functional impairment. Its pathophysiology is characterised by a negative protein and energy balance driven by a variable combination of reduced food intake and abnormal metabolism. The agreed diagnostic criterion for cachexia was weight loss greater than 5%, or weight loss greater than 2% in individuals already showing depletion according to current bodyweight and height (body-mass index [BMI] definition and classification of cancer cachexia. After validation, this should aid clinical trial design, development of practice guidelines, and, eventually, routine clinical management.

  8. Prediction of Breast Cancer using Rule Based Classification

    Directory of Open Access Journals (Sweden)

    Nagendra Kumar SINGH

    2015-12-01

    Full Text Available The current work proposes a model for prediction of breast cancer using the classification approach in data mining. The proposed model is based on various parameters, including symptoms of breast cancer, gene mutation and other risk factors causing breast cancer. Mutations have been predicted in breast cancer causing genes with the help of alignment of normal and abnormal gene sequences; then predicting the class label of breast cancer (risky or safe on the basis of IF-THEN rules, using Genetic Algorithm (GA. In this work, GA has used variable gene encoding mechanisms for chromosomes encoding, uniform population generations and selects two chromosomes by Roulette-Wheel selection technique for two-point crossover, which gives better solutions. The performance of the model is evaluated using the F score measure, Matthews Correlation Coefficient (MCC and Receiver Operating Characteristic (ROC by plotting points (Sensitivity V/s 1- Specificity.

  9. Classification of Rat FTIR Colon Cancer Data Using Waveletsand BPNN

    Institute of Scientific and Technical Information of China (English)

    CHENG,Cungui; XIONG,Wei; TIAN,Yumei

    2009-01-01

    A feature extracting method based on wavelets for horizontal attenuated total reflectance Fourier transform in-frared spectroscopy (HATR-FTIR) and the cancer classification using artificial neural network trained with back-propagation algorithm is presented. The FTIR data collected from 36 normal Sprague-dawley (SD) rats, 60 1,2-DMH-induced SD rats, and 44 second generation rats of those induced rats were first preprocessed. Then, 12 feature variants were extracted using continuous wavelet analysis. Based on BPNN classification, all spectra were classified into two categories: normal and abnormal ones. The accuracy values of identifying normal, dysplastic, early carcinoma, and advanced carcinoma were 100%, 94%, 97.5%, and 100%, respectively. This result indicated that FTIR with continuous wavelet transform (CWT) and the back-propagation neural network (BPNN) could ef- fectively and easily diagnose colon cancer in its early stages.

  10. Classification of Cancer Recurrence with Alpha-Beta BAM

    Directory of Open Access Journals (Sweden)

    María Elena Acevedo

    2009-01-01

    Full Text Available Bidirectional Associative Memories (BAMs based on first model proposed by Kosko do not have perfect recall of training set, and their algorithm must iterate until it reaches a stable state. In this work, we use the model of Alpha-Beta BAM to classify automatically cancer recurrence in female patients with a previous breast cancer surgery. Alpha-Beta BAM presents perfect recall of all the training patterns and it has a one-shot algorithm; these advantages make to Alpha-Beta BAM a suitable tool for classification. We use data from Haberman database, and leave-one-out algorithm was applied to analyze the performance of our model as classifier. We obtain a percentage of classification of 99.98%.

  11. 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...

  12. Computer aided decision support system for cervical cancer classification

    Science.gov (United States)

    Rahmadwati, Rahmadwati; Naghdy, Golshah; Ros, Montserrat; Todd, Catherine

    2012-10-01

    Conventional analysis of a cervical histology image, such a pap smear or a biopsy sample, is performed by an expert pathologist manually. This involves inspecting the sample for cellular level abnormalities and determining the spread of the abnormalities. Cancer is graded based on the spread of the abnormal cells. This is a tedious, subjective and time-consuming process with considerable variations in diagnosis between the experts. This paper presents a computer aided decision support system (CADSS) tool to help the pathologists in their examination of the cervical cancer biopsies. The main aim of the proposed CADSS system is to identify abnormalities and quantify cancer grading in a systematic and repeatable manner. The paper proposes three different methods which presents and compares the results using 475 images of cervical biopsies which include normal, three stages of pre cancer, and malignant cases. This paper will explore various components of an effective CADSS; image acquisition, pre-processing, segmentation, feature extraction, classification, grading and disease identification. Cervical histological images are captured using a digital microscope. The images are captured in sufficient resolution to retain enough information for effective classification. Histology images of cervical biopsies consist of three major sections; background, stroma and squamous epithelium. Most diagnostic information are contained within the epithelium region. This paper will present two levels of segmentations; global (macro) and local (micro). At the global level the squamous epithelium is separated from the background and stroma. At the local or cellular level, the nuclei and cytoplasm are segmented for further analysis. Image features that influence the pathologists' decision during the analysis and classification of a cervical biopsy are the nuclei's shape and spread; the ratio of the areas of nuclei and cytoplasm as well as the texture and spread of the abnormalities

  13. The impact of REACH on classification for human health hazards.

    Science.gov (United States)

    Oltmanns, J; Bunke, D; Jenseit, W; Heidorn, C

    2014-11-01

    The REACH Regulation represents a major piece of chemical legislation in the EU and requires manufacturers and importers of chemicals to assess the safety of their substances. The classification of substances for their hazards is one of the crucial elements in this process. We analysed the effect of REACH on classification for human health endpoints by comparing information from REACH registration dossiers with legally binding, harmonised classifications. The analysis included 142 chemicals produced at very high tonnages in the EU, the majority of which have already been assessed in the past. Of 20 substances lacking a harmonised classification, 12 chemicals were classified in REACH registration dossiers. More importantly, 37 substances with harmonised classifications for human health endpoints had stricter classifications in registration dossiers and 29 of these were classified for at least one additional endpoint not covered by the harmonised classification. Substance-specific analyses suggest that one third of these additional endpoints emerged from experimental studies performed to fulfil information requirements under REACH, while two thirds resulted from a new assessment of pre-REACH studies. We conclude that REACH leads to an improved hazard characterisation even for substances with a potentially good data basis.

  14. Shape variability and classification of human hair: a worldwide approach.

    Science.gov (United States)

    De la Mettrie, Roland; Saint-Léger, Didier; Loussouarn, Geneviève; Garcel, Annelise; Porter, Crystal; Langaney, André

    2007-06-01

    Human hair has been commonly classified according to three conventional ethnic human subgroups, that is, African, Asian, and European. Such broad classification hardly accounts for the high complexity of human biological diversity, resulting from both multiple and past or recent mixed origins. The research reported here is intended to develop a more factual and scientific approach based on physical features of human hair. The aim of the study is dual: (1) to define hair types according to specific shape criteria through objective and simple measurements taken on hairs from 1442 subjects from 18 different countries and (2) to define such hair types without referring to human ethnicity. The driving principle is simple: Because hair can be found in many different human subgroups, defining a straight or a curly hair should provide a more objective approach than a debatable ethnicity-based classification. The proposed method is simple to use and requires the measurement of only three easily accessible descriptors of hair shape: curve diameter (CD), curl index (i), and number of waves (w). This method leads to a worldwide coherent classification of hair in eight well-defined categories. The new hair categories, as described, should be more appropriate and more reliable than conventional standards in cosmetic and forensic sciences. Furthermore, the classification can be useful for testing whether hair shape diversity follows the continuous geographic and historical pattern suggested for human genetic variation or presents major discontinuities between some large human subdivisions, as claimed by earlier classical anthropology.

  15. Colon cancer associated transcripts in human cancers.

    Science.gov (United States)

    Chen, Yincong; Xie, Haibiao; Gao, Qunjun; Zhan, Hengji; Xiao, Huizhong; Zou, Yifan; Zhang, Fuyou; Liu, Yuchen; Li, Jianfa

    2017-08-02

    Long non-coding RNAs serve as important regulators in complicated cellular activities, including cell differentiation, proliferation and death. Dysregulation of long non-coding RNAs occurs in the formation and progression of cancers. The family of colon cancer associated transcripts, long non-coding RNAs colon cancer associated transcript-1 and colon cancer associated transcript-2 are known as oncogenes involved in various cancers. Colon cancer associated transcript-1 is a novel lncRNA located in 8q24.2, and colon cancer associated transcript-2 maps to the 8q24.21 region encompassing rs6983267. Colon cancer associated transcripts have close associations with clinical characteristics, such as lymph node metastasis, high TNM stage and short overall survival. Knockdown of them can reverse the malignant phenotypes of cancer cells, including proliferation, migration, invasion and apoptosis. Moreover, they can increase the expression level of c-MYC and oncogenic microRNAs via activating a series of complex mechanisms. In brief, the family of colon cancer associated transcripts may serve as potential biomarkers or therapeutic targets for human cancers. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  16. [New molecular classification of colorectal cancer, pancreatic cancer and stomach cancer: Towards "à la carte" treatment?].

    Science.gov (United States)

    Dreyer, Chantal; Afchain, Pauline; Trouilloud, Isabelle; André, Thierry

    2016-01-01

    This review reports 3 of recently published molecular classifications of the 3 main gastro-intestinal cancers: gastric, pancreatic and colorectal adenocarcinoma. In colorectal adenocarcinoma, 6 independent classifications were combined to finally hold 4 molecular sub-groups, Consensus Molecular Subtypes (CMS 1-4), linked to various clinical, molecular and survival data. CMS1 (14% MSI with immune activation); CMS2 (37%: canonical with epithelial differentiation and activation of the WNT/MYC pathway); CMS3 (13% metabolic with epithelial differentiation and RAS mutation); CMS4 (23%: mesenchymal with activation of TGFβ pathway and angiogenesis with stromal invasion). In gastric adenocarcinoma, 4 groups were established: subtype "EBV" (9%, high frequency of PIK3CA mutations, hypermetylation and amplification of JAK2, PD-L1 and PD-L2), subtype "MSI" (22%, high rate of mutation), subtype "genomically stable tumor" (20%, diffuse histology type and mutations of RAS and genes encoding integrins and adhesion proteins including CDH1) and subtype "tumors with chromosomal instability" (50%, intestinal type, aneuploidy and receptor tyrosine kinase amplification). In pancreatic adenocarcinomas, a classification in four sub-groups has been proposed, stable subtype (20%, aneuploidy), locally rearranged subtype (30%, focal event on one or two chromosoms), scattered subtype (36%,200 structural variation events, defects in DNA maintenance). Although currently away from the care of patients, these classifications open the way to "à la carte" treatment depending on molecular biology.

  17. Research and Application of Human Capital Strategic Classification Tool: Human Capital Classification Matrix Based on Biological Natural Attribute

    Directory of Open Access Journals (Sweden)

    Yong Liu

    2014-12-01

    Full Text Available In order to study the causes of weak human capital structure strategic classification management in China, we analyze that enterprises around the world face increasingly difficult for human capital management. In order to provide strategically sound answers, the HR managers need the critical information provided by the right technology processing and analytical tools. In this study, there are different types and levels of human capital in formal organization management, which is not the same contribution to a formal organization. An important guarantee for sustained and healthy development of the formal or informal organization is lower human capital risk. To resist this risk is primarily dependent on human capital hedge force and appreciation force in value, which is largely dependent on the strategic value of the performance of senior managers. Based on the analysis of high-level managers perspective, we also discuss the value and configuration of principles and methods to be followed in human capital strategic classification based on Boston Consulting Group (BCG matrix and build Human Capital Classification (HCC matrix based on biological natural attribute to effectively realize human capital structure strategic classification.

  18. Use of DNA methylation for cancer detection and molecular classification.

    Science.gov (United States)

    Zhu, Jingde; Yao, Xuebiao

    2007-03-31

    Conjugation of the methyl group at the fifth carbon of cytosines within the palindromic dinucleotide 5'-CpG-3' sequence (DNA methylation) is the best studied epigenetic mechanism, which acts together with other epigenetic entities: histone modification, chromatin remodeling and microRNAs to shape the chromatin structure of DNA according to its functional state. The cancer genome is frequently characterized by hypermethylation of specific genes concurrently with an overall decrease in the level of 5-methyl cytosine, the pathological implication of which to the cancerous state has been well established. While the latest genome-wide technologies have been applied to classify and interpret the epigenetic layer of gene regulation in the physiological and disease states, the epigenetic testing has also been seriously explored in clinical practice for early detection, refining tumor staging and predicting disease recurrence. This critique reviews the latest research findings on the use of DNA methylation in cancer diagnosis, prognosis and staging/classification.

  19. Report: Human cancer genetics

    Institute of Scientific and Technical Information of China (English)

    LI Marilyn; ALBERTSON Donna

    2006-01-01

    The short report will be focused on the genetic basis and possible mechanisms of tumorigenesis, common types of cancer, the importance of genetic diagnosis of cancer, and the methodology of cancer genetic diagnosis. They will also review presymptomatic testing of hereditary cancers, and the application of expression profiling to identify patients likely to benefit from particular therapeutic approaches.

  20. Human cancer genetics*

    OpenAIRE

    2006-01-01

    The short report will be focused on the genetic basis and possible mechanisms of tumorigenesis, common types of cancer, the importance of genetic diagnosis of cancer, and the methodology of cancer genetic diagnosis. They will also review presymptomatic testing of hereditary cancers, and the application of expression profiling to identify patients likely to benefit from particular therapeutic approaches.

  1. Registration and classification of adolescent and young adult cancer cases.

    Science.gov (United States)

    Pollock, Brad H; Birch, Jillian M

    2008-05-01

    Cancer registries are an important research resource that facilitate the study of etiology, tumor biology, patterns of delayed diagnosis and health planning needs. When outcome data are included, registries can track secular changes in survival related to improvements in early detection or treatment. The surveillance, epidemiology, and end results (SEER) registry has been used to identify major gaps in survival for older adolescent and young adult (AYA) patients compared with younger children and older adults. In order to determine the reasons for this gap, the complete registration and accurate classification of AYA malignancies is necessary. There are inconsistencies in defining the age limits for AYAs although the Adolescent and Young Adult Oncology Progress Review Group proposed a definition of ages 15 through 39 years. The central registration and classification issues for AYAs are case-finding, defining common data elements (CDE) collected across different registries and the diagnostic classification of these malignancies. Goals to achieve by 2010 include extending and validating current diagnostic classification schemes and expanding the CDE to support AYA oncology research, including the collection of tracking information to assess long-term outcomes. These efforts will advance preventive, etiologic, therapeutic, and health services-related research for this understudied age group.

  2. Classification of breast cancer cytological specimen using convolutional neural network

    Science.gov (United States)

    Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

    2017-01-01

    The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

  3. Improved PCA + LDA Applies to Gastric Cancer Image Classification Process

    Science.gov (United States)

    Gan, Lan; Lv, Wenya; Zhang, Xu; Meng, Xiuming

    Principal component analysis (PCA) and linear discriminant analysis (LDA) are two most widely used pattern recognition methods in the field of feature extraction,while PCA + LDA is often used in image recognition.Here,we apply PCA + LDA to gastric cancer image feature classification, but the traditional PCA + LDA dimension reduction method has good effect on the training sample dimensionality and clustering, the effect on test samples dimension reduction and clustering is very poor, that is, the traditional PCA + LDA exists Generalization problem on the test samples. To solve this problem, this paper proposes an improved PCA + LDA method, which mainly considers from the LDA transform; improves the traditional PCA + LDA;increase the generalization performance of LDA on test samples and increases the classification accuracy on test samples. The experiment proves that the method can achieve good clustering.

  4. 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.

  5. Molecular Classification of Gastric Cancer: A new paradigm

    Science.gov (United States)

    Shah, Manish A.; Khanin, Raya; Tang, Laura; Janjigian, Yelena Y.; Klimstra, David S.; Gerdes, Hans; Kelsen, David P.

    2011-01-01

    Purpose Gastric cancer may be subdivided into three distinct subtypes –proximal, diffuse, and distal gastric cancer– based on histopathologic and anatomic criteria. Each subtype is associated with unique epidemiology. Our aim is to test the hypothesis that these distinct gastric cancer subtypes may also be distinguished by gene expression analysis. Experimental Design Patients with localized gastric adenocarcinoma being screened for a phase II preoperative clinical trial (NCI 5917) underwent endoscopic biopsy for fresh tumor procurement. 4–6 targeted biopsies of the primary tumor were obtained. Macrodissection was performed to ensure >80% carcinoma in the sample. HG-U133A GeneChip (Affymetrix) was used for cDNA expression analysis, and all arrays were processed and analyzed using the Bioconductor R-package. Results Between November 2003 and January 2006, 57 patients were screened to identify 36 patients with localized gastric cancer who had adequate RNA for expression analysis. Using supervised analysis, we built a classifier to distinguish the three gastric cancer subtypes, successfully classifying each into tightly grouped clusters. Leave-one-out cross validation error was 0.14, suggesting that >85% of samples were classified correctly. Gene set analysis with the False Discovery Rate set at 0.25 identified several pathways that were differentially regulated when comparing each gastric cancer subtype to adjacent normal stomach. Conclusions Subtypes of gastric cancer that have epidemiologic and histologic distinction are also distinguished by gene expression data. These preliminary data suggest a new classification of gastric cancer with implications for improving our understanding of disease biology and identification of unique molecular drivers for each gastric cancer subtype. PMID:21430069

  6. Epidemiology of human fascioliasis: a review and proposed new classification.

    Science.gov (United States)

    Mas-Coma, M S; Esteban, J G; Bargues, M D

    1999-01-01

    The epidemiological picture of human fascioliasis has changed in recent years. The number of reports of humans infected with Fasciola hepatica has increased significantly since 1980 and several geographical areas have been described as endemic for the disease in humans, with prevalence and intensity ranging from low to very high. High prevalence of fascioliasis in humans does not necessarily occur in areas where fascioliasis is a major veterinary problem. Human fascioliasis can no longer be considered merely as a secondary zoonotic disease but must be considered to be an important human parasitic disease. Accordingly, we present in this article a proposed new classification for the epidemiology of human fascioliasis. The following situations are distinguished: imported cases; autochthonous, isolated, nonconstant cases; hypo-, meso-, hyper-, and holoendemics; epidemics in areas where fascioliasis is endemic in animals but not humans; and epidemics in human endemic areas.

  7. Captan: transition from 'B2' to 'not likely'. How pesticide registrants affected the EPA Cancer Classification Update.

    Science.gov (United States)

    Gordon, Elliot

    2007-01-01

    On 24 November 2004 EPA changed the cancer classification of captan from a 'probable human carcinogen' (Category B2) to 'not likely' when used according to label directions. The new cancer classification considers captan to be a potential carcinogen at prolonged high doses that cause cytotoxicity and regenerative cell hyperplasia. These high doses of captan are many orders of magnitude above those likely to be consumed in the diet, or encountered by individuals in occupational or residential settings. This revised cancer classification reflects EPA's implementation of their new cancer guidelines. The procedures involved in the reclassification effort were agreed upon with EPA and involved an Independent Transparent Review as it related to four components that formed the basis of the original 1986 B2 classification: mouse tumors; rat tumors; mutagenicity; and structural similarity to other carcinogens. A Peer Review Panel organized and administered by Toxicology Excellence for Risk Assessment (TERA) met on 2-3 September 2003. The Panel concluded that captan acted through a non-mutagenic threshold mode of action that required prolonged irritation of the duodenal villi as the initial key event. EPA's Cancer Assessment Review Committee (CARC) met on 9 June 2004 and endorsed the Peer Review findings. EPA intended to have the FIFRA Scientific Advisory Panel (SAP) consider the basis for this reclassification but found the science was robust and judged that a SAP review was not warranted. Using the revised classification, the margin of exposure is approximately 1,200,000, supporting the 'not likely' characterization.

  8. Integrated Classification of Prostate Cancer Reveals a Novel Luminal Subtype with Poor Outcome.

    Science.gov (United States)

    You, Sungyong; Knudsen, Beatrice S; Erho, Nicholas; Alshalalfa, Mohammed; Takhar, Mandeep; Al-Deen Ashab, Hussam; Davicioni, Elai; Karnes, R Jeffrey; Klein, Eric A; Den, Robert B; Ross, Ashley E; Schaeffer, Edward M; Garraway, Isla P; Kim, Jayoung; Freeman, Michael R

    2016-09-01

    Prostate cancer is a biologically heterogeneous disease with variable molecular alterations underlying cancer initiation and progression. Despite recent advances in understanding prostate cancer heterogeneity, better methods for classification of prostate cancer are still needed to improve prognostic accuracy and therapeutic outcomes. In this study, we computationally assembled a large virtual cohort (n = 1,321) of human prostate cancer transcriptome profiles from 38 distinct cohorts and, using pathway activation signatures of known relevance to prostate cancer, developed a novel classification system consisting of three distinct subtypes (named PCS1-3). We validated this subtyping scheme in 10 independent patient cohorts and 19 laboratory models of prostate cancer, including cell lines and genetically engineered mouse models. Analysis of subtype-specific gene expression patterns in independent datasets derived from luminal and basal cell models provides evidence that PCS1 and PCS2 tumors reflect luminal subtypes, while PCS3 represents a basal subtype. We show that PCS1 tumors progress more rapidly to metastatic disease in comparison with PCS2 or PCS3, including PSC1 tumors of low Gleason grade. To apply this finding clinically, we developed a 37-gene panel that accurately assigns individual tumors to one of the three PCS subtypes. This panel was also applied to circulating tumor cells (CTC) and provided evidence that PCS1 CTCs may reflect enzalutamide resistance. In summary, PCS subtyping may improve accuracy in predicting the likelihood of clinical progression and permit treatment stratification at early and late disease stages. Cancer Res; 76(17); 4948-58. ©2016 AACR.

  9. Classification of Cancer-related Death Certificates using Machine Learning

    Directory of Open Access Journals (Sweden)

    Luke Butt

    2013-05-01

    Full Text Available BackgroundCancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities.AimsIn this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated.Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes.ResultsDeath certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032 and false negative rate (0.0297 while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers.ConclusionThe selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with

  10. Survival of patients with nonseminomatous germ cell cancer: a review of the IGCC classification by Cox regression and recursive partitioning.

    Science.gov (United States)

    van Dijk, M R; Steyerberg, E W; Stenning, S P; Dusseldorp, E; Habbema, J D F

    2004-03-22

    The 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 markers alpha-fetoprotein (AFP), human chorionic gonadotrophin (HCG) and lactic dehydrogenase (LDH). The IGCC classification is easy to use and remember, but lacks flexibility. We aimed to examine the extent of any loss in discrimination within the IGCC classification in comparison with alternative modelling by formal weighing of the risk factors. We analysed survival of 3048 NSGCT patients with Cox regression and recursive partitioning for alternative classifications. Good, intermediate and poor prognosis groups were based on predicted 5-year survival. Classifications were further refined by subgrouping within the poor prognosis group. Performance was measured primarily by a bootstrap corrected c-statistic to indicate discriminative ability for future patients. The weights of the risk factors in the alternative classifications differed slightly from the implicit weights in the IGCC classification. Discriminative ability, however, did not increase clearly (IGCC classification, c=0.732; Cox classification, c=0.730; Recursive partitioning classification, c=0.709). Three subgroups could be identified within the poor prognosis groups, resulting in classifications with five prognostic groups and slightly better discriminative ability (c=0.740). In conclusion, the IGCC classification in three prognostic groups is largely supported by Cox regression and recursive partitioning. Cox regression was the most promising tool to define a more refined classification. British Journal of Cancer (2004) 90, 1176-1183. doi:10.1038/sj.bjc.6601665 www.bjcancer.com Published online 24 February 2004

  11. HUMAN PROSTATE CANCER RISK FACTORS

    Science.gov (United States)

    Prostate cancer has the highest prevalence of any non-skin cancer in the human body, with similar likelihood of neoplastic foci found within the prostates of men around the world regardless of diet, occupation, lifestyle, or other factors. Essentially all men with circulating an...

  12. Classifications of multispectral colorectal cancer tissues using convolution neural network

    Directory of Open Access Journals (Sweden)

    Hawraa Haj-Hassan

    2017-01-01

    Full Text Available Background: Colorectal cancer (CRC is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs to predict three tissue types related to the progression of CRC: benign hyperplasia (BH, intraepithelial neoplasia (IN, and carcinoma (Ca. Methods: Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca. An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. Results: An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques. Conclusions: Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.

  13. Multi-aspect angle classification of human radar signatures

    Science.gov (United States)

    Karabacak, C.; Gürbüz, S. Z.; Guldogan, M. B.; Gürbüz, A. C.

    2013-05-01

    The human micro-Doppler signature is a unique signature caused by the time-varying motion of each point on the human body, which can be used to discriminate humans from other targets exhibiting micro-Doppler, such as vehicles, tanks, helicopters, and even other animals. Classification of targets based on micro-Doppler generally involves joint timefrequency analysis of the radar return coupled with extraction of features that may be used to identify the target. Although many techniques have been investigated, including artificial neural networks and support vector machines, almost all suffer a drastic drop in classification performance as the aspect angle of human motion relative to the radar increases. This paper focuses on the use of radar networks to obtain multi-aspect angle data and thereby ameliorate the dependence of classification performance on aspect angle. Knowledge of human walking kinematics is exploited to generate a fuse spectrogram that incorporates estimates of model parameters obtained from each radar in the network. It is shown that the fused spectrogram better approximates the truly underlying motion of the target observed as compared with spectrograms generated from individual nodes.

  14. Molecular voting for glioma classification reflecting heterogeneity in the continuum of cancer progression.

    Science.gov (United States)

    Fuller, Gregory N; Mircean, Cristian; Tabus, Ioan; Taylor, Ellen; Sawaya, Raymond; Bruner, Janet M; Shmulevich, Ilya; Zhang, Wei

    2005-09-01

    Gliomas, the most common brain tumors, are generally categorized into two lineages (astrocytic and oligodendrocytic) and further classified as low-grade (astrocytoma and oligodendroglioma), mid-grade (anaplastic astrocytoma and anaplastic oligodendroglioma), and high-grade (glioblastoma multiforme) based on morphological features. A strict classification scheme has limitations because a specific glioma can be at any stage of the continuum of cancer progression and may contain mixed features. Thus, a more comprehensive classification based on molecular signatures may reflect the biological nature of specific tumors more accurately. In this study, we used microarray technology to profile the gene expression of 49 human brain tumors and applied the k-nearest neighbor algorithm for classification. We first trained the classification gene set with 19 of the most typical glioma cases and selected a set of genes that provide the lowest cross-validation classification error with k=5. We then applied this gene set to the 30 remaining cases, including several that do not belong to gliomas such as atypical meningioma. The results showed that not only does the algorithm correctly classify most of the gliomas, but the detailed voting results also provide more subtle information regarding the molecular similarities to neighboring classes. For atypical meningioma, the voting was equally split among the four classes, indicating a difficulty in placement of meningioma into the four classes of gliomas. Thus, the actual voting results, which are typically used only to decide the winning class label in k-nearest neighbor algorithms, provide a useful method for gaining deeper insight into the stage of a tumor in the continuum of cancer development.

  15. 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.

  16. Human Gait Gender Classification in Spatial and Temporal Reasoning

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2012-09-01

    Full Text Available Biometrics technology already becomes one of many application needs for identification. Every organ in the human body might be used as an identification unit because they tend to be unique characteristics. Many researchers had their focus on human organ biometrics physical characteristics such as fingerprint, human face, palm print, eye iris, DNA, and even behavioral characteristics such as a way of talk, voice and gait walking. Human Gait as the recognition object is the famous biometrics system recently. One of the important advantage in this recognition compare to other is it does not require observed subject’s attention and assistance. This paper proposed Gender classification using Human Gait video data. There are many human gait datasets created within the last 10 years. Some databases that widely used are University of South Florida (USF Gait Dataset, Chinese Academy of Sciences (CASIA Gait Dataset, and Southampton University (SOTON Gait Dataset. This paper classifies human gender in Spatial Temporal reasoning using CASIA Gait Database. Using Support Vector Machine as a Classifier, the classification result is 97.63% accuracy.

  17. Improving accuracy for cancer classification with a new algorithm for genes selection

    Directory of Open Access Journals (Sweden)

    Zhang Hongyan

    2012-11-01

    Full Text Available Abstract Background Even though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish an effective method that can select a parsimonious set of relevant genes. So far, most methods for gene selection in literature focus on screening individual or pairs of genes without considering the possible interactions among genes. Here we introduce a new computational method named the Binary Matrix Shuffling Filter (BMSF. It not only overcomes the difficulty associated with the search schemes of traditional wrapper methods and overfitting problem in large dimensional search space but also takes potential gene interactions into account during gene selection. This method, coupled with Support Vector Machine (SVM for implementation, often selects very small number of genes for easy model interpretability. Results We applied our method to 9 two-class gene expression datasets involving human cancers. During the gene selection process, the set of genes to be kept in the model was recursively refined and repeatedly updated according to the effect of a given gene on the contributions of other genes in reference to their usefulness in cancer classification. The small number of informative genes selected from each dataset leads to significantly improved leave-one-out (LOOCV classification accuracy across all 9 datasets for multiple classifiers. Our method also exhibits broad generalization in the genes selected since multiple commonly used classifiers achieved either equivalent or much higher LOOCV accuracy than those reported in literature. Conclusions Evaluation of a gene’s contribution to binary cancer classification is better to be considered after adjusting for the joint effect of a large number of other genes. A computationally efficient search scheme was provided to perform effective search in the extensive

  18. TSG: a new algorithm for binary and multi-class cancer classification and informative genes selection

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    Wang Haiyan

    2013-01-01

    Full Text Available Abstract Background One of the challenges in classification of cancer tissue samples based on gene expression data is to establish an effective method that can select a parsimonious set of informative genes. The Top Scoring Pair (TSP, k-Top Scoring Pairs (k-TSP, Support Vector Machines (SVM, and prediction analysis of microarrays (PAM are four popular classifiers that have comparable performance on multiple cancer datasets. SVM and PAM tend to use a large number of genes and TSP, k-TSP always use even number of genes. In addition, the selection of distinct gene pairs in k-TSP simply combined the pairs of top ranking genes without considering the fact that the gene set with best discrimination power may not be the combined pairs. The k-TSP algorithm also needs the user to specify an upper bound for the number of gene pairs. Here we introduce a computational algorithm to address the problems. The algorithm is named Chisquare-statistic-based Top Scoring Genes (Chi-TSG classifier simplified as TSG. Results The TSG classifier starts with the top two genes and sequentially adds additional gene into the candidate gene set to perform informative gene selection. The algorithm automatically reports the total number of informative genes selected with cross validation. We provide the algorithm for both binary and multi-class cancer classification. The algorithm was applied to 9 binary and 10 multi-class gene expression datasets involving human cancers. The TSG classifier outperforms TSP family classifiers by a big margin in most of the 19 datasets. In addition to improved accuracy, our classifier shares all the advantages of the TSP family classifiers including easy interpretation, invariant to monotone transformation, often selects a small number of informative genes allowing follow-up studies, resistant to sampling variations due to within sample operations. Conclusions Redefining the scores for gene set and the classification rules in TSP family

  19. Automated ancillary cancer history classification for mesothelioma patients from free-text clinical reports.

    Science.gov (United States)

    Wilson, Richard A; Chapman, Wendy W; Defries, Shawn J; Becich, Michael J; Chapman, Brian E

    2010-10-11

    Clinical records are often unstructured, free-text documents that create information extraction challenges and costs. Healthcare delivery and research organizations, such as the National Mesothelioma Virtual Bank, require the aggregation of both structured and unstructured data types. Natural language processing offers techniques for automatically extracting information from unstructured, free-text documents. Five hundred and eight history and physical reports from mesothelioma patients were split into development (208) and test sets (300). A reference standard was developed and each report was annotated by experts with regard to the patient's personal history of ancillary cancer and family history of any cancer. The Hx application was developed to process reports, extract relevant features, perform reference resolution and classify them with regard to cancer history. Two methods, Dynamic-Window and ConText, for extracting information were evaluated. Hx's classification responses using each of the two methods were measured against the reference standard. The average Cohen's weighted kappa served as the human benchmark in evaluating the system. Hx had a high overall accuracy, with each method, scoring 96.2%. F-measures using the Dynamic-Window and ConText methods were 91.8% and 91.6%, which were comparable to the human benchmark of 92.8%. For the personal history classification, Dynamic-Window scored highest with 89.2% and for the family history classification, ConText scored highest with 97.6%, in which both methods were comparable to the human benchmark of 88.3% and 97.2%, respectively. We evaluated an automated application's performance in classifying a mesothelioma patient's personal and family history of cancer from clinical reports. To do so, the Hx application must process reports, identify cancer concepts, distinguish the known mesothelioma from ancillary cancers, recognize negation, perform reference resolution and determine the experiencer. Results

  20. Automated ancillary cancer history classification for mesothelioma patients from free-text clinical reports

    Directory of Open Access Journals (Sweden)

    Richard A Wilson

    2010-01-01

    Full Text Available Background: Clinical records are often unstructured, free-text documents that create information extraction challenges and costs. Healthcare delivery and research organizations, such as the National Mesothelioma Virtual Bank, require the aggregation of both structured and unstructured data types. Natural language processing offers techniques for automatically extracting information from unstructured, free-text documents. Methods: Five hundred and eight history and physical reports from mesothelioma patients were split into development (208 and test sets (300. A reference standard was developed and each report was annotated by experts with regard to the patient′s personal history of ancillary cancer and family history of any cancer. The Hx application was developed to process reports, extract relevant features, perform reference resolution and classify them with regard to cancer history. Two methods, Dynamic-Window and ConText, for extracting information were evaluated. Hx′s classification responses using each of the two methods were measured against the reference standard. The average Cohen′s weighted kappa served as the human benchmark in evaluating the system. Results: Hx had a high overall accuracy, with each method, scoring 96.2%. F-measures using the Dynamic-Window and ConText methods were 91.8% and 91.6%, which were comparable to the human benchmark of 92.8%. For the personal history classification, Dynamic-Window scored highest with 89.2% and for the family history classification, ConText scored highest with 97.6%, in which both methods were comparable to the human benchmark of 88.3% and 97.2%, respectively. Conclusion: We evaluated an automated application′s performance in classifying a mesothelioma patient′s personal and family history of cancer from clinical reports. To do so, the Hx application must process reports, identify cancer concepts, distinguish the known mesothelioma from ancillary cancers, recognize negation

  1. Classification of normal and cancerous lung tissues by electrical impendence tomography.

    Science.gov (United States)

    Gao, Jianling; Yue, Shihong; Chen, Jun; Wang, Huaxiang

    2014-01-01

    Biological tissue impedance spectroscopy can provide rich physiological and pathological information by measuring the variation of the complex impedance of biological tissues under various frequencies of driven current. Electrical Impedance Tomography (EIT) technique can measure the impedance spectroscopy of biological tissue in medical field. Before application, a key problem must be solved on how to generally distinguish normal tissues from the cancerous in terms of measurable EIT data. In this paper, the impedance spectroscopy characteristics of human lung tissue are studied. On the basis of the measured data of 109 lung cancer patients, Cole-Cole Circle radius (CCCR) and the complex modulus are extracted. In terms of the two characteristics, 71.6% and 66.4% samples of cancerous and normal tissues can be correctly classified, respectively. Furthermore, two characteristics of the measured EIT data of each patient consist of a two-dimensional vector and all such vectors comprise a set of vectors. When classifying the vector set, the rate of correctly partitioning normal and cancerous tissues can be raised to 78.2%. The main factors to affect the classification results on normal and cancerous tissues are generally analyzed. The proposed method will play an important role in further working out an efficient and feasible diagnostic method for potential lung cancer patients, and provide theoretical basis and reference data for electrical impedance tomography technology in monitoring pulmonary function.

  2. Classification of human colonic tissues using FTIR spectra and advanced statistical techniques

    Science.gov (United States)

    Zwielly, A.; Argov, S.; Salman, A.; Bogomolny, E.; Mordechai, S.

    2010-04-01

    One of the major public health hazards is colon cancer. There is a great necessity to develop new methods for early detection of cancer. If colon cancer is detected and treated early, cure rate of more than 90% can be achieved. In this study we used FTIR microscopy (MSP), which has shown a good potential in the last 20 years in the fields of medical diagnostic and early detection of abnormal tissues. Large database of FTIR microscopic spectra was acquired from 230 human colonic biopsies. Five different subgroups were included in our database, normal and cancer tissues as well as three stages of benign colonic polyps, namely, mild, moderate and severe polyps which are precursors of carcinoma. In this study we applied advanced mathematical and statistical techniques including principal component analysis (PCA) and linear discriminant analysis (LDA), on human colonic FTIR spectra in order to differentiate among the mentioned subgroups' tissues. Good classification accuracy between normal, polyps and cancer groups was achieved with approximately 85% success rate. Our results showed that there is a great potential of developing FTIR-micro spectroscopy as a simple, reagent-free viable tool for early detection of colon cancer in particular the early stages of premalignancy among the benign colonic polyps.

  3. Conceptual data sampling for breast cancer histology image classification.

    Science.gov (United States)

    Rezk, Eman; Awan, Zainab; Islam, Fahad; Jaoua, Ali; Al Maadeed, Somaya; Zhang, Nan; Das, Gautam; Rajpoot, Nasir

    2017-07-29

    Data analytics have become increasingly complicated as the amount of data has increased. One technique that is used to enable data analytics in large datasets is data sampling, in which a portion of the data is selected to preserve the data characteristics for use in data analytics. In this paper, we introduce a novel data sampling technique that is rooted in formal concept analysis theory. This technique is used to create samples reliant on the data distribution across a set of binary patterns. The proposed sampling technique is applied in classifying the regions of breast cancer histology images as malignant or benign. The performance of our method is compared to other classical sampling methods. The results indicate that our method is efficient and generates an illustrative sample of small size. It is also competing with other sampling methods in terms of sample size and sample quality represented in classification accuracy and F1 measure. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Oncogenes and human cancer

    NARCIS (Netherlands)

    E.C.P. Heisterkamp (Nora); J.H.C. Groffen (John)

    1984-01-01

    textabstractThe first demonstrations that cancer could have an infectious nature was by Ellerman and Bang (1) ~ who showed that leukemia in chickens was transmissible with cell-free extracts and by Rous (2), who found in a similar fashion that naturally occurring chicken sarcomas were transmissible.

  5. Prediction of PAH mutagenicity in human cells by QSAR classification.

    Science.gov (United States)

    Papa, E; Pilutti, P; Gramatica, P

    2008-01-01

    Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants of high environmental concern. The experimental data of a mutagenicity test on human B-lymphoblastoid cells (alternative to the Ames bacterial test) for a set of 70 oxo-, nitro- and unsubstituted PAHs, detected in particulate matter (PM), were modelled by Quantitative Structure-Activity Relationships (QSAR) classification methods (k-NN, k-Nearest Neighbour, and CART, Classification and Regression Tree) based on different theoretical molecular descriptors selected by Genetic Algorithms. The best models were validated for predictivity both externally and internally. For external validation, Self Organizing Maps (SOM) were applied to split the original data set. The best models, developed on the training set alone, show good predictive performance also on the prediction set chemicals (sensitivity 69.2-87.1%, specificity 62.5-87.5%). The classification of PAHs according to their mutagenicity, based only on a few theoretical molecular descriptors, allows a preliminary assessment of the human health risk, and the prioritisation of these compounds.

  6. Human Behavior Classification Using Multi-Class Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Yogameena, B.

    2010-01-01

    Full Text Available Problem statement: In computer vision and robotics, one of the typical tasks is to identify specific objects in an image and to determine each object’s position and orientation relative to coordinate system. This study presented a Multi-class Relevance Vector machine (RVM classification algorithm which classifies different human poses from a single stationary camera for video surveillance applications. Approach: First the foreground blobs and their edges are obtained. Then the relevance vector machine classification scheme classified the normal and abnormal behavior. Results: The performance proposed by our method was compared with Support Vector Machine (SVM and multi-class support vector machine. Experimental results showed the effectiveness of the method. Conclusion: It is evident that RVM has good accuracy and lesser computational than SVM.

  7. Comparing two classifications of cancer cachexia and their association with survival in patients with unresected pancreatic cancer.

    Science.gov (United States)

    Wesseltoft-Rao, Nima; Hjermstad, Marianne J; Ikdahl, Tone; Dajani, Olav; Ulven, Stine M; Iversen, Per Ole; Bye, Asta

    2015-01-01

    There is no universally accepted definition of cancer cachexia. Two classifications have been proposed; the 3-factor classification requiring ≥ 2 of 3 factors; weight loss ≥ 10%, food intake ≤ 1500 kcal/day, and C-reactive protein ≥ 10 mg/l, and the consensus classification requiring weight loss >5% the past 6 mo, or body mass index 2%. Precachexia is the initial stage of the cachexia trajectory, identified by weight loss ≤ 5%, anorexia and metabolic change. We examined the consistency between the 2 classifications, and their association with survival in a palliative cohort of 45 (25 men, median age of 72 yr, range 35-89) unresected pancreatic cancer patients. Computed tomography images were used to determine sarcopenia. Height/weight/C-reactive protein and survival were extracted from medical records. Food intake was self-reported. The agreement for cachexia and noncachexia was 78% across classifications. Survival was poorer in cachexia compared to noncachexia (3-factor classification, P = 0.0052; consensus classification, P = 0.056; when precachexia was included in the consensus classification, P = 0.027). Both classifications showed a trend toward lower median survival (P cachexia. In conclusion, the two classifications showed good overall agreement in defining cachectic pancreatic cancer patients, and cachexia was associated with poorer survival according to both.

  8. Viruses and human cancer

    Energy Technology Data Exchange (ETDEWEB)

    Gallo, R.C.; Haseltine, W.; Klein, G.; Zur Hausen, H.

    1987-01-01

    This book contains papers on the following topics: Immunology and Epidemiology, Biology and Pathogenesis, Models of Pathogenesis and Treatment, Simian and Bovine Retroviruses, Human Papilloma Viruses, EBV and Herpesvirus, and Hepatitis B Virus.

  9. A Classification of Human-to-Human Communication during the Use of Immersive Teleoperation Interfaces

    DEFF Research Database (Denmark)

    Kraus, Martin; Kibsgaard, Martin

    2015-01-01

    We propose a new classification of the human-to-human communication during the use of immersive teleoperation interfaces based on real-life examples. While a large body of research is concerned with communication in collaborative virtual environments (CVEs), less research focuses on cases where...

  10. Update on epidemiology classification, and management of thyroid cancer

    Directory of Open Access Journals (Sweden)

    Heitham Gheriani

    2006-06-01

    Full Text Available Thyroid cancer represents approximately 0.5–1% of all human malignancy1. In the UK the incidence of thyroid cancer is 2-3 per 100,000 populations 2. In geographical areas of low iodine intake and in areas exposed to nuclear disasters the incidence of thyroid cancer is higher. Benign thyroid conditions are much more common. In the UK approximately 8 % of the population have nodular thyroid disease2. Nodular thyroid disease increases with age and is also more common in females and in geographical areas of low iodine intake. Primary thyroid malignancy can be broadly divided into 2 groups. The first group, which generally have much better prognosis, are the well-differentiated thyroid carcinoma, which includes papillary carcinoma, follicular carcinoma and Hürthle cell tumours. The second group includes the poorly differentiated thyroid carcinoma like medullary thyroid carcinoma and the anaplastic thyroid carcinoma. Other rare tumours such as sarcomas, lymphomas and the extremely rare primary squamous cell carcinoma of the thyroid should be included in the second group. Secondary or metastatic thyroid cancer can be from breast, lung, colon and kidney malignancies.

  11. Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Jogendra Kushwah

    2013-06-01

    Full Text Available The free radical gene classification of cancer diseases is challenging job in biomedical data engineering. The improving of classification of gene selection of cancer diseases various classifier are used, but the classification of classifier are not validate. So ensemble classifier is used for cancer gene classification using neural network classifier with random forest tree. The random forest tree is ensembling technique of classifier in this technique the number of classifier ensemble of their leaf node of class of classifier. In this paper we combined neural network with random forest ensemble classifier for classification of cancer gene selection for diagnose analysis of cancer diseases. The proposed method is different from most of the methods of ensemble classifier, which follow an input output paradigm of neural network, where the members of the ensemble are selected from a set of neural network classifier. the number of classifiers is determined during the rising procedure of the forest. Furthermore, the proposed method produces an ensemble not only correct, but also assorted, ensuring the two important properties that should characterize an ensemble classifier. For empirical evaluation of our proposed method we used UCI cancer diseases data set for classification. Our experimental result shows that better result in compression of random forest tree classification.

  12. Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS)

    Science.gov (United States)

    Alexander, Tiffaney Miller

    2017-01-01

    Research results have shown that more than half of aviation, aerospace and aeronautics mishaps incidents are attributed to human error. As a part of Quality within space exploration ground processing operations, the identification and or classification of underlying contributors and causes of human error must be identified, in order to manage human error.This presentation will provide a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.

  13. Human papillomavirus and cervical cancer.

    Science.gov (United States)

    Crosbie, Emma J; Einstein, Mark H; Franceschi, Silvia; Kitchener, Henry C

    2013-09-07

    Cervical cancer is caused by human papillomavirus infection. Most human papillomavirus infection is harmless and clears spontaneously but persistent infection with high-risk human papillomavirus (especially type 16) can cause cancer of the cervix, vulva, vagina, anus, penis, and oropharynx. The virus exclusively infects epithelium and produces new viral particles only in fully mature epithelial cells. Human papillomavirus disrupts normal cell-cycle control, promoting uncontrolled cell division and the accumulation of genetic damage. Two effective prophylactic vaccines composed of human papillomavirus type 16 and 18, and human papillomavirus type 16, 18, 6, and 11 virus-like particles have been introduced in many developed countries as a primary prevention strategy. Human papillomavirus testing is clinically valuable for secondary prevention in triaging low-grade cytology and as a test of cure after treatment. More sensitive than cytology, primary screening by human papillomavirus testing could enable screening intervals to be extended. If these prevention strategies can be implemented in developing countries, many thousands of lives could be saved.

  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. Septin mutations in human cancers

    Directory of Open Access Journals (Sweden)

    Elias T Spiliotis

    2016-11-01

    Full Text Available Septins are GTP-binding proteins that are evolutionarily and structurally related to the RAS oncogenes. Septin expression levels are altered in many cancers and new advances point to how abnormal septin expression may contribute to the progression of cancer. In contrast to the RAS GTPases, which are frequently mutated and actively promote tumorigenesis, little is known about the occurrence and role of septin mutations in human cancers. Here, we review septin missense mutations that are currently in the Catalog of Somatic Mutations in Cancer (COSMIC database. The majority of septin mutations occur in tumors of the large intestine, skin, endometrium and stomach. Over 25% of the annotated mutations in SEPT2, SEPT4 and SEPT9 belong to large intestine tumors. From all septins, SEPT9 and SEPT14 exhibit the highest mutation frequencies in skin, stomach and large intestine cancers. While septin mutations occur with frequencies lower than 3%, recurring mutations in several invariant and highly conserved amino acids are found across different septin paralogs and tumor types. Interestingly, a significant number of these mutations occur in the GTP-binding pocket and septin dimerization interfaces. Future studies may determine how these somatic mutations affect septin structure and function, whether they contribute to the progression of specific cancers and if they could serve as tumor-specific biomarkers.

  16. DBGC: A Database of Human Gastric Cancer.

    Science.gov (United States)

    Wang, Chao; Zhang, Jun; Cai, Mingdeng; Zhu, Zhenggang; Gu, Wenjie; Yu, Yingyan; Zhang, Xiaoyan

    2015-01-01

    The Database of Human Gastric Cancer (DBGC) is a comprehensive database that integrates various human gastric cancer-related data resources. Human gastric cancer-related transcriptomics projects, proteomics projects, mutations, biomarkers and drug-sensitive genes from different sources were collected and unified in this database. Moreover, epidemiological statistics of gastric cancer patients in China and clinicopathological information annotated with gastric cancer cases were also integrated into the DBGC. We believe that this database will greatly facilitate research regarding human gastric cancer in many fields. DBGC is freely available at http://bminfor.tongji.edu.cn/dbgc/index.do.

  17. 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...

  18. An Improved Parallelized mRMR for Gene Subset Selection in Cancer Classification

    Directory of Open Access Journals (Sweden)

    Rohani Mohammad Kusairi

    2017-09-01

    Full Text Available DNA microarray technique has become a more attractive tool for cancer classification in the scientific and industrial fields. Based on the previous researchers, the conventional approach for cancer classification is primarily based on morphological appearance of the tumor. The limitations of this approach are bias in identify the tumors by expert and faced the difficulty in differentiate the cancer subtypes due to most cancers being highly related to the specific biological insight.  Thus, this study propose an improved parallelized Minimum Redundancy Maximum Relevance (mRMR, which is a particularly fast feature selection method for finding a set of both relevant and complementary features. The mRMR can identify genes more relevance to biological context that leads to richer biological interpretations. The proposed method is expected to achieve accurate classification performance using small number of predictive genes when tested using two datasets from Cancer Genome Project and compared to previous methods.

  19. Classification of skin cancer images using local binary pattern and SVM classifier

    Science.gov (United States)

    Adjed, Faouzi; Faye, Ibrahima; Ababsa, Fakhreddine; Gardezi, Syed Jamal; Dass, Sarat Chandra

    2016-11-01

    In this paper, a classification method for melanoma and non-melanoma skin cancer images has been presented using the local binary patterns (LBP). The LBP computes the local texture information from the skin cancer images, which is later used to compute some statistical features that have capability to discriminate the melanoma and non-melanoma skin tissues. Support vector machine (SVM) is applied on the feature matrix for classification into two skin image classes (malignant and benign). The method achieves good classification accuracy of 76.1% with sensitivity of 75.6% and specificity of 76.7%.

  20. The development of the TNM classification of gastric cancer.

    Science.gov (United States)

    Wittekind, Christian

    2015-08-01

    The first tumor, node, metastasis (TNM) classification for stomach tumors was published in the second edition of the TNM classification of malignant tumors in 1974 and was followed by additional editions up to the seventh edition published in 2010. In the Buffalo Meeting 2008 a harmonization between the Eastern (Japanese) and Western stomach tumor classification was achieved with only minor remaing differences. The present TNM classification of stomach tumors has been criticized but it can be considered generally accepted worldwide. For generating data based on this new TNM classification it is important to correctly use TNM and pTNM. The decions on therapy and the estimation of prognosis are based on TNM. New molecular factor studies will be correlated and based on the results of the TNM classification. © 2015 Japanese Society of Pathology and Wiley Publishing Asia Pty Ltd.

  1. [Classification of human sleep stages based on EEG processing using hidden Markov models].

    Science.gov (United States)

    Doroshenkov, L G; Konyshev, V A; Selishchev, S V

    2007-01-01

    The goal of this work was to describe an automated system for classification of human sleep stages. Classification of sleep stages is an important problem of diagnosis and treatment of human sleep disorders. The developed classification method is based on calculation of characteristics of the main sleep rhythms. It uses hidden Markov models. The method is highly accurate and provides reliable identification of the main stages of sleep. The results of automatic classification are in good agreement with the results of sleep stage identification performed by an expert somnologist using Rechtschaffen and Kales rules. This substantiates the applicability of the developed classification system to clinical diagnosis.

  2. Breast Cancer Classification From Histological Images with Multiple Features and Random Subspace Classifier Ensemble

    Science.gov (United States)

    Zhang, Yungang; Zhang, Bailing; Lu, Wenjin

    2011-06-01

    Histological image is important for diagnosis of breast cancer. In this paper, we present a novel automatic breaset cancer classification scheme based on histological images. The image features are extracted using the Curvelet Transform, statistics of Gray Level Co-occurence Matrix (GLCM) and Completed Local Binary Patterns (CLBP), respectively. The three different features are combined together and used for classification. A classifier ensemble approach, called Random Subspace Ensemble (RSE), are used to select and aggregate a set of base neural network classifiers for classification. The proposed multiple features and random subspace ensemble offer the classification rate 95.22% on a publically available breast cancer image dataset, which compares favorably with the previously published result 93.4%.

  3. Methodological Aspects of Prognostic Classifications: Applications in Testicular Cancer

    NARCIS (Netherlands)

    M.R. van Dijk (Merel)

    2007-01-01

    textabstractPatients with similar characteristics can be grouped together in a prognostic classification to estimate a patient’s prognosis and guide treatment decisions. The topic of this thesis is methodological aspects of defining prognosis classifications. We specifically looked at patients wi

  4. Identification of cancer risk lncRNAs and cancer risk pathways regulated by cancer risk lncRNAs based on genome sequencing data in human cancers.

    Science.gov (United States)

    Li, Yiran; Li, Wan; Liang, Binhua; Li, Liansheng; Wang, Li; Huang, Hao; Guo, Shanshan; Wang, Yahui; He, Yuehan; Chen, Lina; He, Weiming

    2016-12-19

    Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. The complexity of cancer can be reduced to a small number of underlying principles like cancer hallmarks which could govern the transformation of normal cells to cancer. Besides, the growth and metastasis of cancer often relate to combined effects of long non-coding RNAs (lncRNAs). Here, we performed comprehensive analysis for lncRNA expression profiles and clinical data of six types of human cancer patients from The Cancer Genome Atlas (TCGA), and identified six risk pathways and twenty three lncRNAs. In addition, twenty three cancer risk lncRNAs which were closely related to the occurrence or development of cancer had a good classification performance for samples of testing datasets of six cancer datasets. More important, these lncRNAs were able to separate samples in the entire cancer dataset into high-risk group and low-risk group with significantly different overall survival (OS), which was further validated in ten validation datasets. In our study, the robust and effective cancer biomarkers were obtained from cancer datasets which had information of normal-tumor samples. Overall, our research can provide a new perspective for the further study of clinical diagnosis and treatment of cancer.

  5. Evolving Cancer Classification in the Era of Personalized Medicine: A Primer for Radiologists

    Science.gov (United States)

    Jagannathan, Jyothi P.; Ramaiya, Nikhil H.

    2017-01-01

    Traditionally tumors were classified based on anatomic location but now specific genetic mutations in cancers are leading to treatment of tumors with molecular targeted therapies. This has led to a paradigm shift in the classification and treatment of cancer. Tumors treated with molecular targeted therapies often show morphological changes rather than change in size and are associated with class specific and drug specific toxicities, different from those encountered with conventional chemotherapeutic agents. It is important for the radiologists to be familiar with the new cancer classification and the various treatment strategies employed, in order to effectively communicate and participate in the multi-disciplinary care. In this paper we will focus on lung cancer as a prototype of the new molecular classification.

  6. Automated classification of histopathology images of prostate cancer using a Bag-of-Words approach

    Science.gov (United States)

    Sanghavi, Foram M.; Agaian, Sos S.

    2016-05-01

    The goals of this paper are (1) test the Computer Aided Classification of the prostate cancer histopathology images based on the Bag-of-Words (BoW) approach (2) evaluate the performance of the classification grade 3 and 4 of the proposed method using the results of the approach proposed by the authors Khurd et al. in [9] and (3) classify the different grades of cancer namely, grade 0, 3, 4, and 5 using the proposed approach. The system performance is assessed using 132 prostate cancer histopathology of different grades. The system performance of the SURF features are also analyzed by comparing the results with SIFT features using different cluster sizes. The results show 90.15% accuracy in detection of prostate cancer images using SURF features with 75 clusters for k-mean clustering. The results showed higher sensitivity for SURF based BoW classification compared to SIFT based BoW.

  7. Evolving cancer classification in the era of personalized medicine: A primer for radiologists

    Energy Technology Data Exchange (ETDEWEB)

    O' Neill, Alibhe C.; Jagannathan, Jyothi P.; Ramaiya, Nikhil H. [Dept. of of Imaging, Dana Farber Cancer Institute, Boston (United States)

    2017-01-15

    Traditionally tumors were classified based on anatomic location but now specific genetic mutations in cancers are leading to treatment of tumors with molecular targeted therapies. This has led to a paradigm shift in the classification and treatment of cancer. Tumors treated with molecular targeted therapies often show morphological changes rather than change in size and are associated with class specific and drug specific toxicities, different from those encountered with conventional chemotherapeutic agents. It is important for the radiologists to be familiar with the new cancer classification and the various treatment strategies employed, in order to effectively communicate and participate in the multi-disciplinary care. In this paper we will focus on lung cancer as a prototype of the new molecular classification.

  8. Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Jogendra Kushwah

    2013-06-01

    Full Text Available The free radical gene classification of cancerdiseasesis challenging job in biomedical dataengineering. The improving of classification of geneselection of cancer diseases various classifier areused, but the classification of classifier are notvalidate. So ensemble classifier is used for cancergene classification using neural network classifierwith random forest tree. The random forest tree isensembling technique of classifier in this techniquethe number of classifier ensemble of their leaf nodeof class of classifier. In this paper we combinedneuralnetwork with random forest ensembleclassifier for classification of cancer gene selectionfor diagnose analysis of cancer diseases.Theproposed method is different from most of themethods of ensemble classifier, which follow aninput output paradigm ofneural network, where themembers of the ensemble are selected from a set ofneural network classifier. the number of classifiersis determined during the rising procedure of theforest. Furthermore, the proposed method producesan ensemble not only correct, but also assorted,ensuring the two important properties that shouldcharacterize an ensemble classifier. For empiricalevaluation of our proposed method we used UCIcancer diseases data set for classification. Ourexperimental result shows that betterresult incompression of random forest tree classification

  9. Efficacy of the Kyoto Classification of Gastritis in Identifying Patients at High Risk for Gastric Cancer.

    Science.gov (United States)

    Sugimoto, Mitsushige; Ban, Hiromitsu; Ichikawa, Hitomi; Sahara, Shu; Otsuka, Taketo; Inatomi, Osamu; Bamba, Shigeki; Furuta, Takahisa; Andoh, Akira

    2017-01-01

    Objective The Kyoto gastritis classification categorizes the endoscopic characteristics of Helicobacter pylori (H. pylori) infection-associated gastritis and identifies patterns associated with a high risk of gastric cancer. We investigated its efficacy, comparing scores in patients with H. pylori-associated gastritis and with gastric cancer. Methods A total of 1,200 patients with H. pylori-positive gastritis alone (n=932), early-stage H. pylori-positive gastric cancer (n=189), and successfully treated H. pylori-negative cancer (n=79) were endoscopically graded according to the Kyoto gastritis classification for atrophy, intestinal metaplasia, fold hypertrophy, nodularity, and diffuse redness. Results The prevalence of O-II/O-III-type atrophy according to the Kimura-Takemoto classification in early-stage H. pylori-positive gastric cancer and successfully treated H. pylori-negative cancer groups was 45.1%, which was significantly higher than in subjects with gastritis alone (12.7%, pgastritis scores of atrophy and intestinal metaplasia in the H. pylori-positive cancer group were significantly higher than in subjects with gastritis alone (all pgastritis classification may thus be useful for detecting these patients.

  10. HCSD: the human cancer secretome database

    Science.gov (United States)

    Feizi, Amir; Banaei-Esfahani, Amir; Nielsen, Jens

    2015-01-01

    The human cancer secretome database (HCSD) is a comprehensive database for human cancer secretome data. The cancer secretome describes proteins secreted by cancer cells and structuring information about the cancer secretome will enable further analysis of how this is related with tumor biology. The secreted proteins from cancer cells are believed to play a deterministic role in cancer progression and therefore may be the key to find novel therapeutic targets and biomarkers for many cancers. Consequently, huge data on cancer secretome have been generated in recent years and the lack of a coherent database is limiting the ability to query the increasing community knowledge. We therefore developed the Human Cancer Secretome Database (HCSD) to fulfil this gap. HCSD contains >80 000 measurements for about 7000 nonredundant human proteins collected from up to 35 high-throughput studies on 17 cancer types. It has a simple and user friendly query system for basic and advanced search based on gene name, cancer type and data type as the three main query options. The results are visualized in an explicit and interactive manner. An example of a result page includes annotations, cross references, cancer secretome data and secretory features for each identified protein. Database URL: www.cancersecretome.org. PMID:26078477

  11. Human Cancer Models Initiative | Office of Cancer Genomics

    Science.gov (United States)

    The Human Cancer Models Initiative (HCMI) is an international consortium that is generating novel human tumor-derived culture models, which are annotated with genomic and clinical data. In an effort to advance cancer research and more fully understand how in vitro findings are related to clinical biology, HCMI-developed models and related data will be available as a community resource for cancer research.

  12. Rule-guided human classification of Volunteered Geographic Information

    Science.gov (United States)

    Ali, Ahmed Loai; Falomir, Zoe; Schmid, Falko; Freksa, Christian

    2017-05-01

    During the last decade, web technologies and location sensing devices have evolved generating a form of crowdsourcing known as Volunteered Geographic Information (VGI). VGI acted as a platform of spatial data collection, in particular, when a group of public participants are involved in collaborative mapping activities: they work together to collect, share, and use information about geographic features. VGI exploits participants' local knowledge to produce rich data sources. However, the resulting data inherits problematic data classification. In VGI projects, the challenges of data classification are due to the following: (i) data is likely prone to subjective classification, (ii) remote contributions and flexible contribution mechanisms in most projects, and (iii) the uncertainty of spatial data and non-strict definitions of geographic features. These factors lead to various forms of problematic classification: inconsistent, incomplete, and imprecise data classification. This research addresses classification appropriateness. Whether the classification of an entity is appropriate or inappropriate is related to quantitative and/or qualitative observations. Small differences between observations may be not recognizable particularly for non-expert participants. Hence, in this paper, the problem is tackled by developing a rule-guided classification approach. This approach exploits data mining techniques of Association Classification (AC) to extract descriptive (qualitative) rules of specific geographic features. The rules are extracted based on the investigation of qualitative topological relations between target features and their context. Afterwards, the extracted rules are used to develop a recommendation system able to guide participants to the most appropriate classification. The approach proposes two scenarios to guide participants towards enhancing the quality of data classification. An empirical study is conducted to investigate the classification of grass

  13. Cuckoo search optimisation for feature selection in cancer classification: a new approach.

    Science.gov (United States)

    Gunavathi, C; Premalatha, K

    2015-01-01

    Cuckoo Search (CS) optimisation algorithm is used for feature selection in cancer classification using microarray gene expression data. Since the gene expression data has thousands of genes and a small number of samples, feature selection methods can be used for the selection of informative genes to improve the classification accuracy. Initially, the genes are ranked based on T-statistics, Signal-to-Noise Ratio (SNR) and F-statistics values. The CS is used to find the informative genes from the top-m ranked genes. The classification accuracy of k-Nearest Neighbour (kNN) technique is used as the fitness function for CS. The proposed method is experimented and analysed with ten different cancer gene expression datasets. The results show that the CS gives 100% average accuracy for DLBCL Harvard, Lung Michigan, Ovarian Cancer, AML-ALL and Lung Harvard2 datasets and it outperforms the existing techniques in DLBCL outcome and prostate datasets.

  14. Constructing the gene regulation-level representation of microarray data for cancer classification.

    Science.gov (United States)

    Wong, Hau-San; Wang, Hong-Qiang

    2008-02-01

    In this paper, we propose a regulation-level representation for microarray data and optimize it using genetic algorithms (GAs) for cancer classification. Compared with the traditional expression-level features, this representation can greatly reduce the dimensionality of microarray data and accommodate noise and variability such that many statistical machine-learning methods now become applicable and efficient for cancer classification. Experimental results on real-world microarray datasets show that the regulation-level representation can consistently converge at a solution with three regulation levels. This verifies the existence of the three regulation levels (up-regulation, down-regulation and non-significant regulation) associated with a particular biological phenotype. The ternary regulation-level representation not only improves the cancer classification capability but also facilitates the visualization of microarray data.

  15. Telomerase activity in human cancer

    Energy Technology Data Exchange (ETDEWEB)

    Griffith, J.

    2000-10-01

    The overall goal of this collaborative project was to investigate the role in malignant cells of both chromosome telomeres, and telomerase, the enzyme that replicates telomeres. Telomeres are highly conserved nucleoprotein complexes located at the ends of eucaryotic chromosomes. Telomere length in somatic cells is reduced by 40--50 nucleotide pairs with every cell division due to incomplete replication of terminal DNA sequences and the absence of telomerase, the ribonucleoprotein that adds telomere DNA to chromosome ends. Although telomerase is active in cells with extended proliferative capacities, including more than 85% of tumors, work performed under this contract demonstrated that the telomeres of human cancer cells are shorter than those of paired normal cells, and that the length of the telomeres is characteristic of particular types of cancers. The extent of telomere shortening ostensibly is related to the number of cell divisions the tumor has undergone. It is believed that ongoing cell proliferation leads to the accumulation and fixation of new mutations in tumor cell lineages.Therefore, it is not unreasonable to assume that the degree of phenotypic variability is related to the proliferative history of the tumor, and therefore to telomere length, implying a correlation with prognosis. In some human tumors, short telomeres are also correlated with genomic instabilities, including interstitial chromosome translocation, loss of heterozygosity, and aneuoploidy. Moreover, unprotected chromosome ends are highly recombinogenic and telomere shortening in cultured human cells correlates with the formation of dicentric chromosomes, suggesting that critically short telomeres not only identify, but also predispose, cells to genomic instability, again implying a correlation with prognosis. Therefore, telomere length or content could be an important predictor of metastatic potential or responsiveness to various therapeutic modalities.

  16. Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

    Science.gov (United States)

    Wutsqa, D. U.; Marwah, M.

    2017-06-01

    In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.

  17. Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

    Science.gov (United States)

    Chung, Hyunkoo; Lu, Guolan; Tian, Zhiqiang; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2016-03-01

    Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

  18. Enhancing the objectivity of the Japanese classification of peritoneal metastases from colorectal cancer.

    Science.gov (United States)

    Kobayashi, Hirotoshi; Kotake, Kenjiro; Sugihara, Kenichi

    2014-10-01

    The Japanese classification of peritoneal metastases from colorectal cancer is easy to use for general surgeons in routine clinical practice. However, the objectivity of this classification has not been determined. This study aimed to clarify the objectivity of the Japanese classification of peritoneal metastases from colorectal cancer. The data of patients with Stage IV colorectal cancer between 1991 and 2007 in 16 hospitals, who were members of the Japanese Society for Cancer of the Colon and Rectum, were investigated. The size, number and extent (nine areas) of peritoneal metastases according to the Japanese classification (P1, P2 and P3) were investigated using Akaike's information criterion. Of the 564 colorectal cancer patients with synchronous peritoneal metastases, 341 had hematogenous metastases. The minimum Akaike's information criterion was obtained with the cutoff value of one area for P1 metastasis and two or more areas for P2 metastasis (P 20 mm and P3 is defined as >10 peritoneal metastases disseminated in two or more areas. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  19. Which is the most suitable classification for colorectal cancer, log odds, the number or the ratio of positive lymph nodes?

    Directory of Open Access Journals (Sweden)

    Yong-Xi Song

    Full Text Available OBJECTIVE: The aim of the current study was to investigate which is the most suitable classification for colorectal cancer, log odds of positive lymph nodes (LODDS classification or the classifications based on the number of positive lymph nodes (pN and positive lymph node ratio(LNR in a Chinese single institutional population. DESIGN: Clinicopathologic and prognostic data of 1297 patients with colorectal cancer were retrospectively studied. The log-rank statistics, Cox's proportional hazards model, the Nagelkerke R(2 index and a Harrell's C statistic were used. RESULTS: Univariate and three-step multivariate analyses identified that LNR was a significant prognostic factor and LNR classification was superior to both the pN and LODDS classifications. Moreover, the results of the Nagelkerke R(2 index (0.130 and a Harrell's C statistic (0.707 of LNR showed that LNR and LODDS classifications were similar and LNR was a little better than the other two classifications. Furthermore, for patients in each LNR classification, prognosis was homologous between those in different pN or LODDS classifications. However, for patients in pN1a, pN1b, LODDS2 and LODDS3 classifications, significant differences in survival were observed among patients in different LNR classifications. CONCLUSIONS: For patients with colorectal cancer, the LNR classification is more suitable than pN and LODDS classifications for prognostic assessment in a Chinese single institutional population.

  20. Cancer stem cells in human gastrointestinal cancer.

    Science.gov (United States)

    Taniguchi, Hiroaki; Moriya, Chiharu; Igarashi, Hisayoshi; Saitoh, Anri; Yamamoto, Hiroyuki; Adachi, Yasushi; Imai, Kohzoh

    2016-11-01

    Cancer stem cells (CSCs) are thought to be responsible for tumor initiation, drug and radiation resistance, invasive growth, metastasis, and tumor relapse, which are the main causes of cancer-related deaths. Gastrointestinal cancers are the most common malignancies and still the most frequent cause of cancer-related mortality worldwide. Because gastrointestinal CSCs are also thought to be resistant to conventional therapies, an effective and novel cancer treatment is imperative. The first reported CSCs in a gastrointestinal tumor were found in colorectal cancer in 2007. Subsequently, CSCs were reported in other gastrointestinal cancers, such as esophagus, stomach, liver, and pancreas. Specific phenotypes could be used to distinguish CSCs from non-CSCs. For example, gastrointestinal CSCs express unique surface markers, exist in a side-population fraction, show high aldehyde dehydrogenase-1 activity, form tumorspheres when cultured in non-adherent conditions, and demonstrate high tumorigenic potential in immunocompromised mice. The signal transduction pathways in gastrointestinal CSCs are similar to those involved in normal embryonic development. Moreover, CSCs are modified by the aberrant expression of several microRNAs. Thus, it is very difficult to target gastrointestinal CSCs. This review focuses on the current research on gastrointestinal CSCs and future strategies to abolish the gastrointestinal CSC phenotype.

  1. Human papillomaviruses and skin cancer.

    Science.gov (United States)

    Smola, Sigrun

    2014-01-01

    Human papillomaviruses (HPVs) infect squamous epithelia and can induce hyperproliferative lesions. More than 120 different HPV types have been characterized and classified into five different genera. While mucosal high-risk HPVs have a well-established causal role in anogenital carcinogenesis, the biology of cutaneous HPVs is less well understood. The clinical relevance of genus beta-PV infection has clearly been demonstrated in patients suffering from epidermodysplasia verruciformis (EV), a rare inherited disease associated with ahigh rate of skin cancer. In the normal population genus beta-PV are suspected to have an etiologic role in skin carcinogenesis as well but this is still controversially discussed. Their oncogenic potency has been investigated in mouse models and in vitro. In 2009, the International Agency for Research on Cancer (IARC) classified the genus beta HPV types 5 and 8 as "possible carcinogenic" biological agents (group 2B) in EV disease. This chapter will give an overview on the knowns and unknowns of infections with genus beta-PV and discuss their potential impact on skin carcinogenesis in the general population.

  2. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks | Center for Cancer Research

    Science.gov (United States)

    The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification.

  3. IARC use of oxidative stress as key mode of action characteristic for facilitating cancer classification: Glyphosate case example illustrating a lack of robustness in interpretative implementation.

    Science.gov (United States)

    Bus, James S

    2017-06-01

    The International Agency for Research on Cancer (IARC) has formulated 10 key characteristics of human carcinogens to incorporate mechanistic data into cancer hazard classifications. The analysis used glyphosate as a case example to examine the robustness of IARC's determination of oxidative stress as "strong" evidence supporting a plausible cancer mechanism in humans. The IARC analysis primarily relied on 14 human/mammalian studies; 19 non-mammalian studies were uninformative of human cancer given the broad spectrum of test species and extensive use of formulations and aquatic testing. The mammalian studies had substantial experimental limitations for informing cancer mechanism including use of: single doses and time points; cytotoxic/toxic test doses; tissues not identified as potential cancer targets; glyphosate formulations or mixtures; technically limited oxidative stress biomarkers. The doses were many orders of magnitude higher than human exposures determined in human biomonitoring studies. The glyphosate case example reveals that the IARC evaluation fell substantially short of "strong" supporting evidence of oxidative stress as a plausible human cancer mechanism, and suggests that other IARC monographs relying on the 10 key characteristics approach should be similarly examined for a lack of robust data integration fundamental to reasonable mode of action evaluations. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. Radar micro-Doppler based human activity classification for indoor and outdoor environments

    Science.gov (United States)

    Zenaldin, Matthew; Narayanan, Ram M.

    2016-05-01

    This paper presents the results of our experimental investigation into how different environments impact the classification of human motion using radar micro-Doppler (MD) signatures. The environments studied include free space, through-thewall, leaf tree foliage, and needle tree foliage. Results on presented on classification of the following three motions: crawling, walking, and jogging. The classification task was designed how to best separate these movements. The human motion data were acquired using a monostatic coherent Doppler radar operating in the C-band at 6.5 GHz from a total of six human subjects. The received signals were analyzed in the time-frequency domain using the Short-time Fourier Transform (STFT) which was used for feature extraction. Classification was performed using a Support Vector Machine (SVM) using a Radial Basis Function (RBF). Classification accuracies in the range 80-90% were achieved to separate the three movements mentioned.

  5. Hybrid SPR algorithm to select predictive genes for effectual cancer classification

    OpenAIRE

    2012-01-01

    Designing an automated system for classifying DNA microarray data is an extremely challenging problem because of its high dimension and low amount of sample data. In this paper, a hybrid statistical pattern recognition algorithm is proposed to reduce the dimensionality and select the predictive genes for the classification of cancer. Colon cancer gene expression profiles having 62 samples of 2000 genes were used for the experiment. A gene subset of 6 highly informative genes was selecte...

  6. Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI

    Energy Technology Data Exchange (ETDEWEB)

    Dikaios, Nikolaos; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit [University College London, Centre for Medical Imaging, London (United Kingdom); University College London Hospital, Departments of Radiology, London (United Kingdom); Alkalbani, Jokha; Sidhu, Harbir Singh [University College London, Centre for Medical Imaging, London (United Kingdom); Abd-Alazeez, Mohamed; Ahmed, Hashim U.; Emberton, Mark [University College London, Research Department of Urology, Division of Surgery and Interventional Science, London (United Kingdom); Kirkham, Alex [University College London Hospital, Departments of Radiology, London (United Kingdom); Freeman, Alex [University College London Hospital, Department of Histopathology, London (United Kingdom)

    2015-09-15

    To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer. Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models. The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer. LR-models dependent on DCE-MRI parameters alone are not interchangeable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application. (orig.)

  7. CRITERIA FOR AN UPDATED CLASSIFICATION OF HUMAN TRANSCRIPTION FACTOR DNA-BINDING DOMAINS

    NARCIS (Netherlands)

    Wingender, Edgar

    2013-01-01

    By binding to cis-regulatory elements in a sequence-specific manner, transcription factors regulate the activity of nearby genes. Here, we discuss the criteria for a comprehensive classification of human TFs based on their DNA-binding domains. In particular, classification of basic leucine zipper (b

  8. CRITERIA FOR AN UPDATED CLASSIFICATION OF HUMAN TRANSCRIPTION FACTOR DNA-BINDING DOMAINS

    NARCIS (Netherlands)

    Wingender, Edgar

    By binding to cis-regulatory elements in a sequence-specific manner, transcription factors regulate the activity of nearby genes. Here, we discuss the criteria for a comprehensive classification of human TFs based on their DNA-binding domains. In particular, classification of basic leucine zipper

  9. Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification.

    Directory of Open Access Journals (Sweden)

    Xiang Zhang

    Full Text Available Advances in DNA microarray technologies have made gene expression profiles a significant candidate in identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train a classifier, but they are inconvenient for practical application because labels are quite expensive in the clinical cancer research community. This paper proposes a semi-supervised projective non-negative matrix factorization method (Semi-PNMF to learn an effective classifier from both labeled and unlabeled samples, thus boosting subsequent cancer classification performance. In particular, Semi-PNMF jointly learns a non-negative subspace from concatenated labeled and unlabeled samples and indicates classes by the positions of the maximum entries of their coefficients. Because Semi-PNMF incorporates statistical information from the large volume of unlabeled samples in the learned subspace, it can learn more representative subspaces and boost classification performance. We developed a multiplicative update rule (MUR to optimize Semi-PNMF and proved its convergence. The experimental results of cancer classification for two multiclass cancer gene expression profile datasets show that Semi-PNMF outperforms the representative methods.

  10. Optimization of human cancer radiotherapy

    CERN Document Server

    Swan, George W

    1981-01-01

    The mathematical models in this book are concerned with a variety of approaches to the manner in which the clinical radiologic treatment of human neoplasms can be improved. These improvements comprise ways of delivering radiation to the malignan­ cies so as to create considerable damage to tumor cells while sparing neighboring normal tissues. There is no unique way of dealing with these improvements. Accord­ ingly, in this book a number of different presentations are given. Each presentation has as its goal some aspect of the improvement, or optimization, of radiotherapy. This book is a collection of current ideas concerned with the optimization of human cancer radiotherapy. It is hoped that readers will build on this collection and develop superior approaches for the understanding of the ways to improve therapy. The author owes a special debt of thanks to Kathy Prindle who breezed through the typing of this book with considerable dexterity. TABLE OF CONTENTS Chapter GENERAL INTRODUCTION 1. 1 Introduction 1...

  11. High dimensional multiclass classification with applications to cancer diagnosis

    DEFF Research Database (Denmark)

    Vincent, Martin

    Probabilistic classifiers are introduced and it is shown that the only regular linear probabilistic classifier with convex risk is multinomial regression. Penalized empirical risk minimization is introduced and used to construct supervised learning methods for probabilistic classifiers. A sparse...... and a simulation based domain adaption strategy is presented. It is shown that the presented computational contamination approach drastically improves the primary tumor site classification of lever contaminated biopsies of metastases. A final classifier for identification of the primary tumor site is developed...

  12. Early Detection and Classification of Melanoma Skin Cancer

    Directory of Open Access Journals (Sweden)

    Abbas Hanon. Alasadi

    2015-10-01

    Full Text Available Melanoma is a form of cancer that begins in melanocytes (cells that make the pigment melanin. It can affect the skin only, or it may spread to the organs and bones. It is less common, but more serious and aggressive than other types of skin cancer. Melanoma can be of benign or malignant. Malignant melanoma is the dangerous condition, while benign is not. In order to reduce the death rate due to malignant melanoma skin cancer, it is necessary to diagnose it at an early stage. In this paper, a detection system has been designed for diagnosing melanoma in early stages by using digital image processing techniques. The system consists of two phases: the first phase detects whether the pigmented skin lesion is malignant or benign; the second phase recognizes malignant melanoma skin cancer types. Both first and second phases have several stages. The experimental results are acceptable.

  13. Future of the TNM classification and staging system in head and neck cancer.

    NARCIS (Netherlands)

    Takes, R.P.; Rinaldo, A.; Silver, C.E.; Piccirillo, J.F.; Haigentz Jr, M.; Suarez, C.; Poorten, V.L. van der; Hermans, R.; Rodrigo, J.P.; Devaney, K.O.; Ferlito, A.

    2010-01-01

    Staging systems for cancer, including the most universally used TNM classification system, have been based almost exclusively on anatomic information. However, the question arises whether staging systems should be based on this information alone. Other parameters have been identified that should be

  14. Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification.

    Science.gov (United States)

    Huang, Lingkang; Zhang, Hao Helen; Zeng, Zhao-Bang; Bushel, Pierre R

    2013-01-01

    Microarray techniques provide promising tools for cancer diagnosis using gene expression profiles. However, molecular diagnosis based on high-throughput platforms presents great challenges due to the overwhelming number of variables versus the small sample size and the complex nature of multi-type tumors. Support vector machines (SVMs) have shown superior performance in cancer classification due to their ability to handle high dimensional low sample size data. The multi-class SVM algorithm of Crammer and Singer provides a natural framework for multi-class learning. Despite its effective performance, the procedure utilizes all variables without selection. In this paper, we propose to improve the procedure by imposing shrinkage penalties in learning to enforce solution sparsity. The original multi-class SVM of Crammer and Singer is effective for multi-class classification but does not conduct variable selection. We improved the method by introducing soft-thresholding type penalties to incorporate variable selection into multi-class classification for high dimensional data. The new methods were applied to simulated data and two cancer gene expression data sets. The results demonstrate that the new methods can select a small number of genes for building accurate multi-class classification rules. Furthermore, the important genes selected by the methods overlap significantly, suggesting general agreement among different variable selection schemes. High accuracy and sparsity make the new methods attractive for cancer diagnostics with gene expression data and defining targets of therapeutic intervention. The source MATLAB code are available from http://math.arizona.edu/~hzhang/software.html.

  15. Comparison of linear discriminant analysis methods for the classification of cancer based on gene expression data

    Directory of Open Access Journals (Sweden)

    He Miao

    2009-12-01

    Full Text Available Abstract Background More studies based on gene expression data have been reported in great detail, however, one major challenge for the methodologists is the choice of classification methods. The main purpose of this research was to compare the performance of linear discriminant analysis (LDA and its modification methods for the classification of cancer based on gene expression data. Methods The classification performance of linear discriminant analysis (LDA and its modification methods was evaluated by applying these methods to six public cancer gene expression datasets. These methods included linear discriminant analysis (LDA, prediction analysis for microarrays (PAM, shrinkage centroid regularized discriminant analysis (SCRDA, shrinkage linear discriminant analysis (SLDA and shrinkage diagonal discriminant analysis (SDDA. The procedures were performed by software R 2.80. Results PAM picked out fewer feature genes than other methods from most datasets except from Brain dataset. For the two methods of shrinkage discriminant analysis, SLDA selected more genes than SDDA from most datasets except from 2-class lung cancer dataset. When comparing SLDA with SCRDA, SLDA selected more genes than SCRDA from 2-class lung cancer, SRBCT and Brain dataset, the result was opposite for the rest datasets. The average test error of LDA modification methods was lower than LDA method. Conclusions The classification performance of LDA modification methods was superior to that of traditional LDA with respect to the average error and there was no significant difference between theses modification methods.

  16. Prevalence of Human Papillomavirus in endometrial cancer

    DEFF Research Database (Denmark)

    Olesen, Tina Bech; Svahn, Malene Frøsig; Faber, Mette Tuxen

    2014-01-01

    HPV is a common sexually transmitted infection and is considered to be a necessary cause of cervical cancer. The anatomical proximity to the cervix has led researchers to investigate whether Human Papillomavirus (HPV) has a role in the etiology of endometrial cancer.......HPV is a common sexually transmitted infection and is considered to be a necessary cause of cervical cancer. The anatomical proximity to the cervix has led researchers to investigate whether Human Papillomavirus (HPV) has a role in the etiology of endometrial cancer....

  17. HCSD: the human cancer secretome database

    DEFF Research Database (Denmark)

    Feizi, Amir; Banaei-Esfahani, Amir; Nielsen, Jens

    2015-01-01

    database is limiting the ability to query the increasing community knowledge. We therefore developed the Human Cancer Secretome Database (HCSD) to fulfil this gap. HCSD contains >80 000 measurements for about 7000 nonredundant human proteins collected from up to 35 high-throughput studies on 17 cancer...... types. It has a simple and user friendly query system for basic and advanced search based on gene name, cancer type and data type as the three main query options. The results are visualized in an explicit and interactive manner. An example of a result page includes annotations, cross references, cancer...

  18. Identifying Nursing Interventions in a Cancer Screening Program Using Nursing Interventions Classification Taxonomy.

    Science.gov (United States)

    Benito, Llucia; Lluch, María Teresa; Falcó, Anna Marta; García, Montse; Puig, Montse

    2017-04-01

    This study aimed to investigate which Nursing Interventions Classification (NIC) labels correspond to specific nursing interventions provided during cancer screening to establish a nursing documentation system. This descriptive study was conducted to identify and classify the interventions that cancer screening nurses perform based on an initial list. The initial list was grouped into 15 interventions that corresponded to four domains and eight classes. The study found expert consensus regarding the duties of cancer screening nurses and identified 15 interventions that should be implemented in clinical practice for cancer screening care, according to the NIC taxonomy. This study is the first step in developing indicators to assess nursing performance in cancer screening, and it helps to establish the core competency requirements for cancer screening nurses. © 2015 NANDA International, Inc.

  19. A review on ultrasound-based thyroid cancer tissue characterization and automated classification.

    Science.gov (United States)

    Acharya, U R; Swapna, G; Sree, S V; Molinari, F; Gupta, S; Bardales, R H; Witkowska, A; Suri, J S

    2014-08-01

    In this paper, we review the different studies that developed Computer Aided Diagnostic (CAD) for automated classification of thyroid cancer into benign and malignant types. Specifically, we discuss the different types of features that are used to study and analyze the differences between benign and malignant thyroid nodules. These features can be broadly categorized into (a) the sonographic features from the ultrasound images, and (b) the non-clinical features extracted from the ultrasound images using statistical and data mining techniques. We also present a brief description of the commonly used classifiers in ultrasound based CAD systems. We then review the studies that used features based on the ultrasound images for thyroid nodule classification and highlight the limitations of such studies. We also discuss and review the techniques used in studies that used the non-clinical features for thyroid nodule classification and report the classification accuracies obtained in these studies.

  20. Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes

    Directory of Open Access Journals (Sweden)

    Eils Roland

    2005-11-01

    Full Text Available Abstract Background The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods. Results In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85% were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis. Conclusion Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and

  1. Overview of the 8th Edition TNM Classification for Head and Neck Cancer.

    Science.gov (United States)

    Huang, Shao Hui; O'Sullivan, Brian

    2017-07-01

    The main purpose of the TNM system is to provide an anatomic-based classification to adequately depict cancer prognosis. Accurate cancer staging is important for treatment selection and outcome prediction, research design, and cancer control activities. To maintain clinical relevance, periodical updates to TNM are necessary. The recently published 8th edition TNM classification institutes the following changes to the staging of head and neck (excluding thyroid cancer): new stage classifications [HPV-related oropharyngeal cancer (HPV+ OPC) and soft tissue sarcoma of the head and neck (HN-STS)] and modification of T and N categories [T and N categories for nasopharyngeal cancer (NPC), T categories for oral cavity squamous cell carcinomas (OSCC), N categories for non-viral related head and neck cancer and unknown primary (CUP), and T categories for head and neck cutaneous carcinoma]. These changes reflect better understanding tumor biology and clinical behavior (e.g., HPV+ OPC and HN-STS), improved outcomes associated with technical advances in diagnosis and treatment (e.g., NPC), evolving knowledge about additional prognostic factors and risk stratification from research and observation (e.g., inclusion of depth of invasion variable for OSCC, inclusion of extranodal extension variable for all non-viral head and neck cancer, and reintroduction of size criteria for non-Merkel cell cutaneous carcinoma of the head and neck). This review summarizes the changes and potential advantages and limitations/caveats associated with them. Further evidence is needed to evaluate whether these changes would result in improvement in TNM stage performance to better serve the needs for clinical care, research, and cancer control.

  2. Human mammary microenvironment better regulates the biology of human breast cancer in humanized mouse model.

    Science.gov (United States)

    Zheng, Ming-Jie; Wang, Jue; Xu, Lu; Zha, Xiao-Ming; Zhao, Yi; Ling, Li-Jun; Wang, Shui

    2015-02-01

    During the past decades, many efforts have been made in mimicking the clinical progress of human cancer in mouse models. Previously, we developed a human breast tissue-derived (HB) mouse model. Theoretically, it may mimic the interactions between "species-specific" mammary microenvironment of human origin and human breast cancer cells. However, detailed evidences are absent. The present study (in vivo, cellular, and molecular experiments) was designed to explore the regulatory role of human mammary microenvironment in the progress of human breast cancer cells. Subcutaneous (SUB), mammary fat pad (MFP), and HB mouse models were developed for in vivo comparisons. Then, the orthotopic tumor masses from three different mouse models were collected for primary culture. Finally, the biology of primary cultured human breast cancer cells was compared by cellular and molecular experiments. Results of in vivo mouse models indicated that human breast cancer cells grew better in human mammary microenvironment. Cellular and molecular experiments confirmed that primary cultured human breast cancer cells from HB mouse model showed a better proliferative and anti-apoptotic biology than those from SUB to MFP mouse models. Meanwhile, primary cultured human breast cancer cells from HB mouse model also obtained the migratory and invasive biology for "species-specific" tissue metastasis to human tissues. Comprehensive analyses suggest that "species-specific" mammary microenvironment of human origin better regulates the biology of human breast cancer cells in our humanized mouse model of breast cancer, which is more consistent with the clinical progress of human breast cancer.

  3. A Hybrid Reduction Approach for Enhancing Cancer Classification of Microarray Data

    Directory of Open Access Journals (Sweden)

    Abeer M. Mahmoud

    2014-10-01

    Full Text Available This paper presents a novel hybrid machine learning (MLreduction approach to enhance cancer classification accuracy of microarray data based on two ML gene ranking techniques (T-test and Class Separability (CS. The proposed approach is integrated with two ML classifiers; K-nearest neighbor (KNN and support vector machine (SVM; for mining microarray gene expression profiles. Four public cancer microarray databases are used for evaluating the proposed approach and successfully accomplish the mining process. These are Lymphoma, Leukemia SRBCT, and Lung Cancer. The strategy to select genes only from the training samples and totally excluding the testing samples from the classifier building process is utilized for more accurate and validated results. Also, the computational experiments are illustrated in details and comprehensively presented with literature related results. The results showed that the proposed reduction approach reached promising results of the number of genes supplemented to the classifiers as well as the classification accuracy.

  4. Expression of polarity genes in human cancer.

    Science.gov (United States)

    Lin, Wan-Hsin; Asmann, Yan W; Anastasiadis, Panos Z

    2015-01-01

    Polarity protein complexes are crucial for epithelial apical-basal polarity and directed cell migration. Since alterations of these processes are common in cancer, polarity proteins have been proposed to function as tumor suppressors or oncogenic promoters. Here, we review the current understanding of polarity protein functions in epithelial homeostasis, as well as tumor formation and progression. As most previous studies focused on the function of single polarity proteins in simplified model systems, we used a genomics approach to systematically examine and identify the expression profiles of polarity genes in human cancer. The expression profiles of polarity genes were distinct in different human tissues and classified cancer types. Additionally, polarity expression profiles correlated with disease progression and aggressiveness, as well as with identified cancer types, where specific polarity genes were commonly altered. In the case of Scribble, gene expression analysis indicated its common amplification and upregulation in human cancer, suggesting a tumor promoting function.

  5. Antiangiogenic Steroids in Human Cancer Therapy

    OpenAIRE

    2005-01-01

    Despite advances in the early detection of tumors and in the use of chemotherapy, radiotherapy and surgery for disease management, the worldwide mortality from human cancer remains unacceptably high. The treatment of cancer may benefit from the introduction of novel therapies derived from natural products. Natural products have served to provide a basis for many of the pharmaceutical agents in current use in cancer therapy. Emerging research indicates that progressive growth and spread of ...

  6. Breast cancer tumor classification using LASSO method selection approach

    Energy Technology Data Exchange (ETDEWEB)

    Celaya P, J. M.; Ortiz M, J. A.; Martinez B, M. R.; Solis S, L. O.; Castaneda M, R.; Garza V, I.; Martinez F, M.; Ortiz R, J. M., E-mail: morvymm@yahoo.com.mx [Universidad Autonoma de Zacatecas, Av. Ramon Lopez Velarde 801, Col. Centro, 98000 Zacatecas, Zac. (Mexico)

    2016-10-15

    Breast cancer is one of the leading causes of deaths worldwide among women. Early tumor detection is key in reducing breast cancer deaths and screening mammography is the widest available method for early detection. Mammography is the most common and effective breast cancer screening test. However, the rate of positive findings is very low, making the radiologic interpretation monotonous and biased toward errors. In an attempt to alleviate radiological workload, this work presents a computer-aided diagnosis (CAD x) method aimed to automatically classify tumor lesions into malign or benign as a means to a second opinion. The CAD x methos, extracts image features, and classifies the screening mammogram abnormality into one of two categories: subject at risk of having malignant tumor (malign), and healthy subject (benign). In this study, 143 abnormal segmentation s (57 malign and 86 benign) from the Breast Cancer Digital Repository (BCD R) public database were used to train and evaluate the CAD x system. Percentile-rank (p-rank) was used to standardize the data. Using the LASSO feature selection methodology, the model achieved a Leave-one-out-cross-validation area under the receiver operating characteristic curve (Auc) of 0.950. The proposed method has the potential to rank abnormal lesions with high probability of malignant findings aiding in the detection of potential malign cases as a second opinion to the radiologist. (Author)

  7. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.

    Science.gov (United States)

    Li, Hui; Zhu, Yitan; Burnside, Elizabeth S; Huang, Erich; Drukker, Karen; Hoadley, Katherine A; Fan, Cheng; Conzen, Suzanne D; Zuley, Margarita; Net, Jose M; Sutton, Elizabeth; Whitman, Gary J; Morris, Elizabeth; Perou, Charles M; Ji, Yuan; Giger, Maryellen L

    2016-01-01

    Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute's multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER-, PR+ versus PR-, HER2+ versus HER2-, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P = 0.04 for lesions ≤ 2 cm; P = 0.02 for lesions >2 to ≤5 cm) as with the entire data set (P-value = 0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a

  8. HUMAN PAPILLOMAVIRUS INFECTIONS IN LARYNGEAL CANCER

    NARCIS (Netherlands)

    Torrente, Mariela C.; Rodrigo, Juan P.; Haigentz, Missak; Dikkers, Frederik G.; Rinaldo, Alessandra; Takes, Robert P.; Olofsson, Jan; Ferlito, Alfio

    2011-01-01

    Although the association and clinical significance of human papillomavirus (HPV) infections with a subset of head and neck cancers, particularly for oropharyngeal carcinoma, has recently been well documented, the involvement of HPV in laryngeal cancer has been inadequately evaluated. Herein we revie

  9. HUMAN PAPILLOMAVIRUS INFECTIONS IN LARYNGEAL CANCER

    NARCIS (Netherlands)

    Torrente, Mariela C.; Rodrigo, Juan P.; Haigentz, Missak; Dikkers, Frederik G.; Rinaldo, Alessandra; Takes, Robert P.; Olofsson, Jan; Ferlito, Alfio

    2011-01-01

    Although the association and clinical significance of human papillomavirus (HPV) infections with a subset of head and neck cancers, particularly for oropharyngeal carcinoma, has recently been well documented, the involvement of HPV in laryngeal cancer has been inadequately evaluated. Herein we revie

  10. Human papillomavirus infections in laryngeal cancer

    NARCIS (Netherlands)

    Torrente, M.C.; Rodrigo, J.P.; Haigentz Jr., M.; Dikkers, F.G.; Rinaldo, A.; Takes, R.P.; Olofsson, J.; Ferlito, A.

    2011-01-01

    Although the association and clinical significance of human papillomavirus (HPV) infections with a subset of head and neck cancers, particularly for oropharyngeal carcinoma, has recently been well documented, the involvement of HPV in laryngeal cancer has been inadequately evaluated. Herein we revie

  11. Human papillomavirus infections in laryngeal cancer

    NARCIS (Netherlands)

    Torrente, M.C.; Rodrigo, J.P.; Haigentz Jr., M.; Dikkers, F.G.; Rinaldo, A.; Takes, R.P.; Olofsson, J.; Ferlito, A.

    2011-01-01

    Although the association and clinical significance of human papillomavirus (HPV) infections with a subset of head and neck cancers, particularly for oropharyngeal carcinoma, has recently been well documented, the involvement of HPV in laryngeal cancer has been inadequately evaluated. Herein we

  12. HUMAN PAPILLOMAVIRUS INFECTIONS IN LARYNGEAL CANCER

    NARCIS (Netherlands)

    Torrente, Mariela C.; Rodrigo, Juan P.; Haigentz, Missak; Dikkers, Frederik G.; Rinaldo, Alessandra; Takes, Robert P.; Olofsson, Jan; Ferlito, Alfio

    Although the association and clinical significance of human papillomavirus (HPV) infections with a subset of head and neck cancers, particularly for oropharyngeal carcinoma, has recently been well documented, the involvement of HPV in laryngeal cancer has been inadequately evaluated. Herein we

  13. Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification.

    Science.gov (United States)

    Algamal, Zakariya Yahya; Lee, Muhammad Hisyam

    2015-12-01

    Cancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. The adaptive elastic net originally used elastic net estimates as the initial weight, however, using this weight may not be preferable for certain reasons: First, the elastic net estimator is biased in selecting genes. Second, it does not perform well when the pairwise correlations between variables are not high. Adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously. The real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods. Additionally, the classification performance of AAElastic is comparable to the adaptive elastic net and better than other regularization methods. Thus, we can conclude that AAElastic is a reliable adaptive regularized logistic regression method in the field of high-dimensional cancer classification.

  14. Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis.

    Science.gov (United States)

    Al-Rajab, Murad; Lu, Joan; Xu, Qiang

    2017-07-01

    This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Looking at the ICF and human communication through the lens of classification theory.

    Science.gov (United States)

    Walsh, Regina

    2011-08-01

    This paper explores the insights that classification theory can provide about the application of the International Classification of Functioning, Disability and Health (ICF) to communication. It first considers the relationship between conceptual models and classification systems, highlighting that classification systems in speech-language pathology (SLP) have not historically been based on conceptual models of human communication. It then overviews the key concepts and criteria of classification theory. Applying classification theory to the ICF and communication raises a number of issues, some previously highlighted through clinical application. Six focus questions from classification theory are used to explore these issues, and to propose the creation of an ICF-related conceptual model of communicating for the field of communication disability, which would address some of the issues raised. Developing a conceptual model of communication for SLP purposes closely articulated with the ICF would foster productive intra-professional discourse, while at the same time allow the profession to continue to use the ICF for purposes in inter-disciplinary discourse. The paper concludes by suggesting the insights of classification theory can assist professionals to apply the ICF to communication with the necessary rigour, and to work further in developing a conceptual model of human communication.

  16. Potential Prognostic Markers for Human Prostate Cancer

    Science.gov (United States)

    2001-03-01

    Prostate 35: 185-192, 1998 osteoblasts on prostate carcinoma proliferation and chemo- 32. Trikha M, Cai Y, Grignon D, Honn KV: Identification taxis ...Markers for Human Prostate Cancer PRINCIPAL INVESTIGATOR: Bruce R. Zetter, Ph.D. CONTRACTING ORGANIZATION: Children’s Hospital Boston, Massachusetts...March 2001 Final (1 Sep 98 - 28 Feb 01) 4. TITLE AND SUBTITLE 5. FUNDING NUMBERS Potential Prognostic Markers for Human Prostate Cancer DAMD17-98-1

  17. Sensor Data Acquisition and Processing Parameters for Human Activity Classification

    Directory of Open Access Journals (Sweden)

    Sebastian D. Bersch

    2014-03-01

    Full Text Available It is known that parameter selection for data sampling frequency and segmentation techniques (including different methods and window sizes has an impact on the classification accuracy. For Ambient Assisted Living (AAL, no clear information to select these parameters exists, hence a wide variety and inconsistency across today’s literature is observed. This paper presents the empirical investigation of different data sampling rates, segmentation techniques and segmentation window sizes and their effect on the accuracy of Activity of Daily Living (ADL event classification and computational load for two different accelerometer sensor datasets. The study is conducted using an ANalysis Of VAriance (ANOVA based on 32 different window sizes, three different segmentation algorithm (with and without overlap, totaling in six different parameters and six sampling frequencies for nine common classification algorithms. The classification accuracy is based on a feature vector consisting of Root Mean Square (RMS, Mean, Signal Magnitude Area (SMA, Signal Vector Magnitude (here SMV, Energy, Entropy, FFTPeak, Standard Deviation (STD. The results are presented alongside recommendations for the parameter selection on the basis of the best performing parameter combinations that are identified by means of the corresponding Pareto curve.

  18. Validation of the Stage Groupings in the Eighth Edition of the TNM Classification for Lung Cancer.

    Science.gov (United States)

    Sui, Xizhao; Jiang, Wei; Chen, Haiqing; Yang, Fan; Wang, Jun; Wang, Qun

    2017-08-04

    The aim of this study was to validate stage groupings in the eighth edition of the TNM classification in an independent Chinese cohort. We retrospectively analyzed a total of 3599 patients with pathological stage IA to IIIA (seventh edition of the TNM) NSCLC who underwent surgical treatment in two surgical centers in the People's Republic of China between 2005 and 2012. All patients were reclassified according to the eighth edition of the TNM classification. Survival was compared between adjacent stage groupings by using a log-rank test and a Cox regression model. R(2) was calculated to evaluate the discrimination of the two TNM stage classifications. The median follow-up time was 48.7 months. According to the eighth edition of the TNM classification, the overall survival (OS) of adjacent stage groupings showed significant differences except for IA3 vs. IB. The eighth edition of the TNM classification yielded a slightly higher R(2) than the seventh edition (0.172 vs. 0.162). This study provided an external validation of the stage groupings in the eighth edition of the TNM classification for lung cancer among surgically treated Chinese patients with NSCLC. Copyright © 2017. Published by Elsevier Inc.

  19. TNM staging and classification (familial and nonfamilial of breast cancer in Jordanian females

    Directory of Open Access Journals (Sweden)

    M F Atoum

    2010-01-01

    Full Text Available Purpose : Staging of breast tumor has important implications for treatment and prognosis. This study aims at pinpointing the frequency of each stage among familial and nonfamilial breast cancers. Materials and Methods : Ninety-nine Jordanian females diagnosed with familial and nonfamilial breast cancer between 2000 and 2002 were enrolled in this study All breast cancer cases were staged according to the TNM classification into in situ, early invasive, advanced invasive and metastatic. Results : Forty-three cases were familial breast cancer and 56 were nonfamilial. One female breast cancer was diagnosed with ductal carcinoma in situ (DCIS cancer. Fifty cases were diagnosed in early stages of invasive breast cancer, of which 31 cases were familial, 29 cases were classified as advanced invasive, where 21 cases were nonfamilial and 19 cases were metastatic stage of breast cancer, with 16 nonfamilial cases. Stage 2b was the most common stage of early invasive cases and represented 48% of the early stage of breast cancer. On the other hand, among cases diagnosed with advanced invasive breast cancer, stage 3a was the most common stage and represented 89.6% of the advanced stage. Interestingly, all cases of stage 3a belonged to TNM stages of T2N2M0 and T3N1M0. The tumor size in all cases of Jordanian females diagnosed with advanced invasive breast cancer exceeded 2 cm in size due to selection bias from symptomatic women in our study. Conclusion : The incidence of nonfamilial breast cancer was slightly higher than that of the familial type amongst studied the Jordanian females studied. The early invasive stage of breast cancer was more common in the familial while the advanced invasive and metastatic breast cancer cases were encountered more often in the nonfamilial type. Our study was based on a small sample and symptomatic women. Therefore, more research with larger population samples is needed to confirm this conclusion.

  20. Impact of full field digital mammography on the classification and mammographic characteristics of interval breast cancers

    Energy Technology Data Exchange (ETDEWEB)

    Knox, Mark, E-mail: marktknox@gmail.com; O’Brien, Angela, E-mail: angelaobrien@doctors.org.uk; Szabó, Endre, E-mail: endrebacsi@freemail.hu; Smith, Clare S., E-mail: csmith@mater.ie; Fenlon, Helen M., E-mail: helen.fenlon@cancerscreening.ie; McNicholas, Michelle M., E-mail: michelle.mcnicholas@cancerscreening.ie; Flanagan, Fidelma L., E-mail: fidelma.flanagan@cancerscreening.ie

    2015-06-15

    Highlights: • Digital mammography has changed the presentation of interval breast cancer. • Less interval breast cancers are associated with microcalcifications following FFDM. • Interval breast cancer audit remains a key feature of any breast screening program. - Abstract: Objective: Full field digital mammography (FFDM) is increasingly replacing screen film mammography (SFM) in breast screening programs. Interval breast cancers are an issue in all screening programs and the purpose of our study is to assess the impact of FFDM on the classification of interval breast cancers at independent blind review and to compare the mammographic features of interval cancers at FFDM and SFM. Materials and methods: This study included 138 cases of interval breast cancer, 76 following an FFDM screening examination and 62 following screening with SFM. The prior screening mammogram was assessed by each of five consultant breast radiologists who were blinded to the site of subsequent cancer. Subsequent review of the diagnostic mammogram was performed and cases were classified as missed, minimal signs, occult or true interval. Mammographic features of the interval cancer at diagnosis and any abnormality identified on the prior screening mammogram were recorded. Results: The percentages of cancers classified as missed at FFDM and SFM did not differ significantly, 10.5% (8 of 76) at FFDM and 8.1% (5 of 62) at SFM (p = .77). There were significantly less interval cancers presenting as microcalcifications (alone or in association with another abnormality) following screening with FFDM, 16% (12 of 76) than following a SFM examination, 32% (20 of 62) (p = .02). Conclusion: Interval breast cancers continue to pose a problem at FFDM. The switch to FFDM has changed the mammographic presentation of interval breast cancer, with less interval cancers presenting in association with microcalcifications.

  1. From Molecular Classification to Targeted Therapeutics: The Changing Face of Systemic Therapy in Metastatic Gastroesophageal Cancer

    Directory of Open Access Journals (Sweden)

    Adrian Murphy

    2015-01-01

    Full Text Available Histological classification of adenocarcinoma or squamous cell carcinoma for esophageal cancer or using the Lauren classification for intestinal and diffuse type gastric cancer has limited clinical utility in the management of advanced disease. Germline mutations in E-cadherin (CDH1 or mismatch repair genes (Lynch syndrome were identified many years ago but given their rarity, the identification of these molecular alterations does not substantially impact treatment in the advanced setting. Recent molecular profiling studies of upper GI tumors have added to our knowledge of the underlying biology but have not led to an alternative classification system which can guide clinician’s therapeutic decisions. Recently the Cancer Genome Atlas Research Network has proposed four subtypes of gastric cancer dividing tumors into those positive for Epstein-Barr virus, microsatellite unstable tumors, genomically stable tumors, and tumors with chromosomal instability. Unfortunately to date, many phase III clinical trials involving molecularly targeted agents have failed to meet their survival endpoints due to their use in unselected populations. Future clinical trials should utilize molecular profiling of individual tumors in order to determine the optimal use of targeted therapies in preselected patients.

  2. From molecular classification to targeted therapeutics: the changing face of systemic therapy in metastatic gastroesophageal cancer.

    Science.gov (United States)

    Murphy, Adrian; Kelly, Ronan J

    2015-01-01

    Histological classification of adenocarcinoma or squamous cell carcinoma for esophageal cancer or using the Lauren classification for intestinal and diffuse type gastric cancer has limited clinical utility in the management of advanced disease. Germline mutations in E-cadherin (CDH1) or mismatch repair genes (Lynch syndrome) were identified many years ago but given their rarity, the identification of these molecular alterations does not substantially impact treatment in the advanced setting. Recent molecular profiling studies of upper GI tumors have added to our knowledge of the underlying biology but have not led to an alternative classification system which can guide clinician's therapeutic decisions. Recently the Cancer Genome Atlas Research Network has proposed four subtypes of gastric cancer dividing tumors into those positive for Epstein-Barr virus, microsatellite unstable tumors, genomically stable tumors, and tumors with chromosomal instability. Unfortunately to date, many phase III clinical trials involving molecularly targeted agents have failed to meet their survival endpoints due to their use in unselected populations. Future clinical trials should utilize molecular profiling of individual tumors in order to determine the optimal use of targeted therapies in preselected patients.

  3. EMT is the dominant program in human colon cancer

    Directory of Open Access Journals (Sweden)

    Tollenaar Rob AEM

    2011-01-01

    Full Text Available Abstract Background Colon cancer has been classically described by clinicopathologic features that permit the prediction of outcome only after surgical resection and staging. Methods We performed an unsupervised analysis of microarray data from 326 colon cancers to identify the first principal component (PC1 of the most variable set of genes. PC1 deciphered two primary, intrinsic molecular subtypes of colon cancer that predicted disease progression and recurrence. Results Here we report that the most dominant pattern of intrinsic gene expression in colon cancer (PC1 was tightly correlated (Pearson R = 0.92, P -135 with the EMT signature-- both in gene identity and directionality. In a global micro-RNA screen, we further identified the most anti-correlated microRNA with PC1 as MiR200, known to regulate EMT. Conclusions These data demonstrate that the biology underpinning the native, molecular classification of human colon cancer--previously thought to be highly heterogeneous-- was clarified through the lens of comprehensive transcriptome analysis.

  4. Biological stoichiometry in human cancer.

    Directory of Open Access Journals (Sweden)

    James J Elser

    Full Text Available BACKGROUND: A growing tumor in the body can be considered a complex ecological and evolutionary system. A new eco-evolutionary hypothesis (the "Growth Rate Hypothesis", GRH proposes that tumors have elevated phosphorus (P demands due to increased allocation to P-rich nucleic acids, especially ribosomal RNA, to meet the protein synthesis demands of accelerated proliferation. METHODOLOGY/PRINCIPAL FINDINGS: We determined the elemental (C, N, P and nucleic acid contents of paired malignant and normal tissues from colon, lung, liver, or kidney for 121 patients. Consistent with the GRH, lung and colon tumors were significantly higher (by approximately two-fold in P content (fraction of dry weight and RNA content and lower in nitrogen (N:P ratio than paired normal tissue, and P in RNA contributed a significantly larger fraction of total biomass P in malignant relative to normal tissues. Furthermore, patient-specific differences for %P between malignant and normal tissues were positively correlated with such differences for %RNA, both for the overall data and within three of the four organ sites. However, significant differences in %P and %RNA between malignant and normal tissues were not seen in liver and kidney and, overall, RNA contributed only approximately 11% of total tissue P content. CONCLUSIONS/SIGNIFICANCE: Data for lung and colon tumors provide support for the GRH in human cancer. The two-fold amplification of P content in colon and lung tumors may set the stage for potential P-limitation of their proliferation, as such differences often do for rapidly growing biota in ecosystems. However, data for kidney and liver do not support the GRH. To account for these conflicting observations, we suggest that local environments in some organs select for neoplastic cells bearing mutations increasing cell division rate ("r-selected," as in colon and lung while conditions elsewhere may select for reduced mortality rate ("K-selected," as in liver and

  5. AN ADABOOST OPTIMIZED CCFIS BASED CLASSIFICATION MODEL FOR BREAST CANCER DETECTION

    Directory of Open Access Journals (Sweden)

    CHANDRASEKAR RAVI

    2017-06-01

    Full Text Available Classification is a Data Mining technique used for building a prototype of the data behaviour, using which an unseen data can be classified into one of the defined classes. Several researchers have proposed classification techniques but most of them did not emphasis much on the misclassified instances and storage space. In this paper, a classification model is proposed that takes into account the misclassified instances and storage space. The classification model is efficiently developed using a tree structure for reducing the storage complexity and uses single scan of the dataset. During the training phase, Class-based Closed Frequent ItemSets (CCFIS were mined from the training dataset in the form of a tree structure. The classification model has been developed using the CCFIS and a similarity measure based on Longest Common Subsequence (LCS. Further, the Particle Swarm Optimization algorithm is applied on the generated CCFIS, which assigns weights to the itemsets and their associated classes. Most of the classifiers are correctly classifying the common instances but they misclassify the rare instances. In view of that, AdaBoost algorithm has been used to boost the weights of the misclassified instances in the previous round so as to include them in the training phase to classify the rare instances. This improves the accuracy of the classification model. During the testing phase, the classification model is used to classify the instances of the test dataset. Breast Cancer dataset from UCI repository is used for experiment. Experimental analysis shows that the accuracy of the proposed classification model outperforms the PSOAdaBoost-Sequence classifier by 7% superior to other approaches like Naïve Bayes Classifier, Support Vector Machine Classifier, Instance Based Classifier, ID3 Classifier, J48 Classifier, etc.

  6. Human Talent Prediction in HRM using C4.5 Classification Algorithm

    Directory of Open Access Journals (Sweden)

    Hamidah Jantan,

    2010-11-01

    Full Text Available In HRM, among the challenges for HR professionals is to manage an organization’s talents, especially to ensure the right person for the right job at the right time. Human talent prediction is an alternative to handle this issue. Due to that reason, classification and prediction in data mining which is commonly used in many areas can also be implemented to human talent. There are many classification techniques in data mining techniques such as Decision Tree, Neural Network, Rough Set Theory, Bayesian theory and Fuzzy logic. Decision tree is among the popular classification techniques, which can produce the interpretable rules or logic statement. Thegenerated rules from the selected technique can be used for future prediction. In this article, we present the study on how the potential human talent can be predicted using a decision tree classifier. By using this technique, the pattern of talent performance can be identified through the classification process. In that case, the hidden and valuable knowledge discovered in the related databases will be summarized in the decision tree structure. In this study, we use decision tree C4.5 classification algorithm to generate the classification rules for human talent performance records. Finally, the generated rules are evaluated using the unseen data in order to estimate the accuracy of the prediction result.

  7. Epidemiology of human fascioliasis: a review and proposed new classification.

    OpenAIRE

    Mas-Coma, M. S.; Esteban, J. G.; Bargues, M. D.

    1999-01-01

    The epidemiological picture of human fascioliasis has changed in recent years. The number of reports of humans infected with Fasciola hepatica has increased significantly since 1980 and several geographical areas have been described as endemic for the disease in humans, with prevalence and intensity ranging from low to very high. High prevalence of fascioliasis in humans does not necessarily occur in areas where fascioliasis is a major veterinary problem. Human fascioliasis can no longer be c...

  8. Identifying Cancer Biomarkers Via Node Classification within a Mapreduce Framework

    Directory of Open Access Journals (Sweden)

    Taysir Hassan A. Soliman

    2015-12-01

    Full Text Available Big data are giving new research challenges in the life sciences domain because of their variety, volume, veracity, velocity, and value. Predicting gene biomarkers is one of the vital research issues in bioinformatics field, where microarray gene expression and network based methods can be used. These datasets suffer from the huge data voluminous, causing main memory problems. In this paper, a Random Committee Node Classifier algorithm (RCNC is proposed for identifying cancer biomarkers, which is based on microarray gene expression data and Protein-Protein Interaction (PPI data. Data are enriched from other public databases, such as IntACT1 and UniProt2 and Gene Ontology3 (GO. Cancer Biomarkers are identified when applied to different datasets with an accuracy rate an accuracy rate 99.16%, 99.96% precision, 99.24% recall, 99.16% F1-measure and 99.6 ROC. To speed up the performance, it is run within a MapReduce framework, where RCNC MapReduce algorithm is much faster than RCNC sequential algorithm when having large datasets.

  9. Prediction and Classification of Human G-protein Coupled Receptors Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    Yun-Fei Wang; Huan Chen; Yan-Hong Zhou

    2005-01-01

    A computational system for the prediction and classification of human G-protein coupled receptors (GPCRs) has been developed based on the support vector machine (SVM) method and protein sequence information. The feature vectors used to develop the SVM prediction models consist of statistically significant features selected from single amino acid, dipeptide, and tripeptide compositions of protein sequences. Furthermore, the length distribution difference between GPCRsand non-GPCRs has also been exploited to improve the prediction performance.The testing results with annotated human protein sequences demonstrate that this system can get good performance for both prediction and classification of human GPCRs.

  10. Classification of Dukes' B and C colorectal cancers using expression arrays

    DEFF Research Database (Denmark)

    Frederiksen, C.M.; Knudsen, Steen; Laurberg, S.;

    2003-01-01

    Purpose. Colorectal cancer is one of the most common malignancies. Substaging of the cancer is of importance not only to prognosis but also to treatment. Classification of substages based on DNA microarray technology is currently the most promising approach. We therefore investigated if gene...... expression microarrays could be used to classify colorectal tumors. Methods. We used the Affymetrix oligonucleotide arrays to analyze the expression of more than 5,000 genes in samples from the sigmoid and upper rectum of the left colon. Five samples were from normal mucosa and five samples from each......' A and D could not be classified correctly. A number of interesting gene clusters showed a discriminating difference between Dukes' B and C samples. These included mitochondrial genes, stromal remodeling genes, and genes related to cell adhesion. Conclusion. Molecular classification based on gene...

  11. A Novel Segment Classification for Multifocal and Multicentric Breast Cancer to Facilitate Breast-Conservation Treatment.

    Science.gov (United States)

    Tan, Mona P

    2015-01-01

    Breast conservation treatment (BCT) is an appropriate alternative to mastectomy for the treatment of unifocal breast cancer. Multifocal and multicentric breast cancers (MFMCBC) challenge conventional indications for BCT and are often treated with mastectomy. Following progress in treatment strategies for unifocal tumors, there was a movement to evaluate the use of BCT for MFMCBC. Now a growing body of evidence from retrospective data has emerged, demonstrating acceptable local control and overall survival rates with BCT for MFMCBC. Prospective studies are needed to confirm these findings. One of the possible barriers to such trials is the absence of a standardized classification and nomenclature for MFMCBC at this point in time. A novel segment classification is presented in this article in an endeavor to overcome this deficiency and allow future work on this issue.

  12. Effectiveness of Statistical Features for Human Emotions Classification using EEG Biosensors

    Directory of Open Access Journals (Sweden)

    Chai Tong Yuen

    2013-05-01

    Full Text Available This study proposes a statistical features-based classification system for human emotions by using Electroencephalogram (EEG bio-sensors. A total of six statistical features are computed from the EEG data and Artificial Neural Network is applied for the classification of emotions. The system is trained and tested with the statistical features extracted from the psychological signals acquired under emotions stimulation experiments. The effectiveness of each statistical feature and combinations of statistical features in classifying different types of emotions has been studied and evaluated. In the experiment of classifying four main types of emotions: Anger, Sad, Happy and Neutral, the overall classification rate as high as 90% is achieved.

  13. Gastric Cancer Risk Analysis in Unhealthy Habits Data with Classification Algorithms

    Directory of Open Access Journals (Sweden)

    Kirshners Arnis

    2015-12-01

    Full Text Available Data mining methods are applied to a medical task that seeks for the information about the influence of Helicobacter Pylori on the gastric cancer risk increase by analysing the adverse factors of individual lifestyle. In the process of data preprocessing, the data are cleared of noise and other factors, reduced in dimensionality, as well as transformed for the task and cleared of non-informative attributes. Data classification using C4.5, CN2 and k-nearest neighbour algorithms is carried out to find relationships between the analysed attributes and the descriptive class attribute – Helicobacter Pylori presence that could have influence on the cancer development risk. Experimental analysis is carried out using the data of the Latvian-based project “Interdisciplinary Research Group for Early Cancer Detection and Cancer Prevention” database.

  14. Cancer pain: A critical review of mechanism-based classification and physical therapy management in palliative care

    Directory of Open Access Journals (Sweden)

    Senthil P Kumar

    2011-01-01

    Full Text Available Mechanism-based classification and physical therapy management of pain is essential to effectively manage painful symptoms in patients attending palliative care. The objective of this review is to provide a detailed review of mechanism-based classification and physical therapy management of patients with cancer pain. Cancer pain can be classified based upon pain symptoms, pain mechanisms and pain syndromes. Classification based upon mechanisms not only addresses the underlying pathophysiology but also provides us with an understanding behind patient′s symptoms and treatment responses. Existing evidence suggests that the five mechanisms - central sensitization, peripheral sensitization, sympathetically maintained pain, nociceptive and cognitive-affective - operate in patients with cancer pain. Summary of studies showing evidence for physical therapy treatment methods for cancer pain follows with suggested therapeutic implications. Effective palliative physical therapy care using a mechanism-based classification model should be tailored to suit each patient′s findings, using a biopsychosocial model of pain.

  15. Conformational SERS Classification of K-Ras Point Mutations for Cancer Diagnostics.

    Science.gov (United States)

    Morla-Folch, Judit; Gisbert-Quilis, Patricia; Masetti, Matteo; Garcia-Rico, Eduardo; Alvarez-Puebla, Ramon A; Guerrini, Luca

    2017-02-20

    Point mutations in Ras oncogenes are routinely screened for diagnostics and treatment of tumors (especially in colorectal cancer). Here, we develop an optical approach based on direct SERS coupled with chemometrics for the study of the specific conformations that single-point mutations impose on a relatively large fragment of the K-Ras gene (141 nucleobases). Results obtained offer the unambiguous classification of different mutations providing a potentially useful insight for diagnostics and treatment of cancer in a sensitive, fast, direct and inexpensive manner.

  16. Lung Cancer Early Diagnosis Using Some Data Mining Classification Techniques: A Survey

    Directory of Open Access Journals (Sweden)

    Thangaraju P

    2014-06-01

    Full Text Available Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining is primarily used to this requirement thus finding its applications in diverse fields such as retail, financial, communication, marketing organizations and medicine. Data Mining plays an important role in healthcare organization because with the growth of population and dangerous deadly diseases like Cancer, SARS, Leprosy, HIV etc, Lung cancer is one of the most dangerous disease. This survey for appropriate medical image mining, Data Preprocessing, Feature Extraction, rule generation and classification, it provides basic framework for further improvement in medical diagnosis.

  17. Lung Cancer Early Diagnosis Using Some Data Mining Classification Techniques: A Survey

    Directory of Open Access Journals (Sweden)

    Thangaraju P

    2015-11-01

    Full Text Available  Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining is primarily used to this requirement thus finding its applications in diverse fields such as retail, financial, communication, marketing organizations and medicine. Data Mining plays an important role in healthcare organization because with the growth of population and dangerous deadly diseases like Cancer, SARS, Leprosy, HIV etc, Lung cancer is one of the most dangerous disease. This survey for appropriate medical image mining, Data Preprocessing, Feature Extraction, rule generation and classification, it provides basic framework for further improvement in medical diagnosis.

  18. Novel innate cancer killing activity in humans

    Directory of Open Access Journals (Sweden)

    Lovato James

    2011-08-01

    Full Text Available Abstract Background In this study, we pilot tested an in vitro assay of cancer killing activity (CKA in circulating leukocytes of 22 cancer cases and 25 healthy controls. Methods Using a human cervical cancer cell line, HeLa, as target cells, we compared the CKA in circulating leukocytes, as effector cells, of cancer cases and controls. The CKA was normalized as percentages of total target cells during selected periods of incubation time and at selected effector/target cell ratios in comparison to no-effector-cell controls. Results Our results showed that CKA similar to that of our previous study of SR/CR mice was present in human circulating leukocytes but at profoundly different levels in individuals. Overall, males have a significantly higher CKA than females. The CKA levels in cancer cases were lower than that in healthy controls (mean ± SD: 36.97 ± 21.39 vs. 46.28 ± 27.22. Below-median CKA was significantly associated with case status (odds ratio = 4.36; 95% Confidence Interval = 1.06, 17.88 after adjustment of gender and race. Conclusions In freshly isolated human leukocytes, we were able to detect an apparent CKA in a similar manner to that of cancer-resistant SR/CR mice. The finding of CKA at lower levels in cancer patients suggests the possibility that it may be of a consequence of genetic, physiological, or pathological conditions, pending future studies with larger sample size.

  19. Classification of breast cancer stroma as a tool for prognosis

    Science.gov (United States)

    Reis, Sara; Gazinska, Patrycja; Hipwell, John H.; Mertzanidou, Thomy; Naidoo, Kalnisha; Pinder, Sarah; Hawkes, David J.

    2016-03-01

    It has been shown that the tumour microenvironment plays a crucial role in regulating tumour progression by a number of different mechanisms, including the remodeling of collagen fibres in tumour-associated stroma. It is still unclear, however, if these stromal changes are of benefit to the host or the tumour. We hypothesise that stromal maturity is an important reflection of tumour biology, and thus can be used to predict prognosis. The aim of this study is to develop a texture analysis methodology which will automatically classify stromal regions from images of hematoxylin and eosin-stained (H and E) sections into two categories: mature and immature. Subsequently we will investigate whether stromal maturity could be used as a predictor of survival and also as a means to better understand the relationship between the radiological imaging signal and the underlying tissue microstructure. We present initial results for 118 regions-of-interest from a dataset of 39 patients diagnosed with invasive breast cancer.

  20. Setting a generalized functional linear model (GFLM for the classification of different types of cancer

    Directory of Open Access Journals (Sweden)

    Miguel Flores

    2016-11-01

    Full Text Available This work aims to classify the DNA sequences of healthy and malignant cancer respectively. For this, supervised and unsupervised classification methods from a functional context are used; i.e. each strand of DNA is an observation. The observations are discretized, for that reason different ways to represent these observations with functions are evaluated. In addition, an exploratory study is done: estimating the mean and variance of each functional type of cancer. For the unsupervised classification method, hierarchical clustering with different measures of functional distance is used. On the other hand, for the supervised classification method, a functional generalized linear model is used. For this model the first and second derivatives are used which are included as discriminating variables. It has been verified that one of the advantages of working in the functional context is to obtain a model to correctly classify cancers by 100%. For the implementation of the methods it has been used the fda.usc R package that includes all the techniques of functional data analysis used in this work. In addition, some that have been developed in recent decades. For more details of these techniques can be consulted Ramsay, J. O. and Silverman (2005 and Ferraty et al. (2006.

  1. Classification and Clinical Management of Variants of Uncertain Significance in High Penetrance Cancer Predisposition Genes.

    Science.gov (United States)

    Moghadasi, Setareh; Eccles, Diana M; Devilee, Peter; Vreeswijk, Maaike P G; van Asperen, Christi J

    2016-04-01

    In 2008, the International Agency for Research on Cancer (IARC) proposed a system for classifying sequence variants in highly penetrant breast and colon cancer susceptibility genes, linked to clinical actions. This system uses a multifactorial likelihood model to calculate the posterior probability that an altered DNA sequence is pathogenic. Variants between 5%-94.9% (class 3) are categorized as variants of uncertain significance (VUS). This interval is wide and might include variants with a substantial difference in pathogenicity at either end of the spectrum. We think that carriers of class 3 variants would benefit from a fine-tuning of this classification. Classification of VUS to a category with a defined clinical significance is very important because for carriers of a pathogenic mutation full surveillance and risk-reducing surgery can reduce cancer incidence. Counselees who are not carriers of a pathogenic mutation can be discharged from intensive follow-up and avoid unnecessary risk-reducing surgery. By means of examples, we show how, in selected cases, additional data can lead to reclassification of some variants to a different class with different recommendations for surveillance and therapy. To improve the clinical utility of this classification system, we suggest a pragmatic adaptation to clinical practice.

  2. Apparent diffusion coefficient value of gastric cancer by diffusion-weighted imaging: Correlations with the histological differentiation and Lauren classification

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Song, E-mail: songliu532909756@gmail.com [Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008 (China); Guan, Wenxian, E-mail: wenxianguan123@126.com [Department of Gastrointestinal Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008 (China); Wang, Hao, E-mail: wanghao20140525@126.com [Department of Gastrointestinal Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008 (China); Pan, Liang, E-mail: panliang2014@126.com [Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008 (China); Zhou, Zhuping, E-mail: zhupingzhou@126.com [Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008 (China); Yu, Haiping, E-mail: haipingyu2012@126.com [Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008 (China); Liu, Tian, E-mail: tianliu2014@126.com [Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322 (United States); Yang, Xiaofeng, E-mail: xiaofengyang2014@126.com [Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322 (United States); He, Jian, E-mail: hjxueren@126.com [Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008 (China); Zhou, Zhengyang, E-mail: zyzhou@nju.edu.cn [Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008 (China)

    2014-12-15

    Highlights: • Gastric cancers’ ADC values were significantly lower than normal gastric wall. • Gastric adenocarcinomas with different differentiation had different ADC values. • Gastric adenocarcinomas’ ADC values correlated with histologic differentiations. • Gastric cancers’ ADC values correlated with Lauren classifications. • Mean ADC value was better than min ADC value in characterizing gastric cancers. - Abstract: Objective: The purpose of this study was to evaluate the correlations between histological differentiation and Lauren classification of gastric cancer and the apparent diffusion coefficient (ADC) value of diffusion weighted imaging (DWI). Materials and methods: Sixty-nine patients with gastric cancer lesions underwent preoperative magnetic resonance imaging (MRI) (3.0T) and surgical resection. DWI was obtained with a single-shot, echo-planar imaging sequence in the axial plane (b values: 0 and 1000 s/mm{sup 2}). Mean and minimum ADC values were obtained for each gastric cancer and normal gastric walls by two radiologists, who were blinded to the histological findings. Histological type, degree of differentiation and Lauren classification of each resected specimen were determined by one pathologist. Mean and minimum ADC values of gastric cancers with different histological types, degrees of differentiation and Lauren classifications were compared. Correlations between ADC values and histological differentiation and Lauren classification were analyzed. Results: The mean and minimum ADC values of gastric cancers, as a whole and separately, were significantly lower than those of normal gastric walls (all p values <0.001). There were significant differences in the mean and minimum ADC values among gastric cancers with different histological types, degrees of differentiation and Lauren classifications (p < 0.05). Mean and minimum ADC values correlated significantly (all p < 0.001) with histological differentiation (r = 0.564, 0.578) and

  3. Transporter Classification Database (TCDB)

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Transporter Classification Database details a comprehensive classification system for membrane transport proteins known as the Transporter Classification (TC)...

  4. Human papilloma viruses (HPV and breast cancer.

    Directory of Open Access Journals (Sweden)

    James Sutherland Lawson

    2015-12-01

    Full Text Available Purpose: Human papillomaviruses (HPV may have a role in some breast cancers. The purpose of this study is to fill important gaps in the evidence. These gaps are: (i confirmation of the presence of high risk for cancer HPVs in breast cancers, (ii evidence of HPV infections in benign breast tissues prior to the development of HPV positive breast cancer in the same patients, (iii evidence that HPVs are biologically active and not harmless passengers in breast cancer.Methods: RNA-seq data from The Cancer Genome Atlas (TCGA was used to identify HPV RNA sequences in breast cancers. We also conducted a retrospective cohort study based on polymerase chain reaction (PCR analyses to identify HPVs in archival specimens from Australian women with benign breast biopsies who later developed breast cancer. To assess whether HPVs in breast cancer were biologically active, the expression of the oncogenic protein HPV E7 was assessed by immunohistochemistry (IHC.Results: Thirty (3.5% low risk and 20 (2.3% high risk HPV types were identified in 855 breast cancers from the TCGA data base. The high risk types were HPV 18 (48%, HPV 113 (24%, HPV 16 (10%, HPV 52 (10%. Data from the PCR cohort study, indicated that HPV type 18 was the most common type identified in breast cancer specimens (55% of 40 breast cancer specimens followed by HPV 16 (13%. The same HPV type was identified in both the benign and subsequent breast cancer in 15 patients. HPV E7 proteins were identified in 72% of benign breast specimens and 59% of invasive breast cancer specimens.Conclusions: There were 4 observations of particular interest: (i confirmation by both NGS and PCR of the presence of high risk HPV gene sequences in breast cancers, (ii a correlation between high risk HPV in benign breast specimens and subsequent HPV positive breast cancer in the same patient, (iii HPVs in breast cancer are likely to be biologically active (as shown by transcription of HPV DNA to RNA plus the expression of

  5. Prevalence of Telomerase Activity in Human Cancer

    Directory of Open Access Journals (Sweden)

    Chi-Hau Chen

    2011-05-01

    Full Text Available Telomerase activity has been measured in a wide variety of cancerous and non-cancerous tissue types, and the vast majority of clinical studies have shown a direct correlation between it and the presence of cancerous cells. Telomerase plays a key role in cellular immortality and tumorigenesis. Telomerase is activated in 80–90% of human carcinomas, but not in normal somatic cells, therefore, its detection holds promise as a diagnostic marker for cancer. Measurable levels of telomerase have been detected in malignant cells from various samples: tissue from gestational trophoblastic neoplasms; squamous carcinoma cells from oral rinses; lung carcinoma cells from bronchial washings; colorectal carcinoma cells from colonic luminal washings; bladder carcinoma cells from urine or bladder washings; and breast carcinoma or thyroid cancer cells from fine needle aspirations. Such clinical tests for telomerase can be useful as non-invasive and cost-effective methods for early detection and monitoring of cancer. In addition, telomerase activity has been shown to correlate with poor clinical outcome in late-stage diseases such as non-small cell lung cancer, colorectal cancer, and soft tissue sarcomas. In such cases, testing for telomerase activity can be used to identify patients with a poor prognosis and to select those who might benefit from adjuvant treatment. Our review of the latest medical advances in this field reveals that telomerase holds great promise as a biomarker for early cancer detection and monitoring, and has considerable potential as the basis for developing new anticancer therapies.

  6. Cancer Metabolomics and the Human Metabolome Database

    Directory of Open Access Journals (Sweden)

    David S. Wishart

    2016-03-01

    Full Text Available The application of metabolomics towards cancer research has led to a renewed appreciation of metabolism in cancer development and progression. It has also led to the discovery of metabolite cancer biomarkers and the identification of a number of novel cancer causing metabolites. The rapid growth of metabolomics in cancer research is also leading to challenges. In particular, with so many cancer-associate metabolites being identified, it is often difficult to keep track of which compounds are associated with which cancers. It is also challenging to track down information on the specific pathways that particular metabolites, drugs or drug metabolites may be affecting. Even more frustrating are the difficulties associated with identifying metabolites from NMR or MS spectra. Fortunately, a number of metabolomics databases are emerging that are designed to address these challenges. One such database is the Human Metabolome Database (HMDB. The HMDB is currently the world’s largest and most comprehensive, organism-specific metabolomics database. It contains more than 40,000 metabolite entries, thousands of metabolite concentrations, >700 metabolic and disease-associated pathways, as well as information on dozens of cancer biomarkers. This review is intended to provide a brief summary of the HMDB and to offer some guidance on how it can be used in metabolomic studies of cancer.

  7. Human Papillomavirus in Head and Neck Cancer

    Directory of Open Access Journals (Sweden)

    Anna Rosa Garbuglia

    2014-08-01

    Full Text Available Human papillomavirus (HPV is currently considered to be a major etiologic factor, in addition to tobacco and alcohol, for oropharyngeal cancer (OPC development. HPV positive OPCs are epidemiologically distinct from HPV negative ones, and are characterized by younger age at onset, male predominance, and strong association with sexual behaviors. HPV16 is the most prevalent types in oral cavity cancer (OCC, moreover the prevalence of beta, and gamma HPV types is higher than that of alpha HPV in oral cavity.

  8. Oral contraceptives, human papillomavirus and cervical cancer.

    Science.gov (United States)

    La Vecchia, Carlo; Boccia, Stefania

    2014-03-01

    Oncogenic human papillomavirus is the key determinant of cervical cancer, but other risk factors interact with it to define individual risk. Among these, there is oral contraceptive (OC) use. A quantitative review of the link between OCs and cervical cancer was performed. Long-term (>5 year) current or recent OC use has been related to an about two-fold excess risk of cervical cancer. Such an excess risk, however, levels off after stopping use, and approaches unity 10 or more years after stopping. The public health implications of OC use for cervical cancer are limited. In any case, such implications are greater in middle-income and low-income countries, as well as in central and eastern Europe and Latin America, where cervical cancer screening and control remain inadequate.

  9. Radiobiology of human cancer radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Andrews, J.R.

    1978-01-01

    The author has systematically collected and collated the scientific literature correlating the basic and clinical sciences in this field in order to produce a definitive treatise. The book thoroughly reviews the biology and biochemistry relevant to radiobiology and describes the critical locus for the extinction of cell reproductive capacity. Extensive coverage is given to oxygen effect, hyperthermia, high linear energy transfer, cell populations, and similar topics. Separate sections cover time, dose, and fractionation; radiation hematology; cancer chemotherapy; and cancer immunology. The book also contains invaluable discussions of techniques for optimizing radiotherapy alone and in combination with other therapies.

  10. Water pipe smoking and human oral cancers.

    Science.gov (United States)

    Rastam, Samer; Li, Fu-Min; Fouad, Fouad M; Al Kamal, Haysam M; Akil, Nizar; Al Moustafa, Ala-Eddin

    2010-03-01

    While cigarette smoking is recognized as an important risk factor in human oral cancers, the effect of water pipe smoking (WPS) on these cancers is not known. WPS is very common in the young adult population, especially in the Middle East, and has been associated with several respiratory problems. However, to date, there have been no studies examining the association between WPS and the progression of human oral cancers. Currently, the role of WPS in human oral cancers remains uncertain because of the limited number of investigations. This raises the question of whether WPS plays a significant role in the development of human oral carcinomas. In this paper, we propose the hypothesis that human oral normal epithelial cells are vulnerable to persistent WPS; moreover, WPS could play an important role in the initiation of a neoplastic transformation of human normal oral epithelial cells. Therefore, we believe that an international collaboration of epidemiological and clinical studies as well as cellular and molecular biology investigations is necessary to answer this important question.

  11. The Future of Asset Management for Human Space Exploration: Supply Classification and an Integrated Database

    Science.gov (United States)

    Shull, Sarah A.; Gralla, Erica L.; deWeck, Olivier L.; Shishko, Robert

    2006-01-01

    One of the major logistical challenges in human space exploration is asset management. This paper presents observations on the practice of asset management in support of human space flight to date and discusses a functional-based supply classification and a framework for an integrated database that could be used to improve asset management and logistics for human missions to the Moon, Mars and beyond.

  12. Correlation coefficient mapping in fluorescence spectroscopy: tissue classification for cancer detection.

    Science.gov (United States)

    Crowell, Ed; Wang, Gufeng; Cox, Jason; Platz, Charles P; Geng, Lei

    2005-03-01

    Correlation coefficient mapping has been applied to intrinsic fluorescence spectra of colonic tissue for the purpose of cancer diagnosis. Fluorescence emission spectra were collected of 57 colonic tissue sites in a range of 4 physiological conditions: normal (29), hyperplastic (2), adenomatous (5), and cancerous tissues (21). The sample-sample correlation was used to examine the ability of correlation coefficient mapping to determine tissue disease state. The correlation coefficient map indicates two main categories of samples. These categories were found to relate to disease states of the tissue. Sensitivity, selectivity, predictive value positive, and predictive value negative for differentiation between normal tissue and all other categories were all above 92%. This was found to be similar to, or higher than, tissue classification using existing methods of data reduction. Wavelength-wavelength correlation among the samples highlights areas of importance for tissue classification. The two-dimensional correlation map reveals absorption by NADH and hemoglobin in the samples as negative correlation, an effect not obvious from the one-dimensional fluorescence spectra alone. The integrity of tissue was examined in a time series of spectra of a single tissue sample taken after tissue resection. The wavelength-wavelength correlation coefficient map shows the areas of significance for each fluorophore and their relation to each other. NADH displays negative correlation to collagen and FAD, from the absorption of emission or fluorescence resonance energy transfer. The wavelength-wavelength correlation map for the decay set also clearly shows that there are only three fluorophores of importance in the samples, by the well-defined pattern of the map. The sample-sample correlation coefficient map reveals the changes over time and their impact on tissue classification. Correlation coefficient mapping proves to be an effective method for sample classification and cancer

  13. Profiling alternatively spliced mRNA isoforms for prostate cancer classification

    Directory of Open Access Journals (Sweden)

    Fan Jian-Bing

    2006-04-01

    Full Text Available Abstract Background Prostate cancer is one of the leading causes of cancer illness and death among men in the United States and world wide. There is an urgent need to discover good biomarkers for early clinical diagnosis and treatment. Previously, we developed an exon-junction microarray-based assay and profiled 1532 mRNA splice isoforms from 364 potential prostate cancer related genes in 38 prostate tissues. Here, we investigate the advantage of using splice isoforms, which couple transcriptional and splicing regulation, for cancer classification. Results As many as 464 splice isoforms from more than 200 genes are differentially regulated in tumors at a false discovery rate (FDR of 0.05. Remarkably, about 30% of genes have isoforms that are called significant but do not exhibit differential expression at the overall mRNA level. A support vector machine (SVM classifier trained on 128 signature isoforms can correctly predict 92% of the cases, which outperforms the classifier using overall mRNA abundance by about 5%. It is also observed that the classification performance can be improved using multivariate variable selection methods, which take correlation among variables into account. Conclusion These results demonstrate that profiling of splice isoforms is able to provide unique and important information which cannot be detected by conventional microarrays.

  14. A NEW FUNCTIONAL CLASSIFICATION OF STOMACH CANCER AND ITS PATHOBIOLOGICAL AND CLINICAL SIGNIFICANCE

    Institute of Scientific and Technical Information of China (English)

    辛彦; 赵风凯; 宫伟; 王艳萍; 张荫昌; 闫瑞方

    1994-01-01

    The functional differentiations of stomach cancer specimens from 121patients were investigated by enzyme-,mucin-,affinity-and immunohistochemical methods,and the stomach cancers were divided into five functionally differentiated types:1)Absorptive Function Differentiation Type (AFDT),19.8%;2)Mucin Secreting Func-tion Differentiation Type (MSFDT),24.0%;3)Absorptive and Mucin-Producing Function Differentiation Type (AMPFDT),47.1%;4)Special Function Differentiation Type (SFDT),0.8%;and 5)Non-Function Differ-entiation Type(NFDT),8.3%.The results indicate that stomach cancer tissues of the same histological type of -ten display differing functional differentiation,and these functionally differentiated types have different invasive and metastatic characteristics.In addition,the functionally differentiated types have particular organic affinities of metastasis and different clinical prognoses.This study suggests that this new functional classification may supple-ment histological classification.The mechanisms of liver and ovary metastases of stomach cancer are also dis-cussed.

  15. Subspace identification and classification of healthy human gait.

    Directory of Open Access Journals (Sweden)

    Vinzenz von Tscharner

    Full Text Available PURPOSE: The classification between different gait patterns is a frequent task in gait assessment. The base vectors were usually found using principal component analysis (PCA is replaced by an iterative application of the support vector machine (SVM. The aim was to use classifyability instead of variability to build a subspace (SVM space that contains the information about classifiable aspects of a movement. The first discriminant of the SVM space will be compared to a discriminant found by an independent component analysis (ICA in the SVM space. METHODS: Eleven runners ran using shoes with different midsoles. Kinematic data, representing the movements during stance phase when wearing the two shoes, was used as input to a PCA and SVM. The data space was decomposed by an iterative application of the SVM into orthogonal discriminants that were able to classify the two movements. The orthogonal discriminants spanned a subspace, the SVM space. It represents the part of the movement that allowed classifying the two conditions. The data in the SVM space was reconstructed for a visual assessment of the movement difference. An ICA was applied to the data in the SVM space to obtain a single discriminant. Cohen's d effect size was used to rank the PCA vectors that could be used to classify the data, the first SVM discriminant or the ICA discriminant. RESULTS: The SVM base contains all the information that discriminates the movement of the two shod conditions. It was shown that the SVM base contains some redundancy and a single ICA discriminant was found by applying an ICA in the SVM space. CONCLUSIONS: A combination of PCA, SVM and ICA is best suited to extract all parts of the gait pattern that discriminates between the two movements and to find a discriminant for the classification of dichotomous kinematic data.

  16. Review on Feature Selection Techniques and the Impact of SVM for Cancer Classification using Gene Expression Profile

    CERN Document Server

    George, G Victo Sudha; 10.5121/ijcses.2011.2302

    2011-01-01

    The DNA microarray technology has modernized the approach of biology research in such a way that scientists can now measure the expression levels of thousands of genes simultaneously in a single experiment. Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. But compared to the number of genes involved, available training data sets generally have a fairly small sample size for classification. These training data limitations constitute a challenge to certain classification methodologies. Feature selection techniques can be used to extract the marker genes which influence the classification accuracy effectively by eliminating the un wanted noisy and redundant genes This paper presents a review of feature selection techniques that have been employed in micro array data based cancer classification and also the predominant role of SVM for cancer classification.

  17. Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

    Energy Technology Data Exchange (ETDEWEB)

    Eswari J, Satya; Chandrakar, Neha [National Institute of Technology Raipur, Raipur (India)

    2016-04-15

    Artificial neural networks (ANNs) can be used to develop a technique to classify lymph node negative breast cancer that is prone to distant metastases based on gene expression signatures. The neural network used is a multilayered feed forward network that employs back propagation algorithm. Once trained with DNA microarraybased gene expression profiles of genes that were predictive of distant metastasis recurrence of lymph node negative breast cancer, the ANNs became capable of correctly classifying all samples and recognizing the genes most appropriate to the classification. To test the ability of the trained ANN models in recognizing lymph node negative breast cancer, we analyzed additional idle samples that were not used beforehand for the training procedure and obtained the correctly classified result in the validation set. For more substantial result, bootstrapping of training and testing dataset was performed as external validation. This study illustrates the potential application of ANN for breast tumor diagnosis and the identification of candidate targets in patients for therapy.

  18. [Classification and characteristics of interval cancers in the Principality of Asturias's Breast Cancer Screening Program].

    Science.gov (United States)

    Prieto García, M A; Delgado Sevillano, R; Baldó Sierra, C; González Díaz, E; López Secades, A; Llavona Amor, J A; Vidal Marín, B

    2013-09-01

    To review and classify the interval cancers found in the Principality of Asturias's Breast Cancer Screening Program (PDPCM). A secondary objective was to determine the histological characteristics, size, and stage of the interval cancers at the time of diagnosis. We included the interval cancers in the PDPCM in the period 2003-2007. Interval cancers were classified according to the breast cancer screening program protocol, with double reading without consensus, without blinding, with arbitration. Mammograms were interpreted by 10 radiologists in the PDPCM. A total of 33.7% of the interval cancers could not be classified; of the interval cancers that could be classified, 40.67% were labeled true interval cancers, 31.4% were labeled false negatives on screening, 23.7% had minimal signs, and 4.23% were considered occult. A total of 70% of the interval cancers were diagnosed in the year of the period between screening examinations and 71.7% were diagnosed after subsequent screening. A total of 76.9% were invasive ductal carcinomas, 61.1% were stage II when detected, and 78.7% were larger than 10mm when detected. The rate of interval cancers and the rate of false negatives in the PDPCM are higher than those recommended in the European guidelines. Interval cancers are diagnosed later than the tumors detected at screening. Studying interval cancers provides significant training for the radiologists in the PDPCM. Copyright © 2011 SERAM. Published by Elsevier Espana. All rights reserved.

  19. Use of multivariate analysis to suggest a new molecular classification of colorectal cancer

    Science.gov (United States)

    Domingo, Enric; Ramamoorthy, Rajarajan; Oukrif, Dahmane; Rosmarin, Daniel; Presz, Michal; Wang, Haitao; Pulker, Hannah; Lockstone, Helen; Hveem, Tarjei; Cranston, Treena; Danielsen, Havard; Novelli, Marco; Davidson, Brian; Xu, Zheng-Zhou; Molloy, Peter; Johnstone, Elaine; Holmes, Christopher; Midgley, Rachel; Kerr, David; Sieber, Oliver; Tomlinson, Ian

    2013-01-01

    Abstract Molecular classification of colorectal cancer (CRC) is currently based on microsatellite instability (MSI), KRAS or BRAF mutation and, occasionally, chromosomal instability (CIN). Whilst useful, these categories may not fully represent the underlying molecular subgroups. We screened 906 stage II/III CRCs from the VICTOR clinical trial for somatic mutations. Multivariate analyses (logistic regression, clustering, Bayesian networks) identified the primary molecular associations. Positive associations occurred between: CIN and TP53 mutation; MSI and BRAF mutation; and KRAS and PIK3CA mutations. Negative associations occurred between: MSI and CIN; MSI and NRAS mutation; and KRAS mutation, and each of NRAS, TP53 and BRAF mutations. Some complex relationships were elucidated: KRAS and TP53 mutations had both a direct negative association and a weaker, confounding, positive association via TP53–CIN–MSI–BRAF–KRAS. Our results suggested a new molecular classification of CRCs: (1) MSI+ and/or BRAF-mutant; (2) CIN+ and/or TP53– mutant, with wild-type KRAS and PIK3CA; (3) KRAS- and/or PIK3CA-mutant, CIN+, TP53-wild-type; (4) KRAS– and/or PIK3CA-mutant, CIN–, TP53-wild-type; (5) NRAS-mutant; (6) no mutations; (7) others. As expected, group 1 cancers were mostly proximal and poorly differentiated, usually occurring in women. Unexpectedly, two different types of CIN+ CRC were found: group 2 cancers were usually distal and occurred in men, whereas group 3 showed neither of these associations but were of higher stage. CIN+ cancers have conventionally been associated with all three of these variables, because they have been tested en masse. Our classification also showed potentially improved prognostic capabilities, with group 3, and possibly group 1, independently predicting disease-free survival. Copyright © 2012 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. PMID:23165447

  20. Spectral Biomimetic Technique for Wood Classification Inspired by Human Echolocation

    Directory of Open Access Journals (Sweden)

    Juan Antonio Martínez Rojas

    2012-01-01

    Full Text Available Palatal clicks are most interesting for human echolocation. Moreover, these sounds are suitable for other acoustic applications due to their regular mathematical properties and reproducibility. Simple and nondestructive techniques, bioinspired by synthetized pulses whose form reproduces the best features of palatal clicks, can be developed. The use of synthetic palatal pulses also allows detailed studies of the real possibilities of acoustic human echolocation without the problems associated with subjective individual differences. These techniques are being applied to the study of wood. As an example, a comparison of the performance of both natural and synthetic human echolocation to identify three different species of wood is presented. The results show that human echolocation has a vast potential.

  1. Classification of alarm processing techniques and human performance issues

    Energy Technology Data Exchange (ETDEWEB)

    Kim, I.S.; O' Hara, J.M.

    1993-01-01

    Human factors reviews indicate that conventional alarm systems based on the one sensor, one alarm approach, have many human engineering deficiencies, a paramount example being too many alarms during major disturbances. As an effort to resolve these deficiencies, various alarm processing systems have been developed using different techniques. To ensure their contribution to operational safety, the impacts of those systems on operating crew performance should be carefully evaluated. This paper briefly reviews some of the human factors research issues associated with alarm processing techniques and then discusses a framework with which to classify the techniques. The dimensions of this framework can be used to explore the effects of alarm processing systems on human performance.

  2. Classification of alarm processing techniques and human performance issues

    Energy Technology Data Exchange (ETDEWEB)

    Kim, I.S.; O`Hara, J.M.

    1993-05-01

    Human factors reviews indicate that conventional alarm systems based on the one sensor, one alarm approach, have many human engineering deficiencies, a paramount example being too many alarms during major disturbances. As an effort to resolve these deficiencies, various alarm processing systems have been developed using different techniques. To ensure their contribution to operational safety, the impacts of those systems on operating crew performance should be carefully evaluated. This paper briefly reviews some of the human factors research issues associated with alarm processing techniques and then discusses a framework with which to classify the techniques. The dimensions of this framework can be used to explore the effects of alarm processing systems on human performance.

  3. Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features

    Science.gov (United States)

    Kumar, Rajesh; Srivastava, Subodh

    2015-01-01

    A framework for automated detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features is proposed and examined. The various stages involved in the proposed methodology include enhancement of microscopic images, segmentation of background cells, features extraction, and finally the classification. An appropriate and efficient method is employed in each of the design steps of the proposed framework after making a comparative analysis of commonly used method in each category. For highlighting the details of the tissue and structures, the contrast limited adaptive histogram equalization approach is used. For the segmentation of background cells, k-means segmentation algorithm is used because it performs better in comparison to other commonly used segmentation methods. In feature extraction phase, it is proposed to extract various biologically interpretable and clinically significant shapes as well as morphology based features from the segmented images. These include gray level texture features, color based features, color gray level texture features, Law's Texture Energy based features, Tamura's features, and wavelet features. Finally, the K-nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application. The performance of the proposed framework is evaluated using well-known parameters for four fundamental tissues (connective, epithelial, muscular, and nervous) of randomly selected 1000 microscopic biopsy images. PMID:27006938

  4. Cancer Biochemistry and Host-Tumor Interactions: A Decimal Classification, (Categories 51.6, 51.7, and 51.8).

    Science.gov (United States)

    Schneider, John H.

    This is a hierarchical decimal classification of information related to cancer biochemistry, to host-tumor interactions (including cancer immunology), and to occurrence of cancer in special types of animals and plants. It is a working draft of categories taken from an extensive classification of many fields of biomedical information. Because the…

  5. Classification of prostate cancer grade using temporal ultrasound: in vivo feasibility study

    Science.gov (United States)

    Ghavidel, Sahar; Imani, Farhad; Khallaghi, Siavash; Gibson, Eli; Khojaste, Amir; Gaed, Mena; Moussa, Madeleine; Gomez, Jose A.; Siemens, D. Robert; Leveridge, Michael; Chang, Silvia; Fenster, Aaron; Ward, Aaron D.; Abolmaesumi, Purang; Mousavi, Parvin

    2016-03-01

    Temporal ultrasound has been shown to have high classification accuracy in differentiating cancer from benign tissue. In this paper, we extend the temporal ultrasound method to classify lower grade Prostate Cancer (PCa) from all other grades. We use a group of nine patients with mostly lower grade PCa, where cancerous regions are also limited. A critical challenge is to train a classifier with limited aggressive cancerous tissue compared to low grade cancerous tissue. To resolve the problem of imbalanced data, we use Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples for the minority class. We calculate spectral features of temporal ultrasound data and perform feature selection using Random Forests. In leave-one-patient-out cross-validation strategy, an area under receiver operating characteristic curve (AUC) of 0.74 is achieved with overall sensitivity and specificity of 70%. Using an unsupervised learning approach prior to proposed method improves sensitivity and AUC to 80% and 0.79. This work represents promising results to classify lower and higher grade PCa with limited cancerous training samples, using temporal ultrasound.

  6. Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

    Science.gov (United States)

    Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

    2015-11-03

    Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

  7. SELDI-TOF Serum Profiling for Prognostic and Diagnostic Classification of Breast Cancers

    Directory of Open Access Journals (Sweden)

    Christine Laronga

    2004-01-01

    Full Text Available Surface enhanced laser desorption/ionization (SELDI time-of-flight mass spectrometry has emerged as a successful tool for serum based detection and differentiation of many cancer types, including breast cancers. In this study, we have applied the SELDI technology to evaluate three potential applications that could extend the effectiveness of established procedures and biomarkers used for prognostication of breast cancers. Paired serum samples obtained from women with breast cancers prior to surgery and post-surgery (6–9 mos. were examined. In 14/16 post-treatment patients, serum protein profiles could be used to distinguish these samples from the pre-treatment cancer samples. When compared to serum samples from normal healthy women, 11 of these post-treatment samples retained global protein profiles not found in healthy women, including five low-mass proteins that remained elevated in both pre-treatment and post-treatment serum groups. In another pilot study, serum profiles were compared for a group of 30 women who were known BRCA-1 mutation carriers, half of whom subsequently developed breast cancer within three years of the sample procurement. SELDI protein profiling accurately classified 13/15 women with BRCA-1 breast cancers from the 15 non-cancer BRCA-1 carriers. Additionally, the ability of SELDI to distinguish between the serum profiles from sentinel lymph node positive and sentinel lymph node negative patients was evaluated. In sentinel lymph node positive samples, 22/27 samples were correctly classified, in comparison to the correct classification of 55/71 sentinel lymph node negative samples. These initial results indicate the utility of protein profiling approaches for developing new diagnostic and prognostic assays for breast cancers.

  8. AFREET: HUMAN-INSPIRED SPATIO-SPECTRAL FEATURE CONSTRUCTION FOR IMAGE CLASSIFICATION WITH SUPPORT VECTOR MACHINES

    Energy Technology Data Exchange (ETDEWEB)

    S. PERKINS; N. HARVEY

    2001-02-01

    The authors examine the task of pixel-by-pixel classification of the multispectral and grayscale images typically found in remote-sensing and medical applications. Simple machine learning techniques have long been applied to remote-sensed image classification, but almost always using purely spectral information about each pixel. Humans can often outperform these systems, and make extensive use of spatial context to make classification decisions. They present AFREET: an SVM-based learning system which attempts to automatically construct and refine spatio-spectral features in a somewhat human-inspired fashion. Comparisons with traditionally used machine learning techniques show that AFREET achieves significantly higher performance. The use of spatial context is particularly useful for medical imagery, where multispectral images are still rare.

  9. Diagnostic Classification of Normal Persons and Cancer Patients by Using Neural Network Based on Trace Metal Contents in Serum Samples

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Artificial neural network with the back-propagation(BP-ANN) approach was applied to the classification of normal persons and various cancer patients based on the elemental contents in serum samples. This method was verified by the cross-validation method. The effects of the net work parameters were investigated and the related problems were discussed. The samples of 72, 42, and 52 for lung, liver, and stomach cancer patients and normal persons, respectively, were used for the classification study. About 95% of the samples can be classified correctly. There fore, the method can be used as an auxiliary means of the diagnosis of cancer.

  10. Isolation of Cancer Stem Cells From Human Prostate Cancer Samples

    Science.gov (United States)

    Vidal, Samuel J.; Quinn, S. Aidan; de la Iglesia-Vicente, Janis; Bonal, Dennis M.; Rodriguez-Bravo, Veronica; Firpo-Betancourt, Adolfo; Cordon-Cardo, Carlos; Domingo-Domenech, Josep

    2014-01-01

    The cancer stem cell (CSC) model has been considerably revisited over the last two decades. During this time CSCs have been identified and directly isolated from human tissues and serially propagated in immunodeficient mice, typically through antibody labeling of subpopulations of cells and fractionation by flow cytometry. However, the unique clinical features of prostate cancer have considerably limited the study of prostate CSCs from fresh human tumor samples. We recently reported the isolation of prostate CSCs directly from human tissues by virtue of their HLA class I (HLAI)-negative phenotype. Prostate cancer cells are harvested from surgical specimens and mechanically dissociated. A cell suspension is generated and labeled with fluorescently conjugated HLAI and stromal antibodies. Subpopulations of HLAI-negative cells are finally isolated using a flow cytometer. The principal limitation of this protocol is the frequently microscopic and multifocal nature of primary cancer in prostatectomy specimens. Nonetheless, isolated live prostate CSCs are suitable for molecular characterization and functional validation by transplantation in immunodeficient mice. PMID:24686446

  11. Classification of human activity on water through micro-Dopplers using deep convolutional neural networks

    Science.gov (United States)

    Kim, Youngwook; Moon, Taesup

    2016-05-01

    Detecting humans and classifying their activities on the water has significant applications for surveillance, border patrols, and rescue operations. When humans are illuminated by radar signal, they produce micro-Doppler signatures due to moving limbs. There has been a number of research into recognizing humans on land by their unique micro-Doppler signatures, but there is scant research into detecting humans on water. In this study, we investigate the micro-Doppler signatures of humans on water, including a swimming person, a swimming person pulling a floating object, and a rowing person in a small boat. The measured swimming styles were free stroke, backstroke, and breaststroke. Each activity was observed to have a unique micro-Doppler signature. Human activities were classified based on their micro-Doppler signatures. For the classification, we propose to apply deep convolutional neural networks (DCNN), a powerful deep learning technique. Rather than using conventional supervised learning that relies on handcrafted features, we present an alternative deep learning approach. We apply the DCNN, one of the most successful deep learning algorithms for image recognition, directly to a raw micro-Doppler spectrogram of humans on the water. Without extracting any explicit features from the micro-Dopplers, the DCNN can learn the necessary features and build classification boundaries using the training data. We show that the DCNN can achieve accuracy of more than 87.8% for activity classification using 5- fold cross validation.

  12. Enigmatic human tails: A review of their history, embryology, classification, and clinical manifestations.

    Science.gov (United States)

    Tubbs, R Shane; Malefant, Jason; Loukas, Marios; Jerry Oakes, W; Oskouian, Rod J; Fries, Fabian N

    2016-05-01

    The presence of a human tail is a rare and intriguing phenomenon. While cases have been reported in the literature, confusion remains with respect to the proper classification, definition, and treatment methods. We review the literature concerning this anatomical derailment. We also consider the importance of excluding underlying congenital anomalies in these patients to prevent neurological deficits and other abnormal manifestations.

  13. Classification of Chemical Substances and Adverse Effects of Chemical Substances on Human Health

    OpenAIRE

    Söyleriz, Yüksel

    2015-01-01

    In this study, classification of chemical substances and adverse effects of chemical substances on human health in European Union and Turkey are assessed. Method In this study, national and international legislation and practices in the countries of the European Union are reviewed.

  14. Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

    Science.gov (United States)

    Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

    2015-06-01

    Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.

  15. Prognostic value of the seventh AJCC/UICC TNM classification of non-cardia gastric cancer

    Science.gov (United States)

    2013-01-01

    Background The TNM staging criteria for gastric carcinoma have seen numerous revisions, the most recent of which are reflected in the seventh edition AJCC TNM cancer staging manual. Methods A retrospective evaluation of the sixth and seventh TNM classification of gastric cancer on a prospective database, regarding patients operated on for primary gastric cancer, was conducted. The end point of the study was prognosis evaluation in terms of overall survival. Patients operated on for primary gastric cancer between September 2003 and March 2012 at our Department of Emergency and General Surgery, were consecutively retrieved in this study; a total of 114 patients were considered. Cardia gastric cancers, gastric lymphomas and gastrointestinal stromal tumors (GIST) were excluded. Median and mean follow-up periods were 22.5 and 27.7 months (range 15 days to 5 years). Both TNM6 and TNM7 were used to evaluate our patients. Overall survival and survival rates at different stages were analyzed using the Kaplan-Meier method and differences were determined using a log-rank test. Cox’s proportional hazard model was used to identify significant factors related to prognosis in a multivariate analysis. Results Overall survival between the sixth and seventh TNM classification was not significantly different. Both the Kaplan-Meier analysis and the multivariate analysis showed that the major negative prognostic factor was lymphovascular invasion (P TNM. Conclusions Even though further studies are needed in order to increase the number of patients studied, the seventh edition seems to provide a more accurate prognosis, especially regarding N1 and N2 tumors, showing that the most important prognostic factor is lymphovascular invasion. PMID:23687939

  16. Are 20 human papillomavirus types causing cervical cancer?

    Science.gov (United States)

    Arbyn, Marc; Tommasino, Massimo; Depuydt, Christophe; Dillner, Joakim

    2014-12-01

    In 2012, the International Agency for Research on Cancer concluded that there was consistent and sufficient epidemiological, experimental and mechanistic evidence of carcinogenicity to humans for 12 HPV types (HPV16, HPV18, HPV31, HPV33, HPV35, HPV39, HPV45, HPV51, HPV52, HPV56, HPV58 and HPV59) for cervical cancer. Therefore, these types were considered as 1A carcinogens. They all belong to the family of the α-Papillomaviridae, in particular to the species α5 (HPV51), α6 (HPV56), α7 (HPV18, HPV39, HPV45, HPV59) and α9 (HPV16, HPV31, HPV33, HPV35, HPV52, HPV58). Less evidence is available for a thirteenth type (HPV68, α7), which is classified as a 2A carcinogen (probably carcinogenic). Moreover, seven other phylogenetically related types (HPV26, HPV53, HPV66, HPV67, HPV68, HPV70 and HPV73) were identified as single HPV infections in certain rare cases of cervical cancer and were considered possibly carcinogenic (2B carcinogens). Recently, Halec et al [7] demonstrated that the molecular signature of HPV-induced carcinogenesis (presence of type-specific spliced E6*| mRNA; increased expression of p16; and decreased expression of cyclin D1, p53 and Rb) was similar in cervical cancers containing single infections with one of the eight afore-mentioned 2A or 2B carcinogens to those in cancers with single infections with group 1 carcinogens. Ninety six percent of cervical cancers are attributable to one of the 13 most common HPV types (groups 1 and 2A). Including the additional seven HPV types (group 2B) added 2.6%, to reach a total of 98.7% of all HPV-positive cervical cancers. From recently updated meta-analyses, it was shown that HPV68, HPV26, HPV66, HPV67, HPV73 and HPV82 were significantly more common in cancer cases than in women with normal cervical cytology, suggesting that for these HPV types, an upgrading of the carcinogen classification could be considered. However, there is no need to include them in HPV screening tests or vaccines, given their rarity in

  17. Validation of TNM classification for metastatic prostatic cancer treated using primary androgen deprivation therapy.

    Science.gov (United States)

    Kadono, Yoshifumi; Nohara, Takahiro; Ueno, Satoru; Izumi, Kouji; Kitagawa, Yasuhide; Konaka, Hiroyuki; Mizokami, Atsushi; Onozawa, Mizuki; Hinotsu, Shiro; Akaza, Hideyuki; Namiki, Mikio

    2016-02-01

    The current tumor-node-metastasis (TNM) classification system has been used for many years. The prognosis of patients with metastatic prostate cancer (mPC) treated using primary androgen deprivation therapy (PADT) was analyzed according to the TNM classification. A total of 5618 cases with lymph node metastases only (N1M0), non-regional lymph node metastasis (M1a), bone metastasis (M1b), and distant metastasis (M1c) were selected from the Japanese Study Group of Prostate Cancer database. Overall survival (OS), cancer-specific survival (CSS), and progression-free survival (PFS) rates were calculated using Kaplan-Meier analysis. The influence of clinical variables on patient prognosis was evaluated using the Cox proportional hazard regression model. The 5-year OS, CSS, and PFS were 76.0, 83.2, and 38.8% in N1M0, 57.5, 69.0, and 23.0% in M1a, 54.0, 63.1, and 23.0% in M1b, and 40.0, 51.5, and 16.6% in M1c, respectively. OS, CSS, and PFS worsened as the stages progressed. OS, CSS, and PFS were all significantly worse in N1M1b compared with N0M1b. Multivariate analysis revealed that OS and CSS were worse in patients with a Gleason score ≥8 and that combined androgen blockade (CAB) treatment provided better OS than non-CAB treatments at any tumor stage. However, OS and CSS were worse in individuals with a prostate-specific antigen >100 ng/ml only in M1b. Patient prognosis worsened with stage progression; therefore, current TNM classification system of mPC for PADT was shown to be trustworthy. Each PC cell that develops bone or lymphoid metastasis may exhibit different characteristics.

  18. Contribution of multiparameter flow cytometry immunophenotyping to the diagnostic screening and classification of pediatric cancer.

    Directory of Open Access Journals (Sweden)

    Cristiane S Ferreira-Facio

    Full Text Available Pediatric cancer is a relatively rare and heterogeneous group of hematological and non-hematological malignancies which require multiple procedures for its diagnostic screening and classification. Until now, flow cytometry (FC has not been systematically applied to the diagnostic work-up of such malignancies, particularly for solid tumors. Here we evaluated a FC panel of markers for the diagnostic screening of pediatric cancer and further classification of pediatric solid tumors. The proposed strategy aims at the differential diagnosis between tumoral vs. reactive samples, and hematological vs. non-hematological malignancies, and the subclassification of solid tumors. In total, 52 samples from 40 patients suspicious of containing tumor cells were analyzed by FC in parallel to conventional diagnostic procedures. The overall concordance rate between both approaches was of 96% (50/52 diagnostic samples, with 100% agreement for all reactive/inflammatory and non-infiltrated samples as well as for those corresponding to solid tumors (n = 35, with only two false negative cases diagnosed with Hodgkin lymphoma and anaplastic lymphoma, respectively. Moreover, clear discrimination between samples infiltrated by hematopoietic vs. non-hematopoietic tumor cells was systematically achieved. Distinct subtypes of solid tumors showed different protein expression profiles, allowing for the differential diagnosis of neuroblastoma (CD56(hi/GD2(+/CD81(hi, primitive neuroectodermal tumors (CD271(hi/CD99(+, Wilms tumors (>1 cell population, rhabdomyosarcoma (nuMYOD1(+/numyogenin(+, carcinomas (CD45(-/EpCAM(+, germ cell tumors (CD56(+/CD45(-/NG2(+/CD10(+ and eventually also hemangiopericytomas (CD45(-/CD34(+. In summary, our results show that multiparameter FC provides fast and useful complementary data to routine histopathology for the diagnostic screening and classification of pediatric cancer.

  19. Molecular classification of familial non-BRCA1/BRCA2 breast cancer.

    Science.gov (United States)

    Hedenfalk, Ingrid; Ringner, Markus; Ben-Dor, Amir; Yakhini, Zohar; Chen, Yidong; Chebil, Gunilla; Ach, Robert; Loman, Niklas; Olsson, Håkan; Meltzer, Paul; Borg, Ake; Trent, Jeffrey

    2003-03-01

    In the decade since their discovery, the two major breast cancer susceptibility genes BRCA1 and BRCA2, have been shown conclusively to be involved in a significant fraction of families segregating breast and ovarian cancer. However, it has become equally clear that a large proportion of families segregating breast cancer alone are not caused by mutations in BRCA1 or BRCA2. Unfortunately, despite intensive effort, the identification of additional breast cancer predisposition genes has so far been unsuccessful, presumably because of genetic heterogeneity, low penetrance, or recessive/polygenic mechanisms. These non-BRCA1/2 breast cancer families (termed BRCAx families) comprise a histopathologically heterogeneous group, further supporting their origin from multiple genetic events. Accordingly, the identification of a method to successfully subdivide BRCAx families into recognizable groups could be of considerable value to further genetic analysis. We have previously shown that global gene expression analysis can identify unique and distinct expression profiles in breast tumors from BRCA1 and BRCA2 mutation carriers. Here we show that gene expression profiling can discover novel classes among BRCAx tumors, and differentiate them from BRCA1 and BRCA2 tumors. Moreover, microarray-based comparative genomic hybridization (CGH) to cDNA arrays revealed specific somatic genetic alterations within the BRCAx subgroups. These findings illustrate that, when gene expression-based classifications are used, BRCAx families can be grouped into homogeneous subsets, thereby potentially increasing the power of conventional genetic analysis.

  20. 乳腺癌的分子分型%Molecular classification of breast cancer

    Institute of Scientific and Technical Information of China (English)

    张百红; 岳红云

    2014-01-01

    乳腺癌是一种分子水平异质性很高的疾病,分子分型可为乳腺癌的个体化治疗提供一个新视野.在分子病理学、分子生物学和系统生物学指导下,乳腺癌经历了4类分型、70种和21种基因蛋白谱以及基因组整合分类等不同分型.这些分型将为乳腺癌的精确治疗提供指导.%Breast cancer is a group of heterogeneous diseases.Molecular portraits provide a new insight for personalized cancer management in breast cancer.According to the molecular pathology,molecular biology and system biology,breast cancer goes through different typing methods,including four subclasses,geneexpression signature and integrated genomic classification.These major subtypes of breast cancer may provide guidance for precise therapeutics.

  1. Gastric Cancer Risk Analysis in Unhealthy Habits Data with Classification Algorithms

    OpenAIRE

    2015-01-01

    Data mining methods are applied to a medical task that seeks for the information about the influence of Helicobacter Pylori on the gastric cancer risk increase by analysing the adverse factors of individual lifestyle. In the process of data pre-processing, the data are cleared of noise and other factors, reduced in dimensionality, as well as transformed for the task and cleared of non-informative attributes. Data classification using C4.5, CN2 and k-nearest neighbour algorithms is carried out...

  2. The classification of benign and malignant human prostate tissue by multivariate analysis of {sup 1}H magnetic resonance spectra

    Energy Technology Data Exchange (ETDEWEB)

    Hahn, P.; Smith, I.; Leboldus, L.; Littman, C.; Somorjai, L.; Bezabeh, T. [Institute for Biodiagnostic, National Research Council, Manitoba (Canada)

    1998-04-01

    {sup 1}H magnetic resonance spectroscopy studies (360 MHz) were performed on specimens of benign (n = 66) and malignant (n = 21) human prostate tissue from 50 patients and the spectral data were subjected to multivariate analysis, specifically linear-discriminant analysis. On the basis of histopathological assessments, an overall classification accuracy of 96.6 % was achieved, with a sensitivity of 100 % and a specificity of 95.5 % in classifying benign prostatic hyperplasia from prostatic cancer. Resonances due to citrate, glutamate, and taurine were among the six spectral subregions identified by our algorithm as having diagnostic potential. Significantly higher levels of citrate were observed in glandular than in stromal benign prostatic hyperplasia (P < 0.05). This method shows excellent promise for the possibility of in vivo assessment of prostate tissue by magnetic resonance. (author)

  3. Regulatory T Cells in Human Ovarian Cancer

    Directory of Open Access Journals (Sweden)

    Dong-Jun Peng

    2012-01-01

    Full Text Available Multiple layers of suppressive components including regulatory T (TReg cells, suppressive antigen-presenting cells, and inhibitory cytokines form suppressive networks in the ovarian cancer microenvironment. It has been demonstrated that as a major suppressive element, TReg cells infiltrate tumor, interact with several types of immune cells, and mediate immune suppression through different molecular and cellular mechanisms. In this paper, we focus on human ovarian cancer and will discuss the nature of TReg cells including their subsets, trafficking, expansion, and function. We will briefly review the development of manipulation of TReg cells in preclinical and clinical settings.

  4. Support Vector Machine-Based Human Behavior Classification in Crowd through Projection and Star Skeletonization

    Directory of Open Access Journals (Sweden)

    Yogameena, B.

    2010-01-01

    Full Text Available Problem statement: Detection of individual’s abnormal human behaviors in the crowd has become a critical problem because in the event of terror strikes. This study presented a real-time video surveillance system which classifies normal and abnormal behaviors in crowds. The aim of this research was to provide a system which can aid in monitoring crowded urban environments. Approach: The proposed behaviour classification was through projection which separated individuals and using star skeletonization the features like body posture and the cyclic motion cues were obtained. Using these cues the Support Vector Machine (SVM classified the normal and abnormal behaviors of human. Results: Experimental results demonstrated the method proposed was robust and efficient in the classification of normal and abnormal human behaviors. A comparative study of classification accuracy between principal component analysis and Support Vector Machine (SVM classification was also presented. Conclusion: The proposed method classified the behavior such as running people in a crowded environment, bending down movement while most are walking or standing, a person carrying a long bar and a person waving hand in the crowd is classified.

  5. Classification moléculaire du cancer du sein au Maroc

    OpenAIRE

    Fouad, Abbass; Yousra, Akasbi; Kaoutar, Znati; Omar, El Mesbahi; Afaf, Amarti; Sanae, Bennis

    2012-01-01

    Introduction La classification moléculaire des cancers du sein basée sur l'expression génique puis sur le profil protéique a permis de distinguer cinq groupes moléculaires: luminal A, luminal B, Her2/neu, basal-like et non-classées. L'objectif de cette étude réalisée au CHU Hassan II de Fès est de classer 335 cancers du sein infiltrant en groupes moléculaires, puis de les corréler avec les caractéristiques clinicopathologiques. Méthodes Etude rétrospective étalée sur 45 mois, comportant 335 p...

  6. Two-Dimensional ARMA Modeling for Breast Cancer Detection and Classification

    CERN Document Server

    Bouaynaya, Nidhal; Schonfeld, Dan

    2009-01-01

    We propose a new model-based computer-aided diagnosis (CAD) system for tumor detection and classification (cancerous v.s. benign) in breast images. Specifically, we show that (x-ray, ultrasound and MRI) images can be accurately modeled by two-dimensional autoregressive-moving average (ARMA) random fields. We derive a two-stage Yule-Walker Least-Squares estimates of the model parameters, which are subsequently used as the basis for statistical inference and biophysical interpretation of the breast image. We use a k-means classifier to segment the breast image into three regions: healthy tissue, benign tumor, and cancerous tumor. Our simulation results on ultrasound breast images illustrate the power of the proposed approach.

  7. Statistical Analysis of Tissue Images for Detection and Classification of Cervical Cancer

    CERN Document Server

    Jagtap, Jaidip; Pandey, Kiran; Agarwa, Asha; Panigrahi, Prasanta K; Pradhan, Asima

    2011-01-01

    Cervical cancer is one of the major health threats in women worldwide. The current "gold standard" for detecting cancer of the epithelial tissue is the histopathology analysis of biopsy samples. However it relies on the pathologist's judgment of the disease. We investigate the utility of statistical parameters as a potential tool for detection and discrimination of the stages of dysplasia. Digital images of the tissue slides are captured with the help of a digital camera plugged to a microscope. Statistical data analysis is performed with the help of software to evaluate parameters such as mean, maxima, full width half maxima, skewness, kurtosis etc. for the images. We believe that these parameters can help effectively to improve the diagnosis and further classify normal and abnormal tissue sections. These parameters can be used independently as well as in tandem with other parameters as features in classification algorithms that involve the use of Neural networks or Principal component analysis.

  8. Classification of lung cancer tumors based on structural and physicochemical properties of proteins by bioinformatics models.

    Science.gov (United States)

    Hosseinzadeh, Faezeh; Ebrahimi, Mansour; Goliaei, Bahram; Shamabadi, Narges

    2012-01-01

    Rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important in diagnosis of this disease. Furthermore sequence-derived structural and physicochemical descriptors are very useful for machine learning prediction of protein structural and functional classes, classifying proteins and the prediction performance. Herein, in this study is the classification of lung tumors based on 1497 attributes derived from structural and physicochemical properties of protein sequences (based on genes defined by microarray analysis) investigated through a combination of attribute weighting, supervised and unsupervised clustering algorithms. Eighty percent of the weighting methods selected features such as autocorrelation, dipeptide composition and distribution of hydrophobicity as the most important protein attributes in classification of SCLC, NSCLC and COMMON classes of lung tumors. The same results were observed by most tree induction algorithms while descriptors of hydrophobicity distribution were high in protein sequences COMMON in both groups and distribution of charge in these proteins was very low; showing COMMON proteins were very hydrophobic. Furthermore, compositions of polar dipeptide in SCLC proteins were higher than NSCLC proteins. Some clustering models (alone or in combination with attribute weighting algorithms) were able to nearly classify SCLC and NSCLC proteins. Random Forest tree induction algorithm, calculated on leaves one-out and 10-fold cross validation) shows more than 86% accuracy in clustering and predicting three different lung cancer tumors. Here for the first time the application of data mining tools to effectively classify three classes of lung cancer tumors regarding the importance of dipeptide composition, autocorrelation and distribution descriptor has been reported.

  9. Classification of lung cancer tumors based on structural and physicochemical properties of proteins by bioinformatics models.

    Directory of Open Access Journals (Sweden)

    Faezeh Hosseinzadeh

    Full Text Available Rapid distinction between small cell lung cancer (SCLC and non-small cell lung cancer (NSCLC tumors is very important in diagnosis of this disease. Furthermore sequence-derived structural and physicochemical descriptors are very useful for machine learning prediction of protein structural and functional classes, classifying proteins and the prediction performance. Herein, in this study is the classification of lung tumors based on 1497 attributes derived from structural and physicochemical properties of protein sequences (based on genes defined by microarray analysis investigated through a combination of attribute weighting, supervised and unsupervised clustering algorithms. Eighty percent of the weighting methods selected features such as autocorrelation, dipeptide composition and distribution of hydrophobicity as the most important protein attributes in classification of SCLC, NSCLC and COMMON classes of lung tumors. The same results were observed by most tree induction algorithms while descriptors of hydrophobicity distribution were high in protein sequences COMMON in both groups and distribution of charge in these proteins was very low; showing COMMON proteins were very hydrophobic. Furthermore, compositions of polar dipeptide in SCLC proteins were higher than NSCLC proteins. Some clustering models (alone or in combination with attribute weighting algorithms were able to nearly classify SCLC and NSCLC proteins. Random Forest tree induction algorithm, calculated on leaves one-out and 10-fold cross validation shows more than 86% accuracy in clustering and predicting three different lung cancer tumors. Here for the first time the application of data mining tools to effectively classify three classes of lung cancer tumors regarding the importance of dipeptide composition, autocorrelation and distribution descriptor has been reported.

  10. Pooling breast cancer datasets has a synergetic effect on classification performance and improves signature stability

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    van de Vijver Marc J

    2008-08-01

    Full Text Available Abstract Background Michiels et al. (Lancet 2005; 365: 488–92 employed a resampling strategy to show that the genes identified as predictors of prognosis from resamplings of a single gene expression dataset are highly variable. The genes most frequently identified in the separate resamplings were put forward as a 'gold standard'. On a higher level, breast cancer datasets collected by different institutions can be considered as resamplings from the underlying breast cancer population. The limited overlap between published prognostic signatures confirms the trend of signature instability identified by the resampling strategy. Six breast cancer datasets, totaling 947 samples, all measured on the Affymetrix platform, are currently available. This provides a unique opportunity to employ a substantial dataset to investigate the effects of pooling datasets on classifier accuracy, signature stability and enrichment of functional categories. Results We show that the resampling strategy produces a suboptimal ranking of genes, which can not be considered to be a 'gold standard'. When pooling breast cancer datasets, we observed a synergetic effect on the classification performance in 73% of the cases. We also observe a significant positive correlation between the number of datasets that is pooled, the validation performance, the number of genes selected, and the enrichment of specific functional categories. In addition, we have evaluated the support for five explanations that have been postulated for the limited overlap of signatures. Conclusion The limited overlap of current signature genes can be attributed to small sample size. Pooling datasets results in more accurate classification and a convergence of signature genes. We therefore advocate the analysis of new data within the context of a compendium, rather than analysis in isolation.

  11. Classification of pelvic ring fractures in skeletonized human remains.

    Science.gov (United States)

    Báez-Molgado, Socorro; Bartelink, Eric J; Jellema, Lyman M; Spurlock, Linda; Sholts, Sabrina B

    2015-01-01

    Pelvic ring fractures are associated with high rates of mortality and thus can provide key information about circumstances surrounding death. These injuries can be particularly informative in skeletonized remains, yet difficult to diagnose and interpret. This study adapted a clinical system of classifying pelvic ring fractures according to their resultant degree of pelvic stability for application to gross human skeletal remains. The modified Tile criteria were applied to the skeletal remains of 22 individuals from the Cleveland Museum of Natural History and Universidad Nacional Autónoma de México that displayed evidence of pelvic injury. Because these categories are tied directly to clinical assessments concerning the severity and treatment of injuries, this approach can aid in the identification of manner and cause of death, as well as interpretations of possible mechanisms of injury, such as those typical in car-to-pedestrian and motor vehicle accidents. © 2014 American Academy of Forensic Sciences.

  12. Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Maolong Xi

    2016-01-01

    Full Text Available This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO for cancer feature gene selection, coupling support vector machine (SVM for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV. Finally, the BQPSO coupling SVM (BQPSO/SVM, binary PSO coupling SVM (BPSO/SVM, and genetic algorithm coupling SVM (GA/SVM are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.

  13. Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine.

    Science.gov (United States)

    Xi, Maolong; Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun

    2016-01-01

    This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.

  14. Accurate and reliable cancer classification based on probabilistic inference of pathway activity.

    Directory of Open Access Journals (Sweden)

    Junjie Su

    Full Text Available With the advent of high-throughput technologies for measuring genome-wide expression profiles, a large number of methods have been proposed for discovering diagnostic markers that can accurately discriminate between different classes of a disease. However, factors such as the small sample size of typical clinical data, the inherent noise in high-throughput measurements, and the heterogeneity across different samples, often make it difficult to find reliable gene markers. To overcome this problem, several studies have proposed the use of pathway-based markers, instead of individual gene markers, for building the classifier. Given a set of known pathways, these methods estimate the activity level of each pathway by summarizing the expression values of its member genes, and use the pathway activities for classification. It has been shown that pathway-based classifiers typically yield more reliable results compared to traditional gene-based classifiers. In this paper, we propose a new classification method based on probabilistic inference of pathway activities. For a given sample, we compute the log-likelihood ratio between different disease phenotypes based on the expression level of each gene. The activity of a given pathway is then inferred by combining the log-likelihood ratios of the constituent genes. We apply the proposed method to the classification of breast cancer metastasis, and show that it achieves higher accuracy and identifies more reproducible pathway markers compared to several existing pathway activity inference methods.

  15. Improving breast cancer classification with mammography, supported on an appropriate variable selection analysis

    Science.gov (United States)

    Pérez, Noel; Guevara, Miguel A.; Silva, Augusto

    2013-02-01

    This work addresses the issue of variable selection within the context of breast cancer classification with mammography. A comprehensive repository of feature vectors was used including a hybrid subset gathering image-based and clinical features. It aimed to gather experimental evidence of variable selection in terms of cardinality, type and find a classification scheme that provides the best performance over the Area Under Receiver Operating Characteristics Curve (AUC) scores using the ranked features subset. We evaluated and classified a total of 300 subsets of features formed by the application of Chi-Square Discretization, Information-Gain, One-Rule and RELIEF methods in association with Feed-Forward Backpropagation Neural Network (FFBP), Support Vector Machine (SVM) and Decision Tree J48 (DTJ48) Machine Learning Algorithms (MLA) for a comparative performance evaluation based on AUC scores. A variable selection analysis was performed for Single-View Ranking and Multi-View Ranking groups of features. Features subsets representing Microcalcifications (MCs), Masses and both MCs and Masses lesions achieved AUC scores of 0.91, 0.954 and 0.934 respectively. Experimental evidence demonstrated that classification performance was improved by combining image-based and clinical features. The most important clinical and image-based features were StromaDistortion and Circularity respectively. Other less important but worth to use due to its consistency were Contrast, Perimeter, Microcalcification, Correlation and Elongation.

  16. Human papillomavirus and gastrointestinal cancer: A review

    Science.gov (United States)

    Bucchi, Dania; Stracci, Fabrizio; Buonora, Nicola; Masanotti, Giuseppe

    2016-01-01

    Human papillomavirus (HPV) is one of the most common sexually transmitted infections worldwide. Exposure to HPV is very common, and an estimated 65%-100% of sexually active adults are exposed to HPV in their lifetime. The majority of HPV infections are asymptomatic, but there is a 10% chance that individuals will develop a persistent infection and have an increased risk of developing a carcinoma. The International Agency for Research on Cancer has found that the following cancer sites have a strong causal relationship with HPV: cervix uteri, penis, vulva, vagina, anus and oropharynx, including the base of the tongue and the tonsils. However, studies of the aetiological role of HPV in colorectal and esophageal malignancies have conflicting results. The aim of this review was to organize recent evidence and issues about the association between HPV infection and gastrointestinal tumours with a focus on esophageal, colorectal and anal cancers. The ultimate goal was to highlight possible implications for prognosis and prevention. PMID:27672265

  17. Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value.

    Directory of Open Access Journals (Sweden)

    Laetitia Marisa

    Full Text Available BACKGROUND: Colon cancer (CC pathological staging fails to accurately predict recurrence, and to date, no gene expression signature has proven reliable for prognosis stratification in clinical practice, perhaps because CC is a heterogeneous disease. The aim of this study was to establish a comprehensive molecular classification of CC based on mRNA expression profile analyses. METHODS AND FINDINGS: Fresh-frozen primary tumor samples from a large multicenter cohort of 750 patients with stage I to IV CC who underwent surgery between 1987 and 2007 in seven centers were characterized for common DNA alterations, including BRAF, KRAS, and TP53 mutations, CpG island methylator phenotype, mismatch repair status, and chromosomal instability status, and were screened with whole genome and transcriptome arrays. 566 samples fulfilled RNA quality requirements. Unsupervised consensus hierarchical clustering applied to gene expression data from a discovery subset of 443 CC samples identified six molecular subtypes. These subtypes were associated with distinct clinicopathological characteristics, molecular alterations, specific enrichments of supervised gene expression signatures (stem cell phenotype-like, normal-like, serrated CC phenotype-like, and deregulated signaling pathways. Based on their main biological characteristics, we distinguished a deficient mismatch repair subtype, a KRAS mutant subtype, a cancer stem cell subtype, and three chromosomal instability subtypes, including one associated with down-regulated immune pathways, one with up-regulation of the Wnt pathway, and one displaying a normal-like gene expression profile. The classification was validated in the remaining 123 samples plus an independent set of 1,058 CC samples, including eight public datasets. Furthermore, prognosis was analyzed in the subset of stage II-III CC samples. The subtypes C4 and C6, but not the subtypes C1, C2, C3, and C5, were independently associated with shorter relapse

  18. Acromioclavicular joint dislocations: radiological correlation between Rockwood classification system and injury patterns in human cadaver species.

    Science.gov (United States)

    Eschler, Anica; Rösler, Klaus; Rotter, Robert; Gradl, Georg; Mittlmeier, Thomas; Gierer, Philip

    2014-09-01

    The classification system of Rockwood and Young is a commonly used classification for acromioclavicular joint separations subdividing types I-VI. This classification hypothesizes specific lesions to anatomical structures (acromioclavicular and coracoclavicular ligaments, capsule, attached muscles) leading to the injury. In recent literature, our understanding for anatomical correlates leading to the radiological-based Rockwood classification is questioned. The goal of this experimental-based investigation was to approve the correlation between the anatomical injury pattern and the Rockwood classification. In four human cadavers (seven shoulders), the acromioclavicular and coracoclavicular ligaments were transected stepwise. Radiological correlates were recorded (Zanca view) with 15-kg longitudinal tension applied at the wrist. The resulting acromio- and coracoclavicular distances were measured. Radiographs after acromioclavicular ligament transection showed joint space enlargement (8.6 ± 0.3 vs. 3.1 ± 0.5 mm, p acromioclavicular joint space width increased to 16.7 ± 2.7 vs. 8.6 ± 0.3 mm, p acromioclavicular joint lesions higher than Rockwood type I and II. The clinical consequence for reconstruction of low-grade injuries might be a solely surgical approach for the acromioclavicular ligaments or conservative treatment. High-grade injuries were always based on additional structural damage to the coracoclavicular ligaments. Rockwood type V lesions occurred while muscle attachments were intact.

  19. The Evaluation of Microcarcinoma in Differentiated Thyroid Cancers According to Old and New TNM Classification

    Directory of Open Access Journals (Sweden)

    Zekiye Hasbek

    2011-12-01

    Full Text Available Objective: In this study, we aimed to evaluate the tumor size for proximal and distant metastases when the new and old TNM clas¬sification is taken into account in differentiated thyroid cancers. Material and Methods: Two hundred sixty eight patients diagnosed with thyroid carcinoma, undergoing bilateral total or subto¬tal thyroidectomy treated with high doses of I-131 were examined retrospectively. The data of these patients were compared after classification, according to tumor size 1 cm. In the same group, according to the revised TNM classification, in 149 of 207 patients (72% the tumor size was 2 cm. Of 187 patients with negative lymph nodes, 15 (8% showed abnormal activity accumulation in the first post I-131 treatment whole-body scan and 10 (40% of 25 patients positive lymph node (p<0.05 involvement. Conclusion: Since the treatment of patients with microcarcinoma is controversial, tumor size should not be the only factor consid¬ered in patients with differentiated thyroid cancer Tissue tumor invasion, age, gender and multifocality should also be taken into account. (MIRT2011;20:94-99

  20. A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search

    Directory of Open Access Journals (Sweden)

    Tahir Muhammad Atif

    2005-01-01

    Full Text Available The introduction of multispectral imaging in pathology problems such as the identification of prostatic cancer is recent. Unlike conventional RGB color space, it allows the acquisition of a large number of spectral bands within the visible spectrum. This results in a feature vector of size greater than 100. For such a high dimensionality, pattern recognition techniques suffer from the well-known curse of dimensionality problem. The two well-known techniques to solve this problem are feature extraction and feature selection. In this paper, a novel feature selection technique using tabu search with an intermediate-term memory is proposed. The cost of a feature subset is measured by leave-one-out correct-classification rate of a nearest-neighbor (1-NN classifier. The experiments have been carried out on the prostate cancer textured multispectral images and the results have been compared with a reported classical feature extraction technique. The results have indicated a significant boost in the performance both in terms of minimizing features and maximizing classification accuracy.

  1. [Postoperative results under the new stage classification of lung cancer: the additional reports for those of JACS in 1996].

    Science.gov (United States)

    Shirakusa, T

    2000-10-01

    This time, in 3008 lung cancer patients, the postoperative results were analyzed under the new stage grouping of TNM classification. All of those patients underwent the operation in 1989, and the 5 year-survival rates had beeb surveyed in 1996 by JACS (The Japanese Association for Chest Surgery). Under the new TNM classification established in 1996 worldwidey, T3N0M0 was transferred from IIIA to IIB. This report is the additional one in the focus of the results accompanied with the change of TNM classification.

  2. Comparative proteomics analysis of human gastric cancer

    Institute of Scientific and Technical Information of China (English)

    Wei Li; Jian-Fang Li; Ying Qu; Xue-Hua Chen; Jian-Min Qin; Qin-Long Gu; Min Yan; Zheng-Gang Zhu; Bing-Ya Liu

    2008-01-01

    AIM: To isolate and identify differentially expressed proteins between cancer and normal tissues of gastric cancer by two-dimensional electrophoresis (2-DE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS).METHODS: Soluble fraction proteins of gastric cancer tissues and paired normal tissues were separated by 2-DE.The differentially expressed proteins were selected and identified by MALDI-TOF-MS and database search.RESULTS: 2-DE profiles with high resolution and reproducibility were obtained.Twenty-three protein spots were excised from sliver staining gel and digested in gel by trypsin,in which fifteen protein spots were identified successfully.Among the identified proteins,there were ten over-expressed and five under-expressed proteins in stomach cancer tissues compared with normal tissues.CONCLUSION: In this study,the well-resolved,reproducible 2-DE patterns of human gastric cancer tissue and paired normal tissue were established and optimized and certain differentially-expressed proteins were identified.The combined use of 2-DE and MS provides an effective approach to screen for potential tumor markers.

  3. Micro-doppler radar classification of human motions under various training scenarios

    Science.gov (United States)

    Fairchild, Dustin P.; Narayanan, Ram M.

    2013-05-01

    The identification and classification of human motions has become a popular area of research due to its broad range of applications. Knowledge of a person's movements can be a useful tool in surveillance, security, military combat, search and rescue operations, and the medical fields. Classification of common stationary human movements has been performed under various scenarios for two different micro-Doppler radar systems: S-band radar and millimeter-wave (mm-wave) radar. Each radar system has been designed for a specific scenario. The S-band radar is intended for through-the-wall situations at close distances, whereas the mm-wave radar is designed for long distance applications and also for through light foliage. Here, the performance of these radars for different training scenarios is investigated. The S-band radar will be analyzed for classification without a wall barrier, through a brick wall, and also through a cinder block wall. The effect of a wall barrier on micro-Doppler signatures will be briefly discussed. The mm-wave radar will be analyzed for classification at distances of 30, 60, and 91 meters.

  4. Human Colon Cancer Cells Cultivated in Space

    Science.gov (United States)

    1995-01-01

    Within five days, bioreactor cultivated human colon cancer cells (shown) grown in Microgravity on the STS-70 mission in 1995, had grown 30 times the volume of the control specimens on Earth. The samples grown in space had a higher level of cellular organization and specialization. Because they more closely resemble tumors found in the body, microgravity grown cell cultures are ideal for research purposes.

  5. Establishment of a human lung cancer cell line with high metastatic potential to multiple organs: gene expression associated with metastatic potential in human lung cancer.

    Science.gov (United States)

    Nakano, Tetsuhiro; Shimizu, Kimihiro; Kawashima, Osamu; Kamiyoshihara, Mitsuhiro; Kakegawa, Seiichi; Sugano, Masayuki; Ibe, Takashi; Nagashima, Toshiteru; Kaira, Kyoichi; Sunaga, Noriaki; Ohtaki, Youichi; Atsumi, Jun; Takeyoshi, Izumi

    2012-11-01

    Convenient and reliable multiple organ metastasis model systems might contribute to understanding the mechanism(s) of metastasis of lung cancer, which may lead to overcoming metastasis and improvement in the treatment outcome of lung cancer. We isolated a highly metastatic subline, PC14HM, from the human pulmonary adenocarcinoma cell line, PC14, using an in vivo selection method. The expression of 34,580 genes was compared between PC14HM and parental PC14 by cDNA microarray analysis. Among the differentially expressed genes, expression of four genes in human lung cancer tissues and adjacent normal lung tissues were compared using real-time reverse transcription polymerase chain reaction. Although BALB/c nude mice inoculated with parental PC14 cells had few metastases, almost all mice inoculated with PC14HM cells developed metastases in multiple organs, including the lung, bone and adrenal gland, the same progression seen in human lung cancer. cDNA microarray analysis revealed that 981 genes were differentially (more than 3-fold) expressed between the two cell lines. Functional classification revealed that many of those genes were associated with cell growth, cell communication, development and transcription. Expression of three upregulated genes (HRB-2, HS3ST3A1 and RAB7) was higher in human cancer tissue compared to normal lung tissue, while expression of EDG1, which was downregulated, was lower in the cancer tissue compared to the normal lung. These results suggest that the newly established PC14HM cell line may provide a mouse model of widespread metastasis of lung cancer. This model system may provide insights into the key genetic determinants of widespread metastasis of lung cancer.

  6. Aspartame bioassay findings portend human cancer hazards.

    Science.gov (United States)

    Huff, James; LaDou, Joseph

    2007-01-01

    The U.S. Food and Drug Administration (FDA) should reevaluate its position on aspartame as being safe under all conditions. Animal bioassay results predict human cancer risks, and a recent animal study confirms that there is a potential aspartame risk to humans. Aspartame is produced and packaged in China for domestic use and global distribution. Japan, France, and the United States are also major producers. No study of long-term adverse occupational health effects on aspartame workers have been conducted. The FDA should consider sponsoring a prospective epidemiologic study of aspartame workers.

  7. 1. HUMAN POPULATION MONITORING FOR CANCER PREVENTION

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    @@Most of the chemicals classified by the International Agency for Research on Cancer (IARC) as human carcinogens are mutagenic across test systems, cf. [www.epa.gov/gapdb ] and induce tumors at multiple sites in rodent species. They are therefore readity detected in short term tests for gene-tic and related effects (GRE), in animal carcinogenesis bioassays and in human monitoring studies. Carcinogens that are not genotoxic may be studied using new toxicogenomic approaches as will be discussed. A Chemical Effects in Biological Systems (CEBS) database is planned by the National Center for Toxicogenomics to contain information on such compounds. The 1992 Preamble to the IARC Monographs

  8. Swarm Intelligence Approach Based on Adaptive ELM Classifier with ICGA Selection for Microarray Gene Expression and Cancer Classification

    Directory of Open Access Journals (Sweden)

    T. Karthikeyan

    2014-05-01

    Full Text Available The aim of this research study is based on efficient gene selection and classification of microarray data analysis using hybrid machine learning algorithms. The beginning of microarray technology has enabled the researchers to quickly measure the position of thousands of genes expressed in an organic/biological tissue samples in a solitary experiment. One of the important applications of this microarray technology is to classify the tissue samples using their gene expression representation, identify numerous type of cancer. Cancer is a group of diseases in which a set of cells shows uncontrolled growth, instance that interrupts upon and destroys nearby tissues and spreading to other locations in the body via lymph or blood. Cancer has becomes a one of the major important disease in current scenario. DNA microarrays turn out to be an effectual tool utilized in molecular biology and cancer diagnosis. Microarrays can be measured to establish the relative quantity of mRNAs in two or additional organic/biological tissue samples for thousands/several thousands of genes at the same time. As the superiority of this technique become exactly analysis/identifying the suitable assessment of microarray data in various open issues. In the field of medical sciences multi-category cancer classification play a major important role to classify the cancer types according to the gene expression. The need of the cancer classification has been become indispensible, because the numbers of cancer victims are increasing steadily identified by recent years. To perform this proposed a combination of Integer-Coded Genetic Algorithm (ICGA and Artificial Bee Colony algorithm (ABC, coupled with an Adaptive Extreme Learning Machine (AELM, is used for gene selection and cancer classification. ICGA is used with ABC based AELM classifier to chose an optimal set of genes which results in an efficient hybrid algorithm that can handle sparse data and sample imbalance. The

  9. Recapitulating Human Gastric Cancer Pathogenesis: Experimental Models of Gastric Cancer

    Science.gov (United States)

    Ding, Lin; El Zaatari, Mohamad

    2017-01-01

    Overview Gastric cancer has been traditionally defined by the Correa paradigm as a progression of sequential pathological events that begins with chronic inflammation [1]. Infection with Helicobacter pylori (H. pylori) is the typical explanation for why the stomach becomes chronically inflamed. Acute gastric inflammation then leads to chronic gastritis, atrophy particularly of acid-secreting parietal cells, metaplasia due to mucous neck cell expansion from trans-differentiation of zymogenic cells to dysplasia and eventually carcinoma [2]. The chapter contains an overview of gastric anatomy and physiology to set the stage for signaling pathways that play a role in gastric tumorigenesis. Finally, the major known mouse models of gastric transformation are critiqued in terms of the rationale behind their generation and contribution to our understanding of human cancer subtypes. PMID:27573785

  10. Segmentation and Classification of Human Actions and Actor Characteristics with 3d Motion Data

    Directory of Open Access Journals (Sweden)

    S. Ali Etemad

    2012-08-01

    Full Text Available In this paper we have used 3D motion capture data with the aim of detecting and classifying specifichuman actions. In addition to recognition of basic action classes, actor styles and characteristics such asgender, age, and energy level have also been subject to classification. We have applied and compared threemain methods: nearest neighbour search, hidden Markov models, and artificial neural networks. Usingthese techniques, we have proposed exhaustive algorithms for detection of actions in a motion piece andsubsequently classifying the segmented actions and respective characteristics of the actors. We have testedthe methods for various sequences and compared the results for a comprehensive evaluation of each of theproposed techniques. Our findings can be largely used for general classification of human motion data formultimedia applications as well as sorting and classifying data sets of human motion data especially thoseacquired using visual marker-based motion capture systems such as the one employed in this research.

  11. RVM-Based Human Action Classification in Crowd through Projection and Star Skeletonization

    Directory of Open Access Journals (Sweden)

    V. Abhaikumar

    2009-01-01

    Full Text Available Detection of abnormal human actions in the crowd has become a critical problem in video surveillance applications like terrorist attacks. This paper proposes a real-time video surveillance system which is capable of classifying normal and abnormal actions of individuals in a crowd. The abnormal actions of human such as running, jumping, waving hand, bending, walking and fighting with each other in a crowded environment are considered. In this paper, Relevance Vector Machine (RVM is used to classify the abnormal actions of an individual in the crowd based on the results obtained from projection and skeletonization methods. Experimental results on benchmark datasets demonstrate that the proposed system is robust and efficient. A comparative study of classification accuracy between Relevance Vector Machine and Support Vector Machine (SVM classification is also presented.

  12. RVM-Based Human Action Classification in Crowd through Projection and Star Skeletonization

    Directory of Open Access Journals (Sweden)

    Yogameena B

    2009-01-01

    Full Text Available Abstract Detection of abnormal human actions in the crowd has become a critical problem in video surveillance applications like terrorist attacks. This paper proposes a real-time video surveillance system which is capable of classifying normal and abnormal actions of individuals in a crowd. The abnormal actions of human such as running, jumping, waving hand, bending, walking and fighting with each other in a crowded environment are considered. In this paper, Relevance Vector Machine (RVM is used to classify the abnormal actions of an individual in the crowd based on the results obtained from projection and skeletonization methods. Experimental results on benchmark datasets demonstrate that the proposed system is robust and efficient. A comparative study of classification accuracy between Relevance Vector Machine and Support Vector Machine (SVM classification is also presented.

  13. HUMAN CANCER IS A PARASITE SPREAD VIA INTRUSION IN GENOME

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    Sergey N. Rumyantsev

    2013-03-01

    Full Text Available The present article is devoted to further development of new paradigm about the biology of human cancer: the hypothesis of parasitic nature, origin and evolution of the phenomenon. The study included integrative reconsidering, and reinterpretation of the make-ups, traits and processes existing both in human and animal cancers. It was demonstrated that human cancer possesses nearly analogous set of traits characteristic of transmissible animal cancer. Undoubted analogies are seen in the prevalence, clinical exposure, progression of disease, origin of causative agents, immune response against invasion and especially in the intrinsic deviations of the leading traits of cancerous cells. Both human and animal cancers are highly exceptional pathogens. But in contrast to contagious animal cancers the cells of of human cancer can not pass between individuals as usual infectious agents. Exhaustive evidence of the parasitic nature and evolutionary origin of human cancer was revealed and interpreted. In contrast to animal cancer formed of solitary cell lineage, human cancer consists of a couple of lineages constructed under different genetic regulations and performed different structural and physiological functions. The complex make-up of cancer composition remains stable over sequential propagation. The subsistence of human cancer regularly includes obligatory interchange of its successive forms. Human cancer possesses its own biological watch and the ability to gobble its victim, transmit via the intrusion of the genome, perform intercommunications within the tumor components and between the dispersed subunits of cancer. Such intrinsic traits characterize human cancer as a primitively structured parasite that can be classified in Class Mammalians, Species Genomeintruder malevolent (G.malevolent.

  14. Antiangiogenic Steroids in Human Cancer Therapy

    Directory of Open Access Journals (Sweden)

    Richard J. Pietras

    2005-01-01

    Full Text Available Despite advances in the early detection of tumors and in the use of chemotherapy, radiotherapy and surgery for disease management, the worldwide mortality from human cancer remains unacceptably high. The treatment of cancer may benefit from the introduction of novel therapies derived from natural products. Natural products have served to provide a basis for many of the pharmaceutical agents in current use in cancer therapy. Emerging research indicates that progressive growth and spread of many solid tumors depends, in part, on the formation of an adequate blood supply, and this process of tumor-associated angiogenesis is reported to have prognostic significance in several human cancers. This review focuses on the potential application in antitumor therapy of naturally-occurring steroids that target tumor-associated angiogenesis. Squalamine, a 7,24 dihydroxylated 24-sulfated cholestane steroid conjugated to a spermidine at position C-3, is known to have strong antiangiogenic activity in vitro, and it significantly disrupts tumor proliferation and progression in laboratory studies. Work on the interactions of squalamine with vascular endothelial cells indicate that it binds with cell membranes, inhibits the membrane Na+/H+ exchanger and may further function as a calmodulin chaperone. These primary actions appear to promote inhibition of several vital steps in angiogenesis, such as blockade of mitogen-induced actin polymerization, cell–cell adhesion and cell migration, leading to suppression of endothelial cell proliferation. Preclinical studies with squalamine have shown additive benefits in tumor growth delay when squalamine is combined with cisplatin, paclitaxel, cyclophosphamide, genistein or radiation therapy. This compound has also been assessed in early phase clinical trials in cancer; squalamine was found to exhibit little systemic toxicity and was generally well tolerated by treated patients with various solid tumor malignancies

  15. Antiangiogenic Steroids in Human Cancer Therapy.

    Science.gov (United States)

    Pietras, Richard J; Weinberg, Olga K

    2005-03-01

    Despite advances in the early detection of tumors and in the use of chemotherapy, radiotherapy and surgery for disease management, the worldwide mortality from human cancer remains unacceptably high. The treatment of cancer may benefit from the introduction of novel therapies derived from natural products. Natural products have served to provide a basis for many of the pharmaceutical agents in current use in cancer therapy. Emerging research indicates that progressive growth and spread of many solid tumors depends, in part, on the formation of an adequate blood supply, and this process of tumor-associated angiogenesis is reported to have prognostic significance in several human cancers. This review focuses on the potential application in antitumor therapy of naturally-occurring steroids that target tumor-associated angiogenesis. Squalamine, a 7,24 dihydroxylated 24-sulfated cholestane steroid conjugated to a spermidine at position C-3, is known to have strong antiangiogenic activity in vitro, and it significantly disrupts tumor proliferation and progression in laboratory studies. Work on the interactions of squalamine with vascular endothelial cells indicate that it binds with cell membranes, inhibits the membrane Na(+)/H(+) exchanger and may further function as a calmodulin chaperone. These primary actions appear to promote inhibition of several vital steps in angiogenesis, such as blockade of mitogen-induced actin polymerization, cell-cell adhesion and cell migration, leading to suppression of endothelial cell proliferation. Preclinical studies with squalamine have shown additive benefits in tumor growth delay when squalamine is combined with cisplatin, paclitaxel, cyclophosphamide, genistein or radiation therapy. This compound has also been assessed in early phase clinical trials in cancer; squalamine was found to exhibit little systemic toxicity and was generally well tolerated by treated patients with various solid tumor malignancies, including ovarian, non

  16. Proteomic-Based Biosignatures in Breast Cancer Classification and Prediction of Therapeutic Response

    Science.gov (United States)

    He, Jianbo; Whelan, Stephen A.; Lu, Ming; Shen, Dejun; Chung, Debra U.; Saxton, Romaine E.; Faull, Kym F.; Whitelegge, Julian P.; Chang, Helena R.

    2011-01-01

    Protein-based markers that classify tumor subtypes and predict therapeutic response would be clinically useful in guiding patient treatment. We investigated the LC-MS/MS-identified protein biosignatures in 39 baseline breast cancer specimens including 28 HER2-positive and 11 triple-negative (TNBC) tumors. Twenty proteins were found to correctly classify all HER2 positive and 7 of the 11 TNBC tumors. Among them, galectin-3-binding protein and ALDH1A1 were found preferentially elevated in TNBC, whereas CK19, transferrin, transketolase, and thymosin β4 and β10 were elevated in HER2-positive cancers. In addition, several proteins such as enolase, vimentin, peroxiredoxin 5, Hsp 70, periostin precursor, RhoA, cathepsin D preproprotein, and annexin 1 were found to be associated with the tumor responses to treatment within each subtype. The MS-based proteomic findings appear promising in guiding tumor classification and predicting response. When sufficiently validated, some of these candidate protein markers could have great potential in improving breast cancer treatment. PMID:22110952

  17. Proteomic-Based Biosignatures in Breast Cancer Classification and Prediction of Therapeutic Response

    Directory of Open Access Journals (Sweden)

    Jianbo He

    2011-01-01

    Full Text Available Protein-based markers that classify tumor subtypes and predict therapeutic response would be clinically useful in guiding patient treatment. We investigated the LC-MS/MS-identified protein biosignatures in 39 baseline breast cancer specimens including 28 HER2-positive and 11 triple-negative (TNBC tumors. Twenty proteins were found to correctly classify all HER2 positive and 7 of the 11 TNBC tumors. Among them, galectin-3-binding protein and ALDH1A1 were found preferentially elevated in TNBC, whereas CK19, transferrin, transketolase, and thymosin 4 and 10 were elevated in HER2-positive cancers. In addition, several proteins such as enolase, vimentin, peroxiredoxin 5, Hsp 70, periostin precursor, RhoA, cathepsin D preproprotein, and annexin 1 were found to be associated with the tumor responses to treatment within each subtype. The MS-based proteomic findings appear promising in guiding tumor classification and predicting response. When sufficiently validated, some of these candidate protein markers could have great potential in improving breast cancer treatment.

  18. Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts.

    Science.gov (United States)

    Dashtban, M; Balafar, Mohammadali

    2017-03-01

    Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is proposed to identify predictive genes for cancer classification. A filter method was first applied to reduce the dimensionality of feature space followed by employing an integer-coded genetic algorithm with dynamic-length genotype, intelligent parameter settings, and modified operators. The algorithmic behaviors including convergence trends, mutation and crossover rate changes, and running time were studied, conceptually discussed, and shown to be coherent with literature findings. Two well-known filter methods, Laplacian and Fisher score, were examined considering similarities, the quality of selected genes, and their influences on the evolutionary approach. Several statistical tests concerning choice of classifier, choice of dataset, and choice of filter method were performed, and they revealed some significant differences between the performance of different classifiers and filter methods over datasets. The proposed method was benchmarked upon five popular high-dimensional cancer datasets; for each, top explored genes were reported. Comparing the experimental results with several state-of-the-art methods revealed that the proposed method outperforms previous methods in DLBCL dataset. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. A taxonomy of epithelial human cancer and their metastases

    Directory of Open Access Journals (Sweden)

    De Moor Bart

    2009-12-01

    . Moreover, a signature was developed based on our unsupervised clustering of breast tumors and this was predictive for disease-specific survival in three independent studies. Next, the metastases from ovarian, breast, lung and vulva cluster with their tissue of origin while metastases from colon showed a bimodal distribution. A significant part clusters with tissue of origin while the remaining tumors cluster with the tissue of destination. Conclusion Our molecular taxonomy of epithelial human cancer indicates surprising correlations over tissues. This may have a significant impact on the classification of many cancer sites and may guide pathologists, both in research and daily practice. Moreover, these results based on unsupervised analysis yielded a signature predictive of clinical outcome in breast cancer. Additionally, we hypothesize that metastases from gastrointestinal origin either remember their tissue of origin or adapt to the tissue of destination. More specifically, colon metastases in the liver show strong evidence for such a bimodal tissue specific profile.

  20. Epidemiologic studies of the human microbiome and cancer

    National Research Council Canada - National Science Library

    Vogtmann, Emily; Goedert, James J

    2016-01-01

    .... Previously detected associations of individual bacteria (e.g., Helicobacter pylori), periodontal disease, and inflammation with specific cancers have motivated studies considering the association between the human microbiome and cancer risk...

  1. Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images

    Directory of Open Access Journals (Sweden)

    Henry Joutsijoki

    2016-01-01

    Full Text Available The purpose of this paper is to examine how well the human induced pluripotent stem cell (hiPSC colony images can be classified using error-correcting output codes (ECOC. Our image dataset includes hiPSC colony images from three classes (bad, semigood, and good which makes our classification task a multiclass problem. ECOC is a general framework to model multiclass classification problems. We focus on four different coding designs of ECOC and apply to each one of them k-Nearest Neighbor (k-NN searching, naïve Bayes, classification tree, and discriminant analysis variants classifiers. We use Scaled Invariant Feature Transformation (SIFT based features in classification. The best accuracy (62.4% is obtained with ternary complete ECOC coding design and k-NN classifier (standardized Euclidean distance measure and inverse weighting. The best result is comparable with our earlier research. The quality identification of hiPSC colony images is an essential problem to be solved before hiPSCs can be used in practice in large-scale. ECOC methods examined are promising techniques for solving this challenging problem.

  2. Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images.

    Science.gov (United States)

    Joutsijoki, Henry; Haponen, Markus; Rasku, Jyrki; Aalto-Setälä, Katriina; Juhola, Martti

    2016-01-01

    The purpose of this paper is to examine how well the human induced pluripotent stem cell (hiPSC) colony images can be classified using error-correcting output codes (ECOC). Our image dataset includes hiPSC colony images from three classes (bad, semigood, and good) which makes our classification task a multiclass problem. ECOC is a general framework to model multiclass classification problems. We focus on four different coding designs of ECOC and apply to each one of them k-Nearest Neighbor (k-NN) searching, naïve Bayes, classification tree, and discriminant analysis variants classifiers. We use Scaled Invariant Feature Transformation (SIFT) based features in classification. The best accuracy (62.4%) is obtained with ternary complete ECOC coding design and k-NN classifier (standardized Euclidean distance measure and inverse weighting). The best result is comparable with our earlier research. The quality identification of hiPSC colony images is an essential problem to be solved before hiPSCs can be used in practice in large-scale. ECOC methods examined are promising techniques for solving this challenging problem.

  3. Prevention of the Angiogenic Switch in Human Breast Cancer

    Science.gov (United States)

    2009-03-01

    chronic myeloid leukaemia | colorectal cancer | Down syndrome | infantile haemangiomas | multiple myeloma | non-small-cell lung cancer | rheumatoid...Human Breast Cancer PRINCIPAL INVESTIGATOR: Donald Ingber, M.D., Ph.D. CONTRACTING ORGANIZATION: Children’s Hospital...From - To) 15 FEB 2004 - 14 FEB 2009 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Prevention of the Angiogenic Switch in Human Breast Cancer 5b

  4. Identification of area-level influences on regions of high cancer incidence in Queensland, Australia: a classification tree approach

    Directory of Open Access Journals (Sweden)

    Mengersen Kerrie L

    2011-07-01

    Full Text Available Abstract Background Strategies for cancer reduction and management are targeted at both individual and area levels. Area-level strategies require careful understanding of geographic differences in cancer incidence, in particular the association with factors such as socioeconomic status, ethnicity and accessibility. This study aimed to identify the complex interplay of area-level factors associated with high area-specific incidence of Australian priority cancers using a classification and regression tree (CART approach. Methods Area-specific smoothed standardised incidence ratios were estimated for priority-area cancers across 478 statistical local areas in Queensland, Australia (1998-2007, n = 186,075. For those cancers with significant spatial variation, CART models were used to identify whether area-level accessibility, socioeconomic status and ethnicity were associated with high area-specific incidence. Results The accessibility of a person's residence had the most consistent association with the risk of cancer diagnosis across the specific cancers. Many cancers were likely to have high incidence in more urban areas, although male lung cancer and cervical cancer tended to have high incidence in more remote areas. The impact of socioeconomic status and ethnicity on these associations differed by type of cancer. Conclusions These results highlight the complex interactions between accessibility, socioeconomic status and ethnicity in determining cancer incidence risk.

  5. [WHO classification 2016 and first S3 guidelines on renal cell cancer: What is important for the practice?].

    Science.gov (United States)

    Moch, H

    2016-03-01

    The first S3 guidelines on renal cell cancer cover the practical aspects of imaging, diagnostics and therapy as well as the clinical relevance of pathology reporting. This review summarizes the changes in renal tumor classification and the new recommendations for reporting renal cell tumors. The S3 guidelines recommend the 2016 World Health Organization (WHO) classification of renal cell tumors. Novel renal cell tumor entities and provisional or emerging renal cell tumor entities of the 2016 WHO classification of renal tumors are discussed. The S3 guidelines for renal cell cancer also recommend the use of the WHO/International Society of Urologic Pathology (ISUP) grading system for clear cell and for papillary renal cell carcinomas, which replaces the previously used Fuhrman grading system.

  6. Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods

    Directory of Open Access Journals (Sweden)

    Mark Burton

    2012-01-01

    Full Text Available Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients.

  7. Application of local binary pattern and human visual Fibonacci texture features for classification different medical images

    Science.gov (United States)

    Sanghavi, Foram; Agaian, Sos

    2017-05-01

    The goal of this paper is to (a) test the nuclei based Computer Aided Cancer Detection system using Human Visual based system on the histopathology images and (b) Compare the results of the proposed system with the Local Binary Pattern and modified Fibonacci -p pattern systems. The system performance is evaluated using different parameters such as accuracy, specificity, sensitivity, positive predictive value, and negative predictive value on 251 prostate histopathology images. The accuracy of 96.69% was observed for cancer detection using the proposed human visual based system compared to 87.42% and 94.70% observed for Local Binary patterns and the modified Fibonacci p patterns.

  8. Finding Combination of Features from Promoter Regions for Ovarian Cancer-related Gene Group Classification

    KAUST Repository

    Olayan, Rawan S.

    2012-12-01

    In classification problems, it is always important to use the suitable combination of features that will be employed by classifiers. Generating the right combination of features usually results in good classifiers. In the situation when the problem is not well understood, data items are usually described by many features in the hope that some of these may be the relevant or most relevant ones. In this study, we focus on one such problem related to genes implicated in ovarian cancer (OC). We try to recognize two important OC-related gene groups: oncogenes, which support the development and progression of OC, and oncosuppressors, which oppose such tendencies. For this, we use the properties of promoters of these genes. We identified potential “regulatory features” that characterize OC-related oncogenes and oncosuppressors promoters. In our study, we used 211 oncogenes and 39 oncosuppressors. For these, we identified 538 characteristic sequence motifs from their promoters. Promoters are annotated by these motifs and derived feature vectors used to develop classification models. We made a comparison of a number of classification models in their ability to distinguish oncogenes from oncosuppressors. Based on 10-fold cross-validation, the resultant model was able to separate the two classes with sensitivity of 96% and specificity of 100% with the complete set of features. Moreover, we developed another recognition model where we attempted to distinguish oncogenes and oncosuppressors as one group from other OC-related genes. That model achieved accuracy of 82%. We believe that the results of this study will help in discovering other OC-related oncogenes and oncosuppressors not identified as yet.

  9. Reliable classification of two-class cancer data using evolutionary algorithms.

    Science.gov (United States)

    Deb, Kalyanmoy; Raji Reddy, A

    2003-11-01

    In the area of bioinformatics, the identification of gene subsets responsible for classifying available disease samples to two or more of its variants is an important task. Such problems have been solved in the past by means of unsupervised learning methods (hierarchical clustering, self-organizing maps, k-mean clustering, etc.) and supervised learning methods (weighted voting approach, k-nearest neighbor method, support vector machine method, etc.). Such problems can also be posed as optimization problems of minimizing gene subset size to achieve reliable and accurate classification. The main difficulties in solving the resulting optimization problem are the availability of only a few samples compared to the number of genes in the samples and the exorbitantly large search space of solutions. Although there exist a few applications of evolutionary algorithms (EAs) for this task, here we treat the problem as a multiobjective optimization problem of minimizing the gene subset size and minimizing the number of misclassified samples. Moreover, for a more reliable classification, we consider multiple training sets in evaluating a classifier. Contrary to the past studies, the use of a multiobjective EA (NSGA-II) has enabled us to discover a smaller gene subset size (such as four or five) to correctly classify 100% or near 100% samples for three cancer samples (Leukemia, Lymphoma, and Colon). We have also extended the NSGA-II to obtain multiple non-dominated solutions discovering as much as 352 different three-gene combinations providing a 100% correct classification to the Leukemia data. In order to have further confidence in the identification task, we have also introduced a prediction strength threshold for determining a sample's belonging to one class or the other. All simulation results show consistent gene subset identifications on three disease samples and exhibit the flexibilities and efficacies in using a multiobjective EA for the gene subset identification task.

  10. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

    Directory of Open Access Journals (Sweden)

    Wang Lily

    2008-07-01

    Full Text Available Abstract Background Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. Results In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. Conclusion We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.

  11. Gene profile identifies zinc transporters differentially expressed in normal human organs and human pancreatic cancer.

    Science.gov (United States)

    Yang, J; Zhang, Y; Cui, X; Yao, W; Yu, X; Cen, P; Hodges, S E; Fisher, W E; Brunicardi, F C; Chen, C; Yao, Q; Li, M

    2013-03-01

    Deregulated expression of zinc transporters was linked to several cancers. However, the detailed expression profile of all human zinc transporters in normal human organs and in human cancer, especially in pancreatic cancer is not available. The objectives of this study are to investigate the complete expression patterns of 14 ZIP and 10 ZnT transporters in a large number of normal human organs and in human pancreatic cancer tissues and cell lines. We examined the expression patterns of ZIP and ZnT transporters in 22 different human organs and tissues, 11 pairs of clinical human pancreatic cancer specimens and surrounding normal/benign tissues, as well as 10 established human pancreatic cancer cell lines plus normal human pancreatic ductal epithelium (HPDE) cells, using real time RT-PCR and immunohistochemistry. The results indicate that human zinc transporters have tissue specific expression patterns, and may play different roles in different organs or tissues. Almost all the ZIPs except for ZIP4, and most ZnTs were down-regulated in human pancreatic cancer tissues compared to the surrounding benign tissues. The expression patterns of individual ZIPs and ZnTs are similar among different pancreatic cancer lines. Those results and our previous studies suggest that ZIP4 is the only zinc transporter that is significantly up-regulated in human pancreatic cancer and might be the major zinc transporter that plays an important role in pancreatic cancer growth. ZIP4 might serve as a novel molecular target for pancreatic cancer diagnosis and therapy.

  12. Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and BIRADS classification in a population of cancers with subtle mammographic findings.

    Science.gov (United States)

    Andersson, Ingvar; Ikeda, Debra M; Zackrisson, Sophia; Ruschin, Mark; Svahn, Tony; Timberg, Pontus; Tingberg, Anders

    2008-12-01

    The main purpose was to compare breast cancer visibility in one-view breast tomosynthesis (BT) to cancer visibility in one- or two-view digital mammography (DM). Thirty-six patients were selected on the basis of subtle signs of breast cancer on DM. One-view BT was performed with the same compression angle as the DM image in which the finding was least/not visible. On BT, 25 projections images were acquired over an angular range of 50 degrees, with double the dose of one-view DM. Two expert breast imagers classified one- and two-view DM, and BT findings for cancer visibility and BIRADS cancer probability in a non-blinded consensus study. Forty breast cancers were found in 37 breasts. The cancers were rated more visible on BT compared to one-view and two-view DM in 22 and 11 cases, respectively, (p BIRADS classification (p BIRADS classification (p < 0.01). The results indicate that the cancer visibility on BT is superior to DM, which suggests that BT may have a higher sensitivity for breast cancer detection.

  13. Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and BIRADS classification in a population of cancers with subtle mammographic findings

    Energy Technology Data Exchange (ETDEWEB)

    Andersson, Ingvar; Zackrisson, Sophia [Malmoe University Hospital, Diagnostic Centre of Imaging and Functional Medicine, Malmoe (Sweden); Ikeda, Debra M. [Stanford University, Stanford Advanced Medicine Center, Department of Radiology, Stanford, CA (United States); Ruschin, Mark [Lund University, Malmoe University Hospital, Department of Medical Radiation Physics, Malmoe (Sweden); University Health Network/Princess Margaret Hospital, Department of Radiation Physics, Toronto, ON (Canada); Svahn, Tony; Timberg, Pontus; Tingberg, Anders [Lund University, Malmoe University Hospital, Department of Medical Radiation Physics, Malmoe (Sweden)

    2008-12-15

    The main purpose was to compare breast cancer visibility in one-view breast tomosynthesis (BT) to cancer visibility in one- or two-view digital mammography (DM). Thirty-six patients were selected on the basis of subtle signs of breast cancer on DM. One-view BT was performed with the same compression angle as the DM image in which the finding was least/not visible. On BT, 25 projections images were acquired over an angular range of 50 degrees, with double the dose of one-view DM. Two expert breast imagers classified one- and two-view DM, and BT findings for cancer visibility and BIRADS cancer probability in a non-blinded consensus study. Forty breast cancers were found in 37 breasts. The cancers were rated more visible on BT compared to one-view and two-view DM in 22 and 11 cases, respectively, (p<0.01 for both comparisons). Comparing one-view DM to one-view BT, 21 patients were upgraded on BIRADS classification (p<0.01). Comparing two-view DM to one-view BT, 12 patients were upgraded on BIRADS classification (p<0.01). The results indicate that the cancer visibility on BT is superior to DM, which suggests that BT may have a higher sensitivity for breast cancer detection. (orig.)

  14. Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks.

    Science.gov (United States)

    Park, Jinhee; Javier, Rios Jesus; Moon, Taesup; Kim, Youngwook

    2016-11-24

    Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on water using radar has not been extensively studied, unlike the case on dry ground, due to its unique challenge. Namely, not only is the radar cross section of a human on water small, but the micro-Doppler signatures are much noisier due to water drops and waves. In this paper, we first investigate whether discriminative signatures could be obtained for activities on water through a simulation study. Then, we show how we can effectively achieve high classification accuracy by applying deep convolutional neural networks (DCNN) directly to the spectrogram of real measurement data. From the five-fold cross-validation on our dataset, which consists of five aquatic activities, we report that the conventional feature-based scheme only achieves an accuracy of 45.1%. In contrast, the DCNN trained using only the collected data attains 66.7%, and the transfer learned DCNN, which takes a DCNN pre-trained on a RGB image dataset and fine-tunes the parameters using the collected data, achieves a much higher 80.3%, which is a significant performance boost.

  15. Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Jinhee Park

    2016-11-01

    Full Text Available Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on water using radar has not been extensively studied, unlike the case on dry ground, due to its unique challenge. Namely, not only is the radar cross section of a human on water small, but the micro-Doppler signatures are much noisier due to water drops and waves. In this paper, we first investigate whether discriminative signatures could be obtained for activities on water through a simulation study. Then, we show how we can effectively achieve high classification accuracy by applying deep convolutional neural networks (DCNN directly to the spectrogram of real measurement data. From the five-fold cross-validation on our dataset, which consists of five aquatic activities, we report that the conventional feature-based scheme only achieves an accuracy of 45.1%. In contrast, the DCNN trained using only the collected data attains 66.7%, and the transfer learned DCNN, which takes a DCNN pre-trained on a RGB image dataset and fine-tunes the parameters using the collected data, achieves a much higher 80.3%, which is a significant performance boost.

  16. Classification and Numbering of Dental Radiographs for an Automated Human Identification System

    Directory of Open Access Journals (Sweden)

    Anny Yuniarti

    2012-03-01

    Full Text Available Dental based human identification is commonly used in forensic. This is due to the teeth are resistant to temperatures up to 200°C and are not easily got rotten. Thus, teeth are suit for victim identification of natural disaster, fire, bombing, etc. In this paper, we developed an automated human identification system based on dental radiographs. The system has two main stages, the first stage is to arrange a database consisting of labeled dental radiographs, and the second stage is the searching process in the database in order to retrieve the identification result. Both stages use a number of image processing techniques, classification methods, and a numbering system in order to generate dental radiograph’s features and patterns. Our experiments using 6 bitewing and 10 panoramic radiographs that consist of 119 tooth objects in total, has shown good performance of classification. The accuracy of dental pattern classification and dental numbering system are 91.6 % and 81.5% respectively.

  17. Hyperspectral hybrid method classification for detecting altered mucosa of the human larynx

    Directory of Open Access Journals (Sweden)

    Martin Ron

    2012-06-01

    Full Text Available Abstract Background In the field of earth observation, hyperspectral detector systems allow precise target detections of surface components from remote sensing platforms. This enables specific land covers to be identified without the need to physically travel to the areas examined. In the medical field, efforts are underway to develop optical technologies that detect altering tissue surfaces without the necessity to perform an excisional biopsy. With the establishment of expedient classification procedures, hyperspectral imaging may provide a non-invasive diagnostic method that allows determination of pathological tissue with high reliability. In this study, we examined the performance of a hyperspectral hybrid method classification for the automatic detection of altered mucosa of the human larynx. Materials and methods Hyperspectral Imaging was performed in vivo and 30 bands from 390 to 680 nm for 5 cases of laryngeal disorders (2x hemorrhagic polyp, 3x leukoplakia were obtained. Image stacks were processed with unsupervised clustering (linear spectral unmixing, spectral signatures were extracted from unlabeled cluster maps and subsequently applied as end-members for supervised classification (spectral angle mapper of further medical cases with identical diagnosis. Results Linear spectral unmixing clearly highlighted altered mucosa as single spectral clusters in all cases. Matching classes were identified, and extracted spectral signatures could readily be applied for supervised classifications. Automatic target detection performed well, as the considered classes showed notable correspondence with pathological tissue locations. Conclusions Using hyperspectral classification procedures derived from remote sensing applications for diagnostic purposes can create concrete benefits for the medical field. The approach shows that it would be rewarding to collect spectral signatures from histologically different lesions of laryngeal disorders in

  18. Alterations of 5-Hydroxymethylcytosine in Human Cancers

    Directory of Open Access Journals (Sweden)

    Ali Yesilkanal

    2013-06-01

    Full Text Available Prior to 2009, 5-methylcytosine (5-mC was thought to be the only biologically significant cytosine modification in mammalian DNA. With the discovery of the TET enzymes, which convert 5-methylcytosine (5-mC to 5-hydroxymethylcytosine (5-hmC, however, intense interest has emerged in determining the biological function of 5-hmC. Here, we review the techniques used to study 5-hmC and evidence that alterations to 5-hmC physiology play a functional role in the molecular pathogenesis of human cancers.

  19. Human papillomavirus-associated diseases and cancers

    Institute of Scientific and Technical Information of China (English)

    Lan Yang; Jianbo Zhu Co-first author; Xiaoyue Song; Yan Qi; Xiaobin Cui; Feng Li 

    2015-01-01

    Human papilomaviruses (HPVs) have been detected in cervical cancer cels and skin papiloma cels, which have a variety of types, including low-risk and high-risk types. HPV genome replication requires the host cel’s DNA synthesis machinery, and HPVs encode proteins that maintain diferentiated epithelial cels in a replication-competent state. HPV types are tissue-specific and generaly produce diferent types of le-sions, either benign or malignant. This review examines diferent HPV types and their associated diseases and presents therapeutic options for the treatment of HPV-positive diseases.

  20. Assessing global transitions in human development and colorectal cancer incidence.

    Science.gov (United States)

    Fidler, Miranda M; Bray, Freddie; Vaccarella, Salvatore; Soerjomataram, Isabelle

    2017-06-15

    Colorectal cancer incidence has paralleled increases in human development across most countries. Yet, marked decreases in incidence are now observed in countries that have attained very high human development. Thus, in this study, we explored the relationship between human development and colorectal cancer incidence, and in particular assessed whether national transitions to very high human development are linked to temporal patterns in colorectal cancer incidence. For these analyses, we utilized the Human Development Index (HDI) and annual incidence data from regional and national cancer registries. Truncated (30-74 years) age-standardized incidence rates were calculated. Yearly incidence rate ratios and HDI ratios, before and after transitioning to very high human development, were also estimated. Among the 29 countries investigated, colorectal cancer incidence was observed to decrease after reaching the very high human development threshold for 12 countries; decreases were also observed in a further five countries, but the age-standardized incidence rates remained higher than that observed at the threshold. Such declines or stabilizations are likely due to colorectal cancer screening in some populations, as well as varying levels of exposure to protective factors. In summary, it appears that there is a threshold at which human development predicts a stabilization or decline in colorectal cancer incidence, though this pattern was not observed for all countries assessed. Future cancer planning must consider the increasing colorectal cancer burden expected in countries transitioning towards higher levels of human development, as well as possible declines in incidence among countries reaching the highest development level. © 2017 UICC.

  1. The Significance of Molecular Classification in Breast Cancer for Prognosis%乳腺癌分子分型对乳腺癌预后的意义

    Institute of Scientific and Technical Information of China (English)

    王会东

    2013-01-01

    Objective To investigate the molecular classification and prognosis of breast cancer and the relationship between . Methods Retrospective analysis of 316 cases of female primary breast cancer patients with the clinical pathological ,average age 54.5 years.According to estrogen receptor (ER),pregnancy hormone receptor (PR) and human epidermal growth factor receptor 2(HER2) immu-nohistochemical findings,breast cancer types as type Luminal A ,type Luminal B,triple-negative and HER2 positive type,differences in mo-lecular classification of breast cancer prognosis were observed ,various types of patients with postoperative disease -free survival were com-pared.Results The patients were followed up for 5~124 months,a median follow-up time of 58 months,32 patients had recurrence of metas-tasis or death,the single factor analysis showed that the breast cancer disease free survival and molecular types had relation .Conclusion The molecular classification of breast cancer accurately reflects the prognosis of breast cancer .Type Luminal has the best prognosis ,while triple-negative has the worst prognosis .%  目的 探讨乳腺癌分子分型与预后之间的关系。方法 回顾性分析316例原发性乳腺癌患者的临床病理资料,患者均为女性,平均年龄54.5岁。根据雌激素受体(ER)、孕激素受体(PR)及人类表皮生长因子受体2(HER2)的免疫组织化学结果,乳腺癌分型为Luminal A型、Luminal B型、三阴型及 HER2阳性型,观察不同分子分型乳腺癌的预后,比较各型患者术后的无病生存期。结果 随访5~124个月,中位随访时间58个月,32例患者复发转移或死亡,单因素分析示乳腺癌无病生存期与分子分型有关。结论 乳腺癌的分子分型能够准确反映乳腺癌的预后,Luminal型预后最好,而三阴型预后最差。

  2. Self-organizing maps classification of epidemiological data and toenail selenium content monitored on cancer and healthy patients from Poland.

    Science.gov (United States)

    Tsakovski, Stefan L; Zukowska, Joanna; Bode, Peter; Bizuk, Marek K; Kowalczyk, Anna

    2010-01-01

    This paper deals with epidemiological multivariate statistical analysis of cancer and health patients from Pomeranian and Lubuskie Voivodships, Poland. The anthropometric and epidemiologic data include 8 parameters: toenail selenium concentration, sex, age, body mass index (BMI), smoking status, taking of Se supplements, health state, and family history of cancer. The self-organizing maps (SOM) are used for simultaneous classification of parameters and patients with relation to cancer diagnosis. Three different patterns (groups) of patients with cancer diagnosis are outlined: (i) older, smoking men with low toenail selenium concentration; (ii) older smoking women with family relation to cancer and toenail selenium deficiency; (iii) middle, aged nonsmokers with high level of selenium toenail concentration. The simultaneous classification of parameters and patients makes it possible to determine discriminating parameters for each pattern and relations between parameters. The relation of each parameter to cancer disease is discussed as special attention is paid to toenail selenium deficiency. More than 80% of patients with cancer diagnosis possess toenail selenium deficiency, accompanied by old age and smoking.

  3. Classification of samples into two or more ordered populations with application to a cancer trial.

    Science.gov (United States)

    Conde, D; Fernández, M A; Rueda, C; Salvador, B

    2012-12-10

    In many applications, especially in cancer treatment and diagnosis, investigators are interested in classifying patients into various diagnosis groups on the basis of molecular data such as gene expression or proteomic data. Often, some of the diagnosis groups are known to be related to higher or lower values of some of the predictors. The standard methods of classifying patients into various groups do not take into account the underlying order. This could potentially result in high misclassification rates, especially when the number of groups is larger than two. In this article, we develop classification procedures that exploit the underlying order among the mean values of the predictor variables and the diagnostic groups by using ideas from order-restricted inference. We generalize the existing methodology on discrimination under restrictions and provide empirical evidence to demonstrate that the proposed methodology improves over the existing unrestricted methodology. The proposed methodology is applied to a bladder cancer data set where the researchers are interested in classifying patients into various groups.

  4. Skin cancer detection by spectroscopic oblique-incidence reflectometry: classification and physiological origins.

    Science.gov (United States)

    Garcia-Uribe, Alejandro; Kehtarnavaz, Nasser; Marquez, Guillermo; Prieto, Victor; Duvic, Madeleine; Wang, Lihong V

    2004-05-01

    Data obtained from 102 skin lesions in vivo by spectroscopic oblique-incidence reflectometry were analyzed. The participating physicians initially divided the skin lesions into two visually distinguishable groups based on the lesions' melanocytic conditions. Group 1 consisted of the following two cancerous and benign subgroups: (1) basal cell carcinomas and squamous cell carcinomas and (2) benign actinic keratoses, seborrheic keratoses, and warts. Group 2 consisted of (1) dysplastic nevi and (2) benign common nevi. For each group, a bootstrap-based Bayes classifier was designed to separate the benign from the dysplastic or cancerous tissues. A genetic algorithm was then used to obtain the most effective combination of spatiospectral features for each classifier. The classifiers, tested with prospective blind studies, reached statistical accuracies of 100% and 95% for groups 1 and 2, respectively. Properties that related to cell-nuclear size, to the concentration of oxyhemoglobin, and to the concentration of deoxyhemoglobin as well as the derived concentration of total hemoglobin and oxygen saturation were defined to explain the origins of the classification outcomes.

  5. Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features.

    Science.gov (United States)

    Magdy, Eman; Zayed, Nourhan; Fakhr, Mahmoud

    2015-01-01

    Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients' lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM) method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR) for classification step. Finally, K-nearest neighbour (KNN), support vector machine (SVM), naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.

  6. Endogenous retroviral promoter exaptation in human cancer

    Directory of Open Access Journals (Sweden)

    Artem Babaian

    2016-12-01

    Full Text Available Abstract Cancer arises from a series of genetic and epigenetic changes, which result in abnormal expression or mutational activation of oncogenes, as well as suppression/inactivation of tumor suppressor genes. Aberrant expression of coding genes or long non-coding RNAs (lncRNAs with oncogenic properties can be caused by translocations, gene amplifications, point mutations or other less characterized mechanisms. One such mechanism is the inappropriate usage of normally dormant, tissue-restricted or cryptic enhancers or promoters that serve to drive oncogenic gene expression. Dispersed across the human genome, endogenous retroviruses (ERVs provide an enormous reservoir of autonomous gene regulatory modules, some of which have been co-opted by the host during evolution to play important roles in normal regulation of genes and gene networks. This review focuses on the “dark side” of such ERV regulatory capacity. Specifically, we discuss a growing number of examples of normally dormant or epigenetically repressed ERVs that have been harnessed to drive oncogenes in human cancer, a process we term onco-exaptation, and we propose potential mechanisms that may underlie this phenomenon.

  7. Modern criteria to establish human cancer etiology.

    Science.gov (United States)

    Carbone, Michele; Klein, George; Gruber, Jack; Wong, May

    2004-08-01

    The Cancer Etiology Branch of the National Cancer Institute hosted a workshop, "Validation of a causal relationship: criteria to establish etiology," to determine whether recent technological advances now make it possible to delineate improved or novel criteria for the rapid establishment for cancer causation. The workshop was held in Washington, D.C., December 11-12, 2003, and participants were among the international leaders in the fields of epidemiology, chemistry, biochemistry, microbiology, virology, environmental and chemical carcinogenesis, immunology, pathology, molecular pathology, genetics, oncology, and surgical oncology. There was a general consensus that the rapid identification of human carcinogens and their removal (when possible) or the establishment of specific preventive and therapeutic measures was the most desirable and effective way to have a rapid and positive impact in the fight against cancer. From a clinical perspective, it may be as important to target initiators, cocarcinogens and promoters, if by removing any one of them tumor growth can be prevented. Future studies should focus on interactions among and between different biological, chemical, and physical agents. Analyses of single agents can at times miss their carcinogenic potential when such agents are carcinogenic only in subgroups of individuals because of their genetic background, diet, exposure to other carcinogens, or microbial infection. Epidemiology, molecular pathology (including chemistry, biochemistry, molecular biology, molecular virology, molecular genetics, epigenetics, genomics, proteomics, and other molecular-based approaches), and animal and tissue culture experiments should all be seen as important integrating evidence in the determination of human carcinogenicity. Concerning the respective roles of epidemiology and molecular pathology, it was noted that epidemiology allows the determination of the overall effect of a given carcinogen in the human population (e

  8. Reverse phase protein array based tumor profiling identifies a biomarker signature for risk classification of hormone receptor-positive breast cancer

    Directory of Open Access Journals (Sweden)

    Johanna Sonntag

    2014-03-01

    Full Text Available A robust subclassification of luminal breast cancer, the most common molecular subtype of human breast cancer, is crucial for therapy decisions. While a part of patients is at higher risk of recurrence and requires chemo-endocrine treatment, the other part is at lower risk and also poorly responds to chemotherapeutic regimens. To approximate the risk of cancer recurrence, clinical guidelines recommend determining histologic grading and abundance of a cell proliferation marker in tumor specimens. However, this approach assigns an intermediate risk to a substantial number of patients and in addition suffers from a high interobserver variability. Therefore, the aim of our study was to identify a quantitative protein biomarker signature to facilitate risk classification. Reverse phase protein arrays (RPPA were used to obtain quantitative expression data for 128 breast cancer relevant proteins in a set of hormone receptor-positive tumors (n = 109. Proteomic data for the subset of histologic G1 (n = 14 and G3 (n = 22 samples were used for biomarker discovery serving as surrogates of low and high recurrence risk, respectively. A novel biomarker selection workflow based on combining three different classification methods identified caveolin-1, NDKA, RPS6, and Ki-67 as top candidates. NDKA, RPS6, and Ki-67 were expressed at elevated levels in high risk tumors whereas caveolin-1 was observed as downregulated. The identified biomarker signature was subsequently analyzed using an independent test set (AUC = 0.78. Further evaluation of the identified biomarker panel by Western blot and mRNA profiling confirmed the proteomic signature obtained by RPPA. In conclusion, the biomarker signature introduced supports RPPA as a tool for cancer biomarker discovery.

  9. Identifying training deficiencies in military pilots by applying the human factors analysis and classification system.

    Science.gov (United States)

    Li, Wen-Chin; Harris, Don

    2013-01-01

    Without accurate analysis, it is difficult to identify training needs and develop the content of training programs required for preventing aviation accidents. The human factors analysis and classification system (HFACS) is based on Reason's system-wide model of human error. In this study, 523 accidents from the Republic of China Air Force were analyzed in which 1762 human errors were categorized. The results of the analysis showed that errors of judgment and poor decision-making were commonly reported amongst pilots. As a result, it was concluded that there was a need for military pilots to be trained specifically in making decisions in tactical environments. However, application of HFACS also allowed the identification of systemic training deficiencies within the organization further contributing to the accidents observed.

  10. Novel round-robin tabu search algorithm for prostate cancer classification and diagnosis using multispectral imagery.

    Science.gov (United States)

    Tahir, Muhammad Atif; Bouridane, Ahmed

    2006-10-01

    Quantitative cell imagery in cancer pathology has progressed greatly in the last 25 years. The application areas are mainly those in which the diagnosis is still critically reliant upon the analysis of biopsy samples, which remains the only conclusive method for making an accurate diagnosis of the disease. Biopsies are usually analyzed by a trained pathologist who, by analyzing the biopsies under a microscope, assesses the normality or malignancy of the samples submitted. Different grades of malignancy correspond to different structural patterns as well as to apparent textures. In the case of prostate cancer, four major groups have to be recognized: stroma, benign prostatic hyperplasia, prostatic intraepithelial neoplasia, and prostatic carcinoma. Recently, multispectral imagery has been used to solve this multiclass problem. Unlike conventional RGB color space, multispectral images allow the acquisition of a large number of spectral bands within the visible spectrum, resulting in a large feature vector size. For such a high dimensionality, pattern recognition techniques suffer from the well-known "curse-of-dimensionality" problem. This paper proposes a novel round-robin tabu search (RR-TS) algorithm to address the curse-of-dimensionality for this multiclass problem. The experiments have been carried out on a number of prostate cancer textured multispectral images, and the results obtained have been assessed and compared with previously reported works. The system achieved 98%-100% classification accuracy when testing on two datasets. It outperformed principal component/linear discriminant classifier (PCA-LDA), tabu search/nearest neighbor classifier (TS-1NN), and bagging/boosting with decision tree (C4.5) classifier.

  11. Breast cancer surgery and diagnosis-related groups (DRGs): patient classification and hospital reimbursement in 11 European countries.

    Science.gov (United States)

    Scheller-Kreinsen, David; Quentin, Wilm; Geissler, Alexander; Busse, Reinhard

    2013-10-01

    Researchers from eleven countries (i.e. Austria, England, Estonia, Finland, France, Germany, Ireland, Netherlands, Poland, Spain, and Sweden) compared how their DRG systems deal with breast cancer surgery patients. DRG algorithms and indicators of resource consumption were assessed for those DRGs that individually contain at least 1% of all breast cancer surgery patients. Six standardised case vignettes were defined and quasi prices according to national DRG-based hospital payment systems were ascertained. European DRG systems classify breast cancer surgery patients according to different sets of classification variables into three to seven DRGs. Quasi prices for an index case treated with partial mastectomy range from €577 in Poland to €5780 in the Netherlands. Countries award their highest payments for very different kinds of patients. Breast cancer specialists and national DRG authorities should consider how other countries' DRG systems classify breast cancer patients in order to identify potential scope for improvement and to ensure fair and appropriate reimbursement.

  12. Modelling mutational landscapes of human cancers in vitro

    Science.gov (United States)

    Olivier, Magali; Weninger, Annette; Ardin, Maude; Huskova, Hana; Castells, Xavier; Vallée, Maxime P.; McKay, James; Nedelko, Tatiana; Muehlbauer, Karl-Rudolf; Marusawa, Hiroyuki; Alexander, John; Hazelwood, Lee; Byrnes, Graham; Hollstein, Monica; Zavadil, Jiri

    2014-03-01

    Experimental models that recapitulate mutational landscapes of human cancers are needed to decipher the rapidly expanding data on human somatic mutations. We demonstrate that mutation patterns in immortalised cell lines derived from primary murine embryonic fibroblasts (MEFs) exposed in vitro to carcinogens recapitulate key features of mutational signatures observed in human cancers. In experiments with several cancer-causing agents we obtained high genome-wide concordance between human tumour mutation data and in vitro data with respect to predominant substitution types, strand bias and sequence context. Moreover, we found signature mutations in well-studied human cancer driver genes. To explore endogenous mutagenesis, we used MEFs ectopically expressing activation-induced cytidine deaminase (AID) and observed an excess of AID signature mutations in immortalised cell lines compared to their non-transgenic counterparts. MEF immortalisation is thus a simple and powerful strategy for modelling cancer mutation landscapes that facilitates the interpretation of human tumour genome-wide sequencing data.

  13. Human-interpretable feature pattern classification system using learning classifier systems.

    Science.gov (United States)

    Ebadi, Toktam; Kukenys, Ignas; Browne, Will N; Zhang, Mengjie

    2014-01-01

    Image pattern classification is a challenging task due to the large search space of pixel data. Supervised and subsymbolic approaches have proven accurate in learning a problem's classes. However, in the complex image recognition domain, there is a need for investigation of learning techniques that allow humans to interpret the learned rules in order to gain an insight about the problem. Learning classifier systems (LCSs) are a machine learning technique that have been minimally explored for image classification. This work has developed the feature pattern classification system (FPCS) framework by adopting Haar-like features from the image recognition domain for feature extraction. The FPCS integrates Haar-like features with XCS, which is an accuracy-based LCS. A major contribution of this work is that the developed framework is capable of producing human-interpretable rules. The FPCS system achieved 91 [Formula: see text] 1% accuracy on the unseen test set of the MNIST dataset. In addition, the FPCS is capable of autonomously adjusting the rotation angle in unaligned images. This rotation adjustment raised the accuracy of FPCS to 95%. Although the performance is competitive with equivalent approaches, this was not as accurate as subsymbolic approaches on this dataset. However, the benefit of the interpretability of rules produced by FPCS enabled us to identify the distribution of the learned angles-a normal distribution around [Formula: see text]-which would have been very difficult in subsymbolic approaches. The analyzable nature of FPCS is anticipated to be beneficial in domains such as speed sign recognition, where underlying reasoning and confidence of recognition needs to be human interpretable.

  14. Distributed human intelligence for colonic polyp classification in computer-aided detection for CT colonography.

    Science.gov (United States)

    Nguyen, Tan B; Wang, Shijun; Anugu, Vishal; Rose, Natalie; McKenna, Matthew; Petrick, Nicholas; Burns, Joseph E; Summers, Ronald M

    2012-03-01

    To assess the diagnostic performance of distributed human intelligence for the classification of polyp candidates identified with computer-aided detection (CAD) for computed tomographic (CT) colonography. This study was approved by the institutional Office of Human Subjects Research. The requirement for informed consent was waived for this HIPAA-compliant study. CT images from 24 patients, each with at least one polyp of 6 mm or larger, were analyzed by using CAD software to identify 268 polyp candidates. Twenty knowledge workers (KWs) from a crowdsourcing platform labeled each polyp candidate as a true or false polyp. Two trials involving 228 KWs were conducted to assess reproducibility. Performance was assessed by comparing the area under the receiver operating characteristic curve (AUC) of KWs with the AUC of CAD for polyp classification. The detection-level AUC for KWs was 0.845 ± 0.045 (standard error) in trial 1 and 0.855 ± 0.044 in trial 2. These were not significantly different from the AUC for CAD, which was 0.859 ± 0.043. When polyp candidates were stratified by difficulty, KWs performed better than CAD on easy detections; AUCs were 0.951 ± 0.032 in trial 1, 0.966 ± 0.027 in trial 2, and 0.877 ± 0.048 for CAD (P = .039 for trial 2). KWs who participated in both trials showed a significant improvement in performance going from trial 1 to trial 2; AUCs were 0.759 ± 0.052 in trial 1 and 0.839 ± 0.046 in trial 2 (P = .041). The performance of distributed human intelligence is not significantly different from that of CAD for colonic polyp classification. © RSNA.

  15. Using convolutional neural networks for human activity classification on micro-Doppler radar spectrograms

    Science.gov (United States)

    Jordan, Tyler S.

    2016-05-01

    This paper presents the findings of using convolutional neural networks (CNNs) to classify human activity from micro-Doppler features. An emphasis on activities involving potential security threats such as holding a gun are explored. An automotive 24 GHz radar on chip was used to collect the data and a CNN (normally applied to image classification) was trained on the resulting spectrograms. The CNN achieves an error rate of 1.65 % on classifying running vs. walking, 17.3 % error on armed walking vs. unarmed walking, and 22 % on classifying six different actions.

  16. Predicting decisions in human social interactions using real-time fMRI and pattern classification.

    Directory of Open Access Journals (Sweden)

    Maurice Hollmann

    Full Text Available Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives.

  17. Proposal of new classification for stage III colon cancer based on the lymph node ratio: analysis of 4,172 patients from multi-institutional database in Japan.

    Science.gov (United States)

    Sugimoto, Kiichi; Sakamoto, Kazuhiro; Tomiki, Yuichi; Goto, Michitoshi; Kotake, Kenjiro; Sugihara, Kenichi

    2015-02-01

    We retrospectively examined the optimal lymph node ratio (LNR) cutoff value and attempted to construct a new classification using the LNR in stage III colon cancer. The clinical and pathological data of 4,172 patients with histologically proven lymph node metastasis who underwent curative surgery for primary colon cancer at multiple institutions between 1995 and 2004 were derived from the multi-institutional database of the Japanese Society for Cancer of the Colon and Rectum (JSCCR). We determined independent prognostic factors and constructed a new classification using these factors. Finally, we compared the discriminatory ability between the new classification and the TNM seventh edition (TNM 7th) classification. The optimal LNR cutoff value was 0.18. Multivariate analysis revealed that year of surgery, age, gender, histological type, TNM 7th T category, lymphatic invasion, venous invasion, TNM 7th N category, and LNR were found to be significant independent prognostic factors. We attempted to construct a new classification based on the combination of TNM 7th T category and LNR. As a result, the cancer-specific survivals were well stratified (P TNM 7th classification with respect to both a better fit and lower complexity. The optimal LNR cutoff value that was found using the Japanese multi-institutional database and the new classification using LNR are considered to be extremely significant. Therefore, these findings strongly support the application of LNR in the stage classification in stage III colon cancer.

  18. Full Intelligent Cancer Classification of Thermal Breast Images to Assist Physician in Clinical Diagnostic Applications.

    Science.gov (United States)

    Lashkari, AmirEhsan; Pak, Fatemeh; Firouzmand, Mohammad

    2016-01-01

    Breast cancer is the most common type of cancer among women. The important key to treat the breast cancer is early detection of it because according to many pathological studies more than 75% - 80% of all abnormalities are still benign at primary stages; so in recent years, many studies and extensive research done to early detection of breast cancer with higher precision and accuracy. Infra-red breast thermography is an imaging technique based on recording temperature distribution patterns of breast tissue. Compared with breast mammography technique, thermography is more suitable technique because it is noninvasive, non-contact, passive and free ionizing radiation. In this paper, a full automatic high accuracy technique for classification of suspicious areas in thermogram images with the aim of assisting physicians in early detection of breast cancer has been presented. Proposed algorithm consists of four main steps: pre-processing & segmentation, feature extraction, feature selection and classification. At the first step, using full automatic operation, region of interest (ROI) determined and the quality of image improved. Using thresholding and edge detection techniques, both right and left breasts separated from each other. Then relative suspected areas become segmented and image matrix normalized due to the uniqueness of each person's body temperature. At feature extraction stage, 23 features, including statistical, morphological, frequency domain, histogram and Gray Level Co-occurrence Matrix (GLCM) based features are extracted from segmented right and left breast obtained from step 1. To achieve the best features, feature selection methods such as minimum Redundancy and Maximum Relevance (mRMR), Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Floating Forward Selection (SFFS), Sequential Floating Backward Selection (SFBS) and Genetic Algorithm (GA) have been used at step 3. Finally to classify and TH labeling procedures

  19. Classification of weakly carcinogenic human papillomavirus types: addressing the limits of epidemiology at the borderline

    Directory of Open Access Journals (Sweden)

    Buonaguro Franco M

    2009-06-01

    Full Text Available Abstract Virtually all cases of cervical cancer are caused by persistent infections with a restricted set of human papillomaviruses (HPV. Some HPV types, like HPV16 and HPV18, are clear and powerful carcinogens. However, the categorization of the most weakly carcinogenic HPV types is extremely challenging. The decisions are important for screening test and vaccine development. This article describes for open discussion an approach recently taken by a World Health Organization International Agency for Research on Cancer (IARC Monographs Working Group to re-assess the carcinogenicity of different HPV types.

  20. Linear classifier and textural analysis of optical scattering images for tumor classification during breast cancer extraction

    Science.gov (United States)

    Eguizabal, Alma; Laughney, Ashley M.; Garcia Allende, Pilar Beatriz; Krishnaswamy, Venkataramanan; Wells, Wendy A.; Paulsen, Keith D.; Pogue, Brian W.; López-Higuera, José M.; Conde, Olga M.

    2013-02-01

    Texture analysis of light scattering in tissue is proposed to obtain diagnostic information from breast cancer specimens. Light scattering measurements are minimally invasive, and allow the estimation of tissue morphology to guide the surgeon in resection surgeries. The usability of scatter signatures acquired with a micro-sampling reflectance spectral imaging system was improved utilizing an empirical approximation to the Mie theory to estimate the scattering power on a per-pixel basis. Co-occurrence analysis is then applied to the scattering power images to extract the textural features. A statistical analysis of the features demonstrated the suitability of the autocorrelation for the classification of notmalignant (normal epithelia and stroma, benign epithelia and stroma, inflammation), malignant (DCIS, IDC, ILC) and adipose tissue, since it reveals morphological information of tissue. Non-malignant tissue shows higher autocorrelation values while adipose tissue presents a very low autocorrelation on its scatter texture, being malignant the middle ground. Consequently, a fast linear classifier based on the consideration of just one straightforward feature is enough for providing relevant diagnostic information. A leave-one-out validation of the linear classifier on 29 samples with 48 regions of interest showed classification accuracies of 98.74% on adipose tissue, 82.67% on non-malignant tissue and 72.37% on malignant tissue, in comparison with the biopsy H and E gold standard. This demonstrates that autocorrelation analysis of scatter signatures is a very computationally efficient and automated approach to provide pathological information in real-time to guide surgeon during tissue resection.

  1. Laser Raman detection for oral cancer based on a Gaussian process classification method

    Science.gov (United States)

    Du, Zhanwei; Yang, Yongjian; Bai, Yuan; Wang, Lijun; Zhang, Chijun; Chen, He; Luo, Yusheng; Su, Le; Chen, Yong; Li, Xianchang; Zhou, Xiaodong; Jia, Jun; Shen, Aiguo; Hu, Jiming

    2013-06-01

    Oral squamous cell carcinoma is the most common neoplasm of the oral cavity. The incidence rate accounts for 80% of total oral cancer and shows an upward trend in recent years. It has a high degree of malignancy and is difficult to detect in terms of differential diagnosis, as a consequence of which the timing of treatment is always delayed. In this work, Raman spectroscopy was adopted to differentially diagnose oral squamous cell carcinoma and oral gland carcinoma. In total, 852 entries of raw spectral data which consisted of 631 items from 36 oral squamous cell carcinoma patients, 87 items from four oral gland carcinoma patients and 134 items from five normal people were collected by utilizing an optical method on oral tissues. The probability distribution of the datasets corresponding to the spectral peaks of the oral squamous cell carcinoma tissue was analyzed and the experimental result showed that the data obeyed a normal distribution. Moreover, the distribution characteristic of the noise was also in compliance with a Gaussian distribution. A Gaussian process (GP) classification method was utilized to distinguish the normal people and the oral gland carcinoma patients from the oral squamous cell carcinoma patients. The experimental results showed that all the normal people could be recognized. 83.33% of the oral squamous cell carcinoma patients could be correctly diagnosed and the remaining ones would be diagnosed as having oral gland carcinoma. For the classification process of oral gland carcinoma and oral squamous cell carcinoma, the correct ratio was 66.67% and the erroneously diagnosed percentage was 33.33%. The total sensitivity was 80% and the specificity was 100% with the Matthews correlation coefficient (MCC) set to 0.447 213 595. Considering the numerical results above, the application prospects and clinical value of this technique are significantly impressive.

  2. Apoptosis of human pancreatic cancer cells induced by Triptolide

    Institute of Scientific and Technical Information of China (English)

    Guo-Xiong Zhou; Xiao-Ling Ding; Jie-Fei Huang; Hong Zhang; Sheng-Bao Wu; Jian-Ping Cheng; Qun Wei

    2008-01-01

    AIM:To investigate apoptosis in human pancreatic cancer ceils induced by Triptolide (TL),and the relationship between this apoptosis and expression of caspase-3' bcl-2 and bax.METHODS:Human pancreatic cancer cell line SW1990 was cultured in DIEM media for this study.MTT assay was used to determine the cell growth inhibitory rate in vitro.Flow cytometry and TUNEL assay were used to detect the apoptosis of human pancreatic cancer cells before and after TL treatment.RT-PCR was used to detect the expression of apoptosis-associated gene caspase-3' bcl-2 and bax.RESULTS:TL inhibited the growth of human pancreatic cancer cells in a dose-and time-dependent manner.TL induced human pancreatic cancer cells to undergo apoptosis with typically apoptotic characteristics.TUNEL assay showed that after the treatment of human pancreatic cancer cells with 40 ng/mL TL for 12 h and 24 h,the apoptotic rates of human pancreatic cancer cells increased significantly.RT-PCR demonstrated that caspase-3 and bax were significantly up-regulated in SW1990 cells treated with TL while bcl-2 mRNA was not.CONCLUSION:TL is able to induce the apoptosis in human pancreatic cancer cells.This apoptosis may be mediated by up-regulating the expression of apoptosisassociated caspase-3 and bax gene.

  3. The Isolation and Characterization of Human Prostate Cancer Stem Cells

    Science.gov (United States)

    2015-05-01

    IGF1, SOX15, BMPR1B, TGFBR1, etc), which fall into distinct GO categories including SC, development, stress response, and wound healing (unpublished...prostate cancer through the elucidation of the role of cancer stem cells in the pathogenesis of the disease. During the past year, we have made the...studies, ii) in vitro co-culture of human prostate cancer cells (established cell lines and primary patient samples) with human prostate fibroblasts

  4. North American Magazine Coverage of Skin Cancer and Recreational Tanning Before and After the WHO/IARC 2009 Classification of Indoor Tanning Devices as Carcinogenic.

    Science.gov (United States)

    McWhirter, Jennifer E; Hoffman-Goetz, Laurie

    2015-09-01

    The mass media is an influential source of skin cancer information for the public. In 2009, the World Health Organization's International Agency for Research on Cancer classified UV radiation from tanning devices as carcinogenic. Our objective was to determine if media coverage of skin cancer and recreational tanning increased in volume or changed in nature after this classification. We conducted a directed content analysis on 29 North American popular magazines (2007-2012) to investigate the overall volume of articles on skin cancer and recreational tanning and, more specifically, the presence of skin cancer risk factors, UV behaviors, and early detection information in article text (n = 410) and images (n = 714). The volume of coverage on skin cancer and recreational tanning did not increase significantly after the 2009 classification of tanning beds as carcinogenic. Key-related messages, including that UV exposure is a risk factor for skin cancer and that indoor tanning should be avoided, were not reported more frequently after the classification, but the promotion of the tanned look as attractive was conveyed more often in images afterwards (p after the classification (p skin cancer risk factors, other UV behaviors, or early detection information over time. The classification of indoor tanning beds as carcinogenic had no significant impact on the volume or nature of skin cancer and recreational tanning coverage in magazines.

  5. CD147 expression in human gastric cancer is associated with tumor recurrence and prognosis.

    Directory of Open Access Journals (Sweden)

    Dake Chu

    Full Text Available CD147 is correlated with tumor aggressiveness in various human malignancies. Here, we investigated CD147 protein expression in 223 patients with gastric cancer by immunohistochemistry and analyzed its association with disease-free and overall survival. CD147 was increased in gastric cancer compared to normal tissues. Additionally, CD147 expression was associated with gastric cancer invasion, metastasis and TNM stage, whereas it was not related to age, sex, differentiation status, tumor site or Lauren classification. Kaplan-Meier analysis confirmed that CD147 was associated with disease-free and overall survival in patients with gastric cancer; i.e., patients with positive CD147 staining tend to have worse disease-free and overall survival. Moreover, Cox's proportional hazards analysis demonstrated that CD147 was an independent marker of disease-free and overall survival for patients with gastric cancer. These results confirm the association of CD147 with gastric cancer invasion and metastasis and prove that CD147 might be an indicator of tumor recurrence and prognosis in gastric cancer.

  6. Establishment of a drug sensitivity panel using human lung cancer cell lines.

    Directory of Open Access Journals (Sweden)

    Matsushita A

    1999-04-01

    Full Text Available We established a drug sensitivity panel consisting of 24 human lung cancer cell lines. Using this panel, we evaluated 26 anti-cancer agents: three alkylators, three platinum compounds, four antimetabolites, one topoisomerase I inhibitor, five topoisomerase II inhibitors, seven antimitotic agents and three tyrosine kinase inhibitors. This panel showed the following: a Drug sensitivity patterns reflected their clinically-established patterns of action. For example, doxorubicin and etoposide were shown to be active against small cell lung cancer cell lines and mitomycin-C and 5-fluorouracil were active against non-small cell lung cancer cell lines, in agreement with clinical data. b Correlation analysis of the mean graphs derived from the logarithm of IC50 values of the drugs gave insight into the mechanism of each drug's action. Thus, two drug combinations with reverse or no correlation, such as the combination of cisplatin and vinorelbine, might be good candidates for the ideal two drug combination in the treatment of lung cancer, as is being confirmed in clinical trials. c Using cluster analysis of the cell lines in the panel with their drug sensitivity patterns, we could classify the cell lines into four groups depending on the drug sensitivity similarity. This classification will be useful to elucidate the cellular mechanism of action and drug resistance. Thus, our drug sensitivity panel will be helpful to explore new drugs or to develop a new combination of anti-cancer agents for the treatment of lung cancer.

  7. Human papillomavirus genotype prevalence in invasive penile cancers from a registry-based United States population

    Directory of Open Access Journals (Sweden)

    Brenda Y Hernandez

    2014-02-01

    Full Text Available Background. Human papillomavirus (HPV is estimated to play an etiologic role in 40%-50% of penile cancers worldwide. Estimates of HPV prevalence in U.S. penile cancer cases are limited. Methods. HPV DNA was evaluated in tumor tissue from 79 invasive penile cancer patients diagnosed in 1998-2005 within the catchment areas of 7 U.S. cancer registries. HPV was genotyped using PCR-based Linear Array and INNO-LiPA assays and compared by demographic, clinical, and pathologic characteristics and survival. Histological classification was also obtained by independent pathology review. Results. HPV DNA was present in 50 of 79 (63% of invasive penile cancer cases. Sixteen viral genotypes were detected. HPV 16, found in 46% (36/79 of all cases (72% of HPV-positive cases was the most prevalent genotype followed equally by HPV 18, 33, and 45, which each comprised 5% of all cases. Multiple genotypes were detected in 18% of viral positive cases. HPV prevalence did not significantly vary by age, race/ethnicity, population size of geographic region, cancer stage, histology, grade, penile subsite, or prior cancer history. Penile cases diagnosed in more recent years were more likely to be HPV positive. Overall survival did not significantly vary by HPV status. Conclusions. The relatively high prevalence of HPV in our study population provides limited evidence of a more prominent and, possibly, increasing role of infection in penile carcinogenesis in the U.S. compared to other parts of the world.

  8. CD147 expression in human gastric cancer is associated with tumor recurrence and prognosis.

    Science.gov (United States)

    Chu, Dake; Zhu, Shaojun; Li, Jipeng; Ji, Gang; Wang, Weizhong; Wu, Guosheng; Zheng, Jianyong

    2014-01-01

    CD147 is correlated with tumor aggressiveness in various human malignancies. Here, we investigated CD147 protein expression in 223 patients with gastric cancer by immunohistochemistry and analyzed its association with disease-free and overall survival. CD147 was increased in gastric cancer compared to normal tissues. Additionally, CD147 expression was associated with gastric cancer invasion, metastasis and TNM stage, whereas it was not related to age, sex, differentiation status, tumor site or Lauren classification. Kaplan-Meier analysis confirmed that CD147 was associated with disease-free and overall survival in patients with gastric cancer; i.e., patients with positive CD147 staining tend to have worse disease-free and overall survival. Moreover, Cox's proportional hazards analysis demonstrated that CD147 was an independent marker of disease-free and overall survival for patients with gastric cancer. These results confirm the association of CD147 with gastric cancer invasion and metastasis and prove that CD147 might be an indicator of tumor recurrence and prognosis in gastric cancer.

  9. Discrete Wavelet Transform Based Classification of Human Emotions Using Electroencephalogram Signals

    Directory of Open Access Journals (Sweden)

    Mohamed Rizon

    2010-01-01

    Full Text Available Problem statement: The aim of this study was to report the human emotion assessment using Electroencephalogram (EEG. Approach: An audio-visual induction based protocol was designed for inducing five different emotions (happy, surprise, fear, disgust and neutral on 20 subjects in the age group of 19~39 years. EEG signals are recorded from 64 channels placed over entire scalp according to International 10-10 system. We firstly applied Spatial Filtering technique to remove the noises and artifacts from the EEG signals. Three wavelet functions ("db8", "sym8" and "coif5" were used to decompose the EEG signal into five different frequency bands namely: delta, theta, alpha, beta and gamma. A set of new statistical features related to energy were extracted from the EEG frequency bands to construct the feature vector for classifying the emotions. Two simple linear classifiers (K Nearest Neighbor (KNN and Linear Discriminant Analysis (LDA were used for mapping the feature vector into corresponding emotions. Furthermore, we compared the efficacy of emotion classification with a reduced set of channels (24 channels for evaluating the reliability of the emotion recognition system. Results: In this study, 62 channels outperform 24 channels by giving the maximum average classification accuracy of 79.65% using KNN and 78.52% using LDA. Conclusion: In this study we presented an approach to discrete emotion recognition based on the processing of EEG signals. The preliminary results resented in this study address the classifiability of human emotions using original and reduced set of EEG channels. The results presented in this study indicated that, statistical features extracted from time-frequency analysis (wavelet transform works well in the context of discrete emotion classification.

  10. Automated adipose study for assessing cancerous human breast tissue using optical coherence tomography (Conference Presentation)

    Science.gov (United States)

    Gan, Yu; Yao, Xinwen; Chang, Ernest W.; Bin Amir, Syed A.; Hibshoosh, Hanina; Feldman, Sheldon; Hendon, Christine P.

    2017-02-01

    Breast cancer is the third leading cause of death in women in the United States. In human breast tissue, adipose cells are infiltrated or replaced by cancer cells during the development of breast tumor. Therefore, an adipose map can be an indicator of identifying cancerous region. We developed an automated classification method to generate adipose map within human breast. To facilitate the automated classification, we first mask the B-scans from OCT volumes by comparing the signal noise ratio with a threshold. Then, the image was divided into multiple blocks with a size of 30 pixels by 30 pixels. In each block, we extracted texture features such as local standard deviation, entropy, homogeneity, and coarseness. The features of each block were input to a probabilistic model, relevance vector machine (RVM), which was trained prior to the experiment, to classify tissue types. For each block within the B-scan, RVM identified the region with adipose tissue. We calculated the adipose ratio as the number of blocks identified as adipose over the total number of blocks within the B-scan. We obtained OCT images from patients (n = 19) in Columbia medical center. We automatically generated the adipose maps from 24 B-scans including normal samples (n = 16) and cancerous samples (n = 8). We found the adipose regions show an isolated pattern that in cancerous tissue while a clustered pattern in normal tissue. Moreover, the adipose ratio (52.30 ± 29.42%) in normal tissue was higher than the that in cancerous tissue (12.41 ± 10.07%).

  11. Bacterial protein toxins in human cancers.

    Science.gov (United States)

    Rosadi, Francesca; Fiorentini, Carla; Fabbri, Alessia

    2016-02-01

    Many bacteria causing persistent infections produce toxins whose mechanisms of action indicate that they could have a role in carcinogenesis. Some toxins, like CDT and colibactin, directly attack the genome by damaging DNA whereas others, as for example CNF1, CagA and BFT, impinge on key eukaryotic processes, such as cellular signalling and cell death. These bacterial toxins, together with other less known toxins, mimic carcinogens and tumour promoters. The aim of this review is to fulfil an up-to-date analysis of toxins with carcinogenic potential that have been already correlated to human cancers. Bacterial toxins-induced carcinogenesis represents an emerging aspect in bacteriology, and its significance is increasingly recognized.

  12. Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

    Directory of Open Access Journals (Sweden)

    M. Sreeshakthy

    2016-01-01

    Full Text Available Department of Computer Science and Engineering,Anna University Regional Centre, Coimbatore, Indiam.sribtechit@gmail.comJ. PreethiDepartment of Computer Science and EngineeringAnna University Regional Centre, Coimbatore, Indiapreethi17j@yahoo.comEmotions are very important in human decision handling, interaction and cognitive process. In this paper describes that recognize the human emotions from DEAP EEG dataset with different kind of methods. Audio – video based stimuli is used to extract the emotions. EEG signal is divided into different bands using discrete wavelet transformation with db8 wavelet function for further process. Statistical and energy based features are extracted from the bands, based on the features emotions are classified with feed forward neural network with weight optimized algorithm like PSO. Before that the particular band has to be selected based on the training performance of neural networks and then the emotions are classified. In this experimental result describes that the gamma and alpha bands are provides the accurate classification result with average classification rate of 90.3% of using NNRBF, 90.325% of using PNN, 96.3% of using PSO trained NN, 98.1 of using Cuckoo trained NN. At last the emotions are classified into two different groups like valence and arousal. Based on that identifies the person normal and abnormal behavioral using classified emotion.

  13. Disability, human rights, and the International Classification of Functioning, Disability, and Health: systematic review.

    Science.gov (United States)

    Aluas, Maria; Colombetti, Elena; Osimani, Barbara; Musio, Alessio; Pessina, Adriano

    2012-02-01

    This literature review focuses on the literature on disability from the ethical and human rights perspective in the light of the International Classification of Functioning, Disability, and Health in the period from January 1, 2008, to June 30, 2010. This article identifies and examines studies that deal with the subject of disability with reference to rights, ethical issues, and justice. A total of 42 articles and 33 books were selected. The subject most frequently dealt with in studies on disability is that of human rights (76% of the articles and 79% of the books examined), followed by topics relating to welfare (52% of articles and 64% of books), International Classification of Functioning, Disability, and Health (38% of articles and 45% of books), justice (24% of articles and 48% of books), education (21% of articles and 61% of books), and work (19% of articles and 39% of books). The subject of disability is dealt with in various fields of study and various disciplines. Most of the studies are based on the legal approach. It is to be hoped that there will be an increase in the philosophical and ethical study of disability, which has only recently entered the European debate.

  14. Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

    Directory of Open Access Journals (Sweden)

    M. Sreeshakthy

    2016-01-01

    Full Text Available Department of Computer Science and Engineering,Anna University Regional Centre, Coimbatore, Indiam.sribtechit@gmail.comJ. PreethiDepartment of Computer Science and EngineeringAnna University Regional Centre, Coimbatore, Indiapreethi17j@yahoo.comEmotions are very important in human decision handling, interaction and cognitive process. In this paper describes that recognize the human emotions from DEAP EEG dataset with different kind of methods. Audio – video based stimuli is used to extract the emotions. EEG signal is divided into different bands using discrete wavelet transformation with db8 wavelet function for further process. Statistical and energy based features are extracted from the bands, based on the features emotions are classified with feed forward neural network with weight optimized algorithm like PSO. Before that the particular band has to be selected based on the training performance of neural networks and then the emotions are classified. In this experimental result describes that the gamma and alpha bands are provides the accurate classification result with average classification rate of 90.3% of using NNRBF, 90.325% of using PNN, 96.3% of using PSO trained NN, 98.1 of using Cuckoo trained NN. At last the emotions are classified into two different groups like valence and arousal. Based on that identifies the person normal and abnormal behavioral using classified emotion.

  15. An image-based approach for classification of human micro-doppler radar signatures

    Science.gov (United States)

    Tivive, Fok Hing Chi; Phung, Son Lam; Bouzerdoum, Abdesselam

    2013-05-01

    With the advances in radar technology, there is an increasing interest in automatic radar-based human gait identification. This is because radar signals can penetrate through most dielectric materials. In this paper, an image-based approach is proposed for classifying human micro-Doppler radar signatures. The time-varying radar signal is first converted into a time-frequency representation, which is then cast as a two-dimensional image. A descriptor is developed to extract micro-Doppler features from local time-frequency patches centered along the torso Doppler frequency. Experimental results based on real data collected from a 24-GHz Doppler radar showed that the proposed approach achieves promising classification performance.

  16. Human action classification using adaptive key frame interval for feature extraction

    Science.gov (United States)

    Lertniphonphan, Kanokphan; Aramvith, Supavadee; Chalidabhongse, Thanarat H.

    2016-01-01

    Human action classification based on the adaptive key frame interval (AKFI) feature extraction is presented. Since human movement periods are different, the action intervals that contain the intensive and compact motion information are considered in this work. We specify AKFI by analyzing an amount of motion through time. The key frame is defined to be the local minimum interframe motion, which is computed by using frame differencing between consecutive frames. Once key frames are detected, the features within a segmented period are encoded by adaptive motion history image and key pose history image. The action representation consists of the local orientation histogram of the features during AKFI. The experimental results on Weizmann dataset, KTH dataset, and UT Interaction dataset demonstrate that the features can effectively classify action and can classify irregular cases of walking compared to other well-known algorithms.

  17. A novel thyroid cancer nodal map classification system to facilitate nodal localization and surgical management: The A to D map.

    Science.gov (United States)

    Cunnane, Marybeth; Kyriazidis, Natalia; Kamani, Dipti; Juliano, Amy F; Kelly, Hillary R; Curtin, Hugh D; Barber, Samuel R; Randolph, Gregory W

    2016-11-30

    To evaluate the effectiveness, reproducibility, and usability of our proposed nodal nomenclature and classification system employed for several years in our high-volume thyroid cancer unit, for the adequate localization and mapping of lymph nodes in thyroid cancer patients with extensive nodal disease. Retrospective review. Thirty-three thyroid cancer patients with extensive nodal disease treated from January 2004 to May 2013 were included in our study. Preoperative ultrasound and computed tomography scans of these patients were reanalyzed by blinded radiologists to investigate the feasibility for the assignment of abnormal lymph nodes to compartments defined in our proposed nodal classification system and to identify areas of difficulty in the assignment. Analysis of nodal localization revealed a discrepancy in compartment agreement between the two radiologists in the assignment of abnormal nodes in nine patients (9/33, 27%). In six patients (6/33, 18%), discrepancy existed in labeling paratracheal and pretracheal nodes. In three patients (3/33, 9%), disagreement arose in the classification of retrocarotid nodes into lateral versus central compartment. A further refinement of the definition of key borderline regions of the pretracheal versus paratracheal and retrocarotid regions of our classification improved the agreement and demonstrated a complete concordance (100%) amongst the reviewing radiologists. The proposed nodal classification system, derived specifically for differentiated thyroid carcinoma, with readily identifiable anatomic boundaries on imaging and at surgery, facilitates communication among multidisciplinary physicians and aids in creating a uniform and reproducible radiographic nodal map to guide surgical therapy. 4 Laryngoscope, 2016. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

  18. Transduction motif analysis of gastric cancer based on a human signaling network

    Energy Technology Data Exchange (ETDEWEB)

    Liu, G.; Li, D.Z.; Jiang, C.S.; Wang, W. [Fuzhou General Hospital of Nanjing Command, Department of Gastroenterology, Fuzhou, China, Department of Gastroenterology, Fuzhou General Hospital of Nanjing Command, Fuzhou (China)

    2014-04-04

    To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR, MAPK14, BCL2L1, KRT18, PTPN6, CASP3, TGFBR2, AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

  19. Transduction motif analysis of gastric cancer based on a human signaling network

    Directory of Open Access Journals (Sweden)

    G. Liu

    2014-05-01

    Full Text Available To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR, MAPK14, BCL2L1, KRT18, PTPN6, CASP3, TGFBR2, AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

  20. Cell nuclei attributed relational graphs for efficient representation and classification of gastric cancer in digital histopathology

    Science.gov (United States)

    Sharma, Harshita; Zerbe, Norman; Heim, Daniel; Wienert, Stephan; Lohmann, Sebastian; Hellwich, Olaf; Hufnagl, Peter

    2016-03-01

    This paper describes a novel graph-based method for efficient representation and subsequent classification in histological whole slide images of gastric cancer. Her2/neu immunohistochemically stained and haematoxylin and eosin stained histological sections of gastric carcinoma are digitized. Immunohistochemical staining is used in practice by pathologists to determine extent of malignancy, however, it is laborious to visually discriminate the corresponding malignancy levels in the more commonly used haematoxylin and eosin stain, and this study attempts to solve this problem using a computer-based method. Cell nuclei are first isolated at high magnification using an automatic cell nuclei segmentation strategy, followed by construction of cell nuclei attributed relational graphs of the tissue regions. These graphs represent tissue architecture comprehensively, as they contain information about cell nuclei morphology as vertex attributes, along with knowledge of neighborhood in the form of edge linking and edge attributes. Global graph characteristics are derived and ensemble learning is used to discriminate between three types of malignancy levels, namely, non-tumor, Her2/neu positive tumor and Her2/neu negative tumor. Performance is compared with state of the art methods including four texture feature groups (Haralick, Gabor, Local Binary Patterns and Varma Zisserman features), color and intensity features, and Voronoi diagram and Delaunay triangulation. Texture, color and intensity information is also combined with graph-based knowledge, followed by correlation analysis. Quantitative assessment is performed using two cross validation strategies. On investigating the experimental results, it can be concluded that the proposed method provides a promising way for computer-based analysis of histopathological images of gastric cancer.

  1. Characterizing metabolic changes in human colorectal cancer.

    Science.gov (United States)

    Williams, Michael D; Zhang, Xing; Park, Jeong-Jin; Siems, William F; Gang, David R; Resar, Linda M S; Reeves, Raymond; Hill, Herbert H

    2015-06-01

    Colorectal cancer (CRC) remains a leading cause of cancer death worldwide, despite the fact that it is a curable disease when diagnosed early. The development of new screening methods to aid in early diagnosis or identify precursor lesions at risk for progressing to CRC will be vital to improving the survival rate of individuals predisposed to CRC. Metabolomics is an advancing area that has recently seen numerous applications to the field of cancer research. Altered metabolism has been studied for many years as a means to understand and characterize cancer. However, further work is required to establish standard procedures and improve our ability to identify distinct metabolomic profiles that can be used to diagnose CRC or predict disease progression. The present study demonstrates the use of direct infusion traveling wave ion mobility mass spectrometry to distinguish metabolic profiles from CRC samples and matched non-neoplastic epithelium as well as metastatic and primary tumors at different stages of disease (T1-T4). By directly infusing our samples, the analysis time was reduced significantly, thus increasing the speed and efficiency of this method compared to traditional metabolomics platforms. Partial least squares discriminant analysis was used to visualize differences between the metabolic profiles of sample types and to identify the specific m/z features that led to this differentiation. Identification of the distinct m/z features was made using the human metabolome database. We discovered alterations in fatty acid biosynthesis and oxidative, glycolytic, and polyamine pathways that distinguish tumors from non-malignant colonic epithelium as well as various stages of CRC. Although further studies are needed, our results indicate that colonic epithelial cells undergo metabolic reprogramming during their evolution to CRC, and the distinct metabolites could serve as diagnostic tools or potential targets in therapy or primary prevention. Graphical Abstract

  2. The IASLC Lung Cancer Staging Project: Summary of Proposals for Revisions of the Classification of Lung Cancers with Multiple Pulmonary Sites of Involvement in the Forthcoming Eighth Edition of the TNM Classification.

    Science.gov (United States)

    Detterbeck, Frank C; Nicholson, Andrew G; Franklin, Wilbur A; Marom, Edith M; Travis, William D; Girard, Nicolas; Arenberg, Douglas A; Bolejack, Vanessa; Donington, Jessica S; Mazzone, Peter J; Tanoue, Lynn T; Rusch, Valerie W; Crowley, John; Asamura, Hisao; Rami-Porta, Ramón

    2016-05-01

    Patients with lung cancer who harbor multiple pulmonary sites of disease have been challenging to classify; a subcommittee of the International Association for the Study of Lung Cancer Staging and Prognostic Factors Committee was charged with developing proposals for the eighth edition of the tumor, node, and metastasis (TNM) classification to address this issue. A systematic literature review and analysis of the International Association for the Study of Lung Cancer database was performed to develop proposals for revision in an iterative process involving multispecialty international input and review. Details of the evidence base are summarized in other articles. Four patterns of disease are recognized; the clinical presentation, pathologic correlates, and biologic behavior of these suggest specific applications of the TNM classification rules. First, it is proposed that second primary lung cancers be designated with a T, N, and M category for each tumor. Second, tumors with a separate tumor nodule of the same histologic type (either suspected or proved) should be classified according to the location of the separate nodule relative to the index tumor-T3 for a same-lobe, T4 for a same-side (different lobe), and M1a for an other-side location-with a single N and M category. Third, multiple tumors with prominent ground glass (imaging) or lepidic (histologic) features should be designated by the T category of the highest T lesion, the number or m in parentheses (#/m) to indicate the multiplicity, and a collective N and M category for all. Finally, it is proposed that diffuse pneumonic-type lung cancers be designated by size (or T3) if in one lobe, T4 if involving multiple same-side lobes, and M1a if involving both lungs with a single N and M category for all areas of involvement. We propose to tailor TNM classification of multiple pulmonary sites of lung cancer to reflect the unique aspects of four different patterns of presentation. We hope that this will lead to

  3. Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System

    Directory of Open Access Journals (Sweden)

    Jie Su

    2016-01-01

    Full Text Available We proposed a method for automatic detection of cervical cancer cells in images captured from thin liquid based cytology slides. We selected 20,000 cells in images derived from 120 different thin liquid based cytology slides, which include 5000 epithelial cells (normal 2500, abnormal 2500, lymphoid cells, neutrophils, and junk cells. We first proposed 28 features, including 20 morphologic features and 8 texture features, based on the characteristics of each cell type. We then used a two-level cascade integration system of two classifiers to classify the cervical cells into normal and abnormal epithelial cells. The results showed that the recognition rates for abnormal cervical epithelial cells were 92.7% and 93.2%, respectively, when C4.5 classifier or LR (LR: logical regression classifier was used individually; while the recognition rate was significantly higher (95.642% when our two-level cascade integrated classifier system was used. The false negative rate and false positive rate (both 1.44% of the proposed automatic two-level cascade classification system are also much lower than those of traditional Pap smear review.

  4. Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis

    DEFF Research Database (Denmark)

    Karemore, Gopal Raghunath; Mascarenhas, Kim Komal; Patil, Choudhary

    2008-01-01

    the classification accuracy of LVQ with other classifiers (eg. SVM and Multi Layer Perceptron) for the same data set. Good agreement has been obtained between LVQ based classification of spectroscopy data and histopathology results which demonstrate the use of LVQ classifier in bladder cancer diagnosis....

  5. Reprogramming of human cancer cells to pluripotency for models of cancer progression

    Science.gov (United States)

    Kim, Jungsun; Zaret, Kenneth S

    2015-01-01

    The ability to study live cells as they progress through the stages of cancer provides the opportunity to discover dynamic networks underlying pathology, markers of early stages, and ways to assess therapeutics. Genetically engineered animal models of cancer, where it is possible to study the consequences of temporal-specific induction of oncogenes or deletion of tumor suppressors, have yielded major insights into cancer progression. Yet differences exist between animal and human cancers, such as in markers of progression and response to therapeutics. Thus, there is a need for human cell models of cancer progression. Most human cell models of cancer are based on tumor cell lines and xenografts of primary tumor cells that resemble the advanced tumor state, from which the cells were derived, and thus do not recapitulate disease progression. Yet a subset of cancer types have been reprogrammed to pluripotency or near-pluripotency by blastocyst injection, by somatic cell nuclear transfer and by induced pluripotent stem cell (iPS) technology. The reprogrammed cancer cells show that pluripotency can transiently dominate over the cancer phenotype. Diverse studies show that reprogrammed cancer cells can, in some cases, exhibit early-stage phenotypes reflective of only partial expression of the cancer genome. In one case, reprogrammed human pancreatic cancer cells have been shown to recapitulate stages of cancer progression, from early to late stages, thus providing a model for studying pancreatic cancer development in human cells where previously such could only be discerned from mouse models. We discuss these findings, the challenges in developing such models and their current limitations, and ways that iPS reprogramming may be enhanced to develop human cell models of cancer progression. PMID:25712212

  6. Genetic Fuzzy System (GFS based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis

    Directory of Open Access Journals (Sweden)

    Meenakshi M. Pawar

    2016-09-01

    Full Text Available Breast cancer is significant health problem diagnosed mostly in women worldwide. Therefore, early detection of breast cancer is performed with the help of digital mammography, which can reduce mortality rate. This paper presents wrapper based feature selection approach for wavelet co-occurrence feature (WCF using Genetic Fuzzy System (GFS in mammogram classification problem. The performance of GFS algorithm is explained using mini-MIAS database. WCF features are obtained from detail wavelet coefficients at each level of decomposition of mammogram image. At first level of decomposition, 18 features are applied to GFS algorithm, which selects 5 features with an average classification success rate of 39.64%. Subsequently, at second level it selects 9 features from 36 features and the classification success rate is improved to 56.75%. For third level, 16 features are selected from 54 features and average success rate is improved to 64.98%. Lastly, at fourth level 72 features are applied to GFS, which selects 16 features and thereby increasing average success rate to 89.47%. Hence, GFS algorithm is the effective way of obtaining optimal set of feature in breast cancer diagnosis.

  7. CONSTRUCTION AND EXPRESSION OF A HUMAN-MOUSE CHIMERIC ANTIBODY AGAINST HUMAN BLADDER CANCER

    Institute of Scientific and Technical Information of China (English)

    白银; 王琰; 周丽君; 俞莉章

    2001-01-01

    To construct and express a human-mouse chimeric antibody against human bladder cancer. Method: The variable region genes of anti-human bladder cancer monoclonal antibody BDI-1 were cloned by RT-PCR. A human-mouse chimeric antibody expression vector was constructed and transfected into CHO cells. The chimeric antibody against bladder cancer was expressed and characterized. Result: Eukaryotic expression vector of the chimeric antibody against human bladder carcinoma was successfully constructed, and was expressed in eukaryotic cells; the expressed chimeric antibody ch-BDI showed same specificity as its parent McAb against human bladder cancer cells. Conclusion: The constructed chimeric antibody was expressed successfully in eukaryotic cells, and the chimeric antibody had desired affinity against human bladder cancer cells.

  8. Qualitative analysis of cancer patients' experiences using donated human milk.

    Science.gov (United States)

    Rough, Susanne M; Sakamoto, Pauline; Fee, Caroline H; Hollenbeck, Clarie B

    2009-05-01

    This represents the first published account from the patient's perspective of the use of human milk as cancer therapy. Purposive sampling was used to select a sample of 10 participants. Five were patients and 5 were family proxies. Individual interviews were conducted using confirmatory interviewing technique to obtain individual perspectives on the motivation for cancer patients to take donated human milk. Human milk therapy improved the quality of life (QOL) measures in the physical, psychological, and spiritual domains for most patients interviewed. The patients continued their use of human milk despite cost, taste, and discouragement from the conventional medical community. The study results support the theory that QOL may be more important to cancer patients than cancer outcomes and may improve patient medical care overall. These interviews offer information to cancer patients, their practitioners, and donor milk banks on outcomes and symptom relief from this therapy.

  9. Negative node count improvement prognostic prediction of the seventh edition of the TNM classification for gastric cancer.

    Science.gov (United States)

    Deng, Jingyu; Zhang, Rupeng; Zhang, Li; Liu, Yong; Hao, Xishan; Liang, Han

    2013-01-01

    To demonstrate that the seventh edition of the tumor-node-metastasis (TNM) classification for gastric cancer (GC) should be updated with the number of negative lymph nodes for the improvement of its prognostic prediction accuracy. Clinicopathological data of 769 GC patients who underwent curative gastrectomy with lymphadenectomy between 1997 and 2006 were retrospectively analyzed to demonstrate the superiority of prognostic efficiency of the seventh edition of the TNM classification, which can be improved by combining the number of negative lymph nodes. With the Cox regression multivariate analysis, the seventh edition of the TNM classification, the number of negative nodes, the type of gastrectomy, and the depth of tumor invasion (T stage) were identified as independent factors for predicting the overall survival of GC patients. Furthermore, we confirmed that the T stage-N stage-number of negative lymph nodes-metastasis (TNnM) classification is the most appropriate prognostic predictor of GC patients by using case-control matched fashion and multinominal logistic regression. Finally, we were able to clarify that TNnM classification may provide more precise survival differences among the different TNM sub-stages of GC by using the measure of agreement (Kappa coefficient), the McNemar value, the Akaike information criterion, and the Bayesian Information Criterion compared with the seventh edition of the TNM classification. The number of negative nodes, as an important prognostic predictor of GC, can improve the prognostic prediction efficiency of the seventh edition of the TNM classification for GC, which should be recommended for conventional clinical applications.

  10. Negative node count improvement prognostic prediction of the seventh edition of the TNM classification for gastric cancer.

    Directory of Open Access Journals (Sweden)

    Jingyu Deng

    Full Text Available OBJECTIVE: To demonstrate that the seventh edition of the tumor-node-metastasis (TNM classification for gastric cancer (GC should be updated with the number of negative lymph nodes for the improvement of its prognostic prediction accuracy. METHODS: Clinicopathological data of 769 GC patients who underwent curative gastrectomy with lymphadenectomy between 1997 and 2006 were retrospectively analyzed to demonstrate the superiority of prognostic efficiency of the seventh edition of the TNM classification, which can be improved by combining the number of negative lymph nodes. RESULTS: With the Cox regression multivariate analysis, the seventh edition of the TNM classification, the number of negative nodes, the type of gastrectomy, and the depth of tumor invasion (T stage were identified as independent factors for predicting the overall survival of GC patients. Furthermore, we confirmed that the T stage-N stage-number of negative lymph nodes-metastasis (TNnM classification is the most appropriate prognostic predictor of GC patients by using case-control matched fashion and multinominal logistic regression. Finally, we were able to clarify that TNnM classification may provide more precise survival differences among the different TNM sub-stages of GC by using the measure of agreement (Kappa coefficient, the McNemar value, the Akaike information criterion, and the Bayesian Information Criterion compared with the seventh edition of the TNM classification. CONCLUSION: The number of negative nodes, as an important prognostic predictor of GC, can improve the prognostic prediction efficiency of the seventh edition of the TNM classification for GC, which should be recommended for conventional clinical applications.

  11. Flow cytometric investigation of immune-response-related surface molecules on human colorectal cancers

    DEFF Research Database (Denmark)

    Diederichsen, Axel Cosmus Pyndt; Stenholm, A C; Kronborg, O;

    1998-01-01

    Our purpose was to clarify whether human colorectal cancer cells are equipped to present tumour-associated-antigens to the immune system, and whether this ability correlates with lymphoid infiltration, the Dukes' stage and Jass classification. Enzymatically dissociated tumour cells from 70...... molecules, but not the class II, was correlated with lymphoid infiltration and the Jass classification. Expression of these surface molecules was not correlated with the Dukes' stage. The tumour cells were generally equipped to present antigens to the effector arm of the immune system since HLA class I...... is expressed, but the tumour cells were not optimal in stimulating an immune response, since HLA class II and CD58 were only marginally expressed and CD80 and CD54 were absent....

  12. The global cancer burden and human development: A review.

    Science.gov (United States)

    Fidler, Miranda M; Bray, Freddie; Soerjomataram, Isabelle

    2017-06-01

    This review examines the links between human development and cancer overall and for specific types of cancer, as well as cancer-related risk-factors and outcomes, such as disability and life expectancy. To assess human development, the Human Development Index was utilized continuously and according to four levels (low, medium, high, very high), where the low and very high categories include the least and most developed countries, respectively. All studies that assessed aspects of the global cancer burden using this measure were reviewed. Although the present cancer incidence burden is greater in higher Human Development Index countries, a greater proportion of the global mortality burden is observed in less developed countries, with a higher mean fatality rate in the latter countries. Further, the future cancer burden is expected to disproportionally affect less developed regions; in particular, it has been estimated that low and medium Human Development Index countries will experience a 100% and 81% increase in cancer incidence from 2008 to 2030, respectively. Disparities were also observed in risk factors and average health outcomes, such as a greater number of years of life lost prematurely and fewer cancer-related gains in life expectancy observed in lower versus higher Human Development Index settings. From a global perspective, there remain clear disparities in the cancer burden according to national Human Development Index scores. International efforts are needed to aid countries in social and economic transition in order to efficiently plan, implement and evaluate cancer control initiatives as a means to reduce the widening gap in cancer occurrence and survival worldwide.

  13. T Cell Coinhibition and Immunotherapy in Human Breast Cancer

    OpenAIRE

    Janakiram, Murali; Abadi, Yael M.; Sparano, Joseph A.; Zang, Xingxing

    2012-01-01

    Costimulation and coinhibition generated by the B7 family and their receptor CD28 family have key roles in regulating T lymphocyte activation and tolerance. These pathways are very attractive therapeutic targets for human cancers including breast cancer. Gene polymorphisms of B7x (B7-H4/B7S1), PD-1 (CD279), and CTLA-4 (CD152) are associated with increased risk of developing breast cancer although the underlying mechanisms are unclear. In human breast cancer microenvironment, up-regulation of ...

  14. Clinical study of quantitative diagnosis of early cervical cancer based on the classification of acetowhitening kinetics

    Science.gov (United States)

    Wu, Tao; Cheung, Tak-Hong; Yim, So-Fan; Qu, Jianan Y.

    2010-03-01

    A quantitative colposcopic imaging system for the diagnosis of early cervical cancer is evaluated in a clinical study. This imaging technology based on 3-D active stereo vision and motion tracking extracts diagnostic information from the kinetics of acetowhitening process measured from the cervix of human subjects in vivo. Acetowhitening kinetics measured from 137 cervical sites of 57 subjects are analyzed and classified using multivariate statistical algorithms. Cross-validation methods are used to evaluate the performance of the diagnostic algorithms. The results show that an algorithm for screening precancer produced 95% sensitivity (SE) and 96% specificity (SP) for discriminating normal and human papillomavirus (HPV)-infected tissues from cervical intraepithelial neoplasia (CIN) lesions. For a diagnostic algorithm, 91% SE and 90% SP are achieved for discriminating normal tissue, HPV infected tissue, and low-grade CIN lesions from high-grade CIN lesions. The results demonstrate that the quantitative colposcopic imaging system could provide objective screening and diagnostic information for early detection of cervical cancer.

  15. Differential network analysis in human cancer research.

    Science.gov (United States)

    Gill, Ryan; Datta, Somnath; Datta, Susmita

    2014-01-01

    A complex disease like cancer is hardly caused by one gene or one protein singly. It is usually caused by the perturbation of the network formed by several genes or proteins. In the last decade several research teams have attempted to construct interaction maps of genes and proteins either experimentally or reverse engineer interaction maps using computational techniques. These networks were usually created under a certain condition such as an environmental condition, a particular disease, or a specific tissue type. Lately, however, there has been greater emphasis on finding the differential structure of the existing network topology under a novel condition or disease status to elucidate the perturbation in a biological system. In this review/tutorial article we briefly mention some of the research done in this area; we mainly illustrate the computational/statistical methods developed by our team in recent years for differential network analysis using publicly available gene expression data collected from a well known cancer study. This data includes a group of patients with acute lymphoblastic leukemia and a group with acute myeloid leukemia. In particular, we describe the statistical tests to detect the change in the network topology based on connectivity scores which measure the association or interaction between pairs of genes. The tests under various scores are applied to this data set to perform a differential network analysis on gene expression for human leukemia. We believe that, in the future, differential network analysis will be a standard way to view the changes in gene expression and protein expression data globally and these types of tests could be useful in analyzing the complex differential signatures.

  16. Studies of Differentially-Expressed Genes in Human Endometrial Cancer of Various Differentiated Grades

    Institute of Scientific and Technical Information of China (English)

    Bin Cai; David Hogg; Guangzhong Lu; Ling Liu; Xiaowei Xi; Wei Xu; Huifang Lu; Yongbin Yang; Xiaoping Wan

    2007-01-01

    OBJECTIVE To study the gene expression profiles of human endometrial cancers at various differentiaOted grade levels and to identify the genes related to differentiation of the endometrial cancers. METHODS cDNA microarray technology was used to analyze the differentially-expressed genes among different differentiated grades of 32 cases of endometrial cancer. Hierarchical cluster analysis (HCA) for the gene expression profiles of the cases was employed. RESULTS The tissue samples were grouped based on the various dif ferentiated tumor grades with 33 differentiation-related genes identified out (P<0.001). Based on the results from the HCA, the conformity rate was 91% among the 33 differentially-expressed genes and the analysis of pathological classification.CONCLUSION Genes related to the differentiation of endometrial cancer can be identified by using gene chips to analyze the expression profiles of endometrial cancers at various differentiated grades; HCA of the gene expression profiles can be helpful for distinguishing high-risk endometrial cancers before surgery.

  17. [Human functioning and disability: exploring the scope of the World Health Organization's international classification].

    Science.gov (United States)

    Sampaio, Rosana Ferreira; Luz, Madel Terezinha

    2009-03-01

    The theoretical discussion on disability is dichotomized according to the medical and social perspectives. The biomedical model focuses on impairment, disease, or physical abnormality and how these factors produce disability. The social approach suggests that the meaning of disability and impairment emerges from specific social and cultural contexts. The WHO created the International Classification of Functioning, Disability and Health (ICF), with a classification system and theoretical model based on the combination of the medical and social models and using a biopsychosocial approach to integrate the health dimensions. Despite the importance and immediacy of the ICF, some concepts were insufficiently detailed and justified and could lead to distinct interpretations. This essay proposes to describe the ICF model and analyze the scope of the biopsychosocial theory for exploring the relational nature of the 'disability' and 'impairment' categories, as well as the universal nature of the WHO proposal. One of the most positive aspects of the ICF is to highlight the interactive nature of disability and the division of the phenomenon into three dimensions, thus demonstrating the degree of complexity in the process of human functioning and disability.

  18. Contact-state classification in human-demonstrated robot compliant motion tasks using the boosting algorithm.

    Science.gov (United States)

    Cabras, Stefano; Castellanos, María Eugenia; Staffetti, Ernesto

    2010-10-01

    Robot programming by demonstration is a robot programming paradigm in which a human operator directly demonstrates the task to be performed. In this paper, we focus on programming by demonstration of compliant motion tasks, which are tasks that involve contacts between an object manipulated by the robot and the environment in which it operates. Critical issues in this paradigm are to distinguish essential actions from those that are not relevant for the correct execution of the task and to transform this information into a robot-independent representation. Essential actions in compliant motion tasks are the contacts that take place, and therefore, it is important to understand the sequence of contact states that occur during a demonstration, called contact classification or contact segmentation. We propose a contact classification algorithm based on a supervised learning algorithm, in particular on a stochastic gradient boosting algorithm. The approach described in this paper is accurate and does not depend on the geometric model of the objects involved in the demonstration. It neither relies on the kinestatic model of the contact interactions nor on the contact state graph, whose computation is usually of prohibitive complexity even for very simple geometric object models.

  19. EXPRESSION OF Fas LIGAND IN HUMAN COLON CANCER CELL LINES

    Institute of Scientific and Technical Information of China (English)

    张建军; 丁尔迅; 王强; 陈学云; 付志仁

    2001-01-01

    To investigate the expression of Fas ligand in human colon carcinoma cell lines. Methods: A total of six human colon cancer cell lines were examined for the expression of Fas ligand mRNA and cell surface protein by using RT-PCR and flow cytometry respectively. Results: The results showed that Fas ligand mRNA was expressed in all of the six cancer cell lines and Fas ligand cell surface protein was expressed in part of them. Conclusion: These data suggest that Fas ligand was expressed, at least in part, in human colon cancer cell lines and might facilitate to escape from immune surveillance of the host.

  20. One Health and cancer: A comparative study of human and canine cancers in Nairobi

    Directory of Open Access Journals (Sweden)

    Nyariaro Kelvin Momanyi

    2016-11-01

    Full Text Available Aim: Recent trends in comparative animal and human research inform us that collaborative research plays a key role in deciphering and solving cancer challenges. Globally, cancer is a devastating diagnosis with an increasing burden in both humans and dogs and ranks as the number three killer among humans in Kenya. This study aimed to provide comparative information on cancers affecting humans and dogs in Nairobi, Kenya. Materials and Methods: Dog data collection was by cancer case finding from five veterinary clinics and two diagnostic laboratories, whereas the human dataset was from the Nairobi Cancer Registry covering the period 2002-2012. The analysis was achieved using IBM SPSS Statistics® v.20 (Dog data and CanReg5 (human data. The human population was estimated from the Kenya National Census, whereas the dog population was estimated from the human using a human:dog ratio of 4.1:1. Results: A total of 15,558 human and 367 dog cancer cases were identified. In humans, females had higher cancer cases 8993 (an age-standardized rate of 179.3 per 100,000 compared to 6565 in males (122.1 per 100,000. This order was reversed in dogs where males had higher cases 198 (14.9 per 100,000 compared to 169 (17.5 per 100,000 in females. The incident cancer cases increased over the 11-year study period in both species. Common cancers affecting both humans and dogs were: Prostate (30.4, 0.8, the respiratory tract (8.3, 1.3, lymphoma (5.6, 1.4, and liver and biliary tract (6.3, 0.5, whereas, in females, they were: Breast (44.5, 3.6, lip, oral cavity, and pharynx (8.8, 0.6, liver and biliary tract (6.5, 1.2, and lymphoma (6.0, 0.6, respectively, per 100,000. Conclusion: The commonality of some of the cancers in both humans and dogs fortifies that it may be possible to use dogs as models and sentinels in studying human cancers in Kenya and Africa. We further infer that developing joint animalhuman cancer registries and integrated cancer surveillance systems may

  1. The study of a patient's immune system may prove to be a useful noninvasive tool for stage classification in colon cancer.

    Science.gov (United States)

    Pellegrini, Patrizia; Berghella, Anna Maria; Contasta, Ida; Del Beato, Tiziana; Adorno, Domenico

    2006-10-01

    Therapy, and, therefore, prognosis, is strictly related to cancer stage, and hence, screening tests that can contribute to the early classification of disease stage represent a step forward in treatment. Unfortunately, few prognostic indices are available, especially noninvasive ones. Our study of the physiological network of the immune response, however, leads us to believe that it may well be possible to define immunological indices for the classification of cancer stage using blood parameters. In this paper, we show how the study of a patient's immune system can be used as a noninvasive tool for early-stage classification.

  2. Automated classification of immunostaining patterns in breast tissue from the human protein Atlas

    Directory of Open Access Journals (Sweden)

    Issac Niwas Swamidoss

    2013-01-01

    Full Text Available Background: The Human Protein Atlas (HPA is an effort to map the location of all human proteins (http://www.proteinatlas.org/. It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples. Materials and Methods: The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM features, complex wavelet co-occurrence matrix (CWCM features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM and linear discriminant analysis (LDA classifier. Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue. Results: We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert. Conclusions: Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for

  3. Unified Structured Learning for Simultaneous Human Pose Estimation and Garment Attribute Classification

    Science.gov (United States)

    Shen, Jie; Liu, Guangcan; Chen, Jia; Fang, Yuqiang; Xie, Jianbin; Yu, Yong; Yan, Shuicheng

    2014-11-01

    In this paper, we utilize structured learning to simultaneously address two intertwined problems: human pose estimation (HPE) and garment attribute classification (GAC), which are valuable for a variety of computer vision and multimedia applications. Unlike previous works that usually handle the two problems separately, our approach aims to produce a jointly optimal estimation for both HPE and GAC via a unified inference procedure. To this end, we adopt a preprocessing step to detect potential human parts from each image (i.e., a set of "candidates") that allows us to have a manageable input space. In this way, the simultaneous inference of HPE and GAC is converted to a structured learning problem, where the inputs are the collections of candidate ensembles, the outputs are the joint labels of human parts and garment attributes, and the joint feature representation involves various cues such as pose-specific features, garment-specific features, and cross-task features that encode correlations between human parts and garment attributes. Furthermore, we explore the "strong edge" evidence around the potential human parts so as to derive more powerful representations for oriented human parts. Such evidences can be seamlessly integrated into our structured learning model as a kind of energy function, and the learning process could be performed by standard structured Support Vector Machines (SVM) algorithm. However, the joint structure of the two problems is a cyclic graph, which hinders efficient inference. To resolve this issue, we compute instead approximate optima by using an iterative procedure, where in each iteration the variables of one problem are fixed. In this way, satisfactory solutions can be efficiently computed by dynamic programming. Experimental results on two benchmark datasets show the state-of-the-art performance of our approach.

  4. Effect of World Health Organization (WHO) Histological Classification on Predicting Lymph Node Metastasis and Recurrence in Early Gastric Cancer

    Science.gov (United States)

    Lai, Ji Fu; Xu, Wen Na; Noh, Sung Hoon; Lu, Wei Qin

    2016-01-01

    Background The World Health Organization (WHO) histological classification for gastric cancer is widely accepted and used. However, its impact on predicting lymph node metastasis and recurrence in early gastric cancer (EGC) is not well studied. Material/Methods From 1987 to 2005, 2873 EGC patients with known WHO histological type who had undergone curative resection were enrolled in this study. In all, 637 well-differentiated adenocarcinomas (WD), 802 moderately-differentiated adenocarcinomas (MD), 689 poorly-differentiated adenocarcinomas (PD), and 745 signet-ring cell adenocarcinomas (SRC) were identified. Results The distribution of demographic and clinical features in early gastric cancer among WD, MD, PD, and SRC were significantly different. Lymph node metastasis was observed in 317 patients (11.0%), with the lymph node metastasis rate being 5.3%, 14.8%, 17.0%, and 6.3% in WD, MD, PD, and SRC, respectively. Univariate and multivariate analyses indicated that gender, tumor size, gross appearance, depth of invasion, and WHO classification were significantly associated with lymph node metastasis. Recurrence was observed in 83 patients (2.9%), with the recurrence rate being 2.2%, 4.5%, 3.0%, and 1.6% in WD, MD, PD, and SRC, respectively. Multivariate analysis confirmed that MD, elevated gross type, and lymph node metastasis were independent risk factors for recurrence in EGC. MD patients showed worse disease-free survival than non-MD patients (P=0.001). Conclusions WHO classification is useful and necessary to evaluate during the perioperative management of EGC. Treatment strategies for EGC should be made prudently according to WHO classification, especially for MD patients. PMID:27595490

  5. Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution.

    Science.gov (United States)

    Pak, Fatemeh; Kanan, Hamidreza Rashidy; Alikhassi, Afsaneh

    2015-11-01

    Breast cancer is one of the most perilous diseases among women. Breast screening is a method of detecting breast cancer at a very early stage which can reduce the mortality rate. Mammography is a standard method for the early diagnosis of breast cancer. In this paper, a new algorithm is proposed for breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution (SR). The presented algorithm includes three main parts including pre-processing, feature extraction and classification. In the pre-processing stage, after determining the region of interest (ROI) by an automatic technique, the quality of image is improved using NSCT and SR algorithm. In the feature extraction part, several features of the image components are extracted and skewness of each feature is calculated. Finally, AdaBoost algorithm is used to classify and determine the probability of benign and malign disease. The obtained results on Mammographic Image Analysis Society (MIAS) database indicate the significant performance and superiority of the proposed method in comparison with the state of the art approaches. According to the obtained results, the proposed technique achieves 91.43% and 6.42% as a mean accuracy and FPR, respectively.

  6. Statistical control chart and neural network classification for improving human fall detection

    KAUST Repository

    Harrou, Fouzi

    2017-01-05

    This paper proposes a statistical approach to detect and classify human falls based on both visual data from camera and accelerometric data captured by accelerometer. Specifically, we first use a Shewhart control chart to detect the presence of potential falls by using accelerometric data. Unfortunately, this chart cannot distinguish real falls from fall-like actions, such as lying down. To bypass this difficulty, a neural network classifier is then applied only on the detected cases through visual data. To assess the performance of the proposed method, experiments are conducted on the publicly available fall detection databases: the University of Rzeszow\\'s fall detection (URFD) dataset. Results demonstrate that the detection phase play a key role in reducing the number of sequences used as input into the neural network classifier for classification, significantly reducing computational burden and achieving better accuracy.

  7. [Human papillomavirus detection in cervical cancer prevention].

    Science.gov (United States)

    Picconi, María Alejandra

    2013-01-01

    Cervical cancer (CC), which is strongly associated to high-risk human papillomavirus (hr-HPV) infection, continues being a significant health problem in Latin America. The use of conventional cytology to detect precancerous cervical lesions has had no major impact on reducing CC incidence and mortality rates, which are still high in the region. New screening tools to detect precancerous lesions became available, which provide great opportunities for CC prevention, as do highly efficacious HPV vaccines able to prevent nearly all lesions associated with HPV-16 and -18 when applied before viral exposure. Currently, hr-HPV testing represents an invaluable component of clinical guidelines for screening, management and treatment of CC and their precursor lesions. Many testing strategies have been developed that can detect a broad spectrum of hr-HPV types in a single assay; however, only a small subset of them has documented clinical performance for any of the standard HPV testing indications. HPV tests that have not been validated and lack proof of reliability, reproducibility and accuracy should not be used in clinical management. Once incorporated into the lab, it is essential to submit the whole procedure of HPV testing to continuous and rigorous quality assurance to avoid sub-optimal, potentially harmful practices. Recent progress and current status of these methods are discussed in this article.

  8. Towards the human colorectal cancer microbiome.

    Directory of Open Access Journals (Sweden)

    Julian R Marchesi

    Full Text Available Multiple factors drive the progression from healthy mucosa towards sporadic colorectal carcinomas and accumulating evidence associates intestinal bacteria with disease initiation and progression. Therefore, the aim of this study was to provide a first high-resolution map of colonic dysbiosis that is associated with human colorectal cancer (CRC. To this purpose, the microbiomes colonizing colon tumor tissue and adjacent non-malignant mucosa were compared by deep rRNA sequencing. The results revealed striking differences in microbial colonization patterns between these two sites. Although inter-individual colonization in CRC patients was variable, tumors consistently formed a niche for Coriobacteria and other proposed probiotic bacterial species, while potentially pathogenic Enterobacteria were underrepresented in tumor tissue. As the intestinal microbiota is generally stable during adult life, these findings suggest that CRC-associated physiological and metabolic changes recruit tumor-foraging commensal-like bacteria. These microbes thus have an apparent competitive advantage in the tumor microenvironment and thereby seem to replace pathogenic bacteria that may be implicated in CRC etiology. This first glimpse of the CRC microbiome provides an important step towards full understanding of the dynamic interplay between intestinal microbial ecology and sporadic CRC, which may provide important leads towards novel microbiome-related diagnostic tools and therapeutic interventions.

  9. International Classification of Functioning, Disability, and Health in women with breast cancer: a proposal for measurement instruments.

    Science.gov (United States)

    Carvalho, Flávia Nascimento de; Koifman, Rosalina Jorge; Bergmann, Anke

    2013-06-01

    The International Classification of Functioning, Disability, and Health (ICF) aims at standardization, but its applicability requires consistent instruments. In Brazil, invasive therapeutic approaches are frequent, leading to functional alterations. The current study thus aimed to identify and discuss instruments capable of measuring ICF core set codes for breast cancer. The review included ICF studies in women with breast cancer diagnosis and studies with the objective of translating and validating instruments for the Brazilian population, and consistent with the codes. Review studies, systematic or not, were excluded. Eight instruments were selected, and the WHOQOL-Bref was the most comprehensive. The use of various instruments showed 19 coinciding codes, and the instruments as a whole covered 58 of the total of 81 codes. The use of multiple instruments is time-consuming, so new studies are needed to propose parsimonious tools capable of measuring functioning in women treated for breast cancer.

  10. Classification moléculaire du cancer du sein au Maroc

    Science.gov (United States)

    Fouad, Abbass; Yousra, Akasbi; Kaoutar, Znati; Omar, El Mesbahi; Afaf, Amarti; Sanae, Bennis

    2012-01-01

    Introduction La classification moléculaire des cancers du sein basée sur l'expression génique puis sur le profil protéique a permis de distinguer cinq groupes moléculaires: luminal A, luminal B, Her2/neu, basal-like et non-classées. L'objectif de cette étude réalisée au CHU Hassan II de Fès est de classer 335 cancers du sein infiltrant en groupes moléculaires, puis de les corréler avec les caractéristiques clinicopathologiques. Méthodes Etude rétrospective étalée sur 45 mois, comportant 335 patientes colligées au CHU pour le diagnostic et le suivi. Les tumeurs sont analysées histologiquement et classées après une étude immunohistochimique en groupes: luminal A, luminal B, Her2+, basal-like et non-classées. Résultats 54.3% des tumeurs sont du groupe luminal A, 16% luminal B, 11.3% Her2+, 11.3% basal-like et 7% non-classées. Le groupe luminal A renferme le plus faible taux de grade III, d'emboles vasculaires ainsi que de métastases; alors que le groupe des non-classées et basal-like représentent un taux élevé de grade III, une faible proportion d'emboles vasculaires et d'envahissement ganglionnaire. Ces facteurs sont significativement élevés dans les groupes luminal B et Her2+ avec un taux de survie globale de 78% et 76% respectivement. Dans le groupe luminal A, la survie globale des patientes est élevée (87%) alors qu'elle n'est que de 49% dans le groupe des triples négatifs (basal-like et non-classés). Conclusion Le groupe luminal B est différent du luminal A et il est de pronostic péjoratif vis à vis du groupe Her2+. Les caractéristiques clinicopathologiques concordent avec le profil moléculaire donc devraient être pris en considération comme facteurs pronostiques. PMID:23396646

  11. Clinicopathological significance of altered metallothionein 2A expression in gastric cancer according to Lauren's classification

    Institute of Scientific and Technical Information of China (English)

    PAN Yuan-ming; XING Rui; CUI Jian-tao; LI Wen-mei; L(U) You-yong

    2013-01-01

    Background Dysregulated metallothionein 2A (MT2A) has been implicated in carcinogenesis.The purpose of this study was to investigate the expression of MT2A in gastric cancer (GC) and its correlation with prognosis.Methods Reverse transcription-polymerase chain reaction and real-time polymerase chain reaction were used to detect the mRNA expression of MT2A in 12 GC cell lines,normal gastric epithelial GES-1 cells,and 36 GC and adjacent normal tissues.MT2A protein expression was determined in 258 GC tissues and 171 adjacent normal tissues by immunohistochemistry.Results MT2A mRNA expression was lower in GC cells and primary tumors than in GES-1 cells and adjacent normal tissues,respectively.High protein expression of MT2A was present in 130 of 171 normal tissues (76.0%) and in 56 of 258 GC tissues (21.7%; P <0.001).MT2A protein expression was higher in well/moderately differentiated GC (22/54;40.7%) than in poorly differentiated GC (34/204; 16.7%; P <0.001).Moreover,the protein expression of MT2A was lower in diffuse-type GC (6/82; 7.3%) than in intestinal-type GC (50/176; 28.4%; P=0.0001).Importantly,MT2A expression was an independent prognostic factor for GC,and decreased MT2A expression was associated with poor clinical outcome (P <0.001).The expression status of MT2A could predict prognosis in intestinal and diffuse-type GCs.Conclusion Expression status of MT2A might be a useful prognostic biomarker for GC,especially when used in combination with Lauren's classification.

  12. Static micro-array isolation, dynamic time series classification, capture and enumeration of spiked breast cancer cells in blood: the nanotube-CTC chip

    Science.gov (United States)

    Khosravi, Farhad; Trainor, Patrick J.; Lambert, Christopher; Kloecker, Goetz; Wickstrom, Eric; Rai, Shesh N.; Panchapakesan, Balaji

    2016-11-01

    We demonstrate the rapid and label-free capture of breast cancer cells spiked in blood using nanotube-antibody micro-arrays. 76-element single wall carbon nanotube arrays were manufactured using photo-lithography, metal deposition, and etching techniques. Anti-epithelial cell adhesion molecule (anti-EpCAM), Anti-human epithelial growth factor receptor 2 (anti-Her2) and non-specific IgG antibodies were functionalized to the surface of the nanotube devices using 1-pyrene-butanoic acid succinimidyl ester. Following device functionalization, blood spiked with SKBR3, MCF7 and MCF10A cells (100/1000 cells per 5 μl per device, 170 elements totaling 0.85 ml of whole blood) were adsorbed on to the nanotube device arrays. Electrical signatures were recorded from each device to screen the samples for differences in interaction (specific or non-specific) between samples and devices. A zone classification scheme enabled the classification of all 170 elements in a single map. A kernel-based statistical classifier for the ‘liquid biopsy’ was developed to create a predictive model based on dynamic time warping series to classify device electrical signals that corresponded to plain blood (control) or SKBR3 spiked blood (case) on anti-Her2 functionalized devices with ˜90% sensitivity, and 90% specificity in capture of 1000 SKBR3 breast cancer cells in blood using anti-Her2 functionalized devices. Screened devices that gave positive electrical signatures were confirmed using optical/confocal microscopy to hold spiked cancer cells. Confocal microscopic analysis of devices that were classified to hold spiked blood based on their electrical signatures confirmed the presence of cancer cells through staining for DAPI (nuclei), cytokeratin (cancer cells) and CD45 (hematologic cells) with single cell sensitivity. We report 55%-100% cancer cell capture yield depending on the active device area for blood adsorption with mean of 62% (˜12 500 captured off 20 000 spiked cells in 0.1 ml

  13. Catalog of genetic progression of human cancers: breast cancer.

    Science.gov (United States)

    Desmedt, Christine; Yates, Lucy; Kulka, Janina

    2016-03-01

    With the rapid development of next-generation sequencing, deeper insights are being gained into the molecular evolution that underlies the development and clinical progression of breast cancer. It is apparent that during evolution, breast cancers acquire thousands of mutations including single base pair substitutions, insertions, deletions, copy number aberrations, and structural rearrangements. As a consequence, at the whole genome level, no two cancers are identical and few cancers even share the same complement of "driver" mutations. Indeed, two samples from the same cancer may also exhibit extensive differences due to constant remodeling of the genome over time. In this review, we summarize recent studies that extend our understanding of the genomic basis of cancer progression. Key biological insights include the following: subclonal diversification begins early in cancer evolution, being detectable even in in situ lesions; geographical stratification of subclonal structure is frequent in primary tumors and can include therapeutically targetable alterations; multiple distant metastases typically arise from a common metastatic ancestor following a "metastatic cascade" model; systemic therapy can unmask preexisting resistant subclones or influence further treatment sensitivity and disease progression. We conclude the review by describing novel approaches such as the analysis of circulating DNA and patient-derived xenografts that promise to further our understanding of the genomic changes occurring during cancer evolution and guide treatment decision making.

  14. Mastectomy or breast conserving surgery? Factors affecting type of surgical treatment for breast cancer – a classification tree approach

    Directory of Open Access Journals (Sweden)

    O'Neill Terry

    2006-04-01

    Full Text Available Abstract Background A critical choice facing breast cancer patients is which surgical treatment – mastectomy or breast conserving surgery (BCS – is most appropriate. Several studies have investigated factors that impact the type of surgery chosen, identifying features such as place of residence, age at diagnosis, tumor size, socio-economic and racial/ethnic elements as relevant. Such assessment of "propensity" is important in understanding issues such as a reported under-utilisation of BCS among women for whom such treatment was not contraindicated. Using Western Australian (WA data, we further examine the factors associated with the type of surgical treatment for breast cancer using a classification tree approach. This approach deals naturally with complicated interactions between factors, and so allows flexible and interpretable models for treatment choice to be built that add to the current understanding of this complex decision process. Methods Data was extracted from the WA Cancer Registry on women diagnosed with breast cancer in WA from 1990 to 2000. Subjects' treatment preferences were predicted from covariates using both classification trees and logistic regression. Results Tumor size was the primary determinant of patient choice, subjects with tumors smaller than 20 mm in diameter preferring BCS. For subjects with tumors greater than 20 mm in diameter factors such as patient age, nodal status, and tumor histology become relevant as predictors of patient choice. Conclusion Classification trees perform as well as logistic regression for predicting patient choice, but are much easier to interpret for clinical use. The selected tree can inform clinicians' advice to patients.

  15. Role of ARPC2 in Human Gastric Cancer

    Directory of Open Access Journals (Sweden)

    Jun Zhang

    2017-01-01

    Full Text Available Gastric cancer continues to be the second most frequent cause of cancer deaths worldwide. However, the exact molecular mechanisms are still unclear. Further research to find potential targets for therapy is critical and urgent. In this study, we found that ARPC2 promoted cell proliferation and invasion in the human cancer cell line MKN-28 using a cell total number assay, MTT (3-(4,5-dimethyl-2-thiazolyl-2,5-diphenyl-2-H-tetrazolium bromide assay, cell colony formation assay, migration assay, invasion assay, and wound healing assay. For downstream pathways, CTNND1, EZH2, BCL2L2, CDH2, VIM, and EGFR were upregulated by ARPC2, whereas PTEN, BAK, and CDH1 were downregulated by ARPC2. In a clinical study, we examined the expression of ARPC2 in 110 cases of normal human gastric tissues and 110 cases of human gastric cancer tissues. ARPC2 showed higher expression in gastric cancer tissues than in normal gastric tissues. In the association analysis of 110 gastric cancer tissues, ARPC2 showed significant associations with large tumor size, lymph node invasion, and high tumor stage. In addition, ARPC2-positive patients exhibited lower RFS and OS rates compared with ARPC2-negative patients. We thus identify that ARPC2 plays an aneretic role in human gastric cancer and provided a new target for gastric cancer therapy.

  16. Human Papillomavirus and the Development of Different Cancers.

    Science.gov (United States)

    Gao, Ge; Smith, David I

    2017-03-01

    Human papillomaviruses (HPV) are responsible for the development of almost all cervical cancers. HPV is also found in 85% of anal cancer and in 50% of penile, vulvar, and vaginal cancers, and they are increasingly found in a subset of head and neck cancers, i.e., oropharyngeal squamous cell carcinomas (OPSCC). The model for how HPV causes cancer is derived from several decades of study on cervical cancer, and it is just presumed that this model is not only completely valid for cervical cancer but for all other HPV-driven cancers as well. Next-generation sequencing (NGS) has now provided the necessary tools to characterize genomic alterations in cancer cells and can precisely determine the physical status of HPV in those cells as well. We discuss recent discoveries from different applications of NGS in both cervical cancer and OPSCCs, including whole-genome sequencing and mate-pair NGS. We also discuss what NGS studies have revealed about the different ways that HPV can be involved in cancer formation, specifically comparing cervical cancer and OPSCC.

  17. Body mass index: different nutritional status according to WHO, OPAS and Lipschitz classifications in gastrointestinal cancer patients

    Directory of Open Access Journals (Sweden)

    Katia Barao

    2012-06-01

    Full Text Available CONTEXT: The body mass index (BMI is the most common marker used on diagnoses of the nutritional status. The great advantage of this index is the easy way to measure, the low cost, the good correlation with the fat mass and the association to morbidity and mortality. OBJECTIVE: To compare the BMI differences according to the WHO, OPAS and Lipschitz classification. METHODS: A prospective study on 352 patients with esophageal, gastric or colorectal cancer was done. The BMI was calculated and analyzed by the classification of WHO, Lipschitz and OPAS. RESULTS: The mean age was 62.1 ± 12.4 years and 59% of them had more than 59 years. The BMI had not difference between the genders in patients <59 years (P = 0.75, but over 59 years the BMI was higher in women (P<0.01. The percentage of undernourished was 7%, 18% and 21% (P<0.01 by WHO, Lipschitz and OPAS, respectively. The overweight/obesity was also different among the various classifications (P<0.01. CONCLUSIONS: Most of the patients with gastrointestinal cancer had more than 65 years. A different cut off must be used for this patients, because undernourished patients may be wrongly considered well nourished.

  18. Toward automated classification of consumers' cancer-related questions with a new taxonomy of expected answer types.

    Science.gov (United States)

    McRoy, Susan; Jones, Sean; Kurmally, Adam

    2016-09-01

    This article examines methods for automated question classification applied to cancer-related questions that people have asked on the web. This work is part of a broader effort to provide automated question answering for health education. We created a new corpus of consumer-health questions related to cancer and a new taxonomy for those questions. We then compared the effectiveness of different statistical methods for developing classifiers, including weighted classification and resampling. Basic methods for building classifiers were limited by the high variability in the natural distribution of questions and typical refinement approaches of feature selection and merging categories achieved only small improvements to classifier accuracy. Best performance was achieved using weighted classification and resampling methods, the latter yielding an accuracy of F1 = 0.963. Thus, it would appear that statistical classifiers can be trained on natural data, but only if natural distributions of classes are smoothed. Such classifiers would be useful for automated question answering, for enriching web-based content, or assisting clinical professionals to answer questions.

  19. Fourier-transform infrared spectroscopy coupled with a classification machine for the analysis of blood plasma or serum: a novel diagnostic approach for ovarian cancer.

    Science.gov (United States)

    Gajjar, Ketan; Trevisan, Júlio; Owens, Gemma; Keating, Patrick J; Wood, Nicholas J; Stringfellow, Helen F; Martin-Hirsch, Pierre L; Martin, Francis L

    2013-07-21

    Currently available screening tests do not deliver the required sensitivity and specificity for accurate diagnosis of ovarian or endometrial cancer. Infrared (IR) spectroscopy of blood plasma or serum is a rapid, versatile, and relatively non-invasive approach which could characterize biomolecular alterations due to cancer and has potential to be utilized as a screening or diagnostic tool. In the past, no such approach has been investigated for its applicability in screening and/or diagnosis of gynaecological cancers. We set out to determine whether attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy coupled with a proposed classification machine could be applied to IR spectra obtained from plasma and serum for accurate class prediction (cancer vs. normal). Plasma and serum samples were obtained from ovarian cancer cases (n = 30), endometrial cancer cases (n = 30) and non-cancer controls (n = 30), and subjected to ATR-FTIR spectroscopy. Four derived datasets were processed to estimate the real-world diagnosis of ovarian and endometrial cancer. Classification results for ovarian cancer were remarkable (up to 96.7%), whereas endometrial cancer was classified with a relatively high accuracy (up to 81.7%). The results from different combinations of feature extraction and classification methods, and also classifier ensembles, were compared. No single classification system performed best for all different datasets. This demonstrates the need for a framework that can accommodate a diverse set of analytical methods in order to be adaptable to different datasets. This pilot study suggests that ATR-FTIR spectroscopy of blood is a robust tool for accurate diagnosis, and carries the potential to be utilized as a screening test for ovarian cancer in primary care settings. The proposed classification machine is a powerful tool which could be applied to classify the vibrational spectroscopy data of different biological systems (e.g., tissue, urine, saliva

  20. Calorimetric signatures of human cancer cells and their nuclei

    Energy Technology Data Exchange (ETDEWEB)

    Todinova, S. [Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 21, Sofia 1113 (Bulgaria); Stoyanova, E. [Department of Molecular Immunology, Institute of Biology and Immunology of Reproduction, Bulgarian Academy of Sciences, Tzarigradsko shose Blvd. 73, Sofia 1113 (Bulgaria); Krumova, S., E-mail: sakrumo@gmail.com [Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 21, Sofia 1113 (Bulgaria); Iliev, I. [Institute of Experimental Morphology, Pathology and Anthropology with Museum, Acad. G. Bonchev Str., Bl. 25, Sofia 1113 (Bulgaria); Taneva, S.G. [Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 21, Sofia 1113 (Bulgaria)

    2016-01-10

    Graphical abstract: - Highlights: • Two temperature ranges are distinguished in the thermograms of cells/nuclei. • Different thermodynamic properties of cancer and normal human cells/nuclei. • Dramatic reduction of the enthalpy of the low-temperature range in cancer cells. • Oxaliplatin and 5-FU affect the nuclear matrix proteins and the DNA stability. - Abstract: The human cancer cell lines HeLa, JEG-3, Hep G2, SSC-9, PC-3, HT-29, MCF7 and their isolated nuclei were characterized by differential scanning calorimetry. The calorimetric profiles differed from normal human fibroblast (BJ) cells in the two well distinguished temperature ranges—the high-temperature range (H{sub T}, due to DNA-containing structures) and the low-temperature range (L{sub T}, assigned to the nuclear matrix and cellular proteins). The enthalpy of the L{sub T} range, and, respectively the ratio of the enthalpies of the L{sub T}- vs. H{sub T}-range, ΔH{sub L}/ΔH{sub H}, is strongly reduced for all cancer cells compared to normal fibroblasts. On the contrary, for most of the cancer nuclei this ratio is higher compared to normal nuclei. The HT-29 human colorectal cancer cells/nuclei differed most drastically from normal human fibroblast cells/nuclei. Our data also reveal that the treatment of HT-29 cancer cells with cytostatic drugs affects not only the DNA replication but also the cellular proteome.

  1. TP53 mutations, expression and interaction networks in human cancers.

    Science.gov (United States)

    Wang, Xiaosheng; Sun, Qingrong

    2017-01-03

    Although the associations of p53 dysfunction, p53 interaction networks and oncogenesis have been widely explored, a systematic analysis of TP53 mutations and its related interaction networks in various types of human cancers is lacking. Our study explored the associations of TP53 mutations, gene expression, clinical outcomes, and TP53 interaction networks across 33 cancer types using data from The Cancer Genome Atlas (TCGA). We show that TP53 is the most frequently mutated gene in a number of cancers, and its mutations appear to be early events in cancer initiation. We identified genes potentially repressed by p53, and genes whose expression correlates significantly with TP53 expression. These gene products may be especially important nodes in p53 interaction networks in human cancers. This study shows that while TP53-truncating mutations often result in decreased TP53 expression, other non-truncating TP53 mutations result in increased TP53 expression in some cancers. Survival analyses in a number of cancers show that patients with TP53 mutations are more likely to have worse prognoses than TP53-wildtype patients, and that elevated TP53 expression often leads to poor clinical outcomes. We identified a set of candidate synthetic lethal (SL) genes for TP53, and validated some of these SL interactions using data from the Cancer Cell Line Project. These predicted SL genes are promising candidates for experimental validation and the development of personalized therapeutics for patients with TP53-mutated cancers.

  2. Telmisartan inhibits human urological cancer cell growth through early apoptosis

    Science.gov (United States)

    MATSUYAMA, MASAHIDE; FUNAO, KIYOAKI; KURATSUKURI, KATSUYUKI; TANAKA, TOMOAKI; KAWAHITO, YUTAKA; SANO, HAJIME; CHARGUI, JAMEL; TOURAINE, JEAN-LOUIS; YOSHIMURA, NORIO; YOSHIMURA, RIKIO

    2010-01-01

    Angiotensin II receptor blockers (ARBs) are widely used as hypertensive therapeutic agents. In addition, studies have provided evidence that ARBs have the potential to inhibit the growth of several types of cancer cells. It was reported that telmisartan (a type of ARB) has peroxisome proliferator-activated receptor (PPAR)-γ activation activity. We previously reported that the PPAR-γ ligand induces growth arrest in human urological cancer cells through apoptosis. In this study, we evaluated the effects of telmisartan and other ARBs on cell proliferation in renal cell carcinoma (RCC), bladder cancer (BC), prostate cancer (PC) and testicular cancer (TC) cell lines. The inhibitory effects of telmisartan and other ARBs (candesartan, valsartan, irbesartan and losartan) on the growth of the RCC, BC, PC and TC cell lines was investigated using an MTT assay. Flow cytometry and Hoechst staining were used to determine whether the ARBs induced apoptosis. Telmisartan caused marked growth inhibition in the urological cancer cells in a dose- and time-dependent manner. Urological cancer cells treated with 100 μM telmisartan underwent early apoptosis and DNA fragmentation. However, the other ARBs had no effect on cell proliferation in any of the urological cancer cell lines. Telmisartan may mediate potent anti-proliferative effects in urological cancer cells through PPAR-γ. Thus, telmisartan is a potent target for the prevention and treatment of human urological cancer. PMID:22993542

  3. A novel SCID mouse model for studying spontaneous metastasis of human lung cancer to human tissue.

    Science.gov (United States)

    Teraoka, S; Kyoizumi, S; Seyama, T; Yamakido, M; Akiyama, M

    1995-05-01

    We established a novel severe combined immunodeficient (SCID) mouse model for the study of human lung cancer metastasis to human lung. Implantation of both human fetal and adult lung tissue into mammary fat pads of SCID mice showed a 100% rate of engraftment, but only fetal lung implants revealed normal morphology of human lung tissue. Using these chimeric mice, we analyzed human lung cancer metastasis to both mouse and human lungs by subcutaneous inoculation of human squamous cell carcinoma and adenocarcinoma cell lines into the mice. In 60 to 70% of SCID mice injected with human-lung squamous-cell carcinoma, RERF-LC-AI, cancer cells were found to have metastasized to both mouse lungs and human fetal lung implants but not to human adult lung implants 80 days after cancer inoculation. Furthermore, human-lung adenocarcinoma cells, RERF-LC-KJ, metastasized to the human lung implants within 90 days in about 40% of SCID mice, whereas there were no metastases to the lungs of the mice. These results demonstrate the potential of this model for the in vivo study of human lung cancer metastasis.

  4. Polarimetry based partial least square classification of ex vivo healthy and basal cell carcinoma human skin tissues.

    Science.gov (United States)

    Ahmad, Iftikhar; Ahmad, Manzoor; Khan, Karim; Ikram, Masroor

    2016-06-01

    Optical polarimetry was employed for assessment of ex vivo healthy and basal cell carcinoma (BCC) tissue samples from human skin. Polarimetric analyses revealed that depolarization and retardance for healthy tissue group were significantly higher (ppolarimetry together with PLS statistics hold promise for automated pathology classification. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. Fluorescent marker-based and marker-free discrimination between healthy and cancerous human tissues using hyper-spectral imaging

    Science.gov (United States)

    Arnold, Thomas; De Biasio, Martin; Leitner, Raimund

    2015-06-01

    Two problems are addressed in this paper (i) the fluorescent marker-based and the (ii) marker-free discrimination between healthy and cancerous human tissues. For both applications the performance of hyper-spectral methods are quantified. Fluorescent marker-based tissue classification uses a number of fluorescent markers to dye specific parts of a human cell. The challenge is that the emission spectra of the fluorescent dyes overlap considerably. They are, furthermore disturbed by the inherent auto-fluorescence of human tissue. This results in ambiguities and decreased image contrast causing difficulties for the treatment decision. The higher spectral resolution introduced by tunable-filter-based spectral imaging in combination with spectral unmixing techniques results in an improvement of the image contrast and therefore more reliable information for the physician to choose the treatment decision. Marker-free tissue classification is based solely on the subtle spectral features of human tissue without the use of artificial markers. The challenge in this case is that the spectral differences between healthy and cancerous tissues are subtle and embedded in intra- and inter-patient variations of these features. The contributions of this paper are (i) the evaluation of hyper-spectral imaging in combination with spectral unmixing techniques for fluorescence marker-based tissue classification, (ii) the evaluation of spectral imaging for marker-free intra surgery tissue classification. Within this paper, we consider real hyper-spectral fluorescence and endoscopy data sets to emphasize the practical capability of the proposed methods. It is shown that the combination of spectral imaging with multivariate statistical methods can improve the sensitivity and specificity of the detection and the staging of cancerous tissues compared to standard procedures.

  6. Developing a novel nodal grading system to standardize nodal classification in gastric cancer patients with limited lymph node resection

    Institute of Scientific and Technical Information of China (English)

    2015-01-01

    Objective:To develop an easy applicable novel nodal grading system to improve the standardization of nodal classification in patients with limited lymphadenectomy. Methods: We formulated a new approach of nodal classification to classify this category of patients. Log-rank test was used for univariate analysis and Cox proportional hazards model was used for univariate and multivariate analysis. We used linear trendχ2 tests, likelihood ratioχ2 test and Akaike information criterion (AIC) value to assess the homogeneity, discriminatory ability and monotonicity of gradients of the two nodal staging systems.Results:Statistical analysis supported that both the hypothesized N’ stage and hypothesized TN’M stage outperforms the present AJCC/UICC staging system.Conclusion:We developed an easy applicable and reproducible novel nodal grading system that has a greater predicting value than the current AJCC/UICC staging system to classify gastric cancer patients with limited lymphadenectomy.

  7. Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification.

    Science.gov (United States)

    Ramyachitra, D; Sofia, M; Manikandan, P

    2015-09-01

    Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.

  8. Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification

    Directory of Open Access Journals (Sweden)

    D. Ramyachitra

    2015-09-01

    Full Text Available Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM, K-nearest neighbor (KNN, Interval Valued Classification (IVC and the improvised Interval Value based Particle Swarm Optimization (IVPSO algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.

  9. Human Papilloma Viruses and Breast Cancer – Assessment of Causality

    Science.gov (United States)

    Lawson, James Sutherland; Glenn, Wendy K.; Whitaker, Noel James

    2016-01-01

    High risk human papilloma viruses (HPVs) may have a causal role in some breast cancers. Case–control studies, conducted in many different countries, consistently indicate that HPVs are more frequently present in breast cancers as compared to benign breast and normal breast controls (odds ratio 4.02). The assessment of causality of HPVs in breast cancer is difficult because (i) the HPV viral load is extremely low, (ii) HPV infections are common but HPV associated breast cancers are uncommon, and (iii) HPV infections may precede the development of breast and other cancers by years or even decades. Further, HPV oncogenesis can be indirect. Despite these difficulties, the emergence of new evidence has made the assessment of HPV causality, in breast cancer, a practical proposition. With one exception, the evidence meets all the conventional criteria for a causal role of HPVs in breast cancer. The exception is “specificity.” HPVs are ubiquitous, which is the exact opposite of specificity. An additional reservation is that the prevalence of breast cancer is not increased in immunocompromised patients as is the case with respect to HPV-associated cervical cancer. This indicates that HPVs may have an indirect causal influence in breast cancer. Based on the overall evidence, high-risk HPVs may have a causal role in some breast cancers. PMID:27747193

  10. Integrating Colon Cancer Microarray Data: Associating Locus-Specific Methylation Groups to Gene Expression-Based Classifications

    Directory of Open Access Journals (Sweden)

    Ana Barat

    2015-11-01

    Full Text Available Recently, considerable attention has been paid to gene expression-based classifications of colorectal cancers (CRC and their association with patient prognosis. In addition to changes in gene expression, abnormal DNA-methylation is known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Large-scale technologies, such as methylation microarray assays and specific sequencing of methylated DNA, have been used to determine whole genome profiles of CpG island methylation in tissue samples. In this article, publicly available microarray-based gene expression and methylation data sets are used to characterize expression subtypes with respect to locus-specific methylation. A major objective was to determine whether integration of these data types improves previously characterized subtypes, or provides evidence for additional subtypes. We used unsupervised clustering techniques to determine methylation-based subgroups, which are subsequently annotated with three published expression-based classifications, comprising from three to six subtypes. Our results showed that, while methylation profiles provide a further basis for segregation of certain (Inflammatory and Goblet-like finer-grained expression-based subtypes, they also suggest that other finer-grained subtypes are not distinctive and can be considered as a single subtype.

  11. Detection and Classification of Human Body Odor Using an Electronic Nose

    Directory of Open Access Journals (Sweden)

    Teerakiat Kerdcharoen

    2009-09-01

    Full Text Available An electronic nose (E-nose has been designed and equipped with software that can detect and classify human armpit body odor. An array of metal oxide sensors was used for detecting volatile organic compounds. The measurement circuit employs a voltage divider resistor to measure the sensitivity of each sensor. This E-nose was controlled by in-house developed software through a portable USB data acquisition card with a principle component analysis (PCA algorithm implemented for pattern recognition and classification. Because gas sensor sensitivity in the detection of armpit odor samples is affected by humidity, we propose a new method and algorithms combining hardware/software for the correction of the humidity noise. After the humidity correction, the E-nose showed the capability of detecting human body odor and distinguishing the body odors from two persons in a relative manner. The E-nose is still able to recognize people, even after application of deodorant. In conclusion, this is the first report of the application of an E-nose for armpit odor recognition.

  12. Epigenetic changes in virus-associated human cancers

    Institute of Scientific and Technical Information of China (English)

    Hsin Pai LI; Yu Wei LEU; Yu Sun CHANG

    2005-01-01

    Epigenetics of human cancer becomes an area of emerging research direction due to a growing understanding of specific epigenetic pathways and rapid development of detection technologies. Aberrant promoter hypermethylation is a prevalent phenonmena in human cancers. Tumor suppressor genes are often hypermethylated due to the increased activity or deregulation of DNMTs. Increasing evidence also reveals that viral genes are one of the key players in regulating DNA methylation. In this review, we will focus on hypermethylation and tumor suppressor gene silencing and the signal pathways that are involved, particularly in cancers closely associated with the hepatitis B virus, simian virus 40 (SV40), and Epstein-Barr virus. In addition, we will discuss current technologies for genome-wide detection of epigenetically regulated targets, which allow for systematic DNA hypermethylation analysis. The study of epigenetic changes should provide a global view of gene profile in cancer, and epigenetic markers could be used for early detection,prognosis, and therapy of cancer.

  13. Tea and cancer prevention: studies in animals and humans.

    Science.gov (United States)

    Chung, Fung-Lung; Schwartz, Joel; Herzog, Christopher R; Yang, Yang-Ming

    2003-10-01

    The role of tea in protection against cancer has been supported by ample evidence from studies in cell culture and animal models. However, epidemiological studies have generated inconsistent results, some of which associated tea with reduced risk of cancer, whereas others found that tea lacks protective activity against certain human cancers. These results raise questions about the actual role of tea in human cancer that needs to be addressed. This article is intended to provide a better perspective on this controversy by summarizing the laboratory studies in animals and humans with emphasis on animal tumor bioassays on skin, lung, mammary glands and colon, and the molecular and cellular mechanisms affected by tea. Finally, a recent small pilot intervention study with green tea in smokers is presented.

  14. Defining the cellular precursors to human breast cancer

    Science.gov (United States)

    Keller, Patricia J.; Arendt, Lisa M.; Skibinski, Adam; Logvinenko, Tanya; Klebba, Ina; Dong, Shumin; Smith, Avi E.; Prat, Aleix; Perou, Charles M.; Gilmore, Hannah; Schnitt, Stuart; Naber, Stephen P.; Garlick, Jonathan A.; Kuperwasser, Charlotte

    2012-01-01

    Human breast cancers are broadly classified based on their gene-expression profiles into luminal- and basal-type tumors. These two major tumor subtypes express markers corresponding to the major differentiation states of epithelial cells in the breast: luminal (EpCAM+) and basal/myoepithelial (CD10+). However, there are also rare types of breast cancers, such as metaplastic carcinomas, where tumor cells exhibit features of alternate cell types that no longer resemble breast epithelium. Until now, it has been difficult to identify the cell type(s) in the human breast that gives rise to these various forms of breast cancer. Here we report that transformation of EpCAM+ epithelial cells results in the formation of common forms of human breast cancer, including estrogen receptor-positive and estrogen receptor-negative tumors with luminal and basal-like characteristics, respectively, whereas transformation of CD10+ cells results in the development of rare metaplastic tumors reminiscent of the claudin-low subtype. We also demonstrate the existence of CD10+ breast cells with metaplastic traits that can give rise to skin and epidermal tissues. Furthermore, we show that the development of metaplastic breast cancer is attributable, in part, to the transformation of these metaplastic breast epithelial cells. These findings identify normal cellular precursors to human breast cancers and reveal the existence of a population of cells with epidermal progenitor activity within adult human breast tissues. PMID:21940501

  15. A SEMI-AUTOMATIC RULE SET BUILDING METHOD FOR URBAN LAND COVER CLASSIFICATION BASED ON MACHINE LEARNING AND HUMAN KNOWLEDGE

    Directory of Open Access Journals (Sweden)

    H. Y. Gu

    2017-09-01

    Full Text Available Classification rule set is important for Land Cover classification, which refers to features and decision rules. The selection of features and decision are based on an iterative trial-and-error approach that is often utilized in GEOBIA, however, it is time-consuming and has a poor versatility. This study has put forward a rule set building method for Land cover classification based on human knowledge and machine learning. The use of machine learning is to build rule sets effectively which will overcome the iterative trial-and-error approach. The use of human knowledge is to solve the shortcomings of existing machine learning method on insufficient usage of prior knowledge, and improve the versatility of rule sets. A two-step workflow has been introduced, firstly, an initial rule is built based on Random Forest and CART decision tree. Secondly, the initial rule is analyzed and validated based on human knowledge, where we use statistical confidence interval to determine its threshold. The test site is located in Potsdam City. We utilised the TOP, DSM and ground truth data. The results show that the method could determine rule set for Land Cover classification semi-automatically, and there are static features for different land cover classes.

  16. Endocrine therapy of human breast cancer grown in nude mice

    DEFF Research Database (Denmark)

    Brünner, N; Osborne, C K; Spang-Thomsen, M

    1987-01-01

    mice bearing transplanted human breast tumors have been proposed as such a model. This review therefore discusses the use of the athymic nude mouse model of the study of human breast cancer biology, and focuses on four subjects: 1. biological characteristics of heterotransplanted breast tumors; 2...

  17. Human cancer long non-coding RNA transcriptomes.

    Directory of Open Access Journals (Sweden)

    Ewan A Gibb

    Full Text Available Once thought to be a part of the 'dark matter' of the genome, long non-coding RNAs (lncRNAs are emerging as an integral functional component of the mammalian transcriptome. LncRNAs are a novel class of mRNA-like transcripts which, despite no known protein-coding potential, demonstrate a wide range of structural and functional roles in cellular biology. However, the magnitude of the contribution of lncRNA expression to normal human tissues and cancers has not been investigated in a comprehensive manner. In this study, we compiled 272 human serial analysis of gene expression (SAGE libraries to delineate lncRNA transcription patterns across a broad spectrum of normal human tissues and cancers. Using a novel lncRNA discovery pipeline we parsed over 24 million SAGE tags and report lncRNA expression profiles across a panel of 26 different normal human tissues and 19 human cancers. Our findings show extensive, tissue-specific lncRNA expression in normal tissues and highly aberrant lncRNA expression in human cancers. Here, we present a first generation atlas for lncRNA profiling in cancer.

  18. Human Papillomavirus (HPV) and Oropharyngeal Cancer

    Science.gov (United States)

    ... HPV? People get HPV from another person during intimate sexual contact. Most of the time, people get ... 17, 2017 Page last updated: July 17, 2017 Content source: Division of Cancer Prevention and Control, Centers ...

  19. Study of apoptosis in human liver cancers

    Institute of Scientific and Technical Information of China (English)

    Chang-Min Shan; Juan Li

    2002-01-01

    AIM: To investigate the action of apoptosis in occurrence ofliver cacinornas in vivo and the biological effect of Solanumlyratum Thumb on BEL-7404 cell line inducing apoptosis invitro.METHODS: The apoptosis in the liver carcinoma wasdetected with terminal deoxynucl neotidyl transferasemediated dUTP nick end labelling (TUNEL); the cancer cellscultured in DMED medium were treated with extract ofSolanum lyratum Thumb and observed under microscope,and their DNA was assayed by gel electrophoresis.RESULTS: In vivo apoptotic cells in the cancer adjacenttissues inceased; in vitro treatment of liver cancers withextract of Solanum lyratum Thumb could induce the cells tomanifest a typical apoptotic morphology. Their DNA wasfractured and a characteristic ladder pattem could be foundusing electrophoresis.CONCLUSION: In vivo the apoptosis of carcinomas waslower; maybe the cells divided quickly and then the cancersoccurred. In the cancer adjacent tissues, the apoptosispricked up, and in vitro Solarium lyratum Thumb couldinduce the apoptosis of BEL-7404 cells.

  20. Comparison of breast cancer mucin (BCM) and CA 15-3 in human breast cancer

    NARCIS (Netherlands)

    Garcia, M.B.; Blankenstein, M.A.; Wall, E. van der; Nortier, J.W.R.; Schornagel, J.H.; Thijssen, J.H.H.

    1990-01-01

    The Breast Cancer Mucin (BCM) enzyme immunoassay utilizes two monoclonal antibodies (Mab), M85/34 and F36/22, for the identification of a mucin-like glycoprotein in serum of breast cancer patients. We have compared BCM with CA 15-3, another member of the human mammary epithelial antigen

  1. Distribution of trace metal concentrations in paired cancerous and non-cancerous human stomach tissues

    Institute of Scientific and Technical Information of China (English)

    Mehmet Yaman; Gokce Kaya; Hayrettin Yekeler

    2007-01-01

    AIM: To assess whether trace metal concentrations (which influence metabolism as both essential and non-essential elements) are increased or decreased in cancerous tissues and to understand the precise role of these metals in carcinogenesis.METHODS: Concentrations of trace metals including Cd,Ni, Cu, Zn, Fe, Mg and Ca in both cancerous and noncancerous stomach tissue samples were determined by atomic absorption spectrometry (AAS). Tissue samples were digested using microwave energy. Slotted tube atom trap was used to improve the sensitivity of copper and cadmium in flame AAS determinations.RESULTS: From the obtained data in this study,the concentrations of nickel, copper and iron in the cancerous human stomach were found to be significantly higher than those in the non-cancerous tissues, by using t-test for the paired samples. Furthermore, the average calcium concentrations in the cancerous stomach tissue samples were found to be significantly lower than those in the non-cancerous stomach tissue samples by using t-test. Exceedingly high Zn concentrations (207-826 mg/kg) were found in two paired stomach tissue samples from both cancerous and non-cancerous parts.CONCLUSION: In contrast to the literature data for Cu and Fe, the concentrations of copper, iron and nickel in cancerous tissue samples are higher than those in the non-cancerous samples. Furthermore, the Ca levels are lower in cancerous tissue samples than in non-cancerous tissue samples.

  2. Human papillomavirus in cervical cancer and oropharyngeal cancer: One cause, two diseases.

    Science.gov (United States)

    Berman, Tara A; Schiller, John T

    2017-06-15

    Human papillomavirus (HPV) causes greater than 5% of cancers worldwide, including all cervical cancers and an alarmingly increasing proportion of oropharyngeal cancers (OPCs). Despite markedly reduced cervical cancer incidence in industrialized nations with organized screening programs, cervical cancer remains the second most common cause of death from cancer in women worldwide, as developing countries lack resources for universal, high-quality screening. In the United States, HPV-related OPC is only 1 of 5 cancers with a rising incidence since 1975 and now has taken over the cervix as the most common site of HPV-related cancer. Similar trends follow throughout North America and Europe. The need for early detection and prevention is paramount. Despite the common etiologic role of HPV in the development of cervical cancer and HPV-associated OPC, great disparity exists between incidence, screening modalities (or lack thereof), treatment, and prevention in these 2 very distinct cohorts. These differences in cervical cancer and HPV-associated OPC and their impact are discussed here. Cancer 2017;123:2219-2229. © 2017 American Cancer Society. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

  3. Chestnut extract induces apoptosis in AGS human gastric cancer cells.

    Science.gov (United States)

    Lee, Hyun Sook; Kim, Eun Ji; Kim, Sun Hyo

    2011-06-01

    In Korea, chestnut production is increasing each year, but consumption is far below production. We investigated the effect of chestnut extracts on antioxidant activity and anticancer effects. Ethanol extracts of raw chestnut (RCE) or chestnut powder (CPE) had dose-dependent superoxide scavenging activity. Viable numbers of MDA-MD-231 human breast cancer cells, DU145 human prostate cancer cells, and AGS human gastric cancer cells decreased by 18, 31, and 69%, respectively, following treatment with 200 µg/mL CPE for 24 hr. CPE at various concentrations (0-200 µg/mL) markedly decreased AGS cell viability and increased apoptotic cell death dose and time dependently. CPE increased the levels of cleaved caspase-8, -7, -3, and poly (ADP-ribose) polymerase in a dose-dependent manner but not cleaved caspase-9. CPE exerted no effects on Bcl-2 and Bax levels. The level of X-linked inhibitor of apoptosis protein decreased within a narrow range following CPE treatment. The levels of Trail, DR4, and Fas-L increased dose-dependently in CPE-treated AGS cells. These results show that CPE decreases growth and induces apoptosis in AGS gastric cancer cells and that activation of the death receptor pathway contributes to CPE-induced apoptosis in AGS cells. In conclusion, CPE had more of an effect on gastric cancer cells than breast or prostate cancer cells, suggesting that chestnuts would have a positive effect against gastric cancer.

  4. Dietary Acrylamide and Human Cancer: A Systematic Review of Literature

    Science.gov (United States)

    Nagy, Tim R.; Barnes, Stephen; Groopman, John

    2014-01-01

    Cancer remains the second leading cause of death in the United States, and the numbers of cases are expected to continue to rise worldwide. Cancer prevention strategies are crucial for reducing the cancer burden. The carcinogenic potential of dietary acrylamide exposure from cooked foods is unknown. Acrylamide is a by-product of the common Maillard reaction where reducing sugars (i.e., fructose and glucose) react with the amino acid, asparagine. Based on the evidence of acrylamide carcinogenicity in animals, the International Agency for Research on Cancer has classified acrylamide as a group 2A carcinogen for humans. Since the discovery of acrylamide in foods in 2002, a number of studies have explored its potential as a human carcinogen. This paper outlines a systematic review of dietary acrylamide and human cancer, acrylamide exposure and internal dose, exposure assessment methods in the epidemiologic studies, existing data gaps, and future directions. A majority of the studies reported no statistically significant association between dietary acrylamide intake and various cancers, and few studies reported increased risk for renal, endometrial, and ovarian cancers; however, the exposure assessment has been inadequate leading to potential misclassification or underestimation of exposure. Future studies with improved dietary acrylamide exposure assessment are encouraged. PMID:24875401

  5. Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model

    Science.gov (United States)

    Ma, Ling; Lu, Guolan; Wang, Dongsheng; Wang, Xu; Chen, Zhuo Georgia; Muller, Susan; Chen, Amy; Fei, Baowei

    2017-03-01

    Hyperspectral imaging (HSI) is an emerging imaging modality that can provide a noninvasive tool for cancer detection and image-guided surgery. HSI acquires high-resolution images at hundreds of spectral bands, providing big data to differentiating different types of tissue. We proposed a deep learning based method for the detection of head and neck cancer with hyperspectral images. Since the deep learning algorithm can learn the feature hierarchically, the learned features are more discriminative and concise than the handcrafted features. In this study, we adopt convolutional neural networks (CNN) to learn the deep feature of pixels for classifying each pixel into tumor or normal tissue. We evaluated our proposed classification method on the dataset containing hyperspectral images from 12 tumor-bearing mice. Experimental results show that our method achieved an average accuracy of 91.36%. The preliminary study demonstrated that our deep learning method can be applied to hyperspectral images for detecting head and neck tumors in animal models.

  6. Decorin in Human Colon Cancer: Localization In Vivo and Effect on Cancer Cell Behavior In Vitro.

    Science.gov (United States)

    Nyman, Marie C; Sainio, Annele O; Pennanen, Mirka M; Lund, Riikka J; Vuorikoski, Sanna; Sundström, Jari T T; Järveläinen, Hannu T

    2015-09-01

    Decorin is generally recognized as a tumor suppressing molecule. Nevertheless, although decorin has been shown to be differentially expressed in malignant tissues, it has often remained unclear whether, in addition to non-malignant stromal cells, cancer cells also express it. Here, we first used two publicly available databases to analyze the current information about decorin expression and immunoreactivity in normal and malignant human colorectal tissue samples. The analyses demonstrated that decorin expression and immunoreactivity may vary in cancer cells of human colorectal tissues. Therefore, we next examined decorin expression in normal, premalignant and malignant human colorectal tissues in more detail using both in situ hybridization and immunohistochemistry for decorin. Our results invariably demonstrate that malignant cells within human colorectal cancer tissues are devoid of both decorin mRNA and immunoreactivity. Identical results were obtained for cells of neuroendocrine tumors of human colon. Using RT-qPCR, we showed that human colon cancer cell lines are also decorin negative, in accordance with the above in vivo results. Finally, we demonstrate that decorin transduction of human colon cancer cell lines causes a significant reduction in their colony forming capability. Thus, strategies to develop decorin-based adjuvant therapies for human colorectal malignancies are highly rational. © The Author(s) 2015.

  7. Reflections on the New Classification of Tumors by the WHO and Changes in Esophageal Cancer in a High-risk Area

    Institute of Scientific and Technical Information of China (English)

    Zhifeng Chen

    2006-01-01

    In year 2000, a book entitled the Pathology and Genetics of Tumors of the Digestive System was published by the WHO, presenting some new diagnostic criteria and treatment principles. I have analyzed the epidemiologic change of tumors in over 30 years in the high-risk area with esophageal cancer. The following phenomenon was found: accompanied by the sharp decrease in the incidence and mortality of esophageal cancer, there was an increase in the incidence and death rate of stomach cancer involving cardiac cancer. This fact should be considered when analyzing the sharp decrease in esophageal cancer incidence and mortality rate. More attention was given to diagnosis of cardiac cancer; at the same time it is more practical to improve the early screening of cancers. To observe the development of high and lowgrade intraepithelial neoplasms will be an urgent task for esophageal cancer research in the high risk area, according to WHO's new classification.

  8. Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification.

    Science.gov (United States)

    Tian, Xue W; Lim, Joon S

    2015-01-01

    Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.

  9. [Discordance between Clinical and Pathological TNM Classifications in Patients with Oropharyngeal Cancer - Influence on Treatment and Prognosis].

    Science.gov (United States)

    Kordač, P; Kalfeřt, D; Smatanová, K; Laco, J; Vošmik, M; Čelakovský, P; Chrobok, V

    2016-01-01

    The aim of this study was to determine the percentage of discordance between clinical (c) and pathological (p) TNM classifications in cases of oropharyngeal carcinoma and whether it influences recurrence rate and prognosis of primary disease. Fifty-one patients with oropharyngeal carcinoma who underwent primary surgical treatment were included in this retrospective study. Clinical TNM was determined on the basis of clinical examinations and imaging (US, CT, or MRI), and pathological TNM was determined by a histopathologist (analysis of the primary tumor and neck lymph nodes). Concordance and discordance were statistically evaluated. As potential prognostic factors, we statistically analyzed tumor recurrence, specific and nonspecific patient survival, patient age, extent of primary tumor, lymph node positivity, number of removed lymph nodes, and positive tumor margins. Discordance in the TNM classification was found in 27 cases. Disease-free survival was shorter in patients with discordance in T, and this was statistically significant (p = 0.034). Six patients died due to primary disease (11.8%). Disease-specific survival was at the limit of statistical significance (p = 0.069). Discordance between clinical and pathological TNM classifications was 52.9% patients with oropharyngeal carcinoma. Discordance in T is a potential prognostic factor. Improvement in cancer treatment to some extent relies on preoperative staging and should influence the decision about whether or not to administer adjuvant oncological treatment.

  10. Thermographic image analysis for classification of ACL rupture disease, bone cancer, and feline hyperthyroid, with Gabor filters

    Science.gov (United States)

    Alvandipour, Mehrdad; Umbaugh, Scott E.; Mishra, Deependra K.; Dahal, Rohini; Lama, Norsang; Marino, Dominic J.; Sackman, Joseph

    2017-05-01

    Thermography and pattern classification techniques are used to classify three different pathologies in veterinary images. Thermographic images of both normal and diseased animals were provided by the Long Island Veterinary Specialists (LIVS). The three pathologies are ACL rupture disease, bone cancer, and feline hyperthyroid. The diagnosis of these diseases usually involves radiology and laboratory tests while the method that we propose uses thermographic images and image analysis techniques and is intended for use as a prescreening tool. Images in each category of pathologies are first filtered by Gabor filters and then various features are extracted and used for classification into normal and abnormal classes. Gabor filters are linear filters that can be characterized by the two parameters wavelength λ and orientation θ. With two different wavelength and five different orientations, a total of ten different filters were studied. Different combinations of camera views, filters, feature vectors, normalization methods, and classification methods, produce different tests that were examined and the sensitivity, specificity and success rate for each test were produced. Using the Gabor features alone, sensitivity, specificity, and overall success rates of 85% for each of the pathologies was achieved.

  11. Progress toward automatic classification of human brown adipose tissue using biomedical imaging

    Science.gov (United States)

    Gifford, Aliya; Towse, Theodore F.; Walker, Ronald C.; Avison, Malcom J.; Welch, E. B.

    2015-03-01

    Brown adipose tissue (BAT) is a small but significant tissue, which may play an important role in obesity and the pathogenesis of metabolic syndrome. Interest in studying BAT in adult humans is increasing, but in order to quantify BAT volume in a single measurement or to detect changes in BAT over the time course of a longitudinal experiment, BAT needs to first be reliably differentiated from surrounding tissue. Although the uptake of the radiotracer 18F-Fluorodeoxyglucose (18F-FDG) in adipose tissue on positron emission tomography (PET) scans following cold exposure is accepted as an indication of BAT, it is not a definitive indicator, and to date there exists no standardized method for segmenting BAT. Consequently, there is a strong need for robust automatic classification of BAT based on properties measured with biomedical imaging. In this study we begin the process of developing an automated segmentation method based on properties obtained from fat-water MRI and PET-CT scans acquired on ten healthy adult subjects.

  12. Growth-stimulatory effect of resveratrol in human cancer cells.

    Science.gov (United States)

    Fukui, Masayuki; Yamabe, Noriko; Kang, Ki Sung; Zhu, Bao Ting

    2010-08-01

    Earlier studies have shown that resveratrol could induce death in several human cancer cell lines in culture. Here we report our observation that resveratrol can also promote the growth of certain human cancer cells when they are grown either in culture or in athymic nude mice as xenografts. At relatively low concentrations (cells, but this effect was not observed in several other human cell lines tested. Analysis of cell signaling molecules showed that resveratrol induced the activation of JNK, p38, Akt, and NF-kappaB signaling pathways in these cells. Further analysis using pharmacological inhibitors showed that only the NF-kappaB inhibitor (BAY11-7082) abrogated the growth-stimulatory effect of resveratrol in cultured cells. In athymic nude mice, resveratrol at 16.5 mg/kg body weight enhanced the growth of MDA-MB-435s xenografts compared to the control group, while resveratrol at the 33 mg/kg body weight dose did not have a similar effect. Additional analyses confirmed that resveratrol stimulated cancer cell growth in vivo through activation of the NF-kappaB signaling pathway. Taken together, these observations suggest that resveratrol at low concentrations could stimulate the growth of certain types of human cancer cells in vivo. This cell type-specific mitogenic effect of resveratrol may also partly contribute to the procarcinogenic effect of alcohol consumption (rich in resveratrol) in the development of certain human cancers.

  13. Bilateral image subtraction features for multivariate automated classification of breast cancer risk

    Science.gov (United States)

    Celaya-Padilla, Jose M.; Rodriguez-Rojas, Juan; Galván-Tejada, Jorge I.; Martínez-Torteya, Antonio; Treviño, Victor; Tamez-Peña, José G.

    2014-03-01

    Early tumor detection is key in reducing breast cancer deaths and screening mammography is the most widely available method for early detection. However, mammogram interpretation is based on human radiologist, whose radiological skills, experience and workload makes radiological interpretation inconsistent. In an attempt to make mammographic interpretation more consistent, computer aided diagnosis (CADx) systems has been introduced. This paper presents an CADx system aimed to automatically triage normal mammograms form suspicious mammograms. The CADx system co-reregister the left and breast images, then extracts image features from the co-registered mammographic bilateral sets. Finally, an optimal logistic multivariate model is generated by means of an evolutionary search engine. In this study, 440 subjects form the DDSM public data sets were used: 44 normal mammograms, 201 malignant mass mammograms, and 195 mammograms with malignant calci cations. The results showed a cross validation accuracy of 0.88 and an area under receiver operating characteristic (AUC) of 0.89 for the calci cations vs. normal mammograms. The optimal mass vs. normal mammograms model obtained an accuracy of 0.85 and an AUC of 0.88.

  14. The oncogenic potential of human cytomegalovirus and breast cancer.

    Directory of Open Access Journals (Sweden)

    Georges eHerbein

    2014-08-01

    Full Text Available Breast cancer is among the leading causes of cancer-related death among women. The vast majority of breast cancers are carcinomas that originate from cells lining the milk-forming ducts of the mammary gland. Numerous articles indicate that breast tumors exhibit diverse phenotypes depending on their distinct physiopathological signatures, clinical courses and therapeutic possibilities. The human cytomegalovirus (HCMV is a multifaceted highly host specific betaherpesvirus that is regarded as asymptomatic or mildly pathogenic virus in immunocompetent host. HCMV may cause serious in utero infections as well as acute and chronic complications in immunocompromised individual. The involvement of HCMV in late inflammatory complications underscores its possible role in inflammatory diseases and cancer. HCMV targets a variety of cell types in vivo, including macrophages, epithelial cells, endothelial cells, fibroblasts, stromal cells, neuronal cells, smooth muscle cells, and hepatocytes. HCMV can be detected in the milk after delivery and thereby HCMV could spread to adjacent mammary epithelial cells. HCMV also infects macrophages and induces an atypical M1/M2 phenotype, close to the tumor associated macrophage phenotype, which is associated with the release of cytokines involved in cancer initiation or promotion and breast cancer of poor prognosis. HCMV antigens and DNA have been detected in tissue biopsies of breast cancers and elevation in serum HCMV IgG antibody levels has been reported to precede the development of breast cancer in some women. In this review, we will discuss the potential role of HCMV in the initiation and progression of breast cancer.

  15. Systematic variation in gene expression patterns in human cancer cell lines

    Energy Technology Data Exchange (ETDEWEB)

    Ross, Douglas T.; Scherf, Uwe; Eisen, Michael B.; Perou, Charles M.; Rees, Christian; Spellman, Paul; Iyer, Vishwanath; Jeffrey, Stefanie S.; Van de Rijn, Matt; Waltham, Mark; Pergamenschikov, Alexander; Lee, Jeffrey C.F.; Lashkari, Deval; Shalon, Dari; Myers, Timothy G.; Weinstein, John N.; Botstein, David; Brown, Patrick O.

    2000-01-01

    We used cDNA micro arrays to explore the variation in expression of approximately 8,000 unique genes among the 60 cell lines used in the National Cancer Institute s screen for anti-cancer drugs. Classification of the cell lines based solely on the observed patterns of gene expression revealed a correspondence to the ostensible origins of the tumors from which the cell lines were derived. The consistent relationship between the gene expression patterns and the tissue of origin allowed us to recognize outliers whose previous classification appeared incorrect. Specific features of the gene expression patterns appeared to be related to physiological properties of the cell lines, such as their doubling time in culture, drug metabolism or the interferon response. Comparison of gene expression patterns in the cell lines to those observed in normal breast tissue or in breast tumor specimens revealed features of the expression patterns in the tumors that had recognizable counterparts in specific cell lines, reflecting the tumor, stromal and inflammatory components of the tumor tissue. These results provided a novel molecular characterization of this important group of human cell lines and their relationships to tumors in vivo.

  16. Frequency of TERT promoter mutations in human cancers.

    Science.gov (United States)

    Vinagre, João; Almeida, Ana; Pópulo, Helena; Batista, Rui; Lyra, Joana; Pinto, Vasco; Coelho, Ricardo; Celestino, Ricardo; Prazeres, Hugo; Lima, Luis; Melo, Miguel; da Rocha, Adriana Gaspar; Preto, Ana; Castro, Patrícia; Castro, Ligia; Pardal, Fernando; Lopes, José Manuel; Santos, Lúcio Lara; Reis, Rui Manuel; Cameselle-Teijeiro, José; Sobrinho-Simões, Manuel; Lima, Jorge; Máximo, Valdemar; Soares, Paula

    2013-01-01

    Reactivation of telomerase has been implicated in human tumorigenesis, but the underlying mechanisms remain poorly understood. Here we report the presence of recurrent somatic mutations in the TERT promoter in cancers of the central nervous system (43%), bladder (59%), thyroid (follicular cell-derived, 10%) and skin (melanoma, 29%). In thyroid cancers, the presence of TERT promoter mutations (when occurring together with BRAF mutations) is significantly associated with higher TERT mRNA expression, and in glioblastoma we find a trend for increased telomerase expression in cases harbouring TERT promoter mutations. Both in thyroid cancers and glioblastoma, TERT promoter mutations are significantly associated with older age of the patients. Our results show that TERT promoter mutations are relatively frequent in specific types of human cancers, where they lead to enhanced expression of telomerase.

  17. Pre-clinical Orthotopic Murine Model of Human Prostate Cancer.

    Science.gov (United States)

    Shahryari, Varahram; Nip, Hannah; Saini, Sharanjot; Dar, Altaf A; Yamamura, Soichiro; Mitsui, Yozo; Colden, Melissa; Bucay, Nathan; Tabatabai, Laura Z; Greene, Kirsten; Deng, Guoren; Tanaka, Yuichiro; Dahiya, Rajvir; Majid, Shahana

    2016-08-29

    To study the multifaceted biology of prostate cancer, pre-clinical in vivo models offer a range of options to uncover critical biological information about this disease. The human orthotopic prostate cancer xenograft mouse model provides a useful alternative approach for understanding the specific interactions between genetically and molecularly altered tumor cells, their organ microenvironment, and for evaluation of efficacy of therapeutic regimens. This is a well characterized model designed to study the molecular events of primary tumor development and it recapitulates the early events in the metastatic cascade prior to embolism and entry of tumor cells into the circulation. Thus it allows elucidation of molecular mechanisms underlying the initial phase of metastatic disease. In addition, this model can annotate drug targets of clinical relevance and is a valuable tool to study prostate cancer progression. In this manuscript we describe a detailed procedure to establish a human orthotopic prostate cancer xenograft mouse model.

  18. [Use of human recombinant erythropoietin in children with cancer].

    Science.gov (United States)

    Guyot, D; Margueritte, G

    2005-09-01

    Eighty percent of children with cancer suffer from anemia at the time of diagnosis. The physiopathology of anemia is complex. Although anemia can be life threatening, its consequences on the physical, psychological and social state of the child are often minimized. Blood transfusion is the main treatment of anemia: its efficacy is immediate but shortlasting, and it involves infectious and hemolytic risks. The human recombinant erythropoietin has been used for more than 25-years, and is often prescribed to adults with cancer and anemia. The human recombinant erythropoietin rHuEPO is nowadays used when blood transfusion is contra-indicated because of religious or cultural considerations, although several promising studies have been conducted about rHuEPO and children with cancer since 1996: it might be soon the preferential alternative treatment to anemia in children with cancer.

  19. Gene transcriptional networks integrate microenvironmental signals in human breast cancer.

    Science.gov (United States)

    Xu, Ren; Mao, Jian-Hua

    2011-04-01

    A significant amount of evidence shows that microenvironmental signals generated from extracellular matrix (ECM) molecules, soluble factors, and cell-cell adhesion complexes cooperate at the extra- and intracellular level. This synergetic action of microenvironmental cues is crucial for normal mammary gland development and breast malignancy. To explore how the microenvironmental genes coordinate in human breast cancer at the genome level, we have performed gene co-expression network analysis in three independent microarray datasets and identified two microenvironment networks in human breast cancer tissues. Network I represents crosstalk and cooperation of ECM microenvironment and soluble factors during breast malignancy. The correlated expression of cytokines, chemokines, and cell adhesion proteins in Network II implicates the coordinated action of these molecules in modulating the immune response in breast cancer tissues. These results suggest that microenvironmental cues are integrated with gene transcriptional networks to promote breast cancer development.

  20. Anticancer Properties of Capsaicin Against Human Cancer.

    Science.gov (United States)

    Clark, Ruth; Lee, Seong-Ho

    2016-03-01

    There is persuasive epidemiological and experimental evidence that dietary phytochemicals have anticancer activity. Capsaicin is a bioactive phytochemical abundant in red and chili peppers. While the preponderance of the data strongly indicates significant anticancer benefits of capsaicin, more information to highlight molecular mechanisms of its action is required to improve our knowledge to be able to propose a potential therapeutic strategy for use of capsaicin against cancer. Capsaicin has been shown to alter the expression of several genes involved in cancer cell survival, growth arrest, angiogenesis and metastasis. Recently, many research groups, including ours, found that capsaicin targets multiple signaling pathways, oncogenes and tumor-suppressor genes in various types of cancer models. In this review article, we highlight multiple molecular targets responsible for the anticancer mechanism of capsaicin. In addition, we deal with the benefits of combinational use of capsaicin with other dietary or chemotherapeutic compounds, focusing on synergistic anticancer activities.

  1. Breast Cancer Survival Defined by the ER/PR/HER2 Subtypes and a Surrogate Classification according to Tumor Grade and Immunohistochemical Biomarkers

    Directory of Open Access Journals (Sweden)

    Carol A. Parise

    2014-01-01

    Full Text Available Introduction. ER, PR, and HER2 are routinely available in breast cancer specimens. The purpose of this study is to contrast breast cancer-specific survival for the eight ER/PR/HER2 subtypes with survival of an immunohistochemical surrogate for the molecular subtype based on the ER/PR/HER2 subtypes and tumor grade. Methods. We identified 123,780 cases of stages 1–3 primary female invasive breast cancer from California Cancer Registry. The surrogate classification was derived using ER/PR/HER2 and tumor grade. Kaplan-Meier survival analysis and Cox proportional hazards modeling were used to assess differences in survival and risk of mortality for the ER/PR/HER2 subtypes and surrogate classification within each stage. Results. The luminal B/HER2− surrogate classification had a higher risk of mortality than the luminal B/HER2+ for all stages of disease. There was no difference in risk of mortality between the ER+/PR+/HER2− and ER+/PR+/HER2+ in stage 3. With one exception in stage 3, the ER-negative subtypes all had an increased risk of mortality when compared with the ER-positive subtypes. Conclusions. Assessment of survival using ER/PR/HER2 illustrates the heterogeneity of HER2+ subtypes. The surrogate classification provides clear separation in survival and adjusted mortality but underestimates the wide variability within the subtypes that make up the classification.

  2. Mechanism-based classification and physical therapy management of persons with cancer pain: A prospective case series

    Directory of Open Access Journals (Sweden)

    Senthil P Kumar

    2013-01-01

    Full Text Available Context: Mechanism-based classification (MBC was established with current evidence and physical therapy (PT management methods for both cancer and for noncancer pain. Aims: This study aims to describe the efficacy of MBC-based PT in persons with primary complaints of cancer pain. Settings and Design: A prospective case series of patients who attended the physiotherapy department of a multispecialty university-affiliated teaching hospital. Material and Methods: A total of 24 adults (18 female, 6 male aged 47.5 ± 10.6 years, with primary diagnosis of heterogeneous group of cancer, chief complaints of chronic disabling pain were included in the study on their consent for participation The patients were evaluated and classified on the basis of five predominant mechanisms for pain. Physical therapy interventions were recommended based on mechanisms identified and home program was prescribed with a patient log to ensure compliance. Treatments were given in five consecutive weekly sessions for five weeks each of 30 min duration. Statistical Analysis Used: Pre-post comparisons for pain severity (PS and pain interference (PI subscales of Brief pain inventory-Cancer pain (BPI-CP and, European organization for research and treatment in cancer-quality of life questionnaire (EORTC-QLQ-C30 were done using Wilcoxon signed-rank test at 95% confidence interval using SPSS for Windows version 16.0 (SPSS Inc, Chicago, IL. Results: There were statistically significant ( P < 0.05 reduction in pain severity, pain interference and total BPI-CP scores, and the EORTC-QLQ-C30. Conclusion: MBC-PT was effective for improving BPI-CP and EORTC-QLQ-C30 scores in people with cancer pain.

  3. Histological classification and stage of newly diagnosed bladder cancer in a population-based study from the Northeastern United States*

    Science.gov (United States)

    SCHNED, ALAN R.; ANDREW, ANGELINE S.; MARSIT, CARMEN J.; KELSEY, KARL T.; ZENS, MICHAEL S.; KARAGAS, MARGARET R.

    2009-01-01

    Objective There are limited data on the distribution of bladder cancers in the general population, classified by World Health Organization (WHO)/International Society of Urological Pathology (ISUP) criteria. This study evaluated the classification and stage of bladder cancers as part of a population-based epidemiological study of bladder cancer in the Northeastern United States. Material and methods All New Hampshire residents with bladder cancer newly diagnosed from 1998 to 2000 were identified through the state cancer registry. All slides were reviewed by a single pathologist. Tumors were classified by two sets of standard criteria. Results The retrieval rate for cases was over 90%. Of 342 cases reviewed, 15 were excluded for technical reasons or because malignancy was not definitively diagnosed. According to WHO/ISUP criteria, 25.7% of tumors were papillary urothelial neoplasms of low malignant potential (PUNLMP), 34.3% low-grade papillary carcinomas, 22.6% high-grade papillary carcinomas, 10.1% non-papillary urothelial carcinomas and 5.5% carcinoma in situ. By WHO (1973) criteria, 52.5% of tumors were grade 1, 21.4% grade 2 and 26.1% grade 3. Two-thirds of all tumors were stage Ta, 20.8% stage T1 and 7.6% stage ≥T2. 100% of PUNLMPs were non-invasive, 6.3% of low-grade carcinomas were invasive and 64.9% of high-grade carcinomas were invasive. Conclusions Compared to clinic or hospital referral-based series, this study documents a higher percentage of non-invasive tumors and a lower percentage of muscle-invasive tumors. There was also a higher percentage of PUNLMP tumors and fewer high-grade papillary carcinomas than in other series. These results may more accurately reflect prevalence data for bladder cancer grade and stage, although geographic variability may exist. PMID:18432530

  4. Role of ARPC2 in Human Gastric Cancer

    OpenAIRE

    Jun Zhang; Yi Liu; Chang-Jun Yu; Fu Dai; Jie Xiong; Hong-Jun Li; Zheng-Sheng Wu; Rui Ding; Hong Wang

    2017-01-01

    Gastric cancer continues to be the second most frequent cause of cancer deaths worldwide. However, the exact molecular mechanisms are still unclear. Further research to find potential targets for therapy is critical and urgent. In this study, we found that ARPC2 promoted cell proliferation and invasion in the human cancer cell line MKN-28 using a cell total number assay, MTT (3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide) assay, cell colony formation assay, migration assay...

  5. Immune and Inflammatory Cell Composition of Human Lung Cancer Stroma.

    Directory of Open Access Journals (Sweden)

    G-Andre Banat

    Full Text Available Recent studies indicate that the abnormal microenvironment of tumors may play a critical role in carcinogenesis, including lung cancer. We comprehensively assessed the number of stromal cells, especially immune/inflammatory cells, in lung cancer and evaluated their infiltration in cancers of different stages, types and metastatic characteristics potential. Immunohistochemical analysis of lung cancer tissue arrays containing normal and lung cancer sections was performed. This analysis was combined with cyto-/histomorphological assessment and quantification of cells to classify/subclassify tumors accurately and to perform a high throughput analysis of stromal cell composition in different types of lung cancer. In human lung cancer sections we observed a significant elevation/infiltration of total-T lymphocytes (CD3+, cytotoxic-T cells (CD8+, T-helper cells (CD4+, B cells (CD20+, macrophages (CD68+, mast cells (CD117+, mononuclear cells (CD11c+, plasma cells, activated-T cells (MUM1+, B cells, myeloid cells (PD1+ and neutrophilic granulocytes (myeloperoxidase+ compared with healthy donor specimens. We observed all of these immune cell markers in different types of lung cancers including squamous cell carcinoma, adenocarcinoma, adenosquamous cell carcinoma, small cell carcinoma, papillary adenocarcinoma, metastatic adenocarcinoma, and bronchioloalveolar carcinoma. The numbers of all tumor-associated immune cells (except MUM1+ cells in stage III cancer specimens was significantly greater than those in stage I samples. We observed substantial stage-dependent immune cell infiltration in human lung tumors suggesting that the tumor microenvironment plays a critical role during lung carcinogenesis. Strategies for therapeutic interference with lung cancer microenvironment should consider the complexity of its immune cell composition.

  6. Autophagy Therapeutic Potential of Garlic in Human Cancer Therapy

    Directory of Open Access Journals (Sweden)

    Yung-Lin Chu

    2013-07-01

    Full Text Available Cancer is one of the deadliest diseases against humans. To tackle this menace, humans have developed several high-technology therapies, such as chemotherapy, tomotherapy, targeted therapy, and antibody therapy. However, all these therapies have their own adverse side effects. Therefore, recent years have seen increased attention being given to the natural food for complementary therapy, which have less side effects. Garlic 大 蒜 Dà Suàn; Allium sativum, is one of most powerful food used in many of the civilizations for both culinary and medicinal purpose. In general, these foods induce cancer cell death by apoptosis, autophagy, or necrosis. Studies have discussed how natural food factors regulate cell survival or death by autophagy in cancer cells. From many literature reviews, garlic could not only induce apoptosis but also autophagy in cancer cells. Autophagy, which is called type-II programmed cell death, provides new strategy in cancer therapy. In conclusion, we wish that garlic could be the pioneer food of complementary therapy in clinical cancer treatment and increase the life quality of cancer patients.

  7. Expression of Obesity Hormone Leptin in Human Colorectal Cancer

    Institute of Scientific and Technical Information of China (English)

    Jin-chun Cong; Xian-wei Dai; Ming-yang Shen; Jun-jiang Wang; Chun-sheng Chen; Hong Zhang; Lei Qiao

    2009-01-01

    Objective: The obesity hormone, leptin, has been found to participate in the development and proliferation of normal and malignant tissues. The aim of this study was to evaluate the role of leptin in human colorectal cancer.Methods: Serum leptin levels were measured via ABC-ELLSA in 30 colorectal cancers and 24 normal controls. Leptin concentration in colorectal cancer was analyzed in terms of selected clinicopathological features and some oncogenes.Results: The mean concentration of leptin was significantly higher for colorectal cancers(3.54±1.46 ng/ml) than normal controls(2.27±0.99 ng/ml), no gender difference was observed in this study. Leptin expression in poorly differentiated tumors was obviously lower than those in moderately and well differentiated tumors. There were no statistically significant correlations between leptin and the serum CEA and CA199 in colorectal cancers (P>0.05), and between leptin and the expressions of K-RAS, P53, APC, DCC genes in tumor tissues (P>0.05).Conclusion: Leptin is overexpressed in human colorectal cancer, which is related to the differentiation degrees of the tumor. There is no correlation between leptin expression and chages of oncogenes in colorectal cancers.

  8. Bionutrition and oral cancer in humans.

    Science.gov (United States)

    Enwonwu, C O; Meeks, V I

    1995-01-01

    Tobacco (smoking and smokeless) use and excessive consumption of alcohol are considered the main risk factors for oral cancer (ICD9 140-149). Conspicuous national and international variations in oral cancer incidence and mortality rates, as well as observations in migrant populations, raise the possibility that diet and nutritional status could be an important etiologic factor in oral carcinogenesis. As shown in this report, abuse of alcohol and tobacco has serious nutritional implications for the host, and generates increased production of reactive free radicals as well as eliciting immunosuppression. Maintenance of optimal competence of the immune system is critical for cancer surveillance. Active oxygen species and other reactive free radicals mediate phenotypic and genotypic alterations that lead from mutation to neoplasia. Consequently, the most widely used chemopreventive agents against oral cancer (e.g., vitamins A, E, C, and beta-carotene) are anti-oxidants/free radical scavengers. These anti-oxidants, both natural and synthetic, neutralize metabolic products (including reactive oxygen species), interfere with activation of procarcinogens, prevent binding of carcinogens to DNA, inhibit chromosome aberrations, restrain replication of the transformed cell, suppress actions of cancer promoters, and may even induce regression of precancerous oral lesions such as leukoplakia and erythroplakia. Malnutrition is characterized by marked tissue depletion of anti-oxidant nutrients, including GSH (gamma-glutamyl-cysteinyl-glycine), a key cellular anti-oxidant as well as a modulator of T-cell activation. GSH or its precursor cysteine inhibits activation of the nuclear transcription factor kB(NFkB), and has been shown to be protective against chemically induced oral cancer and leukoplakia. Alcohol-, tobacco-, and/or malnutrition-induced immunosuppression promotes impaired salivary gland function and oral mucosal immunity, a prominent reduction in the number of helper CD4

  9. Arsenic Exposure and the Induction of Human Cancers

    Directory of Open Access Journals (Sweden)

    Victor D. Martinez

    2011-01-01

    Full Text Available Arsenic is a metalloid, that is, considered to be a human carcinogen. Millions of individuals worldwide are chronically exposed through drinking water, with consequences ranging from acute toxicities to development of malignancies, such as skin and lung cancer. Despite well-known arsenic-related health effects, the molecular mechanisms involved are not fully understood; however, the arsenic biotransformation process, which includes methylation changes, is thought to play a key role. This paper explores the relationship of arsenic exposure with cancer development and summarizes current knowledge of the potential mechanisms that may contribute to the neoplastic processes observed in arsenic exposed human populations.

  10. Cytotoxicity of dietary flavonoids on different human cancer types

    Directory of Open Access Journals (Sweden)

    Katrin Sak

    2014-01-01

    Full Text Available Flavonoids are ubiquitous in nature. They are also in food, providing an essential link between diet and prevention of chronic diseases including cancer. Anticancer effects of these polyphenols depend on several factors: Their chemical structure and concentration, and also on the type of cancer. Malignant cells from different tissues reveal somewhat different sensitivity toward flavonoids and, therefore, the preferences of the most common dietary flavonoids to various human cancer types are analyzed in this review. While luteolin and kaempferol can be considered as promising candidate agents for treatment of gastric and ovarian cancers, respectively, apigenin, chrysin, and luteolin have good perspectives as potent antitumor agents for cervical cancer; cells from main sites of flavonoid metabolism (colon and liver reveal rather large fluctuations in anticancer activity probably due to exposure to various metabolites with different activities. Anticancer effect of flavonoids toward blood cancer cells depend on their myeloid, lymphoid, or erythroid origin; cytotoxic effects of flavonoids on breast and prostate cancer cells are highly related to the expression of hormone receptors. Different flavonoids are often preferentially present in certain food items, and knowledge about the malignant tissue-specific anticancer effects of flavonoids could be purposely applied both in chemoprevention as well as in cancer treatment.

  11. Immunological responses against human papilloma virus and human papilloma virus induced laryngeal cancer.

    Science.gov (United States)

    Chitose, Shun-ichi; Sakazaki, T; Ono, T; Kurita, T; Mihashi, H; Nakashima, T

    2010-06-01

    This study aimed to clarify the local immune status in the larynx in the presence of infection or carcinogenesis associated with human papilloma virus. Cytological samples (for human papilloma virus detection) and laryngeal secretions (for immunoglobulin assessment) were obtained from 31 patients with laryngeal disease, during microscopic laryngeal surgery. On histological examination, 12 patients had squamous cell carcinoma, four had laryngeal papilloma and 15 had other benign laryngeal disease. Cytological samples were tested for human papilloma virus DNA using the Hybrid Capture 2 assay. High risk human papilloma virus DNA was detected in 25 per cent of patients (three of 12) with laryngeal cancer. Low risk human papilloma virus DNA was detected only in three laryngeal papilloma patients. The mean laryngeal secretion concentrations of immunoglobulins M, G and A and secretory immunoglobulin A in human papilloma virus DNA positive patients were more than twice those in human papilloma virus DNA negative patients. A statistically significant difference was observed between the secretory immunoglobulin A concentrations in the two groups. Patients with laryngeal cancer had higher laryngeal secretion concentrations of each immunoglobulin type, compared with patients with benign laryngeal disease. The study assessed the mean laryngeal secretion concentrations of each immunoglobulin type in the 12 laryngeal cancer patients, comparing human papilloma virus DNA positive patients (n = 3) and human papilloma virus DNA negative patients (n = 9); the mean concentrations of immunoglobulins M, G and A and secretory immunoglobulin A tended to be greater in human papilloma virus DNA positive cancer patients, compared with human papilloma virus DNA negative cancer patients. These results suggest that the local laryngeal immune response is activated by infection or carcinogenesis due to human papilloma virus. The findings strongly suggest that secretory IgA has inhibitory activity

  12. Long-term survivors after pancreatectomy for cancer: the TNM classification is outdated.

    Science.gov (United States)

    Jouffret, Lionel; Turrini, Olivier; Ewald, Jacques; Moutardier, Vincent; Iovanna, Juan Lucio; Delpero, Jean-Robert

    2015-11-01

    According to knowledge, patients with resectable pancreatic adenocarcinoma (PA) should receive adjuvant gemcitabine-based chemotherapy. Thus, the tumour node metastasis (TNM) classification is not used to determine post-operative treatment but rather only to establish patient prognosis. However, the TNM classification does not include strong factors influencing survival, such as perineural invasion or margin status. This study compared the survival of patients with very similar tumours. From 1997 to 2007, 118 patients underwent pancreatectomy for PA. Twenty-six patients (22%) had long-term survival (>5 years; LTS group). According to the major prognostic factors of PA, we matched (1:1) patients in the LTS group with patients who did not have long-term survival (TNM classification. Three patients (12%) in the LTS group had positive margin status, and two patients (8%) had positive lymph node status. Unsurprisingly, the median survival for the control group versus the LTS group was 16 months versus not reached (P TNM classification is outdated because it did not influence adjuvant therapies and did not include two crucial factors: tumour biology and tumour response to chemo/radio therapies. © 2015 Royal Australasian College of Surgeons.

  13. Gene selection and classification for cancer microarray data based on machine learning and similarity measures

    Directory of Open Access Journals (Sweden)

    Liu Qingzhong

    2011-12-01

    Full Text Available Abstract Background Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease status, and how to determine the final gene set that is best for classification. Associations among genetic markers mean one can exploit information redundancy to potentially reduce classification cost in terms of time and money. Results To deal with redundant information and improve classification, we propose a gene selection method, Recursive Feature Addition, which combines supervised learning and statistical similarity measures. To determine the final optimal gene set for prediction and classification, we propose an algorithm, Lagging Prediction Peephole Optimization. By using six benchmark microarray gene expression data sets, we compared Recursive Feature Addition with recently developed gene selection methods: Support Vector Machine Recursive Feature Elimination, Leave-One-Out Calculation Sequential Forward Selection and several others. Conclusions On average, with the use of popular learning machines including Nearest Mean Scaled Classifier, Support Vector Machine, Naive Bayes Classifier and Random Forest, Recursive Feature Addition outperformed other methods. Our studies also showed that Lagging Prediction Peephole Optimization is superior to random strategy; Recursive Feature Addition with Lagging Prediction Peephole Optimization obtained better testing accuracies than the gene selection method varSelRF.

  14. Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data

    DEFF Research Database (Denmark)

    List, Markus; Hauschild, Anne-Christin; Tan, Qihua

    2014-01-01

    on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10...

  15. Classification and Diagnostic Output Prediction of Cancer Using Gene Expression Profiling and Supervised Machine Learning Algorithms

    DEFF Research Database (Denmark)

    Yoo, C.; Gernaey, Krist

    2008-01-01

    In this paper, a new supervised clustering and classification method is proposed. First, the application of discriminant partial least squares (DPLS) for the selection of a minimum number of key genes is applied on a gene expression microarray data set. Second, supervised hierarchical clustering ...

  16. Classification of micro-Doppler signatures of human aquatic activity through simulation and measurement using transferred learning

    Science.gov (United States)

    Kim, Youngwook; Park, Jinhee; Moon, Taesup

    2017-05-01

    Remote detection of human aquatic activity can be applied not only to ocean surveillance but also to rescue operations. When a human is illuminated by electromagnetic waves, a Doppler signal is generated from his or her moving parts. Indeed, bodily movements are what make humans' micro-Doppler signatures unique, offering a chance to classify human motions. Certain studies have analyzed and attempted to recognize human aquatic activity, but the topic has yet to be extensively studied. In the present research, we simulate the micro-Doppler signatures of a swimming person in an attempt to investigate those signatures' characteristics. We model human arms as point scatterers while assuming a simple arm motion. By means of such a simulation, we can obtain spectrograms from a swimming person, then extend our measurement to multiple participants. Measurements are taken from five aquatic activities featuring five participants, comprising freestyle, backstroke, and breaststroke, pulling a boat, and rowing. As suggested by the simulation study, the spectrograms for the five activities show different micro-Doppler signatures; hence, we propose to classify them using a deep convolutional neural network (DCNN). In particular, we suggest the use of a transfer-learned DCNN, which is based on a DCNN pretrained by a large-scale RGB image dataset that is, ImageNet. The classification accuracy is calculated using fivefold cross-validation on our dataset. We find that a DCNN trained through transfer learning achieves the highest accuracy while also providing a significant performance boost over the conventional classification method.

  17. Tissue-engineered models of human tumors for cancer research

    Science.gov (United States)

    Villasante, Aranzazu; Vunjak-Novakovic, Gordana

    2015-01-01

    Introduction Drug toxicity often goes undetected until clinical trials, which are the most costly and dangerous phase of drug development. Both the cultures of human cells and animal studies have limitations that cannot be overcome by incremental improvements in drug-testing protocols. A new generation of bioengineered tumors is now emerging in response to these limitations, with potential to transform drug screening by providing predictive models of tumors within their tissue context, for studies of drug safety and efficacy. An area that could greatly benefit from these models is cancer research. Areas covered In this review, the authors first describe the engineered tumor systems, using Ewing's sarcoma as an example of human tumor that cannot be predictably studied in cell culture and animal models. Then, they discuss the importance of the tissue context for cancer progression and outline the biomimetic principles for engineering human tumors. Finally, they discuss the utility of bioengineered tumor models for cancer research and address the challenges in modeling human tumors for use in drug discovery and testing. Expert opinion While tissue models are just emerging as a new tool for cancer drug discovery, they are already demonstrating potential for recapitulating, in vitro, the native behavior of human tumors. Still, numerous challenges need to be addressed before we can have platforms with a predictive power appropriate for the pharmaceutical industry. Some of the key needs include the incorporation of the vascular compartment, immune system components, and mechanical signals that regulate tumor development and function. PMID:25662589

  18. Classification of human retinal microaneurysms using adaptive optics scanning light ophthalmoscope fluorescein angiography.

    Science.gov (United States)

    Dubow, Michael; Pinhas, Alexander; Shah, Nishit; Cooper, Robert F; Gan, Alexander; Gentile, Ronald C; Hendrix, Vernon; Sulai, Yusufu N; Carroll, Joseph; Chui, Toco Y P; Walsh, Joseph B; Weitz, Rishard; Dubra, Alfredo; Rosen, Richard B

    2014-03-04

    Microaneurysms (MAs) are considered a hallmark of retinal vascular disease, yet what little is known about them is mostly based upon histology, not clinical observation. Here, we use the recently developed adaptive optics scanning light ophthalmoscope (AOSLO) fluorescein angiography (FA) to image human MAs in vivo and to expand on previously described MA morphologic classification schemes. Patients with vascular retinopathies (diabetic, hypertensive, and branch and central retinal vein occlusion) were imaged with reflectance AOSLO and AOSLO FA. Ninety-three MAs, from 14 eyes, were imaged and classified according to appearance into six morphologic groups: focal bulge, saccular, fusiform, mixed, pedunculated, and irregular. The MA perimeter, area, and feret maximum and minimum were correlated to morphology and retinal pathology. Select MAs were imaged longitudinally in two eyes. Adaptive optics scanning light ophthalmoscope fluorescein angiography imaging revealed microscopic features of MAs not appreciated on conventional images. Saccular MAs were most prevalent (47%). No association was found between the type of retinal pathology and MA morphology (P = 0.44). Pedunculated and irregular MAs were among the largest MAs with average areas of 4188 and 4116 μm(2), respectively. Focal hypofluorescent regions were noted in 30% of MAs and were more likely to be associated with larger MAs (3086 vs. 1448 μm(2), P = 0.0001). Retinal MAs can be classified in vivo into six different morphologic types, according to the geometry of their two-dimensional (2D) en face view. Adaptive optics scanning light ophthalmoscope fluorescein angiography imaging of MAs offers the possibility of studying microvascular change on a histologic scale, which may help our understanding of disease progression and treatment response.

  19. Automated breast cancer detection and classification using ultrasound images: A survey

    OpenAIRE

    2010-01-01

    Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown,early detection and diagnos is is the key for breast cancer control,and it can increase the success of treatment,save lives and reduce cost.

  20. Comparison of the prevalence of malnutrition diagnosis in head and neck, gastrointestinal and lung cancer patients by three classification methods

    Science.gov (United States)

    Platek, Mary E.; Popp KPf, Johann V.; Possinger, Candi S.; DeNysschen, Carol A.; Horvath, Peter; Brown, Jean K.

    2011-01-01

    Background Malnutrition is prevalent among patients within certain cancer types. There is lack of universal standard of care for nutrition screening, lack of agreement on an operational definition and on validity of malnutrition indicators. Objective In a secondary data analysis, we investigated prevalence of malnutrition diagnosis by three classification methods using data from medical records of a National Cancer Institute (NCI)-designated comprehensive cancer center. Interventions/Methods Records of 227 patients hospitalized during 1998 with head and neck, gastrointestinal or lung cancer were reviewed for malnutrition based on three methods: 1) physician diagnosed malnutrition related ICD-9 codes; 2) in-hospital nutritional assessment summary conducted by Registered Dietitians; and 3) body mass index (BMI). For patients with multiple admissions, only data from the first hospitalization was included. Results Prevalence of malnutrition diagnosis ranged from 8.8% based on BMI to approximately 26% of all cases based on dietitian assessment. Kappa coefficients between any methods indicated a weak (kappa=0.23, BMI and Dietitians and kappa=0.28, Dietitians and Physicians) to fair strength of agreement (kappa=0.38, BMI and Physicians). Conclusions Available methods to identify patients with malnutrition in an NCI designated comprehensive cancer center resulted in varied prevalence of malnutrition diagnosis. Universal standard of care for nutrition screening that utilizes validated tools is needed. Implications for Practice The Joint Commission on the Accreditation of Healthcare Organizations requires nutritional screening of patients within 24 hours of admission. For this purpose, implementation of a validated tool that can be used by various healthcare practitioners, including nurses, needs to be considered. PMID:21242767

  1. Audiovisual classification of vocal outbursts in human conversation using long-short-term memory networks

    NARCIS (Netherlands)

    Eyben, Florian; Petridis, Stavros; Schuller, Björn; Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

    2011-01-01

    We investigate classification of non-linguistic vocalisations with a novel audiovisual approach and Long Short-Term Memory (LSTM) Recurrent Neural Networks as highly successful dynamic sequence classifiers. As database of evaluation serves this year's Paralinguistic Challenge's Audiovisual Interest

  2. Comparison of staging between the old (6th edition) and new (7th edition) TNM classifications in advanced gastric cancer.

    Science.gov (United States)

    Kikuchi, Shiro; Futawatari, Nobue; Sakuramoto, Shinichi; Katada, Natsuya; Yamashita, Keishi; Shibata, Tomotaka; Nemoto, Masayuki; Watanabe, Masahiko

    2011-06-01

    The aims of the present study were to compare staging between the old (6th edition) and new (7th edition) TNM classifications, and to evaluate the prognostic impact of extended lymph node dissection according to the new nodal staging in advanced gastric cancer. A total of 609 patients with advanced gastric cancer who had undergone curative gastric resection combined with extended lymph node dissection were enrolled in the present study. Survival curves were analyzed according to staging based on the TNM 6th and 7th editions and the Japanese Classification of Gastric Carcinoma (JCGC) 14th edition. The 5-year survival rates and the consecutive stage survival with no significant differences were: IB 88%; II 74%; IIIA 53%; IIIB 39%; and IV 18% (IIIA vs. IIIB, p=0.1307) by the TNM 6th edition; IB 94%; IIA 85%; IIB 71%; IIIA 68%; IIIB 48%; IIIC 23%; and IV 13%; (IIB vs. IIIA, p=0.7665; IIIC vs. IV, p=0.4156) by the TNM 7th and JCGC 14th editions; N0 85%; N1 70%; N2 46%; N3 18%; and M1 13%; (N3 vs. M1, p=0.8640) by the TNM 6th edition; and N0 85%; N1 80%; N2 61%; N3a 46%; N3b 18%; and M1 13%; (N0 vs. N1, p=0.2735; N2 vs. N3a, p=0.0663; N3b vs. M1, p=0.8640) by the TNM 7th and JCGC 14th editions. The new classification according to the TNM 7th and the JCGC 14th editions does not always seem to be superior to the TNM 6th edition for the prognostic stratification of stages in patients who undergo curative resection for advanced gastric cancer. An extended lymph node dissection may be effective for N0-N3a, but not for N3b and M1 stages classified according to the new TNM 7th and JCGC 14th editions.

  3. Benzyl Isothiocyanate Inhibits Epithelial-Mesenchymal Transition in Cultured and Xenografted Human Breast Cancer Cells

    OpenAIRE

    Sehrawat, Anuradha; Singh, Shivendra V.

    2011-01-01

    We showed previously that cruciferous vegetable constituent benzyl isothiocyanate (BITC) inhibits growth of cultured and xenografted human breast cancer cells, and suppresses mammary cancer development in a transgenic mouse model. We now demonstrate, for the first time, that BITC inhibits epithelial-to-mesenchymal transition (EMT) in human breast cancer cells. Exposure of estrogen-independent MDA-MB-231 and estrogen-responsive MCF-7 human breast cancer cell lines and a pancreatic cancer cell ...

  4. IMMUNORESPONSES OF HUMANIZED SCID MICE TO HUMAN LUNG CANCER CELLS

    Institute of Scientific and Technical Information of China (English)

    陈力真; 王树蕙; 张云; 王世真

    1996-01-01

    HuPBL-SCID mice were used to explore how they would response to human ttmoor cells of 801/MLC.Living 801/MLC cells appeared to be fetal to the the mice due to the production of human TNF. The huP-BL-SCID rniee did not generate any noticeable amotmt of specific human immunoglobttlin either by single immunization with living 801/MLC cells or by repeated immunization with irradiated 801/MLC cells. Our preliminary experiments with huPBL-SCID mice showed that such chimeras would he a very useful models for tumor immunological researches.

  5. Epigenetic modifications and human pathologies: cancer and CVD.

    Science.gov (United States)

    Duthie, Susan J

    2011-02-01

    Epigenetic changes are inherited alterations in DNA that affect gene expression and function without altering the DNA sequence. DNA methylation is one epigenetic process implicated in human disease that is influenced by diet. DNA methylation involves addition of a 1-C moiety to cytosine groups in DNA. Methylated genes are not transcribed or are transcribed at a reduced rate. Global under-methylation (hypomethylation) and site-specific over-methylation (hypermethylation) are common features of human tumours. DNA hypomethylation, leading to increased expression of specific proto-oncogenes (e.g. genes involved in proliferation or metastasis) can increase the risk of cancer as can hypermethylation and reduced expression of tumour suppressor (TS) genes (e.g. DNA repair genes). DNA methyltransferases (DNMT), together with the methyl donor S-adenosylmethionine (SAM), facilitate DNA methylation. Abnormal DNA methylation is implicated not only in the development of human cancer but also in CVD. Polyphenols, a group of phytochemicals consumed in significant amounts in the human diet, effect risk of cancer. Flavonoids from tea, soft fruits and soya are potent inhibitors of DNMT in vitro, capable of reversing hypermethylation and reactivating TS genes. Folates, a group of water-soluble B vitamins found in high concentration in green leafy vegetables, regulate DNA methylation through their ability to generate SAM. People who habitually consume the lowest level of folate or with the lowest blood folate concentrations have a significantly increased risk of developing several cancers and CVD. This review describes how flavonoids and folates in the human diet alter DNA methylation and may modify the risk of human colon cancer and CVD.

  6. [A systematic review of worldwide natural history models of colorectal cancer: classification, transition rate and a recommendation for developing Chinese population-specific model].

    Science.gov (United States)

    Li, Z F; Huang, H Y; Shi, J F; Guo, C G; Zou, S M; Liu, C C; Wang, Y; Wang, L; Zhu, S L; Wu, S L; Dai, M

    2017-02-10

    Objective: To review the worldwide studies on natural history models among colorectal cancer (CRC), and to inform building a Chinese population-specific CRC model and developing a platform for further evaluation of CRC screening and other interventions in population in China. Methods: A structured literature search process was conducted in PubMed and the target publication dates were from January 1995 to December 2014. Information about classification systems on both colorectal cancer and precancer on corresponding transition rate, were extracted and summarized. Indicators were mainly expressed by the medians and ranges of annual progression or regression rate. Results: A total of 24 studies were extracted from 1 022 studies, most were from America (n=9), but 2 from China including 1 from the mainland area, mainly based on Markov model (n=22). Classification systems for adenomas included progression risk (n=9) and the sizes of adenoma (n=13, divided into two ways) as follows: 1) Based on studies where adenoma was risk-dependent, the median annual transition rates, from ' normal status' to ' non-advanced adenoma', 'non-advanced' to ' advanced' and ' advanced adenoma' to CRC were 0.016 0 (range: 0.002 2-0.020 0), 0.020 (range: 0.002-0.177) and 0.044 (range: 0.005-0.063), respectively. 2) Median annual transition rates, based on studies where adenoma were classified by sizes, into colorectal cancer was still limited worldwide. Adenoma seemed the most common status setting for precancer model, and the risk-dependent classification for adenoma was consistent with the most commonly used system in clinical practice as well as major cancer screening programs in China. Since the staging systems of cancers varied, and shortage of transition rates based on TNM classification (commonly used in China), there will be a challenge for building Chinese population-specific natural history model of colorectal cancer, information from other classification systems could be

  7. CERVICAL CANCER AND THE HUMAN IMMUNODEFICIENCY ...

    African Journals Online (AJOL)

    as Mexico, Columbia and many developed nations), the reduction in ..... detection among human immunodeficiency virus-infected pregnant Thai women: implications ... Moscicki A. Impact of HPV infection in adolescent populations. J Adolesc ...

  8. The mammographic correlations of a new immunohistochemical classification of invasive breast cancer

    Energy Technology Data Exchange (ETDEWEB)

    Taneja, S. [Nottingham Breast Institute, City Hospital, Hucknall Road, Nottingham NG5 1PB (United Kingdom)], E-mail: sheeba_taneja@yahoo.co.uk; Evans, A.J. [Nottingham Breast Institute, City Hospital, Hucknall Road, Nottingham NG5 1PB (United Kingdom); Rakha, E.A.; Green, A.R. [Division of Pathology, School of Molecular Medical Sciences, Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham (United Kingdom); Ball, G. [Nottingham Trent University, School of Biomedical and Natural Sciences, Nottingham (United Kingdom); Ellis, I.O. [Division of Pathology, School of Molecular Medical Sciences, Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham (United Kingdom)

    2008-11-15

    Aim: Recent protein expression profiling of breast cancer has identified specific subtypes with clinical, biological, and therapeutic implications. The aim of this study was to identify the mammographic correlates of these novel molecular classes of invasive breast cancer. Materials and methods: The mammographic findings of 415 patients with operable breast cancer were correlated with the previously described protein expression classes identified by our group using immunohistochemical (IHC) assessment of a large series of breast cancer cases prepared as tissue microarrays (TMAs). Twenty-five proteins of known relevance in breast cancer were assessed, including hormone receptors, HER-2 status, basal and luminal markers, p53 expression, and E-cadherin. Results: The mammographic background pattern and proportion of lesions that were mammographically occult were similar in all groups. Groups characterized by luminal and hormone receptor positivity had significantly more spiculate lesions at mammography. Groups characterized by HER-2 overexpression, basal characteristics, and E-cadherin positivity had a significantly higher proportion of ill-defined masses. These findings were independent of histological grade. Conclusion: The mammographic features of breast cancer show significant correlation with molecular classes of invasive breast cancer identified by protein expression IHC analysis. The biological reasons for the findings and implications of these regarding imaging protocols require further study and may provide mechanisms for improvement of detection of these lesions.

  9. Synchrotron refractive-index microradiography of human liver cancer tissue

    Institute of Scientific and Technical Information of China (English)

    TONG Yongpeng; ZHANG Guilin; LI Yan; HWU Yeukuang; TSAI Wenli; JE Jung Ho; Margaritondo G.; YUAN Dong

    2005-01-01

    Three human liver tissue samples (~5 mm × 40 mm × 20 mm) were excised from a cancer patient's liver during surgery. The microradiology analysis was performed with a non-standard approach on a synchrotron. High-resolution refractive-index edge-enhanced microradiographs that cover a larger volume of the liver tissue sample were obtained. The cancer tissue and normal tissue could be clearly identified and distinguished based on their different textures. Furthermore, new blood vessel hyperplasia was found near the cancer area. Blood vessels with a diameter smaller than 20 μm could be identified. These findings were fully consistent with the histopathological examination of the same area. Microradiographs of the newly formed blood vessels at different angles were also obtained. This result shows that it is possible to further develop this approach into a technique of microradiographic imaging for clinic diagnosis of liver cancer at the early stage.

  10. Silencing human cancer: identification and uses of microRNAs.

    Science.gov (United States)

    Nicolas, Francisco E; Lopez-Gomollon, Sara; Lopez-Martinez, Alfonso F; Dalmay, Tamas

    2011-01-01

    MicroRNAs (miRNAs) are a new class of negative regulators that repress gene expression by pairing with their target messenger RNAs (mRNAs). There are hundreds of miRNAs coded in the human genome and thousands of target mRNAs participating in a wide variety of physiological processes such as development and cell identity. It is therefore not surprising that several recent reports involved deregulated miRNAs in the complex mechanism of human carcinogenesis, and proposed them as new key regulators to correct the unbalanced expression of oncogenes and tumour suppressor genes exhibited in cancer cells. This review summarises most of the recent patents related to the use of miRNA signatures in cancer diagnosis and prognosis, the detection and profiling of miRNAs from tumour samples and the identification of oncogenes and tumour suppressor genes targeted by miRNAs, as well as new cancer therapies based on miRNA modulators.

  11. Lectins in human cancer: both a devil and an angel?

    Science.gov (United States)

    Dan, Xiu Li; Ng, Tzi Bun

    2013-09-01

    Lectins are a group of proteins which could recognize different sugar structures and specifically initiate reversible binding with them. Lectins are universally expressed in different organisms and undertake important biological roles. Understanding of their inherent roles and mechanisms that they employ has inspired researches with new ideas and applications. For example, along with the revelation of their anti-insect function, plant lectins exhibit great potential in agriculture. In human beings, lectins shoulder great missions in cell communication, differentiation and vesicle trafficking etc., aberrant expression of lectins is always associated with diseases. Mannan-binding lectin deficiency leads to immune disorder and liver sinusoidal endothelial cell lectin is involved in colorectal carcinoma liver metastasis. In this review, we present two contradictory roles of lectins in human cancer: the promotive roles of homologous lectins and suppressive roles of heterologous lectins in cancer development. Hopefully, this review will facilitate a better understanding of tumorigenesis and provide references for cancer treatment.

  12. Strategies of functional food for cancer prevention in human beings.

    Science.gov (United States)

    Zeng, Ya-Wen; Yang, Jia-Zheng; Pu, Xiao-Ying; Du, Juan; Yang, Tao; Yang, Shu-Ming; Zhu, Wei-Hua

    2013-01-01

    Functional food for prevention of chronic diseases is one of this century's key global challenges. Cancer is not only the first or second leading cause of death in China and other countries across the world, but also has diet as one of the most important modifiable risk factors. Major dietary factors now known to promote cancer development are polished grain foods and low intake of fresh vegetables, with general importance for an unhealthy lifestyle and obesity. The strategies of cancer prevention in human being are increased consumption of functional foods like whole grains (brown rice, barley, and buckwheat) and by-products, as well some vegetables (bitter melon, garlic, onions, broccoli, and cabbage) and mushrooms (boletes and Tricholoma matsutake). In addition some beverages (green tea and coffee) may be protective. Southwest China (especially Yunnan Province) is a geographical area where functional crop production is closely related to the origins of human evolution with implications for anticancer influence.

  13. Classification of follicular cell-derived thyroid cancer by global RNA profiling

    DEFF Research Database (Denmark)

    Rossing, Maria

    2013-01-01

    The incidence of thyroid cancer is increasing worldwide and thyroid nodules are a frequent clinical finding. Diagnosing follicular cell-derived cancers is, however, challenging both histopathologically and especially cytopathologically. The advent of high-throughput molecular technologies has...... profiling of follicular cell-derived thyroid cancers....... prompted many researchers to explore the transcriptome and, in recent years, also the miRNome in order to generate new molecular classifiers capable of classifying thyroid tumours more accurately than by conventional cytopathological and histopathological methods. This has led to a number of molecular...

  14. Effect of S1P5 on proliferation and migration of human esophageal cancer cells

    OpenAIRE

    Hu, Wei-Min; Li, Li; Jing, Bao-Qian; Zhao, Yong-Sheng; Wang, Chao-Li; Feng, Li; Xie, Yong-En

    2010-01-01

    AIM: To investigate the sphingosine 1-phosphate (S1P) receptor expression profile in human esophageal cancer cells and the effects of S1P5 on proliferation and migration of human esophageal cancer cells.

  15. Microbial dysbiosis is associated with human breast cancer.

    Directory of Open Access Journals (Sweden)

    Caiyun Xuan

    Full Text Available Breast cancer affects one in eight women in their lifetime. Though diet, age and genetic predisposition are established risk factors, the majority of breast cancers have unknown etiology. The human microbiota refers to the collection of microbes inhabiting the human body. Imbalance in microbial communities, or microbial dysbiosis, has been implicated in various human diseases including obesity, diabetes, and colon cancer. Therefore, we investigated the potential role of microbiota in breast cancer by next-generation sequencing using breast tumor tissue and paired normal adjacent tissue from the same patient. In a qualitative survey of the breast microbiota DNA, we found that the bacterium Methylobacterium radiotolerans is relatively enriched in tumor tissue, while the bacterium Sphingomonas yanoikuyae is relatively enriched in paired normal tissue. The relative abundances of these two bacterial species were inversely correlated in paired normal breast tissue but not in tumor tissue, indicating that dysbiosis is associated with breast cancer. Furthermore, the total bacterial DNA load was reduced in tumor versus paired normal and healthy breast tissue as determined by quantitative PCR. Interestingly, bacterial DNA load correlated inversely with advanced disease, a finding that could have broad implications in diagnosis and staging of breast cancer. Lastly, we observed lower basal levels of antibacterial response gene expression in tumor versus healthy breast tissue. Taken together, these data indicate that microbial DNA is present in the breast and that bacteria or their components may influence the local immune microenvironment. Our findings suggest a previously unrecognized link between dysbiosis and breast cancer which has potential diagnostic and therapeutic implications.

  16. Human Papillomavirus Testing in the Prevention of Cervical Cancer

    Science.gov (United States)

    Wentzensen, Nicolas; Wacholder, Sholom; Kinney, Walter; Gage, Julia C.; Castle, Philip E.

    2011-01-01

    Strong evidence now supports the adoption of cervical cancer prevention strategies that explicitly focus on persistent infection with the causal agent, human papillomavirus (HPV). To inform an evidence-based transition to a new public health approach for cervical cancer screening, we summarize the natural history and cervical carcinogenicity of HPV and discuss the promise and uncertainties of currently available screening methods. New HPV infections acquired at any age are virtually always benign, but persistent infections with one of approximately 12 carcinogenic HPV types explain virtually all cases of cervical cancer. In the absence of an overtly persistent HPV infection, the risk of cervical cancer is extremely low. Thus, HPV test results predict the risk of cervical cancer and its precursors (cervical intraepithelial neoplasia grade 3) better and longer than cytological or colposcopic abnormalities, which are signs of HPV infection. The logical and inevitable move to HPV-based cervical cancer prevention strategies will require longer screening intervals that will disrupt current gynecologic and cytology laboratory practices built on frequent screening. A major challenge will be implementing programs that do not overtreat HPV-positive women who do not have obvious long-term persistence of HPV or treatable lesions at the time of initial evaluation. The greatest potential for reduction in cervical cancer rates from HPV screening is in low-resource regions that can implement infrequent rounds of low-cost HPV testing and treatment. PMID:21282563

  17. Differentially Expressed Genes and Signature Pathways of Human Prostate Cancer.

    Directory of Open Access Journals (Sweden)

    Jennifer S Myers

    Full Text Available Genomic technologies including microarrays and next-generation sequencing have enabled the generation of molecular signatures of prostate cancer. Lists of differentially expressed genes between malignant and non-malignant states are thought to be fertile sources of putative prostate cancer biomarkers. However such lists of differentially expressed genes can be highly variable for multiple reasons. As such, looking at differential expression in the context of gene sets and pathways has been more robust. Using next-generation genome sequencing data from The Cancer Genome Atlas, differential gene expression between age- and stage- matched human prostate tumors and non-malignant samples was assessed and used to craft a pathway signature of prostate cancer. Up- and down-regulated genes were assigned to pathways composed of curated groups of related genes from multiple databases. The significance of these pathways was then evaluated according to the number of differentially expressed genes found in the pathway and their position within the pathway using Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis. The "transforming growth factor-beta signaling" and "Ran regulation of mitotic spindle formation" pathways were strongly associated with prostate cancer. Several other significant pathways confirm reported findings from microarray data that suggest actin cytoskeleton regulation, cell cycle, mitogen-activated protein kinase signaling, and calcium signaling are also altered in prostate cancer. Thus we have demonstrated feasibility of pathway analysis and identified an underexplored area (Ran for investigation in prostate cancer pathogenesis.

  18. The natural immunity to evolutionary atavistic endotoxin for human cancer.

    Science.gov (United States)

    Moncevičiūtė-Eringienė, Elena; Rotkevič, Kristina; Grikienis, Ruta Grikienyte

    2015-11-01

    We propose a new theory of the immunological control of cancer corresponding to the hypothesis that the specific natural immunity to evolutionary atavistic endotoxin has a potential role in human cancer prevention. The results of our studies have shown that IgMNAE, i.e. endogenous or spontaneous IgM class antibodies to enterobacterial lipopolysaccharide molecules (lipid A), control the immune mechanisms responsible for the internal medium stability not only against the damaging impact of the carcinogenic factors, but also against the malignant transformation of its own degenerated cells. Among people who in 1979 and 1982 had IgMNAE in their blood serum, after 15-30years fell ill with cancer 10%, versus 15% among people who had no IgMNAE (pimmunity to endotoxin it is possible to see the formation of the respective evolutionary protective reactions which protect the damaged cells from acquiring resistance to damaging factors and thus from becoming an independent new parasitic population. Thereby the presented theory of the immunological control of cancer has a causal connection with our evolutionary resistance theory of the origin of cancer. Collectively, these data suggest that activation of natural immunity to endotoxin and production of vaccines against evolutionary atavistic endotoxin or gram-negative bacterial endotoxin can be helpful when applied in cancer prophylaxis for persons with a low level of natural immunity to endotoxin and perhaps in creating immunotherapeutic methods for stopping the endogenous parasitism of tumour cells by binding IgMNAE to atavistic endotoxin in cancer patients.

  19. Endothelium specific matrilysin (MMP-7) expression in human cancers

    NARCIS (Netherlands)

    Sier, C.F.M.; Hawinkels, L.J.A.C.; Zijlmans, H.J.M.A.A.; Zuidwijk, K.; Jonge de; Muller, E.S.M.; Ferreira, V.; Hanemaaijer, R.; Mulder-Stapel, A.A.; Kenter, G.G.; Verspaget, H.W.; Gorter, A.

    2008-01-01

    Over-expression of matrilysin (MMP-7) is predominantly associated with epithelial (pre)malignant cells. In the present study MMP-7 expression is also found in endothelial cells in various human cancer types. Endothelial MMP-7 was associated with CD34 and/or CD105 expression. These immunohistochemica

  20. Hanging drop cultures of human testis and testis cancer samples

    DEFF Research Database (Denmark)

    Jørgensen, Anne; Young, J; Nielsen, J E

    2014-01-01

    limited by the lack of experimental models. The aim of this study was to establish an experimental tissue culture model to maintain normal and malignant germ cells within their niche and allow investigation of treatment effects. METHODS: Human testis and testis cancer specimens from orchidectomies were...

  1. Trefoil factor-3 expression in human colon cancer liver metastasis.

    Science.gov (United States)

    Babyatsky, Mark; Lin, Jing; Yio, Xianyang; Chen, Anli; Zhang, Jie-yu; Zheng, Yan; Twyman, Christina; Bao, Xiuliang; Schwartz, Myron; Thung, Swan; Lawrence Werther, J; Itzkowitz, Steven

    2009-01-01

    Deaths from colorectal cancer are often due to liver metastasis. Trefoil factor-3 (TFF3) is expressed by normal intestinal epithelial cells and its expression is maintained throughout the colon adenoma-carcinoma sequence. Our previous work demonstrated a correlation between TFF3 expression and metastatic potential in an animal model of colon cancer. The aim of this study was to determine whether TFF3 is expressed in human colon cancer liver metastasis (CCLM) and whether inhibiting TFF3 expression in colon cancer cells would alter their invasive potential in vitro. Human CCLMs were analyzed at the mRNA and protein level for TFF3 expression. Two highly metastatic rat colon cancer cell lines that either natively express TFF3 (LN cells) or were transfected with TFF3 (LPCRI-2 cells), were treated with two rat TFF3 siRNA constructs (si78 and si365), and analyzed in an in vitro invasion assay. At the mRNA and protein level, TFF3 was expressed in 17/17 (100%) CCLMs and 10/11 (91%) primary colon cancers, but not in normal liver tissue. By real time PCR, TFF3 expression was markedly inhibited by both siRNA constructs in LN and LPCRI-2 cells. The si365 and si78 constructs inhibited invasion by 44% and 53%, respectively, in LN cells, and by 74% and 50%, respectively, in LPCRI-2 cells. These results provide further evidence that TFF3 contributes to the malignant behavior of colon cancer cells. These observations may have relevance for designing new diagnostic and treatment approaches to colorectal cancer.

  2. Classification of a palliative care population in a comprehensive cancer centre

    DEFF Research Database (Denmark)

    Benthien, Kirstine Skov; Nordly, Mie; Videbæk, Katja

    2016-01-01

    PURPOSE: The purposes of the present study were to classify the palliative care population (PCP) in a comprehensive cancer centre by using information on antineoplastic treatment options and to analyse associations between socio-demographic factors, cancer diagnoses, treatment characteristics...... of accelerated transition to SPC at home (NCT01885637). The PCP was classified as patients with incurable cancer and limited or no antineoplastic treatment options. Patients with performance status 2-4 were further classified as the essential palliative care population (EPCP). RESULTS: During the study period......, 3717 patients with cancer were assessed. The PCP comprised 513 patients yielding a prevalence of 14 %. The EPCP comprised 256 patients (7 %). The EPCP was older, more likely inpatients, had a higher comorbidity burden and 38 % received SPC. Women, patients without caregivers and patients with breast...

  3. A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

    NARCIS (Netherlands)

    H.M.J. Sontrop; P.D. Moerland; R. van den Ham; M.J.T. Reinders; W.F.J. Verhaegh

    2009-01-01

    Background: Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for

  4. Classification of bladder cancer cell lines using Raman spectroscopy: a comparison of excitation wavelength, sample substrate and statistical algorithms

    Science.gov (United States)

    Kerr, Laura T.; Adams, Aine; O'Dea, Shirley; Domijan, Katarina; Cullen, Ivor; Hennelly, Bryan M.

    2014-05-01

    Raman microspectroscopy can be applied to the urinary bladder for highly accurate classification and diagnosis of bladder cancer. This technique can be applied in vitro to bladder epithelial cells obtained from urine cytology or in vivo as an optical biopsy" to provide results in real-time with higher sensitivity and specificity than current clinical methods. However, there exists a high degree of variability across experimental parameters which need to be standardised before this technique can be utilized in an everyday clinical environment. In this study, we investigate different laser wavelengths (473 nm and 532 nm), sample substrates (glass, fused silica and calcium fluoride) and multivariate statistical methods in order to gain insight into how these various experimental parameters impact on the sensitivity and specificity of Raman cytology.

  5. Side effects of cancer therapies. International classification and documentation systems; Nebenwirkungen in der Onkologie. Internationale Systematik und Dokumentation

    Energy Technology Data Exchange (ETDEWEB)

    Seegenschmiedt, M.H. [Erlangen-Nuernberg Univ., Erlangen (Germany). Klinik und Poliklinik fuer Strahlentherapie

    1998-11-01

    The publication presents and explains verified, international classification and documentation systems for side effects induced by cancer treatments, applicable in general and clinical practice and clinical research, and covers in a clearly arranged manner the whole range of treatments, including acute and chronic side effects of chemotherapy and radiotherapy, surgery, or combined therapies. The book fills a long-felt need in tumor documentation and is a major contribution to quality assurance in clinical oncology in German-speaking countries. As most parts of the book are bilingual, presenting German and English texts and terminology, it satisfies the principles of interdisciplinarity and internationality. The tabulated form chosen for presentation of classification systems and criteria facilitate the user`s approach as well as application in daily work. (orig./CB) [Deutsch] International abgesicherte Klassifikationen von Nebenwirkungen in der Onkologie - anwendbar in Praxis, Klinik und klinischer Forschung - stehen im Mittelpunkt dieses Werkes. Dies bezieht akute und chronische Nebenwirkungen nach Chemo- und Radiotherapie, Chirurgie oder kombinierten Therapieverfahren mit ein. Das Buch schliesst eine grosse Luecke in der Tumordokumentation und traegt somit wesentlich zur Qualitaetssicherung in der klinischen Onkologie im deutschsprachigen Raum bei. Durch die meist zweisprachige Ausfuehrung in Englisch und Deutsch erfuellt es das Prinzip der Interdisziplinaritaet sowie das Prinzip der Internationalitaet. Die tabellarische Darstellung erleichtert die praktische Arbeit, und die vorgeschlagenen Formblaetter unterstuetzen die praktische Umsetzung im Alltag. (orig.)

  6. Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines.

    Science.gov (United States)

    Torheim, Turid; Malinen, Eirik; Kvaal, Knut; Lyng, Heidi; Indahl, Ulf G; Andersen, Erlend K F; Futsaether, Cecilia M

    2014-08-01

    Dynamic contrast enhanced MRI (DCE-MRI) provides insight into the vascular properties of tissue. Pharmacokinetic models may be fitted to DCE-MRI uptake patterns, enabling biologically relevant interpretations. The aim of our study was to determine whether treatment outcome for 81 patients with locally advanced cervical cancer could be predicted from parameters of the Brix pharmacokinetic model derived from pre-chemoradiotherapy DCE-MRI. First-order statistical features of the Brix parameters were used. In addition, texture analysis of Brix parameter maps was done by constructing gray level co-occurrence matrices (GLCM) from the maps. Clinical factors and first- and second-order features were used as explanatory variables for support vector machine (SVM) classification, with treatment outcome as response. Classification models were validated using leave-one-out cross-model validation. A random value permutation test was used to evaluate model significance. Features derived from first-order statistics could not discriminate between cured and relapsed patients (specificity 0%-20%, p-values close to unity). However, second-order GLCM features could significantly predict treatment outcome with accuracies (~70%) similar to the clinical factors tumor volume and stage (69%). The results indicate that the spatial relations within the tumor, quantified by texture features, were more suitable for outcome prediction than first-order features.

  7. Nasal metastases from renal cell carcinoma are associated with Memorial Sloan-Kettering Cancer Center poor-prognosis classification

    Institute of Scientific and Technical Information of China (English)

    Caroline Victoria Choong; Tiffany Tang; Wen Yee Chay; Christopher Goh; Miah Hiang Tay; Nor Azhari Mohd Zam; Puay Hoon Tan; Min-Han Tan

    2011-01-01

    Unusual sites of metastases are recognized in patients with renai cell carcinoma (RCC). However, the prognostic implications of these sites are not well understood. We used the Memorial Sloan-Kettering Cancer Center (MSKCC) risk classification for metastatic RCC to evaluate 912 consecutive patients with RCC managed at the Singapore General Hospital between 1990 and 2009. Among these patients, 301 had metastases either at diagnosis or during the course of illness. Nasal metastases, all arising from clear cell RCC, were identified histologically in 4 patients (1.3% of those with metastasis). All 4 patients were classified as MSKCC poor prognosis by current risk criteria. Nasal metastases were significantly associated with lung and bone metastases. The frequency of nasal metastases in patients with metastatic RCC is about 1%, occurring predominantly in patients with clear cell RCC. Nasal metastases are associated with poor prognosis as estimated by the MSKCC risk classification, with attendant implications for selection of targeted therapy, and are usually associated with multi-organ dissemination, including concurrent lung and bone involvement.

  8. Long-term Prostate-specific Antigen Velocity in Improved Classification of Prostate Cancer Risk and Mortality

    DEFF Research Database (Denmark)

    Ørsted, David Dynnes; Bojesen, Stig E; Kamstrup, Pia R

    2013-01-01

    already including baseline PSA values and age yielded continuous NRIs of 98-99% and 56-106%, respectively. Used on a nationwide scale (eg, for men aged 60-64 yr), long-term PSAV >0.35 versus ≤0.35 ng/ml per year appropriately reclassified 128 of 10 000 men with PCa and 8095 of 10 000 men with no PCa......BACKGROUND: It remains unclear whether adding long-term prostate-specific antigen velocity (PSAV) to baseline PSA values improves classification of prostate cancer (PCa) risk and mortality in the general population. OBJECTIVE: To determine whether long-term PSAV improves classification of PCa risk...... and mortality in the general population. DESIGN, SETTING, AND PARTICIPANTS: We studied 503 men aged 30-80 yr, with and without PCa, who had repeated PSA measurements over 20 yr and up to 28 yr before PCa diagnosis. These were selected from among 7455 men in the Copenhagen City Heart Study, a prospective...

  9. A risk evaluation model of cervical cancer based on etiology and human leukocyte antigen allele susceptibility

    Directory of Open Access Journals (Sweden)

    Bicheng Hu

    2014-11-01

    Conclusions: This model, based on etiology and HLA allele susceptibility, can estimate the risk of cervical cancer in chronic cervicitis patients after HPV infection. It combines genetic and environmental factors and significantly enhances the accuracy of risk evaluation for cervical cancer. This model could be used to select patients for intervention therapy and to guide patient classification management.

  10. Human Papillomavirus Genotype as a Major Determinant of the Course of Cervical Cancer

    Directory of Open Access Journals (Sweden)

    Niakan M

    2004-01-01

    Full Text Available Introduction: Certain types of human papillomavrus (HPV are associated with cervical intraepithelial neoplasia (CIN and squamous cell carcinoma (SCC. The aim of theobservations reported here was to determine whether the prognosis for invasive cancers of the uterine cervix is related to the type of human papillomavirus asociated with the tumor. Material and Methods: Twenty Patients with invasive cervical cancer were prospectively registered from 2000 to 2001. HPV typing was performed by insitu hybridization(ISH on DNA extracted from frozen, formal in-fixed, paraffin-embedded tumor specimens. The specimens mostly represented classifications SCC Stage 1 and Stage 2 of the International Federation of Gynecology and Obstetrics (Table 1. HPV- DNA was detected by insituhybridization, using three different DNA Probes: types 6/11, 16/18 and 31/33/51. Results: HPV DNA was detected in the nuclei of SCC tumor cells in 13(65% of 20 cases. Of the 13 HPV-DNA positive cases three reacted only with the HPV 31/33/51 probe, two reacted only with the 16/18 probe, three showed strong hybridization for both 31/33/51 and 6/11probes, four showed 6/11 and 16/18 genotypes and one case reacted with 31/33/51,6/11and16/18probes. Conclusion: The prognosis for invasive cancers of the uterine cervix is dependent on the oncogenic potential of the associated HPV type. HPV typing may provide a prognostic indicator for individual patients and is of potential use in defining specific therapies against HPV harboring tumor cells. These findings are consistent with the hypothesis that HPV infection is the primary cause of cervical neoplasia. Furthermore, they support HPV vaccine research to prevent cervical cancer and efforts to develop HPV DNA diagnostic tests.

  11. Identification of immune cell infiltration in hematoxylin-eosin stained breast cancer samples: texture-based classification of tissue morphologies

    Science.gov (United States)

    Turkki, Riku; Linder, Nina; Kovanen, Panu E.; Pellinen, Teijo; Lundin, Johan

    2016-03-01

    The characteristics of immune cells in the tumor microenvironment of breast cancer capture clinically important information. Despite the heterogeneity of tumor-infiltrating immune cells, it has been shown that the degree of infiltration assessed by visual evaluation of hematoxylin-eosin (H and E) stained samples has prognostic and possibly predictive value. However, quantification of the infiltration in H and E-stained tissue samples is currently dependent on visual scoring by an expert. Computer vision enables automated characterization of the components of the tumor microenvironment, and texture-based methods have successfully been used to discriminate between different tissue morphologies and cell phenotypes. In this study, we evaluate whether local binary pattern texture features with superpixel segmentation and classification with support vector machine can be utilized to identify immune cell infiltration in H and E-stained breast cancer samples. Guided with the pan-leukocyte CD45 marker, we annotated training and test sets from 20 primary breast cancer samples. In the training set of arbitrary sized image regions (n=1,116) a 3-fold cross-validation resulted in 98% accuracy and an area under the receiver-operating characteristic curve (AUC) of 0.98 to discriminate between immune cell -rich and - poor areas. In the test set (n=204), we achieved an accuracy of 96% and AUC of 0.99 to label cropped tissue regions correctly into immune cell -rich and -poor categories. The obtained results demonstrate strong discrimination between immune cell -rich and -poor tissue morphologies. The proposed method can provide a quantitative measurement of the degree of immune cell infiltration and applied to digitally scanned H and E-stained breast cancer samples for diagnostic purposes.

  12. Clinicopathological significance of PTPN12 expression in human breast cancer

    Energy Technology Data Exchange (ETDEWEB)

    Yuan, Xunyi [Breast Disease Diagnosis and Treatment Centre, Affiliated Hospital of Medical College, Qingdao University, Qingdao Shandong Province (China); Yuan, Zhentao [Department of Anesthesiology, Shengli Oilfield Central Hospital, Dongying Shandong Province (China); Jiang, Dandan; Li, Funian [Breast Disease Diagnosis and Treatment Centre, Affiliated Hospital of Medical College, Qingdao University, Qingdao Shandong Province (China)

    2012-10-15

    Protein tyrosine phosphatase non-receptor type 12 (PTPN12) is a recently identified tumor suppressor gene (TSG) that is frequently compromised in human triple-negative breast cancer. In the present study, we investigated the expression of PTPN12 protein by patients with breast cancer in a Chinese population and the relationship between PTPN12 expression levels and patient clinicopathological features and prognosis. Additionally, we explored the underlying down-regulation mechanism from the perspective of an epigenetic alteration. We examined PTPN12 mRNA expression in five breast cancer cell lines using semi-quantitative reverse-transcription PCR, and detected PTPN12 protein expression using immunohistochemistry in 150 primary invasive breast cancer cases and paired adjacent non-tumor tissues. Methylation-specific PCR was performed to analyze the promoter CpG island methylation status of PTPN12. PTPN12 was significantly down-regulated in breast cancer cases (48/150) compared to adjacent noncancerous tissues (17/150; P < 0.05). Furthermore, low expression of PTPN12 showed a significant positive correlation with tumor size (P = 0.047), lymph node metastasis (P = 0.001), distant metastasis (P = 0.009), histological grade (P = 0.012), and survival time (P = 0.019). Additionally, promoter CpG island hypermethylation occurs more frequently in breast cancer cases and breast cancer cell lines with low PTPN12 expression. Our findings suggest that PTPN12 is potentially a methylation-silenced TSG for breast cancer that may play an important role in breast carcinogenesis and could potentially serve as an independent prognostic factor for invasive breast cancer patients.

  13. Genome-wide analysis of alternative transcripts in human breast cancer

    Science.gov (United States)

    Wen, Ji; Toomer, Kevin H.

    2016-01-01

    Transcript variants play a critical role in diversifying gene expression. Alternative splicing is a major mechanism for generating transcript variants. A number of genes have been implicated in breast cancer pathogenesis with their aberrant expression of alternative transcripts. In this study, we performed genome-wide analyses of transcript variant expression in breast cancer. With RNA-Seq data from 105 patients, we characterized the transcriptome of breast tumors, by pairwise comparison of gene expression in the breast tumor versus matched healthy tissue from each patient. We identified 2839 genes, ~10 % of protein-coding genes in the human genome, that had differential expression of transcript variants between tumors and healthy tissues. The validity of the computational analysis was confirmed by quantitative RT-PCR assessment of transcript variant expression from four top candidate genes. The alternative transcript profiling led to classification of breast cancer into two subgroups and yielded a novel molecular signature that could be prognostic of patients’ tumor burden and survival. We uncovered nine splicing factors (FOX2, MBNL1, QKI, PTBP1, ELAVL1, HNRNPC, KHDRBS1, SFRS2, and TIAR) that were involved in aberrant splicing in breast cancer. Network analyses for the coordinative patterns of transcript variant expression identified twelve “hub” genes that differentiated the cancerous and normal transcriptomes. Dysregulated expression of alternative transcripts may reveal novel biomarkers for tumor development. It may also suggest new therapeutic targets, such as the “hub” genes identified through the network analyses of transcript variant expression, or splicing factors implicated in the formation of the tumor transcriptome. PMID:25913416

  14. Ensemble modeling coupled with six element concentrations in human blood for cancer diagnosis.

    Science.gov (United States)

    Chen, Hui; Tan, Chao; Wu, Tong

    2011-10-01

    Six important metal contents (i.e., zinc, barium, magnesium, calcium, copper, and selenium) in blood samples coupled with an ensemble classification algorithm have been used for the classification of normal people and cancer patients. A dataset containing 42 healthy samples and 32 cancer samples was used for experiment. The prediction results from this method outperformed those from the newly developed support vector machine, i.e., a sensitivity of 100%, a specificity of 95.2%, and an overall accuracy of 98.6%. It seems that ELDA coupled with blood element analysis can serve as a valuable tool for diagnosing cancer in clinical practice.

  15. Salinomycin as a Drug for Targeting Human Cancer Stem Cells

    Directory of Open Access Journals (Sweden)

    Cord Naujokat

    2012-01-01

    Full Text Available Cancer stem cells (CSCs represent a subpopulation of tumor cells that possess self-renewal and tumor initiation capacity and the ability to give rise to the heterogenous lineages of malignant cells that comprise a tumor. CSCs possess multiple intrinsic mechanisms of resistance to chemotherapeutic drugs, novel tumor-targeted drugs, and radiation therapy, allowing them to survive standard cancer therapies and to initiate tumor recurrence and metastasis. Various molecular complexes and pathways that confer resistance and survival of CSCs, including expression of ATP-binding cassette (ABC drug transporters, activation of the Wnt/β-catenin, Hedgehog, Notch and PI3K/Akt/mTOR signaling pathways, and acquisition of epithelial-mesenchymal transition (EMT, have been identified recently. Salinomycin, a polyether ionophore antibiotic isolated from Streptomyces albus, has been shown to kill CSCs in different types of human cancers, most likely by interfering with ABC drug transporters, the Wnt/β-catenin signaling pathway, and other CSC pathways. Promising results from preclinical trials in human xenograft mice and a few clinical pilote studies reveal that salinomycin is able to effectively eliminate CSCs and to induce partial clinical regression of heavily pretreated and therapy-resistant cancers. The ability of salinomycin to kill both CSCs and therapy-resistant cancer cells may define the compound as a novel and an effective anticancer drug.

  16. Study on Biopharmaceutics Classification and Oral Bioavailability of a Novel Multikinase Inhibitor NCE for Cancer Therapy

    Science.gov (United States)

    Yang, Yang; Fan, Chun-Mei; He, Xuan; Ren, Ke; Zhang, Jin-Kun; He, Ying-Ju; Yu, Luo-Ting; Zhao, Ying-Lan; Gong, Chang-Yang; Zheng, Yu; Song, Xiang-Rong; Zeng, Jun

    2014-01-01

    Specific biopharmaceutics classification investigation and study on phamacokinetic profile of a novel drug candidate (2-methylcarbamoyl-4-{4-[3- (trifluoromethyl) benzamido] phenoxy} pyridinium 4-methylbenzenesulfonate monohydrate, NCE) were carried out. Equilibrium solubility and intrinsic dissolution rate (IDR) of NCE were estimated in different phosphate buffers. Effective intestinal permeability (Peff) of NCE was determined using single-pass intestinal perfusion technique in rat duodenum, jejunum and ileum at three concentrations. Theophylline (high permeability) and ranitidine (low permeability) were also applied to access the permeability of NCE as reference compounds. The bioavailability after intragastrical and intravenous administration was measured in beagle dogs. The solubility of NCE in tested phosphate buffers was quite low with the maximum solubility of 81.73 μg/mL at pH 1.0. The intrinsic dissolution ratio of NCE was 1 × 10−4 mg·min−1·cm−2. The Peff value of NCE in all intestinal segments was more proximate to the high-permeability reference theophylline. Therefore, NCE was classified as class II drug according to Biopharmaceutics Classification System due to its low solubility and high intestinal permeability. In addition, concentration-dependent permeability was not observed in all the segments, indicating that there might be passive transportation for NCE. The absolute oral bioavailability of NCE in beagle dogs was 26.75%. Therefore, dissolution promotion will be crucial for oral formulation development and intravenous administration route will also be suggested for further NCE formulation development. All the data would provide a reference for biopharmaceutics classification research of other novel drug candidates. PMID:24776763

  17. Study on Biopharmaceutics Classification and Oral Bioavailability of a Novel Multikinase Inhibitor NCE for Cancer Therapy

    Directory of Open Access Journals (Sweden)

    Yang Yang

    2014-04-01

    Full Text Available Specific biopharmaceutics classification investigation and study on phamacokinetic profile of a novel drug candidate (2-methylcarbamoyl-4-{4-[3- (trifluoromethyl benzamido] phenoxy} pyridinium 4-methylbenzenesulfonate monohydrate, NCE were carried out. Equilibrium solubility and intrinsic dissolution rate (IDR of NCE were estimated in different phosphate buffers. Effective intestinal permeability (Peff of NCE was determined using single-pass intestinal perfusion technique in rat duodenum, jejunum and ileum at three concentrations. Theophylline (high permeability and ranitidine (low permeability were also applied to access the permeability of NCE as reference compounds. The bioavailability after intragastrical and intravenous administration was measured in beagle dogs. The solubility of NCE in tested phosphate buffers was quite low with the maximum solubility of 81.73 μg/mL at pH 1.0. The intrinsic dissolution ratio of NCE was 1 × 10−4 mg·min−1·cm−2. The Peff value of NCE in all intestinal segments was more proximate to the high-permeability reference theophylline. Therefore, NCE was classified as class II drug according to Biopharmaceutics Classification System due to its low solubility and high intestinal permeability. In addition, concentration-dependent permeability was not observed in all the segments, indicating that there might be passive transportation for NCE. The absolute oral bioavailability of NCE in beagle dogs was 26.75%. Therefore, dissolution promotion will be crucial for oral formulation development and intravenous administration route will also be suggested for further NCE formulation development. All the data would provide a reference for biopharmaceutics classification research of other novel drug candidates.

  18. Study on biopharmaceutics classification and oral bioavailability of a novel multikinase inhibitor NCE for cancer therapy.

    Science.gov (United States)

    Yang, Yang; Fan, Chun-Mei; He, Xuan; Ren, Ke; Zhang, Jin-Kun; He, Ying-Ju; Yu, Luo-Ting; Zhao, Ying-Lan; Gong, Chang-Yang; Zheng, Yu; Song, Xiang-Rong; Zeng, Jun

    2014-04-25

    Specific biopharmaceutics classification investigation and study on phamacokinetic profile of a novel drug candidate (2-methylcarbamoyl-4-{4-[3- (trifluoromethyl) benzamido] phenoxy} pyridinium 4-methylbenzenesulfonate monohydrate, NCE) were carried out. Equilibrium solubility and intrinsic dissolution rate (IDR) of NCE were estimated in different phosphate buffers. Effective intestinal permeability (P(eff)) of NCE was determined using single-pass intestinal perfusion technique in rat duodenum, jejunum and ileum at three concentrations. Theophylline (high permeability) and ranitidine (low permeability) were also applied to access the permeability of NCE as reference compounds. The bioavailability after intragastrical and intravenous administration was measured in beagle dogs. The solubility of NCE in tested phosphate buffers was quite low with the maximum solubility of 81.73 μg/mL at pH 1.0. The intrinsic dissolution ratio of NCE was 1 × 10⁻⁴ mg·min⁻¹·cm⁻². The P(eff) value of NCE in all intestinal segments was more proximate to the high-permeability reference theophylline. Therefore, NCE was classified as class II drug according to Biopharmaceutics Classification System due to its low solubility and high intestinal permeability. In addition, concentration-dependent permeability was not observed in all the segments, indicating that there might be passive transportation for NCE. The absolute oral bioavailability of NCE in beagle dogs was 26.75%. Therefore, dissolution promotion will be crucial for oral formulation development and intravenous administration route will also be suggested for further NCE formulation development. All the data would provide a reference for biopharmaceutics classification research of other novel drug candidates.

  19. Human Papillomavirus 16E6 Oncogene Mutation in Cervical Cancer

    Institute of Scientific and Technical Information of China (English)

    Feng Sun; Xiao-qin Ha; Tong-de Lv; Chuan-ping Xing; Bin Liu; Xiao-zhe Cao

    2009-01-01

    Objective: Cervical cancer (CC) is the second most common type of cancer in women worldwide, after breast cancer. High-risk human papillomaviruses (HR-HPVs) are considered to be the major causes of cervical cancer. HPV16 is the most common type of HR-HPVs and HPV16 E6 gene is one of the major oncogenes. Specific mutations are considered as dangerous factors causing CC. This study was designed to find mutations of HPV16 E6 and the relationship between the mutations and the happening of CC.Methods: The tissue DNA was extracted from 15 biopsies of CC. Part of HPV16 E6 gene (nucleotide 201-523) was amplified by polymerase chain reaction (PCR) from the CC tissue DNA. The PCR fragments were sequenced and analyzed.Results: The result of PCR showed that the positive rate of HPV16 E6 was 93.33% (14/15). After sequencing and analyzing, in the 13 out of 14 PCR fragments, 4 maintained prototype (30.77%), 8 had a same 350G mutation (61.54%), and 1 had a 249G mutation (7.69%).Conclusion: This study suggest that there is a high infection rate of HPV in cervical cancer and most of the HPV16 E6 gene has mutations. Those mutations may have an association with the development of cervical cancer.

  20. [HPV (Human Papilloma Virus) implication in other cancers than gynaecological].

    Science.gov (United States)

    Badoual, C; Tartour, E; Roussel, H; Bats, A S; Pavie, J; Pernot, S; Weiss, L; Mohamed, A Si; Thariat, J; Hoffmann, C; Péré, H

    2015-08-01

    Worldwide, approximately 5 to 10% of the population is infected by a Human Papilloma Virus (HPV). Some of these viruses, with a high oncogenic risk (HPV HR), are responsible for about 5% of cancer. It is now accepted that almost all carcinomas of the cervix and the vulva are due to an HPV HR (HPV16 and 18) infection. However, these viruses are known to be involved in the carcinogenesis of many other cancers (head and neck [SCCHN], penis, anus). For head and neck cancer, HPV infection is considered as a good prognostic factor. The role of HPV HR in anal cancer is also extensively studied in high-risk patient's population. The role of HPV infection in the carcinogenesis of esophageal, bladder, lung, breast or skin cancers is still debated. Given the multiple possible locations of HPV HR infection, the question of optimizing the management of patients with a HPV+ cancer arises in the implementation of a comprehensive clinical and biological monitoring. It is the same in therapeutics with the existence of a preventive vaccination, for example.

  1. Marker evaluation of human breast and bladder cancers

    Energy Technology Data Exchange (ETDEWEB)

    Mayall, B.H.; Carroll, P.R.; Chen, Ling-Chun; Cohen, M.B.; Goodson, W.H. III; Smith, H.S.; Waldman, F.M. (California Univ., San Francisco, CA (USA))

    1990-11-02

    We are investigating multiple markers in human breast and bladder cancers. Our aim is to identify markers that are clinically relevant and that contribute to our understanding of the disease process in individual patients. Good markers accurately assess the malignant potential of a cancer in an individual patient. Thus, they help identify those cancers that will recur, and they may be used to predict more accurately time to recurrence, response to treatment, and overall prognosis. Therapy and patient management may then be optimized to the individual patient. Relevant markers reflect the underlying pathobiology of individual tumors. As a tissue undergoes transformation from benign to malignant, the cells lose their differentiated phenotype. As a generalization, the more the cellular phenotype, cellular proliferation and cellular genotype depart from normal, the more advanced is the tumor in its biological evolution and the more likely it is that the patient has a poor prognosis. We use three studies to illustrate our investigation of potential tumor markers. Breast cancers are labeled in vivo with 5-bromodeoxyuridine (BrdUrd) to give a direct measure of the tumor labeling index. Bladder cancers are analyzed immunocytochemically using an antibody against proliferation. Finally, the techniques of molecular genetics are used to detect allelic loss in breast cancers. 6 refs., 3 figs.

  2. ANALYSES ON DIFFERENTIALLY EXPRESSED GENES ASSOCIATED WITH HUMAN BREAST CANCER

    Institute of Scientific and Technical Information of China (English)

    MENG Xu-li; DING Xiao-wen; XU Xiao-hong

    2006-01-01

    Objective: To investigate the molecular etiology of breast cancer by way of studying the differential expression and initial function of the related genes in the occurrence and development of breast cancer. Methods: Two hundred and eighty-eight human tumor related genes were chosen for preparation of the oligochips probe. mRNA was extracted from 16 breast cancer tissues and the corresponding normal breast tissues, and cDNA probe was prepared through reverse-transcription and hybridized with the gene chip. A laser focused fluorescent scanner was used to scan the chip. The different gene expressions were thereafter automatically compared and analyzed between the two sample groups. Cy3/Cy5>3.5 meant significant up-regulation. Cy3/Cy5<0.25 meant significant down-regulation. Results: The comparison between the breast cancer tissues and their corresponding normal tissues showed that 84 genes had differential expression in the Chip. Among the differently expressed genes, there were 4 genes with significant down-regulation and 6 with significant up-regulation. Compared with normal breast tissues, differentially expressed genes did partially exist in the breast cancer tissues. Conclusion: Changes in multi-gene expression regulations take place during the occurrence and development of breast cancer; and the research on related genes can help understanding the mechanism of tumor occurrence.

  3. KiSS-1 expression in human breast cancer.

    Science.gov (United States)

    Martin, Tracey A; Watkins, Gareth; Jiang, Wen G

    2005-01-01

    The KiSS-1 gene encodes a 145 amino acid residue peptide that is further processed to a final peptide, metastin, a ligand to a G-coupled orphan receptor (OT7T175/AXOR12). KiSS-1 has been identified as a putative human metastasis suppressor gene in melanomas and in breast cancer cell lines. This study aimed to determine the expression and distribution of KiSS-1 and its receptor in human breast cancer tissues and to identify a possible link between expression levels and patient prognosis. Frozen sections from breast cancer primary tumours (matched tumour 124 and background 33) were immuno-stained with KiSS-1 antibody. RNA was reverse transcribed and analyzed by Q-PCR (standardized using beta-actin, and normalized with cytokeratin-19 levels). Levels of expression of KiSS-1 were higher in tumour compared to background tissues (3,124+/-1,262 vs 2,397+/-1,181) and significantly increased in node positive tumours compared to node negative (3,637+/-1,719 vs 2,653+/-1,994, P = 0.02). KiSS-1 expression was also increased with increasing grade and TNM status. There were no such trends with the KiSS-1 receptor. Expression of KiSS-1 was higher in patients who had died from breast cancer than those who had remained healthy (4,631+/-3,024 vs 2,280+/-1,403) whereas expression of the receptor was reduced (480+/-162 vs 195+/-134). Immunohistochemical staining showed increased expression of KiSS-1 in tumour sections. Insertion of the KiSS-1 gene into the human breast cancer cell line MDA-MB-231, resulted in cells that were significantly more motile and invasive in behaviour, with reduced adhesion to matrix, using respective assays. In conclusion, KiSS-1 expression is increased in human breast cancer, particularly in patients with aggressive tumours and with mortality. Over-expression of KiSS-1 in breast cancer cells result in more aggressive phenotype. Together, it suggests that KiSS-1 plays a role beyond the initial metastasis repressor in this cancer type.

  4. Are 20 human papillomavirus types causing cervical cancer?

    OpenAIRE

    Arbyn, Marc; Tommasino, Massimo; Depuydt, Christophe; Dillner, Joakim

    2014-01-01

    Abstract: In 2012, the International Agency for Research on Cancer concluded that there was consistent and sufficient epidemiological, experimental and mechanistic evidence of carcinogenicity to humans for 12 HPV types (HPV16, HPV18, HPV31, HPV33, HPV35, HPV39, HPV45, HPV51, HPV52, HPV56, HPV58 and HPV59) for cervical cancer. Therefore, these types were considered as 1A carcinogens. They all belong to the family of the -Papillomaviridae, in particular to the species 5 (HPV51), 6 (HPV56), 7 (H...

  5. Aurora-A Oncogene in Human Ovarian Cancer

    Science.gov (United States)

    2006-11-01

    in Human Ovarian Cancer 5b. GRANT NUMBER W81XWH-05-1-0021 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER Jin Q. Cheng, M.D...this project : 1) examine the clinicalpathological significance and the mechanism of Aurora-A overexpression/activation in ovarian cancer; 2) determine...kinase is required to localize D-TACC to centro- somes and to regulate astral microtubules. J Cell Biol 2002;156:437–51. 33. Castro A, Mandart E, Lorca T

  6. Moving Forward in Human Cancer Risk Assessment

    OpenAIRE

    Paules, Richard S.; Aubrecht, Jiri; Corvi, Raffaella; Garthoff, Bernward; Kleinjans, Jos C.

    2010-01-01

    Background The current safety paradigm for assessing carcinogenic properties of drugs, cosmetics, industrial chemicals, and environmental exposures relies mainly on in vitro genotoxicity testing followed by 2-year rodent bioassays. This testing battery is extremely sensitive but has low specificity. Furthermore, rodent bioassays are associated with high costs, high animal burden, and limited predictive value for human risks. Objectives We provide a response to a growing appeal for a paradigm ...

  7. Human endogenous retroviruses and cancer prevention: evidence and prospects

    Directory of Open Access Journals (Sweden)

    Cegolon Luca

    2013-01-01

    Full Text Available Abstract Background Cancer is a significant and growing problem worldwide. While this increase may, in part, be attributed to increasing longevity, improved case notifications and risk-enhancing lifestyle (such as smoking, diet and obesity, hygiene-related factors resulting in immuno-regulatory failure may also play a major role and call for a revision of vaccination strategies to protect against a range of cancers in addition to infections. Discussion Human endogenous retroviruses (HERVs are a significant component of a wider family of retroelements that constitutes part of the human genome. They were originated by the integration of exogenous retroviruses into the human genome millions of years ago. HERVs are estimated to comprise about 8% of human DNA and are ubiquitous in somatic and germinal tissues. Physiologic and pathologic processes are influenced by some biologically active HERV families. HERV antigens are only expressed at low levels by the host, but in circumstances of inappropriate control their genes may initiate or maintain pathological processes. Although the precise mechanism leading to abnormal HERVs gene expression has yet to be clearly elucidated, environmental factors seem to be involved by influencing the human immune system. HERV-K expression has been detected in different types of tumors. Among the various human endogenous retroviral families, the K series was the latest acquired by the human species. Probably because of its relatively recent origin, the HERV-K is the most complete and biologically active family. The abnormal expression of HERV-K seemingly triggers pathological processes leading to melanoma onset, but also contributes to the morphological and functional cellular modifications implicated in melanoma maintenance and progression. The HERV-K-MEL antigen is encoded by a pseudo-gene incorporated in the HERV-K env-gene. HERV-K-MEL is significantly expressed in the majority of dysplastic and normal naevi, as well

  8. Xmrk, kras and myc transgenic zebrafish liver cancer models share molecular signatures with subsets of human hepatocellular carcinoma.

    Directory of Open Access Journals (Sweden)

    Weiling Zheng

    Full Text Available Previously three oncogene transgenic zebrafish lines with inducible expression of xmrk, kras or Myc in the liver have been generated and these transgenic lines develop oncogene-addicted liver tumors upon chemical induction. In the current study, comparative transcriptomic approaches were used to examine the correlation of the three induced transgenic liver cancers with human liver cancers. RNA profiles from the three zebrafish tumors indicated relatively small overlaps of significantly deregulated genes and biological pathways. Nevertheless, the three transgenic tumor signatures all showed significant correlation with advanced or very advanced human hepatocellular carcinoma (HCC. Interestingly, molecular signature from each oncogene-induced zebrafish liver tumor correlated with only a small subset of human HCC samples (24-29% and there were conserved up-regulated pathways between the zebrafish and correlated human HCC subgroup. The three zebrafish liver cancer models together represented nearly half (47.2% of human HCCs while some human HCCs showed significant correlation with more than one signature defined from the three oncogene-addicted zebrafish tumors. In contrast, commonly deregulated genes (21 up and 16 down in the three zebrafish tumor models generally showed accordant deregulation in the majority of human HCCs, suggesting that these genes might be more consistently deregulated in a broad range of human HCCs with different molecular mechanisms and thus serve as common diagnosis markers and therapeutic targets. Thus, these transgenic zebrafish models with well-defined oncogene-induced tumors are valuable tools for molecular classification of human HCCs and for understanding of molecular drivers in hepatocarcinogenesis in each human HCC subgroup.

  9. Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data

    Directory of Open Access Journals (Sweden)

    J. Sunil Rao

    2007-01-01

    Full Text Available In gene selection for cancer classifi cation using microarray data, we define an eigenvalue-ratio statistic to measure a gene’s contribution to the joint discriminability when this gene is included into a set of genes. Based on this eigenvalueratio statistic, we define a novel hypothesis testing for gene statistical redundancy and propose two gene selection methods. Simulation studies illustrate the agreement between statistical redundancy testing and gene selection methods. Real data examples show the proposed gene selection methods can select a compact gene subset which can not only be used to build high quality cancer classifiers but also show biological relevance.

  10. Application of SVM classifier in thermographic image classification for early detection of breast cancer

    Science.gov (United States)

    Oleszkiewicz, Witold; Cichosz, Paweł; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał

    2016-09-01

    This article presents the application of machine learning algorithms for early detection of breast cancer on the basis of thermographic images. Supervised learning model: Support vector machine (SVM) and Sequential Minimal Optimization algorithm (SMO) for the training of SVM classifier were implemented. The SVM classifier was included in a client-server application which enables to create a training set of examinations and to apply classifiers (including SVM) for the diagnosis and early detection of the breast cancer. The sensitivity and specificity of SVM classifier were calculated based on the thermographic images from studies. Furthermore, the heuristic method for SVM's parameters tuning was proposed.

  11. A novel gene signature for molecular diagnosis of human prostate cancer by RT-qPCR.

    Directory of Open Access Journals (Sweden)

    Federica Rizzi

    Full Text Available BACKGROUND: Prostate cancer (CaP is one of the most relevant causes of cancer death in Western Countries. Although detection of CaP at early curable stage is highly desirable, actual screening methods present limitations and new molecular approaches are needed. Gene expression analysis increases our knowledge about the biology of CaP and may render novel molecular tools, but the identification of accurate biomarkers for reliable molecular diagnosis is a real challenge. We describe here the diagnostic power of a novel 8-genes signature: ornithine decarboxylase (ODC, ornithine decarboxylase antizyme (OAZ, adenosylmethionine decarboxylase (AdoMetDC, spermidine/spermine N(1-acetyltransferase (SSAT, histone H3 (H3, growth arrest specific gene (GAS1, glyceraldehyde 3-phosphate dehydrogenase (GAPDH and Clusterin (CLU in tumour detection/classification of human CaP. METHODOLOGY/PRINCIPAL FINDINGS: The 8-gene signature was detected by retrotranscription real-time quantitative PCR (RT-qPCR in frozen prostate surgical specimens obtained from 41 patients diagnosed with CaP and recommended to undergo radical prostatectomy (RP. No therapy was given to patients at any time before RP. The bio-bank used for the study consisted of 66 specimens: 44 were benign-CaP paired from the same patient. Thirty-five were classified as benign and 31 as CaP after final pathological examination. Only molecular data were used for classification of specimens. The Nearest Neighbour (NN classifier was used in order to discriminate CaP from benign tissue. Validation of final results was obtained with 10-fold cross-validation procedure. CaP versus benign specimens were discriminated with (80+/-5% accuracy, (81+/-6% sensitivity and (78+/-7% specificity. The method also correctly classified 71% of patients with Gleason score or =7, an important predictor of final outcome. CONCLUSIONS/SIGNIFICANCE: The method showed high sensitivity in a collection of specimens in which a significant

  12. Apoptosis mechanisms of human gastric cancer cell line MKN-45 infected with human mutant p27

    Institute of Scientific and Technical Information of China (English)

    Jin-Shui Zhu; Long Wang; Guo-Qiang Cheng; Qin Li; Zu-Ming Zhu; Li Zhu

    2005-01-01

    AIM: To explore the inducing effect of human mutant p27 gene on the apoptosis of the human gastric cancer cell line MKN-45 and its associated mechanisms. METHODS: The recombinant adenovirus Ad-p27mt was constructed to infect the human gastric cancer cell line MKN-45. Using flow cytometry, TUNEL assay and DNA fragment analysis, we measured the apoptotic effect of Ad-p27mt on the human gastric cancer cells. RESULTS: Ad-p27mt was successfully constructed and the infection efficiency reached 100%. After 18 h of infection, we observed an apoptotic hypodiploid peak on the flow cytometer before G1-S and apoptotic characteristic bands in the DNA electrophoresis. The apoptotic rate detected by TUNEL method was significantly higher in the Ad-p27mt group (89.4±3.12%)compared to the control group (3.12±0.13%, P < 0.01).CONCLUSION: Human mutant p27 can induce apoptosis of the human gastric cancer cells in vitro.

  13. A computational study on convolutional feature combination strategies for grade classification in colon cancer using fluorescence microscopy data

    Science.gov (United States)

    Chowdhury, Aritra; Sevinsky, Christopher J.; Santamaria-Pang, Alberto; Yener, Bülent

    2017-03-01

    The cancer diagnostic workflow is typically performed by highly specialized and trained pathologists, for which analysis is expensive both in terms of time and money. This work focuses on grade classification in colon cancer. The analysis is performed over 3 protein markers; namely E-cadherin, beta actin and colagenIV. In addition, we also use a virtual Hematoxylin and Eosin (HE) stain. This study involves a comparison of various ways in which we can manipulate the information over the 4 different images of the tissue samples and come up with a coherent and unified response based on the data at our disposal. Pre- trained convolutional neural networks (CNNs) is the method of choice for feature extraction. The AlexNet architecture trained on the ImageNet database is used for this purpose. We extract a 4096 dimensional feature vector corresponding to the 6th layer in the network. Linear SVM is used to classify the data. The information from the 4 different images pertaining to a particular tissue sample; are combined using the following techniques: soft voting, hard voting, multiplication, addition, linear combination, concatenation and multi-channel feature extraction. We observe that we obtain better results in general than when we use a linear combination of the feature representations. We use 5-fold cross validation to perform the experiments. The best results are obtained when the various features are linearly combined together resulting in a mean accuracy of 91.27%.

  14. Network-constrained group lasso for high-dimensional multinomial classification with application to cancer subtype prediction.

    Science.gov (United States)

    Tian, Xinyu; Wang, Xuefeng; Chen, Jun

    2014-01-01

    Classic multinomial logit model, commonly used in multiclass regression problem, is restricted to few predictors and does not take into account the relationship among variables. It has limited use for genomic data, where the number of genomic features far exceeds the sample size. Genomic features such as gene expressions are usually related by an underlying biological network. Efficient use of the network information is important to improve classification performance as well as the biological interpretability. We proposed a multinomial logit model that is capable of addressing both the high dimensionality of predictors and the underlying network information. Group lasso was used to induce model sparsity, and a network-constraint was imposed to induce the smoothness of the coefficients with respect to the underlying network structure. To deal with the non-smoothness of the objective function in optimization, we developed a proximal gradient algorithm for efficient computation. The proposed model was compared to models with no prior structure information in both simulations and a problem of cancer subtype prediction with real TCGA (the cancer genome atlas) gene expression data. The network-constrained mode outperformed the traditional ones in both cases.

  15. The "T" now Matters: The Eighth Edition of the Union for International Cancer Control Classification of Pancreatic Adenocarcinoma.

    Science.gov (United States)

    Welsch, Thilo; Seifert, Adrian; Müssle, Benjamin; Distler, Marius; Aust, Daniela E; Weitz, Jürgen

    2017-09-21

    The new 8th edition of the Union for International Cancer Control (UICC) classification of TNM staging includes relevant changes for pancreatic cancer (PC). We report on the survival stratification of the new T and N stages. The 8th edition TNM system was retrospectively applied to patients who underwent curative PC resection at our institution between 2005-2015. Some 256 patients were included. The postoperative TNM stage was pT3 in 96% of the cases according to the 7th edition of UICC staging. When the 8th edition system -which stratifies by tumor size and number of involved lymph nodes- was applied, both T- and N-stage were significant prognostic survival factors. Most interestingly, the old pT3 subgroup was split into four different pT stages of the 8th edition system with the most frequent stage being pT2 (58.6%). In this subgroup, patients with pT1-2 and pT3 tumors had a significantly different survival (P = 0.0474). Compared with the 7th edition of UICC TNM staging, the new N, but also the T system can better discriminate the overall survival of PC patients.

  16. Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging

    Science.gov (United States)

    Klomp, Sander; van der Sommen, Fons; Swager, Anne-Fré; Zinger, Svitlana; Schoon, Erik J.; Curvers, Wouter L.; Bergman, Jacques J.; de With, Peter H. N.

    2017-03-01

    Volumetric Laser Endomicroscopy (VLE) is a promising technique for the detection of early neoplasia in Barrett's Esophagus (BE). VLE generates hundreds of high resolution, grayscale, cross-sectional images of the esophagus. However, at present, classifying these images is a time consuming and cumbersome effort performed by an expert using a clinical prediction model. This paper explores the feasibility of using computer vision techniques to accurately predict the presence of dysplastic tissue in VLE BE images. Our contribution is threefold. First, a benchmarking is performed for widely applied machine learning techniques and feature extraction methods. Second, three new features based on the clinical detection model are proposed, having superior classification accuracy and speed, compared to earlier work. Third, we evaluate automated parameter tuning by applying simple grid search and feature selection methods. The results are evaluated on a clinically validated dataset of 30 dysplastic and 30 non-dysplastic VLE images. Optimal classification accuracy is obtained by applying a support vector machine and using our modified Haralick features and optimal image cropping, obtaining an area under the receiver operating characteristic of 0.95 compared to the clinical prediction model at 0.81. Optimal execution time is achieved using a proposed mean and median feature, which is extracted at least factor 2.5 faster than alternative features with comparable performance.

  17. MicroRNA in human cancer and chronic inflammatory diseases.

    Science.gov (United States)

    Kanwar, Jagat R; Mahidhara, Ganesh; Kanwar, Rupinder K

    2010-06-01

    MicroRNAs (miRNAs) are the non-coding RNAs that act as post-translational regulators to their complimentary messenger RNAs (mRNA). Due to their specific gene silencing property, miRNAs have been implicated in a number of cellular and developmental processes. Also, it has been proposed that a particular set of miRNA spectrum is expressed only in a particular type of tissue. Many interesting findings related to the differential expression of miRNAs in various human diseases including several types of cancers, neurodegenerative diseases and metabolic diseases have been reported. Deregulation of miRNA expression in different types of human diseases and the roles various miRNAs play as tumour suppressors as well as oncogenes, suggest their contribution to cancer and/or in other disease development. These findings have possible implications in the development of diagnostics and/or therapeutics in human malignancies. In this review, we discuss various miRNAs that are differentially expressed in human chronic inflammatory diseases, neurodegenerative diseases, cancer and the further prospective development of miRNA based diagnostics and therapeutics.

  18. MicroRNA Regulation of Human Breast Cancer Stem Cells