WorldWideScience

Sample records for multi-class tumor classification

  1. Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification

    KAUST Repository

    Zhu, Xiaofeng; Xie, Qing; Zhu, Yonghua; Liu, Xingyi; Zhang, Shichao

    2015-01-01

    This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple

  2. Binary Stochastic Representations for Large Multi-class Classification

    KAUST Repository

    Gerald, Thomas; Baskiotis, Nicolas; Denoyer, Ludovic

    2017-01-01

    Classification with a large number of classes is a key problem in machine learning and corresponds to many real-world applications like tagging of images or textual documents in social networks. If one-vs-all methods usually reach top performance

  3. Multi-class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods.

    Science.gov (United States)

    Lodhi, Huma; Muggleton, Stephen; Sternberg, Mike J E

    2010-09-17

    Toxicity prediction is essential for drug design and development of effective therapeutics. In this paper we present an in silico strategy, to identify the mode of action of toxic compounds, that is based on the use of a novel logic based kernel method. The technique uses support vector machines in conjunction with the kernels constructed from first order rules induced by an Inductive Logic Programming system. It constructs multi-class models by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. In order to evaluate the effectiveness of the approach for chemoinformatics problems like predictive toxicology, we apply it to toxicity classification in aquatic systems. The method is used to identify and classify 442 compounds with respect to the mode of action. The experimental results show that the technique successfully classifies toxic compounds and can be useful in assessing environmental risks. Experimental comparison of the performance of the proposed multi-class scheme with the standard multi-class Inductive Logic Programming algorithm and multi-class Support Vector Machine yields statistically significant results and demonstrates the potential power and benefits of the approach in identifying compounds of various toxic mechanisms. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. Binary Stochastic Representations for Large Multi-class Classification

    KAUST Repository

    Gerald, Thomas

    2017-10-23

    Classification with a large number of classes is a key problem in machine learning and corresponds to many real-world applications like tagging of images or textual documents in social networks. If one-vs-all methods usually reach top performance in this context, these approaches suffer of a high inference complexity, linear w.r.t. the number of categories. Different models based on the notion of binary codes have been proposed to overcome this limitation, achieving in a sublinear inference complexity. But they a priori need to decide which binary code to associate to which category before learning using more or less complex heuristics. We propose a new end-to-end model which aims at simultaneously learning to associate binary codes with categories, but also learning to map inputs to binary codes. This approach called Deep Stochastic Neural Codes (DSNC) keeps the sublinear inference complexity but do not need any a priori tuning. Experimental results on different datasets show the effectiveness of the approach w.r.t. baseline methods.

  5. Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification

    KAUST Repository

    Zhu, Xiaofeng

    2015-05-28

    This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.

  6. Vision based nutrient deficiency classification in maize plants using multi class support vector machines

    Science.gov (United States)

    Leena, N.; Saju, K. K.

    2018-04-01

    Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

  7. EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine

    Science.gov (United States)

    Gao, Lin; Cheng, Wei; Zhang, Jinhua; Wang, Jue

    2016-08-01

    Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.

  8. Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

    Science.gov (United States)

    Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

    2015-07-30

    Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

    Science.gov (United States)

    Morisi, Rita; Manners, David Neil; Gnecco, Giorgio; Lanconelli, Nico; Testa, Claudia; Evangelisti, Stefania; Talozzi, Lia; Gramegna, Laura Ludovica; Bianchini, Claudio; Calandra-Buonaura, Giovanna; Sambati, Luisa; Giannini, Giulia; Cortelli, Pietro; Tonon, Caterina; Lodi, Raffaele

    2018-02-01

    In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy. When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required. The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Multi-class machine classification of suicide-related communication on Twitter.

    Science.gov (United States)

    Burnap, Pete; Colombo, Gualtiero; Amery, Rosie; Hodorog, Andrei; Scourfield, Jonathan

    2017-08-01

    The World Wide Web, and online social networks in particular, have increased connectivity between people such that information can spread to millions of people in a matter of minutes. This form of online collective contagion has provided many benefits to society, such as providing reassurance and emergency management in the immediate aftermath of natural disasters. However, it also poses a potential risk to vulnerable Web users who receive this information and could subsequently come to harm. One example of this would be the spread of suicidal ideation in online social networks, about which concerns have been raised. In this paper we report the results of a number of machine classifiers built with the aim of classifying text relating to suicide on Twitter. The classifier distinguishes between the more worrying content, such as suicidal ideation, and other suicide-related topics such as reporting of a suicide, memorial, campaigning and support. It also aims to identify flippant references to suicide. We built a set of baseline classifiers using lexical, structural, emotive and psychological features extracted from Twitter posts. We then improved on the baseline classifiers by building an ensemble classifier using the Rotation Forest algorithm and a Maximum Probability voting classification decision method, based on the outcome of base classifiers. This achieved an F-measure of 0.728 overall (for 7 classes, including suicidal ideation) and 0.69 for the suicidal ideation class. We summarise the results by reflecting on the most significant predictive principle components of the suicidal ideation class to provide insight into the language used on Twitter to express suicidal ideation. Finally, we perform a 12-month case study of suicide-related posts where we further evaluate the classification approach - showing a sustained classification performance and providing anonymous insights into the trends and demographic profile of Twitter users posting content of this type.

  11. A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification

    Science.gov (United States)

    Shyu, Mei-Ling; Sainani, Varsha

    The increasing number of network security related incidents have made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). IDSs are expected to analyze a large volume of data while not placing a significantly added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel data mining assisted multiagent-based intrusion detection system (DMAS-IDS) is proposed, particularly with the support of multiclass supervised classification. These agents can detect and take predefined actions against malicious activities, and data mining techniques can help detect them. Our proposed DMAS-IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDS with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on multiagent platform along with a supervised classification technique.

  12. Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping.

    Science.gov (United States)

    Jung, Segun; Bi, Yingtao; Davuluri, Ramana V

    2015-01-01

    Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised data discretization methods--Equal-width binning, Equal-frequency binning, and k-means clustering--in accurately classifying the four known subtypes of glioblastoma multiforme (GBM) when the classification algorithms were trained on the isoform-level gene expression profiles from exon-array platform and tested on the corresponding profiles from RNA-seq data. We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification. For models trained and tested on exon-array data, the addition of data discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array data and tested on RNA-seq data, the addition of data discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without data discretization, however, only 73.6% accuracy was achieved at most. The classification algorithms, trained and tested on data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for classification and identification of

  13. Assessment of multi class kinematic wave models

    NARCIS (Netherlands)

    Van Wageningen-Kessels, F.L.M.; Van Lint, J.W.C.; Vuik, C.; Hoogendoorn, S.P.

    2012-01-01

    In the last decade many multi class kinematic wave (MCKW) traffic ow models have been proposed. MCKW models introduce heterogeneity among vehicles and drivers. For example, they take into account differences in (maximum) velocities and driving style. Nevertheless, the models are macroscopic and the

  14. A uniform residual tumor (R) classification: integration of the R classification and the circumferential margin status.

    NARCIS (Netherlands)

    Wittekind, C.; Compton, C.; Quirke, P.; Nagtegaal, I.D.; Merkel, S.; Hermanek, P.; Sobin, L.H.

    2009-01-01

    BACKGROUND: Since the introduction of the TNM residual tumor (R) classification, the involvement of resection margins has been defined either as a microscopic (R1) or a macroscopic (R2) demonstration of tumor directly at the resection margin ("tumor transected"). METHODS: The recognition of the

  15. Preoperative classification of ovarial tumors by means of computed tomography

    International Nuclear Information System (INIS)

    Steinbrich, W.; Rohde, U.

    1982-01-01

    127 histologically demonstrated ovarial tumors were studied in a blindfold test in order to find out to what extent a preoperative determination of dignity or diagnosis of the tumor kind is possible by computed tomography. The overall rate of correct determinations of dignity is 82%. In case of functional cysts and cystomas with thin cyst walls, cystadenocarcinomas and dermoid cysts, this rate is about 95%, whereas the classification results are less exact in case of cystic tumors with broadened cyst walls, preponderantly solid tumors and tumor-like lesions. (orig.) [de

  16. Multiparametric classification links tumor microenvironments with tumor cell phenotype.

    Directory of Open Access Journals (Sweden)

    Bojana Gligorijevic

    2014-11-01

    Full Text Available While it has been established that a number of microenvironment components can affect the likelihood of metastasis, the link between microenvironment and tumor cell phenotypes is poorly understood. Here we have examined microenvironment control over two different tumor cell motility phenotypes required for metastasis. By high-resolution multiphoton microscopy of mammary carcinoma in mice, we detected two phenotypes of motile tumor cells, different in locomotion speed. Only slower tumor cells exhibited protrusions with molecular, morphological, and functional characteristics associated with invadopodia. Each region in the primary tumor exhibited either fast- or slow-locomotion. To understand how the tumor microenvironment controls invadopodium formation and tumor cell locomotion, we systematically analyzed components of the microenvironment previously associated with cell invasion and migration. No single microenvironmental property was able to predict the locations of tumor cell phenotypes in the tumor if used in isolation or combined linearly. To solve this, we utilized the support vector machine (SVM algorithm to classify phenotypes in a nonlinear fashion. This approach identified conditions that promoted either motility phenotype. We then demonstrated that varying one of the conditions may change tumor cell behavior only in a context-dependent manner. In addition, to establish the link between phenotypes and cell fates, we photoconverted and monitored the fate of tumor cells in different microenvironments, finding that only tumor cells in the invadopodium-rich microenvironments degraded extracellular matrix (ECM and disseminated. The number of invadopodia positively correlated with degradation, while the inhibiting metalloproteases eliminated degradation and lung metastasis, consistent with a direct link among invadopodia, ECM degradation, and metastasis. We have detected and characterized two phenotypes of motile tumor cells in vivo, which

  17. Deep learning for tumor classification in imaging mass spectrometry.

    Science.gov (United States)

    Behrmann, Jens; Etmann, Christian; Boskamp, Tobias; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter

    2018-04-01

    Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de. Supplementary data are available at Bioinformatics online.

  18. Tumor taxonomy for the developmental lineage classification of neoplasms

    International Nuclear Information System (INIS)

    Berman, Jules J

    2004-01-01

    The new 'Developmental lineage classification of neoplasms' was described in a prior publication. The classification is simple (the entire hierarchy is described with just 39 classifiers), comprehensive (providing a place for every tumor of man), and consistent with recent attempts to characterize tumors by cytogenetic and molecular features. A taxonomy is a list of the instances that populate a classification. The taxonomy of neoplasia attempts to list every known term for every known tumor of man. The taxonomy provides each concept with a unique code and groups synonymous terms under the same concept. A Perl script validated successive drafts of the taxonomy ensuring that: 1) each term occurs only once in the taxonomy; 2) each term occurs in only one tumor class; 3) each concept code occurs in one and only one hierarchical position in the classification; and 4) the file containing the classification and taxonomy is a well-formed XML (eXtensible Markup Language) document. The taxonomy currently contains 122,632 different terms encompassing 5,376 neoplasm concepts. Each concept has, on average, 23 synonyms. The taxonomy populates 'The developmental lineage classification of neoplasms,' and is available as an XML file, currently 9+ Megabytes in length. A representation of the classification/taxonomy listing each term followed by its code, followed by its full ancestry, is available as a flat-file, 19+ Megabytes in length. The taxonomy is the largest nomenclature of neoplasms, with more than twice the number of neoplasm names found in other medical nomenclatures, including the 2004 version of the Unified Medical Language System, the Systematized Nomenclature of Medicine Clinical Terminology, the National Cancer Institute's Thesaurus, and the International Classification of Diseases Oncolology version. This manuscript describes a comprehensive taxonomy of neoplasia that collects synonymous terms under a unique code number and assigns each

  19. Surgical options in benign parotid tumors: a proposal for classification.

    Science.gov (United States)

    Quer, Miquel; Vander Poorten, Vincent; Takes, Robert P; Silver, Carl E; Boedeker, Carsten C; de Bree, Remco; Rinaldo, Alessandra; Sanabria, Alvaro; Shaha, Ashok R; Pujol, Albert; Zbären, Peter; Ferlito, Alfio

    2017-11-01

    Different surgical options are currently available for treating benign tumors of the parotid gland, and the discussion on optimal treatment continues despite several meta-analyses. These options include more limited resections (extracapsular dissection, partial lateral parotidectomy) versus more extensive and traditional options (lateral parotid lobectomy, total parotidectomy). Different schools favor one option or another based on their experience, skills and tradition. This review provides a critical analysis of the literature regarding these options. The main limitation of all the studies is the bias of selection for different surgical approaches. For this reason, we propose a staging system that could facilitate clinical decision making and the comparison of results. We propose four categories based on the size of the tumor and its location within the parotid gland. Category I includes tumors up to 3 cm, which are mobile, close to the outer surface and close to the parotid borders. Category II includes deeper tumors up to 3 cm. Category III comprises tumors greater than 3 cm involving two levels of the parotid gland, and category IV tumors are greater than 3 cm and involve more than 2 levels. For each category and for the various pathologic types, a guideline of surgical extent is proposed. The objective of this classification is to facilitate prospective multicentric studies on surgical techniques in the treatment of benign parotid tumors and to enable the comparison of results of different clinical studies.

  20. Welcoming the new WHO classification of pituitary tumors 2017: revolution in TTF-1-positive posterior pituitary tumors.

    Science.gov (United States)

    Shibuya, Makoto

    2018-04-01

    The fourth edition of the World Health Organization classification of endocrine tumors (EN-WHO2017) was released in 2017. In this new edition, changes in the classification of non-neuroendocrine tumors are proposed particularly in tumors arising in the posterior pituitary. These tumors are a distinct group of low-grade neoplasms of the sellar region that express thyroid transcription factor-1, and include pituicytoma, granular cell tumor of the sellar region, spindle cell oncocytoma, and sellar ependymoma. This short review focuses on the classification of posterior pituitary tumors newly proposed in EN-WHO2017, and controversies in their pathological differential diagnosis are discussed based on recent cases.

  1. Benign fatty tumors: classification, clinical course, imaging appearance, and treatment

    International Nuclear Information System (INIS)

    Bancroft, Laura W.; Kransdorf, Mark J.; Peterson, Jeffrey J.; O'Connor, Mary I.

    2006-01-01

    Lipoma is the most common soft-tissue tumor, with a wide spectrum of clinical presentations and imaging appearances. Several subtypes are described, ranging from lesions entirely composed of mature adipose tissue to tumors intimately associated with nonadipose tissue, to those composed of brown fat. The imaging appearance of these fatty masses is frequently sufficiently characteristic to allow a specific diagnosis. However, in other cases, although a specific diagnosis is not achievable, a meaningful limited differential diagnosis can be established. The purpose of this manuscript is to review the spectrum of benign fatty tumors highlighting the current classification system, clinical presentation and behavior, spectrum of imaging appearances, and treatment. The imaging review emphasizes computed tomography (CT) scanning and magnetic resonance (MR) imaging, differentiating radiologic features. (orig.)

  2. Morphologic classification of ductal breast tumors on ultrasound : differential diagnosis of benign and malignant tumors

    International Nuclear Information System (INIS)

    Won, Mi Sook; Chung, Soo Young; Yang, Ik; Lee, Yul; Park, Hai Jung; Lee, Myoung Hwan; Yoon, In Sook; Koh, Mi Gyoung

    1997-01-01

    To evaluate the morphologic differential diagnosis of benign and malignant ductal breast tumors, as seen on US US findings in 29 pathologically proven cases of ductal breast tumor were retrospectively reviewed. All patients were female and their mean age was 42 years. Nineteen tumors were benign and ten were malignant, and all ductal or cystic lesions showed solid masses. According to the location of the mural nodule, we classified the sonographic appearance of these tumors into three types:intraductal, intracystic and amorphic. The intraductal type was divided into three subtypes:incompletely obstructive, completely obstructive and multiple mural nodules. For the intracystic type, too, three subtypes were designated:the intracystic mural nodule (mural cyst), intracystic mural nodule with the duct (mural cyst+duct) and intracystic multiple mural nodules. The amorphic type is defined as an atypical ductal tumor with the mural nodule extending into adjacent parenchyma. The margin of the duct or cyst was smooth in 68.4% of benign, and irregular in 90% of malignant ductal tumors. Internal echogeneity of the duct or cyst usually showed homogeneity in both benign and malignant tumors. 73.7% of tumors connecting the duct were benign and 50% were malignant. In benign tumors, 52.6% of mural nodule had an irregular margin, while in malignant tumors, the corresponding proportion was 100%;both types usually showed heterogeneous hypoechogeneity. Among benign tumors, the most common morphologic type was the intraductal incompletely obstructive subtype (36.8%);among those that were malignant, the amorphic type was most common, accounting for 40% of tumors. No amorphic type was benign and no incompletely obstructive subtype was malignant. When ductal breast tumors are morphologically classified on the basis of sonographic findings, the intraductal incompletely obstructive subtype suggests benignancy, and the amorphic type, malignancy. The morphologic classification of ductal

  3. Multi-Class Classification for Identifying JPEG Steganography Embedding Methods

    Science.gov (United States)

    2008-09-01

    digital pictures on Web sites or sending them through email (Astrowsky, 2000). Steganography may also be used to allow communication between affiliates...B.H. (2000). STEGANOGRAPHY: Hidden Images, A New Challenge in the Fight Against Child Porn . UPDATE, Volume 13, Number 2, pp. 1-4, Retrieved June 3

  4. A multi-class classification MCLP model with particle swarm ...

    Indian Academy of Sciences (India)

    A M Viswa Bharathy

    clearly show that the proposed model performs better in terms of detection rate, false .... ease the process of target recognition and detection in ... They performed packet-level simulation analysis in ns-2 ... validated using CPLEX and MATLAB.

  5. Malignant fatty tumors: classification, clinical course, imaging appearance and treatment

    International Nuclear Information System (INIS)

    Peterson, J.J.; Kransdorf, M.J.; Bancroft, L.W.; O'Connor, M.I.

    2003-01-01

    Liposarcoma is a relatively common soft tissue malignancy with a wide spectrum of clinical presentations and imaging appearances. Several subtypes are described, ranging from lesions nearly entirely composed of mature adipose tissue, to tumors with very sparse adipose elements. The imaging appearance of these fatty masses is frequently sufficiently characteristic to allow a specific diagnosis, while in other cases, although a specific diagnosis is not achievable, a meaningful limited differential diagnosis can be established. The purpose of this paper is to review the spectrum of malignant fatty tumors, highlighting the current classification system, clinical presentation and behavior, treatment and spectrum of imaging appearances. The imaging review will emphasize CT scanning and MR imaging, and will stress differentiating radiologic features. (orig.)

  6. SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition.

    Science.gov (United States)

    Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

    2007-05-22

    Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at http://svm-fold.c2b2.columbia.edu. Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach

  7. The 2017 World Health Organization classification of tumors of the pituitary gland: a summary.

    Science.gov (United States)

    Lopes, M Beatriz S

    2017-10-01

    The 4th edition of the World Health Organization (WHO) classification of endocrine tumors has been recently released. In this new edition, major changes are recommended in several areas of the classification of tumors of the anterior pituitary gland (adenophypophysis). The scope of the present manuscript is to summarize these recommended changes, emphasizing a few significant topics. These changes include the following: (1) a novel approach for classifying pituitary neuroendocrine tumors according to pituitary adenohypophyseal cell lineages; (2) changes to the histological grading of pituitary neuroendocrine tumors with the elimination of the term "atypical adenoma;" and (3) introduction of new entities like the pituitary blastoma and re-definition of old entities like the null-cell adenoma. This new classification is very practical and mostly based on immunohistochemistry for pituitary hormones, pituitary-specific transcription factors, and other immunohistochemical markers commonly used in pathology practice, not requiring routine ultrastructural analysis of the tumors. Evaluation of tumor proliferation potential, by mitotic count and Ki-67 labeling index, and tumor invasion is strongly recommended on individual case basis to identify clinically aggressive adenomas. In addition, the classification offers the treating clinical team information on tumor prognosis by identifying specific variants of adenomas associated with an elevated risk for recurrence. Changes in the classification of non-neuroendocrine tumors are also proposed, in particular those tumors arising in the posterior pituitary including pituicytoma, granular cell tumor of the posterior pituitary, and spindle cell oncocytoma. These changes endorse those previously published in the 2016 WHO classification of CNS tumors. Other tumors arising in the sellar region are also reviewed in detail including craniopharyngiomas, mesenchymal and stromal tumors, germ cell tumors, and hematopoietic tumors. It is

  8. Changing Histopathological Diagnostics by Genome-Based Tumor Classification

    Directory of Open Access Journals (Sweden)

    Michael Kloth

    2014-05-01

    Full Text Available Traditionally, tumors are classified by histopathological criteria, i.e., based on their specific morphological appearances. Consequently, current therapeutic decisions in oncology are strongly influenced by histology rather than underlying molecular or genomic aberrations. The increase of information on molecular changes however, enabled by the Human Genome Project and the International Cancer Genome Consortium as well as the manifold advances in molecular biology and high-throughput sequencing techniques, inaugurated the integration of genomic information into disease classification. Furthermore, in some cases it became evident that former classifications needed major revision and adaption. Such adaptations are often required by understanding the pathogenesis of a disease from a specific molecular alteration, using this molecular driver for targeted and highly effective therapies. Altogether, reclassifications should lead to higher information content of the underlying diagnoses, reflecting their molecular pathogenesis and resulting in optimized and individual therapeutic decisions. The objective of this article is to summarize some particularly important examples of genome-based classification approaches and associated therapeutic concepts. In addition to reviewing disease specific markers, we focus on potentially therapeutic or predictive markers and the relevance of molecular diagnostics in disease monitoring.

  9. Distributed optimization of multi-class SVMs.

    Directory of Open Access Journals (Sweden)

    Maximilian Alber

    Full Text Available Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained on data involving a large number of classes. The same cannot be stated, however, for the so-called all-in-one SVMs, which require solving a quadratic program of size quadratically in the number of classes. We develop distributed algorithms for two all-in-one SVM formulations (Lee et al. and Weston and Watkins that parallelize the computation evenly over the number of classes. This allows us to compare these models to one-vs.-rest SVMs on unprecedented scale. The results indicate superior accuracy on text classification data.

  10. The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification.

    Science.gov (United States)

    Travis, William D; Brambilla, Elisabeth; Nicholson, Andrew G; Yatabe, Yasushi; Austin, John H M; Beasley, Mary Beth; Chirieac, Lucian R; Dacic, Sanja; Duhig, Edwina; Flieder, Douglas B; Geisinger, Kim; Hirsch, Fred R; Ishikawa, Yuichi; Kerr, Keith M; Noguchi, Masayuki; Pelosi, Giuseppe; Powell, Charles A; Tsao, Ming Sound; Wistuba, Ignacio

    2015-09-01

    The 2015 World Health Organization (WHO) Classification of Tumors of the Lung, Pleura, Thymus and Heart has just been published with numerous important changes from the 2004 WHO classification. The most significant changes in this edition involve (1) use of immunohistochemistry throughout the classification, (2) a new emphasis on genetic studies, in particular, integration of molecular testing to help personalize treatment strategies for advanced lung cancer patients, (3) a new classification for small biopsies and cytology similar to that proposed in the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (4) a completely different approach to lung adenocarcinoma as proposed by the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (5) restricting the diagnosis of large cell carcinoma only to resected tumors that lack any clear morphologic or immunohistochemical differentiation with reclassification of the remaining former large cell carcinoma subtypes into different categories, (6) reclassifying squamous cell carcinomas into keratinizing, nonkeratinizing, and basaloid subtypes with the nonkeratinizing tumors requiring immunohistochemistry proof of squamous differentiation, (7) grouping of neuroendocrine tumors together in one category, (8) adding NUT carcinoma, (9) changing the term sclerosing hemangioma to sclerosing pneumocytoma, (10) changing the name hamartoma to "pulmonary hamartoma," (11) creating a group of PEComatous tumors that include (a) lymphangioleiomyomatosis, (b) PEComa, benign (with clear cell tumor as a variant) and (c) PEComa, malignant, (12) introducing the entity pulmonary myxoid sarcoma with an EWSR1-CREB1 translocation, (13) adding the entities myoepithelioma and myoepithelial carcinomas, which can show EWSR1 gene rearrangements, (14) recognition of usefulness of WWTR1-CAMTA1 fusions in diagnosis of epithelioid

  11. CT and MR imaging findings of endocrine tumor of the pancreas according to WHO classification

    International Nuclear Information System (INIS)

    Rha, Sung Eun; Jung, Seung Eun; Lee, Kang Hoon; Ku, Young Mi; Byun, Jae Young; Lee, Jae Mun

    2007-01-01

    The pancreatic endocrine tumors are rare neuroendocrine tumors of the pancreas originating from totipotential stem cells or differentiated mature endocrine cells within the exocrine gland. Endocrine tumors are usually classified into functioning and non-functioning tumors and presents with a range of benignity or malignancy. In this article, we present the various CT and MR imaging findings of endocrine tumors of pancreas according to recent WHO classification

  12. Multi-class oscillating systems of interacting neurons

    DEFF Research Database (Denmark)

    Ditlevsen, Susanne; Löcherbach, Eva

    2017-01-01

    We consider multi-class systems of interacting nonlinear Hawkes processes modeling several large families of neurons and study their mean field limits. As the total number of neurons goes to infinity we prove that the evolution within each class can be described by a nonlinear limit differential...

  13. A Multi-Class, Interdisciplinary Project Using Elementary Statistics

    Science.gov (United States)

    Reese, Margaret

    2012-01-01

    This article describes a multi-class project that employs statistical computing and writing in a statistics class. Three courses, General Ecology, Meteorology, and Introductory Statistics, cooperated on a project for the EPA's Student Design Competition. The continuing investigation has also spawned several undergraduate research projects in…

  14. Approximations for Markovian multi-class queues with preemptive priorities

    NARCIS (Netherlands)

    van der Heijden, Matthijs C.; van Harten, Aart; Sleptchenko, Andrei

    2004-01-01

    We discuss the approximation of performance measures in multi-class M/M/k queues with preemptive priorities for large problem instances (many classes and servers) using class aggregation and server reduction. We compared our approximations to exact and simulation results and found that our approach

  15. A fast learning method for large scale and multi-class samples of SVM

    Science.gov (United States)

    Fan, Yu; Guo, Huiming

    2017-06-01

    A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

  16. WHO/ISUP classification of the urothelial tumors of the urinary bladder

    Directory of Open Access Journals (Sweden)

    Zdenka Ovčak

    2005-09-01

    Full Text Available Background: The authors present the current classification of urothelial neoplasms of the urinary bladder. The classification of urothelial tumors of the urinary bladder of 1973 was despite some imperfection relatively successfuly used for more than thirty years. The three grade classification of papillary urothelial tumors without invasion has been based on evaluation of variations in architecture of covering epithelium and tumor cell anaplasia. As reccomended by the International Society of Urological Pathologists (ISUP, the World Health Organisation (WHO accepted the new WHO/ ISUP classification in 1998 that was revised in 2002 and finally published in 2004. With intention to avoid unnecessary diagnosis of cancer in patients having papillary urothelial tumors with rare invasive or metastastatic growth, this classification introduced a new entity, the papillary urothelial neoplasia of low malignant potential (PUNLMP. The additional change in classification was the division of invasive urothelial neoplasms only to low and high grade urothelial carcinomas.Conclusions: The authors’ opinion is that although the old classification is not recommended for use anymore the new one is not solving the elementary reproaches to previous classification such as terminological unsuitability and insufficient scientific reasoning. Our proposed solution in classification of papillary urothelial neoplasms would be the application of criteria analogous to that used in diagnostics of papillary noninvasive tumors of the head and neck or alimentary tract.

  17. Automatic SLEEP staging: From young aduslts to elderly patients using multi-class support vector machine

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Jennum, Poul; Sorensen, Helge B. D.

    2013-01-01

    an automatic sleep stage detector, which can separate wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep using only EEG and EOG. Most sleep events, which define the sleep stages, are reduced with age. This is addressed by focusing on the amplitude of the clinical EEG bands......Aging is a process that is inevitable, and makes our body vulnerable to age-related diseases. Age is the most consistent factor affecting the sleep structure. Therefore, new automatic sleep staging methods, to be used in both of young and elderly patients, are needed. This study proposes......, and not the affected sleep events. The age-related influences are then reduced by robust subject-specific scaling. The classification of the three sleep stages are achieved by a multi-class support vector machine using the one-versus-rest scheme. It was possible to obtain a high classification accuracy of 0...

  18. Intrusion detection model using fusion of chi-square feature selection and multi class SVM

    Directory of Open Access Journals (Sweden)

    Ikram Sumaiya Thaseen

    2017-10-01

    Full Text Available Intrusion detection is a promising area of research in the domain of security with the rapid development of internet in everyday life. Many intrusion detection systems (IDS employ a sole classifier algorithm for classifying network traffic as normal or abnormal. Due to the large amount of data, these sole classifier models fail to achieve a high attack detection rate with reduced false alarm rate. However by applying dimensionality reduction, data can be efficiently reduced to an optimal set of attributes without loss of information and then classified accurately using a multi class modeling technique for identifying the different network attacks. In this paper, we propose an intrusion detection model using chi-square feature selection and multi class support vector machine (SVM. A parameter tuning technique is adopted for optimization of Radial Basis Function kernel parameter namely gamma represented by ‘ϒ’ and over fitting constant ‘C’. These are the two important parameters required for the SVM model. The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks. The investigational results on NSL-KDD dataset which is an enhanced version of KDDCup 1999 dataset shows that our proposed approach results in a better detection rate and reduced false alarm rate. An experimentation on the computational time required for training and testing is also carried out for usage in time critical applications.

  19. The new WHO 2016 classification of brain tumors-what neurosurgeons need to know.

    Science.gov (United States)

    Banan, Rouzbeh; Hartmann, Christian

    2017-03-01

    The understanding of molecular alterations of tumors has severely changed the concept of classification in all fields of pathology. The availability of high-throughput technologies such as next-generation sequencing allows for a much more precise definition of tumor entities. Also in the field of brain tumors a dramatic increase of knowledge has occurred over the last years partially calling into question the purely morphologically based concepts that were used as exclusive defining criteria in the WHO 2007 classification. Review of the WHO 2016 classification of brain tumors as well as a search and review of publications in the literature relevant for brain tumor classification from 2007 up to now. The idea of incorporating the molecular features in classifying tumors of the central nervous system led the authors of the new WHO 2016 classification to encounter inevitable conceptual problems, particularly with respect to linking morphology to molecular alterations. As a solution they introduced the concept of a "layered diagnosis" to the classification of brain tumors that still allows at a lower level a purely morphologically based diagnosis while partially forcing the incorporation of molecular characteristics for an "integrated diagnosis" at the highest diagnostic level. In this context the broad availability of molecular assays was debated. On the one hand molecular antibodies specifically targeting mutated proteins should be available in nearly all neuropathological laboratories. On the other hand, different high-throughput assays are accessible only in few first-world neuropathological institutions. As examples oligodendrogliomas are now primarily defined by molecular characteristics since the required assays are generally established, whereas molecular grouping of ependymomas, found to clearly outperform morphologically based tumor interpretation, was rejected from inclusion in the WHO 2016 classification because the required assays are currently only

  20. Action Recognition Using 3D Histograms of Texture and A Multi-Class Boosting Classifier.

    Science.gov (United States)

    Zhang, Baochang; Yang, Yun; Chen, Chen; Yang, Linlin; Han, Jungong; Shao, Ling

    2017-10-01

    Human action recognition is an important yet challenging task. This paper presents a low-cost descriptor called 3D histograms of texture (3DHoTs) to extract discriminant features from a sequence of depth maps. 3DHoTs are derived from projecting depth frames onto three orthogonal Cartesian planes, i.e., the frontal, side, and top planes, and thus compactly characterize the salient information of a specific action, on which texture features are calculated to represent the action. Besides this fast feature descriptor, a new multi-class boosting classifier (MBC) is also proposed to efficiently exploit different kinds of features in a unified framework for action classification. Compared with the existing boosting frameworks, we add a new multi-class constraint into the objective function, which helps to maintain a better margin distribution by maximizing the mean of margin, whereas still minimizing the variance of margin. Experiments on the MSRAction3D, MSRGesture3D, MSRActivity3D, and UTD-MHAD data sets demonstrate that the proposed system combining 3DHoTs and MBC is superior to the state of the art.

  1. NEW CLASSIFICATION AND DIAGNOSIS OF APPENDICEAL CARCINOID TUMORS

    Directory of Open Access Journals (Sweden)

    Vuka Katić

    2012-03-01

    Full Text Available Carcinoid tumours are rare lesions that belong to the APUDoma category having the capacity of Amine Precursor Uptake and Decarboxylase. Gastrointestinal system comprises 90% of all carcinoids in the body and they are the most common type of primary malignant lesions of the appendix. New WHO classification of gastrointestinal carcinoids, diagnostic dilemmas of some carcinoid variants and, sometimes unpredictable prognosis are the reasons for the following study: clinical, macro- and microscopical as well as cytochemical and immunocytochemical examination of the vermiform appendix carcinoids, surgically removed from 16 patients. The appendectomy was induced by acute appendicitis or tumorous mass, without carcinoid syndrome. After two-day fixation in 10% formaldehyde, routinelly processed and embedded in paraffin, laboratory sections were stained with H&E, Fontana-Masson’s, Grimelius’, FIF and ABPAS methods. ABC method has been used for immunohistochemical examination. The antibodies for Chromogranin A, NSE, Synaptophysin, Cytokeratin 7, S-100 protein, Ki67 and CEA (primary antibodies and ABC (secondary antibody (Dako Kopenhagen were tested. The patients had no carcinoid syndrome. The most frequent was classic appendiceal carcinoid, well differentiated - NETG1 (8 cases, without metastases; goblet cell carcinoids were rare (3 cases, one case with liver metastases. The second case of goblet cell carcinoid was associated with cystadenoma papillare mucinosum, complicated by pseudomixoma peritonei and the third case was limited only to appendiceal wall. The patient with liver metastases died five months after appendectomy. The patient with goblet cell carcinoid associated with papillary mucinous cystadenoma and complicated by pseudomixoma peritonei had re-operation with both partial cecal and right ovarial resection, associated with washing the peritoneal cavity. The patient was feeling well during six years from the second operation. Based on our

  2. International society of neuropathology-haarlem consensus guidelines for nervous system tumor classification and grading

    NARCIS (Netherlands)

    Louis, D.N.; Perry, A.; Burger, P.; Ellison, D.W.; Reifenberger, G.; Deimling, A. Von; Aldape, K.; Brat, D.; Collins, V.P.; Eberhart, C.; Figarella-Branger, D.; Fuller, G.N.; Giangaspero, F.; Giannini, C.; Hawkins, C.; Kleihues, P.; Korshunov, A.; Kros, J.M.; Lopes, M. Beatriz; Ng, H.K.; Ohgaki, H.; Paulus, W.; Pietsch, T.; Rosenblum, M.; Rushing, E.; Soylemezoglu, F.; Wiestler, O.; Wesseling, P.

    2014-01-01

    Major discoveries in the biology of nervous system tumors have raised the question of how non-histological data such as molecular information can be incorporated into the next World Health Organization (WHO) classification of central nervous system tumors. To address this question, a meeting of

  3. Computationally efficient SVM multi-class image recognition with confidence measures

    International Nuclear Information System (INIS)

    Makili, Lazaro; Vega, Jesus; Dormido-Canto, Sebastian; Pastor, Ignacio; Murari, Andrea

    2011-01-01

    Typically, machine learning methods produce non-qualified estimates, i.e. the accuracy and reliability of the predictions are not provided. Transductive predictors are very recent classifiers able to provide, simultaneously with the prediction, a couple of values (confidence and credibility) to reflect the quality of the prediction. Usually, a drawback of the transductive techniques for huge datasets and large dimensionality is the high computational time. To overcome this issue, a more efficient classifier has been used in a multi-class image classification problem in the TJ-II stellarator database. It is based on the creation of a hash function to generate several 'one versus the rest' classifiers for every class. By using Support Vector Machines as the underlying classifier, a comparison between the pure transductive approach and the new method has been performed. In both cases, the success rates are high and the computation time with the new method is up to 0.4 times the old one.

  4. A Genomics-Based Classification of Human Lung Tumors

    NARCIS (Netherlands)

    Seidel, Danila; Zander, Thomas; Heukamp, Lukas C.; Peifer, Martin; Bos, Marc; Fernandez-Cuesta, Lynnette; Leenders, Frauke; Lu, Xin; Ansen, Sascha; Gardizi, Masyar; Nguyen, Chau; Berg, Johannes; Russell, Prudence; Wainer, Zoe; Schildhaus, Hans-Ulrich; Rogers, Toni-Maree; Solomon, Benjamin; Pao, William; Carter, Scott L.; Getz, Gad; Hayes, D. Neil; Wilkerson, Matthew D.; Thunnissen, Erik; Travis, William D.; Perner, Sven; Wright, Gavin; Brambilla, Elisabeth; Buettner, Reinhard; Wolf, Juergen; Thomas, Roman; Gabler, Franziska; Wilkening, Ines; Mueller, Christian; Dahmen, Ilona; Menon, Roopika; Koenig, Katharina; Albus, Kerstin; Merkelbach-Bruse, Sabine; Fassunke, Jana; Schmitz, Katja; Kuenstlinger, Helen; Kleine, Michaela; Binot, Elke; Querings, Silvia; Altmueller, Janine; Boessmann, Ingelore; Nuemberg, Peter; Schneider, Peter; Groen, Harry; Timens, Wim

    2013-01-01

    We characterized genome alterations in 1255 clinically annotated lung tumors of all histological subgroups to identify genetically defined and clinically relevant subtypes. More than 55% of all cases had at least one oncogenic genome alteration potentially amenable to specific therapeutic

  5. Texture-based classification of different gastric tumors at contrast-enhanced CT

    Energy Technology Data Exchange (ETDEWEB)

    Ba-Ssalamah, Ahmed, E-mail: ahmed.ba-ssalamah@meduniwien.ac.at [Department of Radiology, Medical University of Vienna (Austria); Muin, Dina; Schernthaner, Ruediger; Kulinna-Cosentini, Christiana; Bastati, Nina [Department of Radiology, Medical University of Vienna (Austria); Stift, Judith [Department of Pathology, Medical University of Vienna (Austria); Gore, Richard [Department of Radiology, University of Chicago Pritzker School of Medicine, Chicago, IL (United States); Mayerhoefer, Marius E. [Department of Radiology, Medical University of Vienna (Austria)

    2013-10-01

    Purpose: To determine the feasibility of texture analysis for the classification of gastric adenocarcinoma, lymphoma, and gastrointestinal stromal tumors on contrast-enhanced hydrodynamic-MDCT images. Materials and methods: The arterial phase scans of 47 patients with adenocarcinoma (AC) and a histologic tumor grade of [AC-G1, n = 4, G1, n = 4; AC-G2, n = 7; AC-G3, n = 16]; GIST, n = 15; and lymphoma, n = 5, and the venous phase scans of 48 patients with AC-G1, n = 3; AC-G2, n = 6; AC-G3, n = 14; GIST, n = 17; lymphoma, n = 8, were retrospectively reviewed. Based on regions of interest, texture analysis was performed, and features derived from the gray-level histogram, run-length and co-occurrence matrix, absolute gradient, autoregressive model, and wavelet transform were calculated. Fisher coefficients, probability of classification error, average correlation coefficients, and mutual information coefficients were used to create combinations of texture features that were optimized for tumor differentiation. Linear discriminant analysis in combination with a k-nearest neighbor classifier was used for tumor classification. Results: On arterial-phase scans, texture-based lesion classification was highly successful in differentiating between AC and lymphoma, and GIST and lymphoma, with misclassification rates of 3.1% and 0%, respectively. On venous-phase scans, texture-based classification was slightly less successful for AC vs. lymphoma (9.7% misclassification) and GIST vs. lymphoma (8% misclassification), but enabled the differentiation between AC and GIST (10% misclassification), and between the different grades of AC (4.4% misclassification). No texture feature combination was able to adequately distinguish between all three tumor types. Conclusion: Classification of different gastric tumors based on textural information may aid radiologists in establishing the correct diagnosis, at least in cases where the differential diagnosis can be narrowed down to two

  6. Texture-based classification of different gastric tumors at contrast-enhanced CT

    International Nuclear Information System (INIS)

    Ba-Ssalamah, Ahmed; Muin, Dina; Schernthaner, Ruediger; Kulinna-Cosentini, Christiana; Bastati, Nina; Stift, Judith; Gore, Richard; Mayerhoefer, Marius E.

    2013-01-01

    Purpose: To determine the feasibility of texture analysis for the classification of gastric adenocarcinoma, lymphoma, and gastrointestinal stromal tumors on contrast-enhanced hydrodynamic-MDCT images. Materials and methods: The arterial phase scans of 47 patients with adenocarcinoma (AC) and a histologic tumor grade of [AC-G1, n = 4, G1, n = 4; AC-G2, n = 7; AC-G3, n = 16]; GIST, n = 15; and lymphoma, n = 5, and the venous phase scans of 48 patients with AC-G1, n = 3; AC-G2, n = 6; AC-G3, n = 14; GIST, n = 17; lymphoma, n = 8, were retrospectively reviewed. Based on regions of interest, texture analysis was performed, and features derived from the gray-level histogram, run-length and co-occurrence matrix, absolute gradient, autoregressive model, and wavelet transform were calculated. Fisher coefficients, probability of classification error, average correlation coefficients, and mutual information coefficients were used to create combinations of texture features that were optimized for tumor differentiation. Linear discriminant analysis in combination with a k-nearest neighbor classifier was used for tumor classification. Results: On arterial-phase scans, texture-based lesion classification was highly successful in differentiating between AC and lymphoma, and GIST and lymphoma, with misclassification rates of 3.1% and 0%, respectively. On venous-phase scans, texture-based classification was slightly less successful for AC vs. lymphoma (9.7% misclassification) and GIST vs. lymphoma (8% misclassification), but enabled the differentiation between AC and GIST (10% misclassification), and between the different grades of AC (4.4% misclassification). No texture feature combination was able to adequately distinguish between all three tumor types. Conclusion: Classification of different gastric tumors based on textural information may aid radiologists in establishing the correct diagnosis, at least in cases where the differential diagnosis can be narrowed down to two

  7. Mass Spectrometry Imaging for the Classification of Tumor Tissue

    NARCIS (Netherlands)

    Mascini, N.E.

    2016-01-01

    Mass spectrometry imaging (MSI) can detect and identify many different molecules without the need for labeling. In addition, it can provide their spatial distributions as ‘molecular maps’. These features make MSI well suited for studying the molecular makeup of tumor tissue. Currently, there is an

  8. Ingenious Snake: An Adaptive Multi-Class Contours Extraction

    Science.gov (United States)

    Li, Baolin; Zhou, Shoujun

    2018-04-01

    Active contour model (ACM) plays an important role in computer vision and medical image application. The traditional ACMs were used to extract single-class of object contours. While, simultaneous extraction of multi-class of interesting contours (i.e., various contours with closed- or open-ended) have not been solved so far. Therefore, a novel ACM model named “Ingenious Snake” is proposed to adaptively extract these interesting contours. In the first place, the ridge-points are extracted based on the local phase measurement of gradient vector flow field; the consequential ridgelines initialization are automated with high speed. Secondly, the contours’ deformation and evolvement are implemented with the ingenious snake. In the experiments, the result from initialization, deformation and evolvement are compared with the existing methods. The quantitative evaluation of the structure extraction is satisfying with respect of effectiveness and accuracy.

  9. Tumor Classification Using High-Order Gene Expression Profiles Based on Multilinear ICA

    Directory of Open Access Journals (Sweden)

    Ming-gang Du

    2009-01-01

    Full Text Available Motivation. Independent Components Analysis (ICA maximizes the statistical independence of the representational components of a training gene expression profiles (GEP ensemble, but it cannot distinguish relations between the different factors, or different modes, and it is not available to high-order GEP Data Mining. In order to generalize ICA, we introduce Multilinear-ICA and apply it to tumor classification using high order GEP. Firstly, we introduce the basis conceptions and operations of tensor and recommend Support Vector Machine (SVM classifier and Multilinear-ICA. Secondly, the higher score genes of original high order GEP are selected by using t-statistics and tabulate tensors. Thirdly, the tensors are performed by Multilinear-ICA. Finally, the SVM is used to classify the tumor subtypes. Results. To show the validity of the proposed method, we apply it to tumor classification using high order GEP. Though we only use three datasets, the experimental results show that the method is effective and feasible. Through this survey, we hope to gain some insight into the problem of high order GEP tumor classification, in aid of further developing more effective tumor classification algorithms.

  10. Breast cancer tumor classification using LASSO method selection approach

    International Nuclear Information System (INIS)

    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.

    2016-10-01

    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)

  11. 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)

  12. Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

    Science.gov (United States)

    Wang, Deng; Miao, Duoqian; Blohm, Gunnar

    2012-01-01

    Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

  13. Intra-axial brain tumors in adults. On the basis of the 2016 WHO classification

    International Nuclear Information System (INIS)

    Peitgen, N.; Papanagiotou, P.

    2017-01-01

    The influence of the World Health Organization (WHO) classification from 2016 on the radiological diagnosis for tumors of the central nervous system (CNS) in adults. Computed tomography (CT), magnetic resonance imaging (MRI) and MR spectroscopy. In order to come as close as possible to the correct diagnosis of CNS tumors, MRI is the long-standing accepted method of choice that can in some cases be supported by the use of CT to demonstrate calcification or bone destruction. In individual cases MRI spectroscopy can be helpful for the differentiation between neoplasms and inflammatory lesions or surveillance of tumor therapy, just as perfusion, which is not discussed in this article. (orig.) [de

  14. Checking the Course of Colorectal Carcinoma Follow up Using Mathematical Classification of Tumor Marker Profiles

    Czech Academy of Sciences Publication Activity Database

    Holubec jr., L.; Botterlich, N.; Topolčan, O.; Finek, J.; Pikner, R.; Pecen, Ladislav; Holubec, L.

    2002-01-01

    Roč. 23, Suppl.1 (2002), s. 57 ISSN 1010-4283. [Meeting of the International Society for Oncodevelopmental Biology and Medicine /30./. 08.09.2002-12.09.2002, Boston] R&D Projects: GA MŠk ME 438 Institutional research plan: AV0Z1030915 Keywords : tumor markers * colorectal CA * mathematical classification Subject RIV: BA - General Mathematics

  15. Radiological classification of renal angiomyolipomas based on 127 tumors

    Directory of Open Access Journals (Sweden)

    Prando Adilson

    2003-01-01

    Full Text Available PURPOSE: Demonstrate radiological findings of 127 angiomyolipomas (AMLs and propose a classification based on the radiological evidence of fat. MATERIALS AND METHODS: The imaging findings of 85 consecutive patients with AMLs: isolated (n = 73, multiple without tuberous sclerosis (TS (n = 4 and multiple with TS (n = 8, were retrospectively reviewed. Eighteen AMLs (14% presented with hemorrhage. All patients were submitted to a dedicated helical CT or magnetic resonance studies. All hemorrhagic and non-hemorrhagic lesions were grouped together since our objective was to analyze the presence of detectable fat. Out of 85 patients, 53 were monitored and 32 were treated surgically due to large perirenal component (n = 13, hemorrhage (n = 11 and impossibility of an adequate preoperative characterization (n = 8. There was not a case of renal cell carcinoma (RCC with fat component in this group of patients. RESULTS: Based on the presence and amount of detectable fat within the lesion, AMLs were classified in 4 distinct radiological patterns: Pattern-I, predominantly fatty (usually less than 2 cm in diameter and intrarenal: 54%; Pattern-II, partially fatty (intrarenal or exophytic: 29%; Pattern-III, minimally fatty (most exophytic and perirenal: 11%; and Pattern-IV, without fat (most exophytic and perirenal: 6%. CONCLUSIONS: This proposed classification might be useful to understand the imaging manifestations of AMLs, their differential diagnosis and determine when further radiological evaluation would be necessary. Small (< 1.5 cm, pattern-I AMLs tend to be intra-renal, homogeneous and predominantly fatty. As they grow they tend to be partially or completely exophytic and heterogeneous (patterns II and III. The rare pattern-IV AMLs, however, can be small or large, intra-renal or exophytic but are always homogeneous and hyperdense mass. Since no renal cell carcinoma was found in our series, from an evidence-based practice, all renal mass with detectable

  16. A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets.

    Science.gov (United States)

    Fernández, Alberto; Carmona, Cristobal José; José Del Jesus, María; Herrera, Francisco

    2017-09-01

    Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes. Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples. For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a Multi-Objective Evolutionary Algorithm, using the C4.5 decision tree as baseline classifier in this wrapper approach. The multi-objective scheme allows taking a double advantage: the search space becomes broader, and we may provide a set of different solutions in order to build an ensemble of classifiers. This proposal has been contrasted versus several state-of-the-art solutions on imbalanced classification showing excellent results in both binary and multi-class problems.

  17. Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features.

    Science.gov (United States)

    Su, Yanni; Wang, Yuanyuan; Jiao, Jing; Guo, Yi

    2011-01-01

    Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity.

  18. Classification of brain tumors by means of proton nuclear magnetic resonance (NMR) spectroscopy

    International Nuclear Information System (INIS)

    Sottile, V.S.; Zanchi, D.E.

    2017-01-01

    In the present work, at the request of health professionals, a computer application named “ViDa” was developed. The aim of this study is to differentiate brain lesions according to whether or not they are tumors, and their subsequent classification into different tumor types using magnetic resonance spectroscopy (SVS) with an echo time of 30 milliseconds. For this development, different areas of knowledge were integrated, among which are Artificial intelligence, physics, programming, physiopathology, images in medicine, among others. Biomedical imaging can be divided into two stages: the pre-processing, performed by the resonator, and post-processing software, performed by ViDa, for the interpretation of the data. This application is included within the Medical Informatics area, as it provides assistance for clinical decision making. The role of the biomedical engineer is fulfilled by developing a health technology in response to a manifested real-life problem. The tool developed shows promising results achieving a 100% Sensitivity, 73% Specificity, 77% Positive Predictive Value and 100% Negative Predictive Value reported in 21 cases tested. The correct classifications of the tumor’s origin reach 70%, the classification of non-astrocytic lesions achieves 67% of correct classifications in that the gradation of astrocytomas achieves a 57% of gradations that agree with biopsies and 43% of slight errors. It was possible to develop an application of assistance to the diagnosis, which together with others medical tests, will make it possible to sharpen the diagnoses of brain tumors. (authors) [es

  19. Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors.

    Science.gov (United States)

    Rodriguez Gutierrez, D; Awwad, A; Meijer, L; Manita, M; Jaspan, T; Dineen, R A; Grundy, R G; Auer, D P

    2014-05-01

    Qualitative radiologic MR imaging review affords limited differentiation among types of pediatric posterior fossa brain tumors and cannot detect histologic or molecular subtypes, which could help to stratify treatment. This study aimed to improve current posterior fossa discrimination of histologic tumor type by using support vector machine classifiers on quantitative MR imaging features. This retrospective study included preoperative MRI in 40 children with posterior fossa tumors (17 medulloblastomas, 16 pilocytic astrocytomas, and 7 ependymomas). Shape, histogram, and textural features were computed from contrast-enhanced T2WI and T1WI and diffusivity (ADC) maps. Combinations of features were used to train tumor-type-specific classifiers for medulloblastoma, pilocytic astrocytoma, and ependymoma types in separation and as a joint posterior fossa classifier. A tumor-subtype classifier was also produced for classic medulloblastoma. The performance of different classifiers was assessed and compared by using randomly selected subsets of training and test data. ADC histogram features (25th and 75th percentiles and skewness) yielded the best classification of tumor type (on average >95.8% of medulloblastomas, >96.9% of pilocytic astrocytomas, and >94.3% of ependymomas by using 8 training samples). The resulting joint posterior fossa classifier correctly assigned >91.4% of the posterior fossa tumors. For subtype classification, 89.4% of classic medulloblastomas were correctly classified on the basis of ADC texture features extracted from the Gray-Level Co-Occurence Matrix. Support vector machine-based classifiers using ADC histogram features yielded very good discrimination among pediatric posterior fossa tumor types, and ADC textural features show promise for further subtype discrimination. These findings suggest an added diagnostic value of quantitative feature analysis of diffusion MR imaging in pediatric neuro-oncology. © 2014 by American Journal of Neuroradiology.

  20. The Pattern Recognition in Cattle Brand using Bag of Visual Words and Support Vector Machines Multi-Class

    Directory of Open Access Journals (Sweden)

    Carlos Silva, Mr

    2018-03-01

    Full Text Available The recognition images of cattle brand in an automatic way is a necessity to governmental organs responsible for this activity. To help this process, this work presents a method that consists in using Bag of Visual Words for extracting of characteristics from images of cattle brand and Support Vector Machines Multi-Class for classification. This method consists of six stages: a select database of images; b extract points of interest (SURF; c create vocabulary (K-means; d create vector of image characteristics (visual words; e train and sort images (SVM; f evaluate the classification results. The accuracy of the method was tested on database of municipal city hall, where it achieved satisfactory results, reporting 86.02% of accuracy and 56.705 seconds of processing time, respectively.

  1. Analysis of classification and surgical treatment of cervical dumbbell-shaped tumors

    Directory of Open Access Journals (Sweden)

    LIU Jia-gang

    2013-11-01

    Full Text Available Objective To investigate the clinical characteristics, classification, surgical approach, complication and prognosis of cervical dumbbell-shaped tumors. Methods Twenty-six consecutive cases with cervical dumbbell-shaped tumors were retrospectively studied. According to tumor location by imaging examination, all tumors were divided into 3 types. Type Ⅰ (17 cases was mostly intravertebral and foraminal. Surgery through posterior approach was performed and internal fixation was operated in 8 cases. Type Ⅱ (4 cases was mostly paravertebral and foraminal. Surgery through the anterolateral approach was performed without internal fixation. Type Ⅲ (5 cases was equalization of intravertebral and paravertebral, and underwent surgery through combined posterior-anterolateral approach and internal fixation was performed in all of those cases. If the unilateral facet joint was destroyed, internal fixation was necessary. Lateral mass screw internal fixation and transpedicular screw fixation supplemented by fusion with autologous iliac bone graft were used to maintain cervical spinal stability. Results Among 26 patients there were 19 schwannomas, 4 neurofibromas, 2 gangliocytoma and 1 spinal meningioma. Total and subtotal tumor resection was achieved in 23 and 3 patients respectively. Among them 50% (13/26 of the cases were used internal fixation including 8 TypeⅠand 5 Type Ⅲ patients. The follow-up period was from 7 to 62 months, and mean time was 30 months. Four cases (15.38% were found local tumor recurrence. Two cases suffered with surgical infection and cerebrospinal fluid leakage. There was no spinal cord injury and spinal deformity. Conclusion In order to increase the total resection rate and decrease recurrence rate, surgical approach should be selected according to the imaging classification of tumors. Stability reconstruction is absolutely necessary for the patients with facet joint destroyed.

  2. Systematic bias in genomic classification due to contaminating non-neoplastic tissue in breast tumor samples.

    Science.gov (United States)

    Elloumi, Fathi; Hu, Zhiyuan; Li, Yan; Parker, Joel S; Gulley, Margaret L; Amos, Keith D; Troester, Melissa A

    2011-06-30

    Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification. To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors. Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability. Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor.

  3. Case based reasoning applied to medical diagnosis using multi-class classifier: A preliminary study

    Directory of Open Access Journals (Sweden)

    D. Viveros-Melo

    2017-02-01

    Full Text Available Case-based reasoning (CBR is a process used for computer processing that tries to mimic the behavior of a human expert in making decisions regarding a subject and learn from the experience of past cases. CBR has demonstrated to be appropriate for working with unstructured domains data or difficult knowledge acquisition situations, such as medical diagnosis, where it is possible to identify diseases such as: cancer diagnosis, epilepsy prediction and appendicitis diagnosis. Some of the trends that may be developed for CBR in the health science are oriented to reduce the number of features in highly dimensional data. An important contribution may be the estimation of probabilities of belonging to each class for new cases. In this paper, in order to adequately represent the database and to avoid the inconveniences caused by the high dimensionality, noise and redundancy, a number of algorithms are used in the preprocessing stage for performing both variable selection and dimension reduction procedures. Also, a comparison of the performance of some representative multi-class classifiers is carried out to identify the most effective one to include within a CBR scheme. Particularly, four classification techniques and two reduction techniques are employed to make a comparative study of multiclass classifiers on CBR

  4. A clinical perspective on the 2016 WHO brain tumor classification and routine molecular diagnostics.

    Science.gov (United States)

    van den Bent, Martin J; Weller, Michael; Wen, Patrick Y; Kros, Johan M; Aldape, Ken; Chang, Susan

    2017-05-01

    The 2007 World Health Organization (WHO) classification of brain tumors did not use molecular abnormalities as diagnostic criteria. Studies have shown that genotyping allows a better prognostic classification of diffuse glioma with improved treatment selection. This has resulted in a major revision of the WHO classification, which is now for adult diffuse glioma centered around isocitrate dehydrogenase (IDH) and 1p/19q diagnostics. This revised classification is reviewed with a focus on adult brain tumors, and includes a recommendation of genes of which routine testing is clinically useful. Apart from assessment of IDH mutational status including sequencing of R132H-immunohistochemistry negative cases and testing for 1p/19q, several other markers can be considered for routine testing, including assessment of copy number alterations of chromosome 7 and 10 and of TERT promoter, BRAF, and H3F3A mutations. For "glioblastoma, IDH mutated" the term "astrocytoma grade IV" could be considered. It should be considered to treat IDH wild-type grades II and III diffuse glioma with polysomy of chromosome 7 and loss of 10q as glioblastoma. New developments must be more quickly translated into further revised diagnostic categories. Quality control and rapid integration of molecular findings into the final diagnosis and the communication of the final diagnosis to clinicians require systematic attention. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  5. Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

    Science.gov (United States)

    Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

    2018-04-01

    Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure

  6. Neuroendocrine tumors of colon and rectum: validation of clinical and prognostic values of the World Health Organization 2010 grading classifications and European Neuroendocrine Tumor Society staging systems.

    Science.gov (United States)

    Shen, Chaoyong; Yin, Yuan; Chen, Huijiao; Tang, Sumin; Yin, Xiaonan; Zhou, Zongguang; Zhang, Bo; Chen, Zhixin

    2017-03-28

    This study evaluated and compared the clinical and prognostic values of the grading criteria used by the World Health Organization (WHO) and the European Neuroendocrine Tumors Society (ENETS). Moreover, this work assessed the current best prognostic model for colorectal neuroendocrine tumors (CRNETs). The 2010 WHO classifications and the ENETS systems can both stratify the patients into prognostic groups, although the 2010 WHO criteria is more applicable to CRNET patients. Along with tumor location, the 2010 WHO criteria are important independent prognostic parameters for CRNETs in both univariate and multivariate analyses through Cox regression (P<0.05). Data from 192 consecutive patients histopathologically diagnosed with CRNETs and had undergone surgical resection from January 2009 to May 2016 in a single center were retrospectively analyzed. Findings suggest that the WHO classifications are superior over the ENETS classification system in predicting the prognosis of CRNETs. Additionally, the WHO classifications can be widely used in clinical practice.

  7. Classification between normal and tumor tissues based on the pair-wise gene expression ratio

    International Nuclear Information System (INIS)

    Yap, YeeLeng; Zhang, XueWu; Ling, MT; Wang, XiangHong; Wong, YC; Danchin, Antoine

    2004-01-01

    Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancer-related signals are generally lacking. Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression to pair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset this transformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). The efficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioning with informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio). Classification results were compared to the original datasets for up to 10-feature model classifiers. 82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasets respectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient of variation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed dataset exhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from 45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV, coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higher classification efficiency, especially with the colon dataset – from 87.1% to 93.5%. Over 90% of the top ten discriminating axes in both datasets showed significant improvement after data transformation. The high classification efficiency achieved suggested

  8. PAI-1 and EGFR expression in adult glioma tumors: toward a molecular prognostic classification

    International Nuclear Information System (INIS)

    Muracciole, Xavier; Romain, Sylvie; Dufour, Henri; Palmari, Jacqueline; Chinot, Olivier; Ouafik, L'Houcine; Grisoli, Francois; Figarella-Branger, Dominique; Martin, Pierre-Marie

    2002-01-01

    Purpose: Molecular classification of gliomas is a major challenge in the effort to improve therapeutic decisions. The plasminogen activator system, including plasminogen activator inhibitor type 1 (PAI-1), plays a key role in tumor invasion and neoangiogenesis. Epidermal growth factor receptor (EGFR) is involved in the control of proliferation. The contribution of PAI-1 and EGFR to the survival of gliomas was retrospectively investigated. Methods and Materials: Fifty-nine adult gliomas treated by neurosurgery and conventional irradiation were analyzed, including 9 low-grade (2) and 50 high-grade (3-4) tumors (WHO classification). PAI-1 was measured on cytosols and EGFR on solubilized membranes using ELISA methods. Results: High PAI-1 levels were strongly associated with high histologic grade (p<0.001) and histologic necrosis (p<0.001). PAI-1 also correlated positively with patient age (p=0.05) and negatively with Karnofsky index (p=0.01). By univariate analysis of the high-grade population, higher PAI-1 (p<0.0001) and EGFR values (p=0.02) were associated with shorter overall survival. Only PAI-1 was an independent factor in multivariate analysis. Grade 3 tumors with low PAI-1 (100% 3-year overall survival rate) presented the same clinical outcome as the low-grade tumors. Conclusions: In this prognostic study, PAI-1 and EGFR expression revealed similarities and differences between high-grade gliomas that were not apparent by traditional clinical criteria. These data strongly support that biologic factors should be included in glioma classification and the design of clinical trials to treat more homogeneous populations

  9. [Categorization of uterine cervix tumors : What's new in the 2014 WHO classification].

    Science.gov (United States)

    Lax, S F; Horn, L-C; Löning, T

    2016-11-01

    In the 2014 WHO classification, squamous cell precursor lesions are classified as low-grade and high-grade intraepithelial lesions. LSIL corresponds to CIN1, HSIL includes CIN2 and CIN3. Only adenocarcinoma in situ (AIS) is accepted as precursor of adenocarcinoma and includes the stratified mucin-producing intraepithelial lesion (SMILE). Although relatively rare, adenocarcinoma and squamous cell carcinoma can be mixed with a poorly differentiated neuroendocrine carcinoma. Most cervical adenocarcinomas are low grade and of endocervical type. Mucinous carcinomas show marked intra- and extracellular mucin production. Almost all squamous cell carcinomas, the vast majority of adenocarcinomas, and many rare carcinoma types are HPV related. For low grade endocervical adenocarcinomas, the pattern-based classification according to Silva should be reported. Neuroendocrine tumors are rare and are classified into low-grade and high-grade, whereby the term carcinoid is still used.

  10. Neuropsychological assessment of individuals with brain tumor: Comparison of approaches used in the classification of impairment

    Directory of Open Access Journals (Sweden)

    Toni Maree Dwan

    2015-03-01

    Full Text Available Approaches to classifying neuropsychological impairment after brain tumor vary according to testing level (individual tests, domains or global index and source of reference (i.e., norms, controls and premorbid functioning. This study aimed to compare rates of impairment according to different classification approaches. Participants were 44 individuals (57% female with a primary brain tumor diagnosis (mean age = 45.6 years and 44 matched control participants (59% female, mean age = 44.5 years. All participants completed a test battery that assesses premorbid IQ (Wechsler Adult Reading Test, attention/processing speed (Digit Span, Trail Making Test A, memory (Hopkins Verbal Learning Test – Revised, Rey-Osterrieth Complex Figure-recall and executive function (Trail Making Test B, Rey-Osterrieth Complex Figure copy, Controlled Oral Word Association Test. Results indicated that across the different sources of reference, 86-93% of participants were classified as impaired at a test-specific level, 61-73% were classified as impaired at a domain-specific level, and 32-50% were classified as impaired at a global level. Rates of impairment did not significantly differ according to source of reference (p>.05; however, at the individual participant level, classification based on estimated premorbid IQ was often inconsistent with classification based on the norms or controls. Participants with brain tumor performed significantly poorer than matched controls on tests of neuropsychological functioning, including executive function (p=.001 and memory (p.05. These results highlight the need to examine individuals’ performance across a multi-faceted neuropsychological test battery to avoid over- or under-estimation of impairment.

  11. A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

    Science.gov (United States)

    Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

    2017-08-01

    Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

  12. CT features of the subtypes of thymic epithelial tumors on the basis of the world health organization classification

    International Nuclear Information System (INIS)

    Guo Xiaoyu; Yu Hong; Xiao Xiangsheng

    2013-01-01

    Thymic epithelial tumors including thymomas and thymic carcinomas have well-known heterogeneous oncologic behaviors and variable histologic features. They show variable and unpredictable evolutions ranging from an indolent non-invasive feature to a highly infiltrative and metastasising one. Currently, CT is a common and efficient imaging method for assessing thymic epithelial tumors. CT evaluation is the main reference for preoperative clinic staging and histological classification. CT features of subtypes of thymic epithelial tumors on the basis of the World Health Organization classification provide the foundation for the diagnosis and predicting prognosis. (authors)

  13. Efficient Identification of miRNAs for Classification of Tumor Origin

    DEFF Research Database (Denmark)

    Søkilde, Rolf; Vincent, Martin; Møller, Anne K

    2014-01-01

    Carcinomas of unknown primary origin constitute 3% to 5% of all newly diagnosed metastatic cancers, with the primary source difficult to classify with current histological methods. Effective cancer treatment depends on early and accurate identification of the tumor; patients with metastases...... of unknown origin have poor prognosis and short survival. Because miRNA expression is highly tissue specific, the miRNA profile of a metastasis may be used to identify its origin. We therefore evaluated the potential of miRNA profiling to identify the primary tumor of known metastases. Two hundred eight...... formalin-fixed, paraffin-embedded samples, representing 15 different histologies, were profiled on a locked nucleic acid-enhanced microarray platform, which allows for highly sensitive and specific detection of miRNA. On the basis of these data, we developed and cross-validated a novel classification...

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  15. Prognostic factors of non-functioning pancreatic neuroendocrine tumor revisited: The value of WHO 2010 classification.

    Science.gov (United States)

    Bu, Jiyoung; Youn, Sangmin; Kwon, Wooil; Jang, Kee Taek; Han, Sanghyup; Han, Sunjong; You, Younghun; Heo, Jin Seok; Choi, Seong Ho; Choi, Dong Wook

    2018-02-01

    Various factors have been reported as prognostic factors of non-functional pancreatic neuroendocrine tumors (NF-pNETs). There remains some controversy as to the factors which might actually serve to successfully prognosticate future manifestation and diagnosis of NF-pNETs. As well, consensus regarding management strategy has never been achieved. The aim of this study is to further investigate potential prognostic factors using a large single-center cohort to help determine the management strategy of NF-pNETs. During the time period 1995 through 2013, 166 patients with NF-pNETs who underwent surgery in Samsung Medical Center were entered in a prospective database, and those factors thought to represent predictors of prognosis were tested in uni- and multivariate models. The median follow-up time was 46.5 months; there was a maximum follow-up period of 217 months. The five-year overall survival and disease-free survival rates were 88.5% and 77.0%, respectively. The 2010 WHO classification was found to be the only prognostic factor which affects overall survival and disease-free survival in multivariate analysis. Also, pathologic tumor size and preoperative image tumor size correlated strongly with the WHO grades ( p <0.001, and p <0.001). Our study demonstrates that 2010 WHO classification represents a valuable prognostic factor of NF-pNETs and tumor size on preoperative image correlated with WHO grade. In view of the foregoing, the preoperative image size is thought to represent a reasonable reference with regard to determination and development of treatment strategy of NF-pNETs.

  16. Classification of malignant and benign liver tumors using a radiomics approach

    Science.gov (United States)

    Starmans, Martijn P. A.; Miclea, Razvan L.; van der Voort, Sebastian R.; Niessen, Wiro J.; Thomeer, Maarten G.; Klein, Stefan

    2018-03-01

    Correct diagnosis of the liver tumor phenotype is crucial for treatment planning, especially the distinction between malignant and benign lesions. Clinical practice includes manual scoring of the tumors on Magnetic Resonance (MR) images by a radiologist. As this is challenging and subjective, it is often followed by a biopsy. In this study, we propose a radiomics approach as an objective and non-invasive alternative for distinguishing between malignant and benign phenotypes. T2-weighted (T2w) MR sequences of 119 patients from multiple centers were collected. We developed an efficient semi-automatic segmentation method, which was used by a radiologist to delineate the tumors. Within these regions, features quantifying tumor shape, intensity, texture, heterogeneity and orientation were extracted. Patient characteristics and semantic features were added for a total of 424 features. Classification was performed using Support Vector Machines (SVMs). The performance was evaluated using internal random-split cross-validation. On the training set within each iteration, feature selection and hyperparameter optimization were performed. To this end, another cross validation was performed by splitting the training sets in training and validation parts. The optimal settings were evaluated on the independent test sets. Manual scoring by a radiologist was also performed. The radiomics approach resulted in 95% confidence intervals of the AUC of [0.75, 0.92], specificity [0.76, 0.96] and sensitivity [0.52, 0.82]. These approach the performance of the radiologist, which were an AUC of 0.93, specificity 0.70 and sensitivity 0.93. Hence, radiomics has the potential to predict the liver tumor benignity in an objective and non-invasive manner.

  17. Deep learning based classification of breast tumors with shear-wave elastography.

    Science.gov (United States)

    Zhang, Qi; Xiao, Yang; Dai, Wei; Suo, Jingfeng; Wang, Congzhi; Shi, Jun; Zheng, Hairong

    2016-12-01

    This study aims to build a deep learning (DL) architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE), and to evaluate the DL architecture in differentiation between benign and malignant breast tumors. We construct a two-layer DL architecture for SWE feature extraction, comprised of the point-wise gated Boltzmann machine (PGBM) and the restricted Boltzmann machine (RBM). The PGBM contains task-relevant and task-irrelevant hidden units, and the task-relevant units are connected to the RBM. Experimental evaluation was performed with five-fold cross validation on a set of 227 SWE images, 135 of benign tumors and 92 of malignant tumors, from 121 patients. The features learned with our DL architecture were compared with the statistical features quantifying image intensity and texture. Results showed that the DL features achieved better classification performance with an accuracy of 93.4%, a sensitivity of 88.6%, a specificity of 97.1%, and an area under the receiver operating characteristic curve of 0.947. The DL-based method integrates feature learning with feature selection on SWE. It may be potentially used in clinical computer-aided diagnosis of breast cancer. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies

    DEFF Research Database (Denmark)

    Perell, Katharina; Vincent, Martin; Vainer, Ben

    2015-01-01

    for normal liver tissue contamination. Performance was estimated by cross-validation, followed by independent validation on 55 liver core biopsies with a tumor content as low as 10%. A microRNA classifier developed, using the statistical contamination model, showed an overall classification accuracy of 74...... on classification. MicroRNA profiling was performed using quantitative Real-Time PCR on formalin-fixed paraffin-embedded samples. 278 primary tumors and liver metastases, representing nine primary tumor classes, as well as normal liver samples were used as a training set. A statistical model was applied to adjust.......5% upon independent validation. Two-thirds of the samples were classified with high-confidence, with an accuracy of 92% on high-confidence predictions. A classifier trained without adjusting for liver tissue contamination, showed a classification accuracy of 38.2%. Our results indicate that surrounding...

  19. Integrating molecular markers into the World Health Organization classification of CNS tumors: a survey of the neuro-oncology community.

    Science.gov (United States)

    Aldape, Kenneth; Nejad, Romina; Louis, David N; Zadeh, Gelareh

    2017-03-01

    Molecular markers provide important biological and clinical information related to the classification of brain tumors, and the integration of relevant molecular parameters into brain tumor classification systems has been a widely discussed topic in neuro-oncology over the past decade. With recent advances in the development of clinically relevant molecular signatures and the 2016 World Health Organization (WHO) update, the views of the neuro-oncology community on such changes would be informative for implementing this process. A survey with 8 questions regarding molecular markers in tumor classification was sent to an email list of Society for Neuro-Oncology members and attendees of prior meetings (n=5065). There were 403 respondents. Analysis was performed using whole group response, based on self-reported subspecialty. The survey results show overall strong support for incorporating molecular knowledge into the classification and clinical management of brain tumors. Across all 7 subspecialty groups, ≥70% of respondents agreed to this integration. Interestingly, some variability is seen among subspecialties, notably with lowest support from neuropathologists, which may reflect their roles in implementing such diagnostic technologies. Based on a survey provided to the neuro-oncology community, we report strong support for the integration of molecular markers into the WHO classification of brain tumors, as well as for using an integrated "layered" diagnostic format. While membership from each specialty showed support, there was variation by specialty in enthusiasm regarding proposed changes. The initial results of this survey influenced the deliberations underlying the 2016 WHO classification of tumors of the central nervous system. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.

  20. Classification of tumor based on magnetic resonance (MR) brain images using wavelet energy feature and neuro-fuzzy model

    Science.gov (United States)

    Damayanti, A.; Werdiningsih, I.

    2018-03-01

    The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.

  1. DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach.

    Directory of Open Access Journals (Sweden)

    Zhiheng Wang

    Full Text Available The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database.The DisoMCS is available at http://cal.tongji.edu.cn/disorder/.

  2. Classification of brain tumors using texture based analysis of T1-post contrast MR scans in a preclinical model

    Science.gov (United States)

    Tang, Tien T.; Zawaski, Janice A.; Francis, Kathleen N.; Qutub, Amina A.; Gaber, M. Waleed

    2018-02-01

    Accurate diagnosis of tumor type is vital for effective treatment planning. Diagnosis relies heavily on tumor biopsies and other clinical factors. However, biopsies do not fully capture the tumor's heterogeneity due to sampling bias and are only performed if the tumor is accessible. An alternative approach is to use features derived from routine diagnostic imaging such as magnetic resonance (MR) imaging. In this study we aim to establish the use of quantitative image features to classify brain tumors and extend the use of MR images beyond tumor detection and localization. To control for interscanner, acquisition and reconstruction protocol variations, the established workflow was performed in a preclinical model. Using glioma (U87 and GL261) and medulloblastoma (Daoy) models, T1-weighted post contrast scans were acquired at different time points post-implant. The tumor regions at the center, middle, and peripheral were analyzed using in-house software to extract 32 different image features consisting of first and second order features. The extracted features were used to construct a decision tree, which could predict tumor type with 10-fold cross-validation. Results from the final classification model demonstrated that middle tumor region had the highest overall accuracy at 79%, while the AUC accuracy was over 90% for GL261 and U87 tumors. Our analysis further identified image features that were unique to certain tumor region, although GL261 tumors were more homogenous with no significant differences between the central and peripheral tumor regions. In conclusion our study shows that texture features derived from MR scans can be used to classify tumor type with high success rates. Furthermore, the algorithm we have developed can be implemented with any imaging datasets and may be applicable to multiple tumor types to determine diagnosis.

  3. On the role of cost-sensitive learning in multi-class brain-computer interfaces.

    Science.gov (United States)

    Devlaminck, Dieter; Waegeman, Willem; Wyns, Bart; Otte, Georges; Santens, Patrick

    2010-06-01

    Brain-computer interfaces (BCIs) present an alternative way of communication for people with severe disabilities. One of the shortcomings in current BCI systems, recently put forward in the fourth BCI competition, is the asynchronous detection of motor imagery versus resting state. We investigated this extension to the three-class case, in which the resting state is considered virtually lying between two motor classes, resulting in a large penalty when one motor task is misclassified into the other motor class. We particularly focus on the behavior of different machine-learning techniques and on the role of multi-class cost-sensitive learning in such a context. To this end, four different kernel methods are empirically compared, namely pairwise multi-class support vector machines (SVMs), two cost-sensitive multi-class SVMs and kernel-based ordinal regression. The experimental results illustrate that ordinal regression performs better than the other three approaches when a cost-sensitive performance measure such as the mean-squared error is considered. By contrast, multi-class cost-sensitive learning enables us to control the number of large errors made between two motor tasks.

  4. Multi-Class load balancing scheme for QoS and energy ...

    African Journals Online (AJOL)

    Multi-Class load balancing scheme for QoS and energy conservation in cloud computing. ... If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs. Alternatively, you can download the PDF file directly to your computer, from ...

  5. Breast tumor classification using axial shear strain elastography: a feasibility study

    International Nuclear Information System (INIS)

    Thitaikumar, Arun; Ophir, Jonathan; Mobbs, Louise M; Kraemer-Chant, Christina M; Garra, Brian S

    2008-01-01

    Recently, the feasibility of visualizing the characteristics of bonding at an inclusion-background boundary using axial-shear strain elastography was demonstrated. In this paper, we report a feasibility study on the utility of the axial-shear strain elastograms in the classification of in vivo breast tumor as being benign or malignant. The study was performed using data sets obtained from 15 benign and 15 malignant cases that were biopsy proven. A total of three independent observers were trained, and their services were utilized for the study. A total of 9 cases were used as training set and the remaining cases were used as testing set. The feature from the axial-shear strain elastogram, namely, the area of the axial-shear region, was extracted by the observers. The observers also outlined the tumor area on the corresponding sonogram, which was used to normalize the area of the axial-shear strain region. There are several observations that can be drawn from the results. First, the result indicates that the observers consistently (∼82% of the cases) noticed the characteristic pattern of the axial-shear strain distribution data as predicted in the previous simulation studies, i.e. alternating regions of positive and negative axial-shear strain values around the tumor-background interface. Second, the analysis of the result suggests that in approximately 57% of the cases in which the observers did not visualize tumor in the sonogram, the elastograms helped them to locate the tumor. Finally, the analysis of the result suggests that for the discriminant feature value of 0.46, the number of unnecessary biopsies could be reduced by 56.3% without compromising on sensitivity and on negative predictive value (NPV). Based on the results in this study, feature values greater than 0.75 appear to be indicative of malignancy, while values less than 0.46 to be indicative of benignity. Feature values between 0.46 and 0.75 may result in an overlap between benign and malignant

  6. 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…

  7. Embedding filtering criteria into a wrapper marker selection method for brain tumor classification: an application on metabolic peak area ratios

    International Nuclear Information System (INIS)

    Kounelakis, M G; Zervakis, M E; Giakos, G C; Postma, G J; Buydens, L M C; Kotsiakis, X

    2011-01-01

    The purpose of this study is to identify reliable sets of metabolic markers that provide accurate classification of complex brain tumors and facilitate the process of clinical diagnosis. Several ratios of metabolites are tested alone or in combination with imaging markers. A wrapper feature selection and classification methodology is studied, employing Fisher's criterion for ranking the markers. The set of extracted markers that express statistical significance is further studied in terms of biological behavior with respect to the brain tumor type and grade. The outcome of this study indicates that the proposed method by exploiting the intrinsic properties of data can actually reveal reliable and biologically relevant sets of metabolic markers, which form an important adjunct toward a more accurate type and grade discrimination of complex brain tumors

  8. 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...

  9. Molecular sub-classification of renal epithelial tumors using meta-analysis of gene expression microarrays.

    Directory of Open Access Journals (Sweden)

    Thomas Sanford

    Full Text Available To evaluate the accuracy of the sub-classification of renal cortical neoplasms using molecular signatures.A search of publicly available databases was performed to identify microarray datasets with multiple histologic sub-types of renal cortical neoplasms. Meta-analytic techniques were utilized to identify differentially expressed genes for each histologic subtype. The lists of genes obtained from the meta-analysis were used to create predictive signatures through the use of a pair-based method. These signatures were organized into an algorithm to sub-classify renal neoplasms. The use of these signatures according to our algorithm was validated on several independent datasets.We identified three Gene Expression Omnibus datasets that fit our criteria to develop a training set. All of the datasets in our study utilized the Affymetrix platform. The final training dataset included 149 samples represented by the four most common histologic subtypes of renal cortical neoplasms: 69 clear cell, 41 papillary, 16 chromophobe, and 23 oncocytomas. When validation of our signatures was performed on external datasets, we were able to correctly classify 68 of the 72 samples (94%. The correct classification by subtype was 19/20 (95% for clear cell, 14/14 (100% for papillary, 17/19 (89% for chromophobe, 18/19 (95% for oncocytomas.Through the use of meta-analytic techniques, we were able to create an algorithm that sub-classified renal neoplasms on a molecular level with 94% accuracy across multiple independent datasets. This algorithm may aid in selecting molecular therapies and may improve the accuracy of subtyping of renal cortical tumors.

  10. Advances in the Genetic Characterization of Cutaneous Mesenchymal Neoplasms: Implications for Tumor Classification and Novel Diagnostic Markers.

    Science.gov (United States)

    Compton, Leigh A; Doyle, Leona A

    2017-06-01

    Cutaneous mesenchymal neoplasms often pose significant diagnostic challenges; many such entities are rare or show clinical and histologic overlap with both other mesenchymal and non-mesenchymal lesions. Recent advances in the genetic classification of many cutaneous mesenchymal neoplasms have not only helped define unique pathologic entities and increase our understanding of their biology, but have also provided new diagnostic markers. This review details these recent discoveries, with a focus on their implications for tumor classification and diagnosis. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Volumetric response classification in metastatic solid tumors on MSCT: Initial results in a whole-body setting

    International Nuclear Information System (INIS)

    Wulff, A.M.; Fabel, M.; Freitag-Wolf, S.; Tepper, M.; Knabe, H.M.; Schäfer, J.P.; Jansen, O.; Bolte, H.

    2013-01-01

    Purpose: To examine technical parameters of measurement accuracy and differences in tumor response classification using RECIST 1.1 and volumetric assessment in three common metastasis types (lung nodules, liver lesions, lymph node metastasis) simultaneously. Materials and methods: 56 consecutive patients (32 female) aged 41–82 years with a wide range of metastatic solid tumors were examined with MSCT for baseline and follow up. Images were evaluated by three experienced radiologists using manual measurements and semi-automatic lesion segmentation. Institutional ethics review was obtained and all patients gave written informed consent. Data analysis comprised interobserver variability operationalized as coefficient of variation and categorical response classification according to RECIST 1.1 for both manual and volumetric measures. Continuous data were assessed for statistical significance with Wilcoxon signed-rank test and categorical data with Fleiss kappa. Results: Interobserver variability was 6.3% (IQR 4.6%) for manual and 4.1% (IQR 4.4%) for volumetrically obtained sum of relevant diameters (p < 0.05, corrected). 4–8 patients’ response to therapy was classified differently across observers by using volumetry compared to standard manual measurements. Fleiss kappa revealed no significant difference in categorical agreement of response classification between manual (0.7558) and volumetric (0.7623) measurements. Conclusion: Under standard RECIST thresholds there was no advantage of volumetric compared to manual response evaluation. However volumetric assessment yielded significantly lower interobserver variability. This may allow narrower thresholds for volumetric response classification in the future

  12. Volumetric response classification in metastatic solid tumors on MSCT: Initial results in a whole-body setting

    Energy Technology Data Exchange (ETDEWEB)

    Wulff, A.M., E-mail: a.wulff@rad.uni-kiel.de [Klinik für Diagnostische Radiologie, Arnold-Heller-Straße 3, Haus 23, 24105 Kiel (Germany); Fabel, M. [Klinik für Diagnostische Radiologie, Arnold-Heller-Straße 3, Haus 23, 24105 Kiel (Germany); Freitag-Wolf, S., E-mail: freitag@medinfo.uni-kiel.de [Institut für Medizinische Informatik und Statistik, Brunswiker Str. 10, 24105 Kiel (Germany); Tepper, M., E-mail: m.tepper@rad.uni-kiel.de [Klinik für Diagnostische Radiologie, Arnold-Heller-Straße 3, Haus 23, 24105 Kiel (Germany); Knabe, H.M., E-mail: h.knabe@rad.uni-kiel.de [Klinik für Diagnostische Radiologie, Arnold-Heller-Straße 3, Haus 23, 24105 Kiel (Germany); Schäfer, J.P., E-mail: jp.schaefer@rad.uni-kiel.de [Klinik für Diagnostische Radiologie, Arnold-Heller-Straße 3, Haus 23, 24105 Kiel (Germany); Jansen, O., E-mail: o.jansen@neurorad.uni-kiel.de [Klinik für Diagnostische Radiologie, Arnold-Heller-Straße 3, Haus 23, 24105 Kiel (Germany); Bolte, H., E-mail: hendrik.bolte@ukmuenster.de [Klinik für Nuklearmedizin, Albert-Schweitzer-Campus 1, Gebäude A1, 48149 Münster (Germany)

    2013-10-01

    Purpose: To examine technical parameters of measurement accuracy and differences in tumor response classification using RECIST 1.1 and volumetric assessment in three common metastasis types (lung nodules, liver lesions, lymph node metastasis) simultaneously. Materials and methods: 56 consecutive patients (32 female) aged 41–82 years with a wide range of metastatic solid tumors were examined with MSCT for baseline and follow up. Images were evaluated by three experienced radiologists using manual measurements and semi-automatic lesion segmentation. Institutional ethics review was obtained and all patients gave written informed consent. Data analysis comprised interobserver variability operationalized as coefficient of variation and categorical response classification according to RECIST 1.1 for both manual and volumetric measures. Continuous data were assessed for statistical significance with Wilcoxon signed-rank test and categorical data with Fleiss kappa. Results: Interobserver variability was 6.3% (IQR 4.6%) for manual and 4.1% (IQR 4.4%) for volumetrically obtained sum of relevant diameters (p < 0.05, corrected). 4–8 patients’ response to therapy was classified differently across observers by using volumetry compared to standard manual measurements. Fleiss kappa revealed no significant difference in categorical agreement of response classification between manual (0.7558) and volumetric (0.7623) measurements. Conclusion: Under standard RECIST thresholds there was no advantage of volumetric compared to manual response evaluation. However volumetric assessment yielded significantly lower interobserver variability. This may allow narrower thresholds for volumetric response classification in the future.

  13. Classifications within molecular subtypes enables identification of BRCA1/BRCA2 mutation carriers by RNA tumor profiling

    DEFF Research Database (Denmark)

    Larsen, Martin J; Kruse, Torben A; Tan, Qihua

    2013-01-01

    Pathogenic germline mutations in BRCA1 or BRCA2 are detected in less than one third of families with a strong history of breast cancer. It is therefore expected that mutations still remain undetected by currently used screening methods. In addition, a growing number of BRCA1/2 sequence variants...... of unclear pathogen significance are found in the families, constituting an increasing clinical challenge. New methods are therefore needed to improve the detection rate and aid the interpretation of the clinically uncertain variants. In this study we analyzed a series of 33 BRCA1, 22 BRCA2, and 128 sporadic...... tumors by RNA profiling to investigate the classification potential of RNA profiles to predict BRCA1/2 mutation status. We found that breast tumors from BRCA1 and BRCA2 mutation carriers display characteristic RNA expression patterns, allowing them to be distinguished from sporadic tumors. The majority...

  14. New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs

    NARCIS (Netherlands)

    Sturm, Dominik; Orr, Brent A.; Toprak, Umut H.; Hovestadt, Volker; Jones, David T. W.; Capper, David; Sill, Martin; Buchhalter, Ivo; Northcott, Paul A.; Leis, Irina; Ryzhova, Marina; Koelsche, Christian; Pfaff, Elke; Allen, Sariah J.; Balasubramanian, Gnanaprakash; Worst, Barbara C.; Pajtler, Kristian W.; Brabetz, Sebastian; Johann, Pascal D.; Sahm, Felix; Reimand, Jüri; Mackay, Alan; Carvalho, Diana M.; Remke, Marc; Phillips, Joanna J.; Perry, Arie; Cowdrey, Cynthia; Drissi, Rachid; Fouladi, Maryam; Giangaspero, Felice; Łastowska, Maria; Grajkowska, Wiesława; Scheurlen, Wolfram; Pietsch, Torsten; Hagel, Christian; Gojo, Johannes; Lötsch, Daniela; Berger, Walter; Slavc, Irene; Haberler, Christine; Jouvet, Anne; Holm, Stefan; Hofer, Silvia; Prinz, Marco; Keohane, Catherine; Fried, Iris; Mawrin, Christian; Scheie, David; Mobley, Bret C.; Schniederjan, Matthew J.; Santi, Mariarita; Buccoliero, Anna M.; Dahiya, Sonika; Kramm, Christof M.; von Bueren, André O.; von Hoff, Katja; Rutkowski, Stefan; Herold-Mende, Christel; Frühwald, Michael C.; Milde, Till; Hasselblatt, Martin; Wesseling, Pieter; Rößler, Jochen; Schüller, Ulrich; Ebinger, Martin; Schittenhelm, Jens; Frank, Stephan; Grobholz, Rainer; Vajtai, Istvan; Hans, Volkmar; Schneppenheim, Reinhard; Zitterbart, Karel; Collins, V. Peter; Aronica, Eleonora; Varlet, Pascale; Puget, Stephanie; Dufour, Christelle; Grill, Jacques; Figarella-Branger, Dominique; Wolter, Marietta; Schuhmann, Martin U.; Shalaby, Tarek; Grotzer, Michael; van Meter, Timothy; Monoranu, Camelia-Maria; Felsberg, Jörg; Reifenberger, Guido; Snuderl, Matija; Forrester, Lynn Ann; Koster, Jan; Versteeg, Rogier; Volckmann, Richard; van Sluis, Peter; Wolf, Stephan; Mikkelsen, Tom; Gajjar, Amar; Aldape, Kenneth; Moore, Andrew S.; Taylor, Michael D.; Jones, Chris; Jabado, Nada; Karajannis, Matthias A.; Eils, Roland; Schlesner, Matthias; Lichter, Peter; von Deimling, Andreas; Pfister, Stefan M.; Ellison, David W.; Korshunov, Andrey; Kool, Marcel

    2016-01-01

    Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children but also affecting adolescents and adults. Herein, we demonstrate that a significant proportion of institutionally

  15. New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs

    NARCIS (Netherlands)

    Sturm, Dominik; Orr, Brent A.; Toprak, Umut H.; Hovestadt, Volker; Jones, David T W; Capper, David; Sill, Martin; Buchhalter, Ivo; Northcott, Paul A.; Leis, Irina; Ryzhova, Marina; Koelsche, Christian; Pfaff, Elke; Allen, Sariah J.; Balasubramanian, Gnanaprakash; Worst, Barbara C.; Pajtler, Kristian W.; Brabetz, Sebastian; Johann, Pascal D.; Sahm, Felix; Reimand, Jüri; Mackay, Alan; Carvalho, Diana M.; Remke, Marc; Phillips, Joanna J.; Perry, Arie; Cowdrey, Cynthia; Drissi, Rachid; Fouladi, Maryam; Giangaspero, Felice; Łastowska, Maria; Grajkowska, Wiesława; Scheurlen, Wolfram; Pietsch, Torsten; Hagel, Christian; Gojo, Johannes; Lötsch, Daniela; Berger, Walter; Slavc, Irene; Haberler, Christine; Jouvet, Anne; Holm, Stefan; Hofer, Silvia; Prinz, Marco; Keohane, Catherine; Fried, Iris; Mawrin, Christian; Scheie, David; Mobley, Bret C.; Schniederjan, Matthew J.; Santi, Mariarita; Buccoliero, Anna M.; Dahiya, Sonika; Kramm, Christof M.; Von Bueren, André O.; Von Hoff, Katja; Rutkowski, Stefan; Herold-Mende, Christel; Frühwald, Michael C.; Milde, Till; Hasselblatt, Martin; Wesseling, Pieter; Rößler, Jochen; Schüller, Ulrich; Ebinger, Martin; Schittenhelm, Jens; Frank, Stephan; Grobholz, Rainer; Vajtai, Istvan; Hans, Volkmar; Schneppenheim, Reinhard; Zitterbart, Karel; Collins, V. Peter; Aronica, Eleonora; Varlet, Pascale; Puget, Stephanie; Dufour, Christelle; Grill, Jacques; Figarella-Branger, Dominique; Wolter, Marietta; Schuhmann, Martin U.; Shalaby, Tarek; Grotzer, Michael; Van Meter, Timothy; Monoranu, Camelia Maria; Felsberg, Jörg; Reifenberger, Guido; Snuderl, Matija; Forrester, Lynn Ann; Koster, Jan; Versteeg, Rogier; Volckmann, Richard; Van Sluis, Peter; Wolf, Stephan; Mikkelsen, Tom; Gajjar, Amar; Aldape, Kenneth; Moore, Andrew S.; Taylor, Michael D.; Jones, Chris; Jabado, Nada; Karajannis, Matthias A.; Eils, Roland; Schlesner, Matthias; Lichter, Peter; Von Deimling, Andreas; Pfister, Stefan M.; Ellison, David W.; Korshunov, Andrey; Kool, Marcel

    2016-01-01

    Summary Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children but also affecting adolescents and adults. Herein, we demonstrate that a significant proportion of

  16. Feasibility of a novel deformable image registration technique to facilitate classification, targeting, and monitoring of tumor and normal tissue

    International Nuclear Information System (INIS)

    Brock, Kristy K.; Dawson, Laura A.; Sharpe, Michael B.; Moseley, Douglas J.; Jaffray, David A.

    2006-01-01

    Purpose: To investigate the feasibility of a biomechanical-based deformable image registration technique for the integration of multimodality imaging, image guided treatment, and response monitoring. Methods and Materials: A multiorgan deformable image registration technique based on finite element modeling (FEM) and surface projection alignment of selected regions of interest with biomechanical material and interface models has been developed. FEM also provides an inherent method for direct tracking specified regions through treatment and follow-up. Results: The technique was demonstrated on 5 liver cancer patients. Differences of up to 1 cm of motion were seen between the diaphragm and the tumor center of mass after deformable image registration of exhale and inhale CT scans. Spatial differences of 5 mm or more were observed for up to 86% of the surface of the defined tumor after deformable image registration of the computed tomography (CT) and magnetic resonance images. Up to 6.8 mm of motion was observed for the tumor after deformable image registration of the CT and cone-beam CT scan after rigid registration of the liver. Deformable registration of the CT to the follow-up CT allowed a more accurate assessment of tumor response. Conclusions: This biomechanical-based deformable image registration technique incorporates classification, targeting, and monitoring of tumor and normal tissue using one methodology

  17. New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs.

    Science.gov (United States)

    Sturm, Dominik; Orr, Brent A; Toprak, Umut H; Hovestadt, Volker; Jones, David T W; Capper, David; Sill, Martin; Buchhalter, Ivo; Northcott, Paul A; Leis, Irina; Ryzhova, Marina; Koelsche, Christian; Pfaff, Elke; Allen, Sariah J; Balasubramanian, Gnanaprakash; Worst, Barbara C; Pajtler, Kristian W; Brabetz, Sebastian; Johann, Pascal D; Sahm, Felix; Reimand, Jüri; Mackay, Alan; Carvalho, Diana M; Remke, Marc; Phillips, Joanna J; Perry, Arie; Cowdrey, Cynthia; Drissi, Rachid; Fouladi, Maryam; Giangaspero, Felice; Łastowska, Maria; Grajkowska, Wiesława; Scheurlen, Wolfram; Pietsch, Torsten; Hagel, Christian; Gojo, Johannes; Lötsch, Daniela; Berger, Walter; Slavc, Irene; Haberler, Christine; Jouvet, Anne; Holm, Stefan; Hofer, Silvia; Prinz, Marco; Keohane, Catherine; Fried, Iris; Mawrin, Christian; Scheie, David; Mobley, Bret C; Schniederjan, Matthew J; Santi, Mariarita; Buccoliero, Anna M; Dahiya, Sonika; Kramm, Christof M; von Bueren, André O; von Hoff, Katja; Rutkowski, Stefan; Herold-Mende, Christel; Frühwald, Michael C; Milde, Till; Hasselblatt, Martin; Wesseling, Pieter; Rößler, Jochen; Schüller, Ulrich; Ebinger, Martin; Schittenhelm, Jens; Frank, Stephan; Grobholz, Rainer; Vajtai, Istvan; Hans, Volkmar; Schneppenheim, Reinhard; Zitterbart, Karel; Collins, V Peter; Aronica, Eleonora; Varlet, Pascale; Puget, Stephanie; Dufour, Christelle; Grill, Jacques; Figarella-Branger, Dominique; Wolter, Marietta; Schuhmann, Martin U; Shalaby, Tarek; Grotzer, Michael; van Meter, Timothy; Monoranu, Camelia-Maria; Felsberg, Jörg; Reifenberger, Guido; Snuderl, Matija; Forrester, Lynn Ann; Koster, Jan; Versteeg, Rogier; Volckmann, Richard; van Sluis, Peter; Wolf, Stephan; Mikkelsen, Tom; Gajjar, Amar; Aldape, Kenneth; Moore, Andrew S; Taylor, Michael D; Jones, Chris; Jabado, Nada; Karajannis, Matthias A; Eils, Roland; Schlesner, Matthias; Lichter, Peter; von Deimling, Andreas; Pfister, Stefan M; Ellison, David W; Korshunov, Andrey; Kool, Marcel

    2016-02-25

    Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children but also affecting adolescents and adults. Herein, we demonstrate that a significant proportion of institutionally diagnosed CNS-PNETs display molecular profiles indistinguishable from those of various other well-defined CNS tumor entities, facilitating diagnosis and appropriate therapy for patients with these tumors. From the remaining fraction of CNS-PNETs, we identify four new CNS tumor entities, each associated with a recurrent genetic alteration and distinct histopathological and clinical features. These new molecular entities, designated "CNS neuroblastoma with FOXR2 activation (CNS NB-FOXR2)," "CNS Ewing sarcoma family tumor with CIC alteration (CNS EFT-CIC)," "CNS high-grade neuroepithelial tumor with MN1 alteration (CNS HGNET-MN1)," and "CNS high-grade neuroepithelial tumor with BCOR alteration (CNS HGNET-BCOR)," will enable meaningful clinical trials and the development of therapeutic strategies for patients affected by poorly differentiated CNS tumors. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs

    DEFF Research Database (Denmark)

    Sturm, Dominik; Orr, Brent A; Toprak, Umut H

    2016-01-01

    with a recurrent genetic alteration and distinct histopathological and clinical features. These new molecular entities, designated "CNS neuroblastoma with FOXR2 activation (CNS NB-FOXR2)," "CNS Ewing sarcoma family tumor with CIC alteration (CNS EFT-CIC)," "CNS high-grade neuroepithelial tumor with MN1 alteration...

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

    International Nuclear Information System (INIS)

    Xyda, Argyro; Haberland, Ulrike; Klotz, Ernst; Jung, Klaus; Bock, Hans Christoph; Schramm, Ramona; Knauth, Michael; Schramm, Peter

    2012-01-01

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

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

    Directory of Open Access Journals (Sweden)

    A.V. Faria

    2011-02-01

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

  1. Classification of primary lung tumors in dogs: 210 cases (1975-1985)

    International Nuclear Information System (INIS)

    Ogilvie, G.K.; Haschek, W.M.; Withrow, S.J.; Richardson, R.C.; Harvey, H.J.; Henderson, R.A.; Fowler, J.D.; Norris, A.M.; Tomlinson, J.; McCaw, D.

    1989-01-01

    Two hundred ten dogs that had primary lung tumors diagnosed between 1975 and 1985 were evaluated. The majority of the tumors were classified as adenocarcinoma (74.8%) and alveolar carcinoma (20%). The most common clinical signs of disease were cough (52%), dyspnea (23.8%), lethargy (18.1%), weight loss (12.4%), and tachypnea (4.8%). The clinical methods that were most successful in directly or indirectly leading to a diagnosis of primary lung tumor were thoracic radiography (77.1%) and cytologic examination of fine-needle aspirate specimens (24.8%)

  2. Histological image classification using biologically interpretable shape-based features

    International Nuclear Information System (INIS)

    Kothari, Sonal; Phan, John H; Young, Andrew N; Wang, May D

    2013-01-01

    Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions

  3. Classification of the lymphatic drainage status of a primary tumor: a proposal

    International Nuclear Information System (INIS)

    Munz, D.L.; Maza, S.; Ivancevic, V.; Geworski, L.

    2000-01-01

    Aim: Creation of a classification of the lymphatic drainage status of a primary tumour. It shall enable comparison of different approaches, standardisation and quality control. Methods: Identification and topographic localisation of the sentinel node(s) using lymphatic radionuclide gamma camera imaging and/or gamma probe detection and/or vital dye mapping. Results: A classification comprising four classes (D-Class I-IV) and distinct subclasses (A-E) proved to be simply to be learned and applicable as well as reliably reproducible. It is based on the number of sentinel lymph nodes and their locations and can be combined with the pathological and molecular biological lymph node status. D-classes/subclasses obtained in 420 patients with malignant melanoma of the skin are presented. Conclusions: The classification is applicable to different approaches. Its diagnostic, therapeutic and prognostic value should be studied prospectively in those primary tumours which preferably metastasise via their draining lymphatic vessels. (orig.) [de

  4. Zonal NePhRO scoring system: a superior renal tumor complexity classification model.

    Science.gov (United States)

    Hakky, Tariq S; Baumgarten, Adam S; Allen, Bryan; Lin, Hui-Yi; Ercole, Cesar E; Sexton, Wade J; Spiess, Philippe E

    2014-02-01

    Since the advent of the first standardized renal tumor complexity system, many subsequent scoring systems have been introduced, many of which are complicated and can make it difficult to accurately measure data end points. In light of these limitations, we introduce the new zonal NePhRO scoring system. The zonal NePhRO score is based on 4 anatomical components that are assigned a score of 1, 2, or 3, and their sum is used to classify renal tumors. The zonal NePhRO scoring system is made up of the (Ne)arness to collecting system, (Ph)ysical location of the tumor in the kidney, (R)adius of the tumor, and (O)rganization of the tumor. In this retrospective study, we evaluated patients exhibiting clinical stage T1a or T1b who underwent open partial nephrectomy performed by 2 genitourinary surgeons. Each renal unit was assigned both a zonal NePhRO score and a RENAL (radius, exophytic/endophytic properties, nearness of tumor to the collecting system or sinus in millimeters, anterior/posterior, location relative to polar lines) score, and a blinded reviewer used the same preoperative imaging study to obtain both scores. Additional data points gathered included age, clamp time, complication rate, urine leak rate, intraoperative blood loss, and pathologic tumor size. One hundred sixty-six patients underwent open partial nephrectomy. There were 37 perioperative complications quantitated using the validated Clavien-Dindo system; their occurrence was predicted by the NePhRO score on both univariate and multivariate analyses (P = .0008). Clinical stage, intraoperative blood loss, and tumor diameter were all correlated with the zonal NePhRO score on univariate analysis only. The zonal NePhRO scoring system is a simpler tool that accurately predicts the surgical complexity of a renal lesion. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. Performance improvement of multi-class detection using greedy algorithm for Viola-Jones cascade selection

    Science.gov (United States)

    Tereshin, Alexander A.; Usilin, Sergey A.; Arlazarov, Vladimir V.

    2018-04-01

    This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.

  6. The multi-class binomial failure rate model for the treatment of common-cause failures

    International Nuclear Information System (INIS)

    Hauptmanns, U.

    1995-01-01

    The impact of common cause failures (CCF) on PSA results for NPPs is in sharp contrast with the limited quality which can be achieved in their assessment. This is due to the dearth of observations and cannot be remedied in the short run. Therefore the methods employed for calculating failure rates should be devised such as to make the best use of the few available observations on CCF. The Multi-Class Binomial Failure Rate (MCBFR) Model achieves this by assigning observed failures to different classes according to their technical characteristics and applying the BFR formalism to each of these. The results are hence determined by a superposition of BFR type expressions for each class, each of them with its own coupling factor. The model thus obtained flexibly reproduces the dependence of CCF rates on failure multiplicity insinuated by the observed failure multiplicities. This is demonstrated by evaluating CCFs observed for combined impulse pilot valves in German NPPs. (orig.) [de

  7. Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework

    Science.gov (United States)

    Chandakkar, Parag S.; Venkatesan, Ragav; Li, Baoxin

    2013-02-01

    Diabetic retinopathy (DR) is a vision-threatening complication from diabetes mellitus, a medical condition that is rising globally. Unfortunately, many patients are unaware of this complication because of absence of symptoms. Regular screening of DR is necessary to detect the condition for timely treatment. Content-based image retrieval, using archived and diagnosed fundus (retinal) camera DR images can improve screening efficiency of DR. This content-based image retrieval study focuses on two DR clinical findings, microaneurysm and neovascularization, which are clinical signs of non-proliferative and proliferative diabetic retinopathy. The authors propose a multi-class multiple-instance image retrieval framework which deploys a modified color correlogram and statistics of steerable Gaussian Filter responses, for retrieving clinically relevant images from a database of DR fundus image database.

  8. Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring.

    Science.gov (United States)

    Ghose, Soumya; Mitra, Jhimli; Karunanithi, Mohan; Dowling, Jason

    2015-01-01

    Home monitoring of chronically ill or elderly patient can reduce frequent hospitalisations and hence provide improved quality of care at a reduced cost to the community, therefore reducing the burden on the healthcare system. Activity recognition of such patients is of high importance in such a design. In this work, a system for automatic human physical activity recognition from smart-phone inertial sensors data is proposed. An ensemble of decision trees framework is adopted to train and predict the multi-class human activity system. A comparison of our proposed method with a multi-class traditional support vector machine shows significant improvement in activity recognition accuracies.

  9. Tumors of the ampulla of vater: histopathologic classification and predictors of survival.

    Science.gov (United States)

    Carter, Jonathan T; Grenert, James P; Rubenstein, Laura; Stewart, Lygia; Way, Lawrence W

    2008-08-01

    The histology and clinical behavior of ampullary tumors vary substantially. We speculated that this might reflect the presence of two kinds of ampullary adenocarcinoma: pancreaticobiliary and intestinal. We analyzed patient demographics, presentation, survival (mean followup 44 months), and tumor histology for 157 consecutive ampullary tumors resected from 1989 to 2006. Histologic features were reviewed by a pathologist blinded to clinical outcomes. Survival was compared using Kaplan-Meier/Cox proportional hazards analysis. There were 33 benign (32 adenomas and 1 paraganglioma) and 124 malignant (118 adenocarcinomas and 6 neuroendocrine) tumors. One hundred fifteen (73%) patients underwent a Whipple procedure, 32 (20%) a local resection, and 10 (7%) a palliative operation. For adenocarcinomas, survival in univariate models was affected by jaundice, histologic grade, lymphovascular, or perineural invasion, T stage, nodal metastasis, and pancreaticobiliary subtype (p jaundice more often than those with the intestinal kind (p = 0.01) and had worse survival. In addition to other factors, tumor type (intestinal versus pancreaticobiliary) had a major effect on survival in patients with ampullary adenocarcinoma. The current concept of ampullary adenocarcinoma as a unique entity, distinct from duodenal and pancreatic adenocarcinoma, might be wrong. Intestinal ampullary adenocarcinomas behaved like their duodenal counterparts, but pancreaticobiliary ones were more aggressive and behaved like pancreatic adenocarcinomas.

  10. Residual tumor size and IGCCCG risk classification predict additional vascular procedures in patients with germ cell tumors and residual tumor resection: a multicenter analysis of the German Testicular Cancer Study Group.

    Science.gov (United States)

    Winter, Christian; Pfister, David; Busch, Jonas; Bingöl, Cigdem; Ranft, Ulrich; Schrader, Mark; Dieckmann, Klaus-Peter; Heidenreich, Axel; Albers, Peter

    2012-02-01

    Residual tumor resection (RTR) after chemotherapy in patients with advanced germ cell tumors (GCT) is an important part of the multimodal treatment. To provide a complete resection of residual tumor, additional surgical procedures are sometimes necessary. In particular, additional vascular interventions are high-risk procedures that require multidisciplinary planning and adequate resources to optimize outcome. The aim was to identify parameters that predict additional vascular procedures during RTR in GCT patients. A retrospective analysis was performed in 402 GCT patients who underwent 414 RTRs in 9 German Testicular Cancer Study Group (GTCSG) centers. Overall, 339 of 414 RTRs were evaluable with complete perioperative data sets. The RTR database was queried for additional vascular procedures (inferior vena cava [IVC] interventions, aortic prosthesis) and correlated to International Germ Cell Cancer Collaborative Group (IGCCCG) classification and residual tumor volume. In 40 RTRs, major vascular procedures (23 IVC resections with or without prosthesis, 11 partial IVC resections, and 6 aortic prostheses) were performed. In univariate analysis, the necessity of IVC intervention was significantly correlated with IGCCCG (14.1% intermediate/poor vs 4.8% good; p=0.0047) and residual tumor size (3.7% size risk features must initially be identified as high-risk patients for vascular procedures and therefore should be referred to specialized surgical centers with the ad hoc possibility of vascular interventions. Copyright © 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved.

  11. An exact solution for the state probabilities of the multi-class, multi-server queue with preemptive priorities

    NARCIS (Netherlands)

    Sleptchenko, Andrei; van Harten, Aart; van der Heijden, Matthijs C.

    2005-01-01

    We consider a multi-class, multi-server queueing system with preemptive priorities. We distinguish two groups of priority classes that consist of multiple customer types, each having their own arrival and service rate. We assume Poisson arrival processes and exponentially distributed service times.

  12. Radiological diagnostics of skeletal tumors

    International Nuclear Information System (INIS)

    Uhl, M.; Herget, G.W.

    2008-01-01

    The book contains contributions concerning the following topics: 1. introduction and fundamentals: WHO classification of bone tumors, imaging diagnostics and their function; localization, typical clinical and radiological criteria, TNM classification and status classification, invasive tumor diagnostics; 2. specific tumor diagnostics: chondrogenic bone tumors, osseous tumors, connective tissue bony tumors, osteoclastoma, osteomyelogenic bone tumors, vascular bone tumors, neurogenic bone tumors, chordoma; adamantinoma of the long tubular bone; tumor-like lesions, bony metastases, bone granulomas, differential diagnostics: tumor-like lesions

  13. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

    Science.gov (United States)

    Huynh, Benjamin Q; Li, Hui; Giger, Maryellen L

    2016-07-01

    Convolutional neural networks (CNNs) show potential for computer-aided diagnosis (CADx) by learning features directly from the image data instead of using analytically extracted features. However, CNNs are difficult to train from scratch for medical images due to small sample sizes and variations in tumor presentations. Instead, transfer learning can be used to extract tumor information from medical images via CNNs originally pretrained for nonmedical tasks, alleviating the need for large datasets. Our database includes 219 breast lesions (607 full-field digital mammographic images). We compared support vector machine classifiers based on the CNN-extracted image features and our prior computer-extracted tumor features in the task of distinguishing between benign and malignant breast lesions. Five-fold cross validation (by lesion) was conducted with the area under the receiver operating characteristic (ROC) curve as the performance metric. Results show that classifiers based on CNN-extracted features (with transfer learning) perform comparably to those using analytically extracted features [area under the ROC curve [Formula: see text

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

    Directory of Open Access Journals (Sweden)

    R. Rajesh Sharma

    2015-01-01

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

  15. Canine classical seminoma: a specific malignant type with human classifications is highly correlated with tumor angiogenesis

    International Nuclear Information System (INIS)

    Kim, Jong-Hyuk; Yu, Chi-Ho; Yhee, Ji-Young; Im, Keum-Soon; Kim, Na-Hyun; Sur, Jung-Hyang

    2010-01-01

    Human seminoma is classified as classical seminoma (SE) and spermatocytic seminoma (SS). Human SE is known to be more malignant and metastasizing more frequently than SS. Tumor angiogenesis is highly related with tumor progression and metastasis, with microvessel density (MVD) being an important parameter of metastatic potential. Canine seminoma is not yet well-established as SE or SS type including correlation with angiogenesis. We classified canine SE and SS, and then compared them to tumor associated vessels. Twenty-three cases of canine seminomas (2 intratubular, 9 diffuse, and 12 intratubular/diffuse seminomas showing both intratubular and diffuse patterns) were classified as SE or SS by immunohistochemistry (IHC) using monoclonal antibody against PLAP and by PAS stain. The histopathological data were then compared to see if there was a correlation with SE or SS. Angiogenesis of seminomas were evaluated by immunohistochemical assay using polyclonal antibody against Von Willebrand factor (vWF) and by calculating the means of MVD, vessels area and perimeters using computerized image analysis. Statistical Package for Social Sciences (SPSS) program was used for various statistical analyses. The numbers of PLAP+/PAS+ canine SEs were 8/23 (34.8%) and PLAP-/PAS- SSs were 15/23 (61.2%). All SE cases (8/8, 100%) were intratubular/diffuse types. SS types included 2 intratubular (2/15, 13.3%), 9 diffuse (9/15, 60%), and 4 intratubular/diffuse (4/15, 26.7%) types. MVD and vascular parameters in SEs were significantly higher than in SSs, showing the highest value in the intratubular/diffuse type. Seminomas observed with neoplastic cells invasion of vessels presented higher perimeter and area values than seminomas without conformed neoplastic cells invasion. In this study, we demonstrated a positive relationship between canine SE and tumor angiogenesis. Furthermore, we also showed that a tumor cells invasion of vessels were a correlated vascular parameter. Although

  16. Canine classical seminoma: a specific malignant type with human classifications is highly correlated with tumor angiogenesis

    Directory of Open Access Journals (Sweden)

    Kim Jong-Hyuk

    2010-05-01

    Full Text Available Abstract Background Human seminoma is classified as classical seminoma (SE and spermatocytic seminoma (SS. Human SE is known to be more malignant and metastasizing more frequently than SS. Tumor angiogenesis is highly related with tumor progression and metastasis, with microvessel density (MVD being an important parameter of metastatic potential. Canine seminoma is not yet well-established as SE or SS type including correlation with angiogenesis. We classified canine SE and SS, and then compared them to tumor associated vessels. Methods Twenty-three cases of canine seminomas (2 intratubular, 9 diffuse, and 12 intratubular/diffuse seminomas showing both intratubular and diffuse patterns were classified as SE or SS by immunohistochemistry (IHC using monoclonal antibody against PLAP and by PAS stain. The histopathological data were then compared to see if there was a correlation with SE or SS. Angiogenesis of seminomas were evaluated by immunohistochemical assay using polyclonal antibody against Von Willebrand factor (vWF and by calculating the means of MVD, vessels area and perimeters using computerized image analysis. Statistical Package for Social Sciences (SPSS program was used for various statistical analyses. Results The numbers of PLAP+/PAS+ canine SEs were 8/23 (34.8% and PLAP-/PAS- SSs were 15/23 (61.2%. All SE cases (8/8, 100% were intratubular/diffuse types. SS types included 2 intratubular (2/15, 13.3%, 9 diffuse (9/15, 60%, and 4 intratubular/diffuse (4/15, 26.7% types. MVD and vascular parameters in SEs were significantly higher than in SSs, showing the highest value in the intratubular/diffuse type. Seminomas observed with neoplastic cells invasion of vessels presented higher perimeter and area values than seminomas without conformed neoplastic cells invasion. Conclusion In this study, we demonstrated a positive relationship between canine SE and tumor angiogenesis. Furthermore, we also showed that a tumor cells invasion of vessels

  17. Tissue classifications in Monte Carlo simulations of patient dose for photon beam tumor treatments

    Science.gov (United States)

    Lin, Mu-Han; Chao, Tsi-Chian; Lee, Chung-Chi; Tung-Chieh Chang, Joseph; Tung, Chuan-Jong

    2010-07-01

    The purpose of this work was to study the calculated dose uncertainties induced by the material classification that determined the interaction cross-sections and the water-to-material stopping-power ratios. Calculations were made for a head- and neck-cancer patient treated with five intensity-modulated radiotherapy fields using 6 MV photon beams. The patient's CT images were reconstructed into two voxelized patient phantoms based on different CT-to-material classification schemes. Comparisons of the depth-dose curve of the anterior-to-posterior field and the dose-volume-histogram of the treatment plan were used to evaluate the dose uncertainties from such schemes. The results indicated that any misassignment of tissue materials could lead to a substantial dose difference, which would affect the treatment outcome. To assure an appropriate material assignment, it is desirable to have different conversion tables for various parts of the body. The assignment of stopping-power ratio should be based on the chemical composition and the density of the material.

  18. Tissue classifications in Monte Carlo simulations of patient dose for photon beam tumor treatments

    International Nuclear Information System (INIS)

    Lin, Mu-Han; Chao, Tsi-Chian; Lee, Chung-Chi; Tung-Chieh Chang, Joseph; Tung, Chuan-Jong

    2010-01-01

    The purpose of this work was to study the calculated dose uncertainties induced by the material classification that determined the interaction cross-sections and the water-to-material stopping-power ratios. Calculations were made for a head- and neck-cancer patient treated with five intensity-modulated radiotherapy fields using 6 MV photon beams. The patient's CT images were reconstructed into two voxelized patient phantoms based on different CT-to-material classification schemes. Comparisons of the depth-dose curve of the anterior-to-posterior field and the dose-volume-histogram of the treatment plan were used to evaluate the dose uncertainties from such schemes. The results indicated that any misassignment of tissue materials could lead to a substantial dose difference, which would affect the treatment outcome. To assure an appropriate material assignment, it is desirable to have different conversion tables for various parts of the body. The assignment of stopping-power ratio should be based on the chemical composition and the density of the material.

  19. [A revolution postponed indefinitely.WHO classification of tumors of the breast 2012: the main changes compared to the 3rd edition (2003)].

    Science.gov (United States)

    Nenutil, Rudolf

    2015-01-01

    In 2012, the new classification of the fourth series WHO blue books of breast tumors was released. The current version represents a fluent evolution, compared to the third edition. Some limited changes regarding terminology, definitions and the inclusion of some diagnostic units were adopted. The information about the molecular biology and genetic background of breast carcinoma has been enriched substantially.

  20. Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data

    Directory of Open Access Journals (Sweden)

    Ayman Habib

    2016-01-01

    Full Text Available 3D modeling of a given site is an important activity for a wide range of applications including urban planning, as-built mapping of industrial sites, heritage documentation, military simulation, and outdoor/indoor analysis of airflow. Point clouds, which could be either derived from passive or active imaging systems, are an important source for 3D modeling. Such point clouds need to undergo a sequence of data processing steps to derive the necessary information for the 3D modeling process. Segmentation is usually the first step in the data processing chain. This paper presents a region-growing multi-class simultaneous segmentation procedure, where planar, pole-like, and rough regions are identified while considering the internal characteristics (i.e., local point density/spacing and noise level of the point cloud in question. The segmentation starts with point cloud organization into a kd-tree data structure and characterization process to estimate the local point density/spacing. Then, proceeding from randomly-distributed seed points, a set of seed regions is derived through distance-based region growing, which is followed by modeling of such seed regions into planar and pole-like features. Starting from optimally-selected seed regions, planar and pole-like features are then segmented. The paper also introduces a list of hypothesized artifacts/problems that might take place during the region-growing process. Finally, a quality control process is devised to detect, quantify, and mitigate instances of partially/fully misclassified planar and pole-like features. Experimental results from airborne and terrestrial laser scanning as well as image-based point clouds are presented to illustrate the performance of the proposed segmentation and quality control framework.

  1. Classification of large circulating tumor cells isolated with ultra-high throughput microfluidic Vortex technology

    Science.gov (United States)

    Che, James; Yu, Victor; Dhar, Manjima; Renier, Corinne; Matsumoto, Melissa; Heirich, Kyra; Garon, Edward B.; Goldman, Jonathan; Rao, Jianyu; Sledge, George W.; Pegram, Mark D.; Sheth, Shruti; Jeffrey, Stefanie S.; Kulkarni, Rajan P.; Sollier, Elodie; Di Carlo, Dino

    2016-01-01

    Circulating tumor cells (CTCs) are emerging as rare but clinically significant non-invasive cellular biomarkers for cancer patient prognosis, treatment selection, and treatment monitoring. Current CTC isolation approaches, such as immunoaffinity, filtration, or size-based techniques, are often limited by throughput, purity, large output volumes, or inability to obtain viable cells for downstream analysis. For all technologies, traditional immunofluorescent staining alone has been employed to distinguish and confirm the presence of isolated CTCs among contaminating blood cells, although cells isolated by size may express vastly different phenotypes. Consequently, CTC definitions have been non-trivial, researcher-dependent, and evolving. Here we describe a complete set of objective criteria, leveraging well-established cytomorphological features of malignancy, by which we identify large CTCs. We apply the criteria to CTCs enriched from stage IV lung and breast cancer patient blood samples using the High Throughput Vortex Chip (Vortex HT), an improved microfluidic technology for the label-free, size-based enrichment and concentration of rare cells. We achieve improved capture efficiency (up to 83%), high speed of processing (8 mL/min of 10x diluted blood, or 800 μL/min of whole blood), and high purity (avg. background of 28.8±23.6 white blood cells per mL of whole blood). We show markedly improved performance of CTC capture (84% positive test rate) in comparison to previous Vortex designs and the current FDA-approved gold standard CellSearch assay. The results demonstrate the ability to quickly collect viable and pure populations of abnormal large circulating cells unbiased by molecular characteristics, which helps uncover further heterogeneity in these cells. PMID:26863573

  2. Breast Cancer Survival Defined by the ER/PR/HER2 Subtypes and a Surrogate Classification according to Tumor Grade and Immunohistochemical Bio markers

    International Nuclear Information System (INIS)

    Parise, C. A.; Caggiano, V.

    2014-01-01

    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.

  3. 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.

  4. Applying Multi-Class Support Vector Machines for performance assessment of shipping operations: The case of tanker vessels

    DEFF Research Database (Denmark)

    Pagoropoulos, Aris; Møller, Anders H.; McAloone, Tim C.

    2017-01-01

    of feature selection algorithms. Afterwards, a model based on Multi- Class Support Vector Machines (SVM) was constructed and the efficacy of the approach is shown through the application of a test set. The results demonstrate the importance and benefits of machine learning algorithms in driving energy....... Identifying the potential of behavioural savings can be challenging, due to the inherent difficulty in analysing the data and operationalizing energy efficiency within the dynamic operating environment of the vessels. This article proposes a supervised learning model for identifying the presence of energy...

  5. Succinate Dehydrogenase Subunit B (SDHB Is Expressed in Neurofibromatosis 1-Associated Gastrointestinal Stromal Tumors (Gists: Implications for the SDHB Expression Based Classification of Gists

    Directory of Open Access Journals (Sweden)

    Jeanny H. Wang, Jerzy Lasota, Markku Miettinen

    2011-01-01

    Full Text Available Gastrointestinal Stromal Tumor (GIST is the most common mesenchymal tumor of the digestive tract. GISTs develop with relatively high incidence in patients with Neurofibromatosis-1 syndrome (NF1. Mutational activation of KIT or PDGFRA is believed to be a driving force in the pathogenesis of familial and sporadic GISTs. Unlike those tumors, NF1-associated GISTs do not have KIT or PGDFRA mutations. Similarly, no mutational activation of KIT or PDGFRA has been identified in pediatric GISTs and in GISTs associated with Carney Triad and Carney-Stratakis Syndrome. KIT and PDGFRA-wild type tumors are expected to have lesser response to imatinib treatment. Recently, Carney Triad and Carney-Stratakis Syndrome -associated GISTs and pediatric GISTs have been shown to have a loss of expression of succinate dehydrogenase subunit B (SDHB, a Krebs cycle/electron transport chain interface protein. It was proposed that GISTs can be divided into SDHB- positive (type 1, and SDHB-negative (type 2 tumors because of similarities in clinical features and response to imatinib treatment. In this study, SDHB expression was examined immunohistochemically in 22 well-characterized NF1-associated GISTs. All analyzed tumors expressed SDHB. Based on SDHB-expression status, NF1-associated GISTs belong to type 1 category; however, similarly to SDHB type 2 tumors, they do not respond well to imatinib treatment. Therefore, a simple categorization of GISTs into SDHB-positive and-negative seems to be incomplete. A classification based on both SDHB expression status and KIT and PDGFRA mutation status characterize GISTs more accurately and allow subdivision of SDHB-positive tumors into different clinico-genetic categories.

  6. Detection of surface cracking in steel pipes based on vibration data using a multi-class support vector machine classifier

    Science.gov (United States)

    Mustapha, S.; Braytee, A.; Ye, L.

    2017-04-01

    In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.

  7. Identification and optimization of classifier genes from multi-class earthworm microarray dataset.

    Directory of Open Access Journals (Sweden)

    Ying Li

    Full Text Available Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes and have used it to profile gene expression in 248 earthworms exposed to TNT, RDX or neither. We assembled a new machine learning pipeline consisting of several well-established feature filtering/selection and classification techniques to analyze the 248-array dataset in order to construct classifier models that can separate earthworm samples into three groups: control, TNT-treated, and RDX-treated. First, a total of 869 genes differentially expressed in response to TNT or RDX exposure were identified using a univariate statistical algorithm of class comparison. Then, decision tree-based algorithms were applied to select a subset of 354 classifier genes, which were ranked by their overall weight of significance. A multiclass support vector machine (MC-SVM method and an unsupervised K-mean clustering method were applied to independently refine the classifier, producing a smaller subset of 39 and 30 classifier genes, separately, with 11 common genes being potential biomarkers. The combined 58 genes were considered the refined subset and used to build MC-SVM and clustering models with classification accuracy of 83.5% and 56.9%, respectively. This study demonstrates that the machine learning approach can be used to identify and optimize a small subset of classifier/biomarker genes from high dimensional datasets and generate classification models of acceptable precision for multiple classes.

  8. Quantitative diffusion weighted imaging parameters in tumor and peritumoral stroma for prediction of molecular subtypes in breast cancer

    Science.gov (United States)

    He, Ting; Fan, Ming; Zhang, Peng; Li, Hui; Zhang, Juan; Shao, Guoliang; Li, Lihua

    2018-03-01

    Breast cancer can be classified into four molecular subtypes of Luminal A, Luminal B, HER2 and Basal-like, which have significant differences in treatment and survival outcomes. We in this study aim to predict immunohistochemistry (IHC) determined molecular subtypes of breast cancer using image features derived from tumor and peritumoral stroma region based on diffusion weighted imaging (DWI). A dataset of 126 breast cancer patients were collected who underwent preoperative breast MRI with a 3T scanner. The apparent diffusion coefficients (ADCs) were recorded from DWI, and breast image was segmented into regions comprising the tumor and the surrounding stromal. Statistical characteristics in various breast tumor and peritumoral regions were computed, including mean, minimum, maximum, variance, interquartile range, range, skewness, and kurtosis of ADC values. Additionally, the difference of features between each two regions were also calculated. The univariate logistic based classifier was performed for evaluating the performance of the individual features for discriminating subtypes. For multi-class classification, multivariate logistic regression model was trained and validated. The results showed that the tumor boundary and proximal peritumoral stroma region derived features have a higher performance in classification compared to that of the other regions. Furthermore, the prediction model using statistical features, difference features and all the features combined from these regions generated AUC values of 0.774, 0.796 and 0.811, respectively. The results in this study indicate that ADC feature in tumor and peritumoral stromal region would be valuable for estimating the molecular subtype in breast cancer.

  9. Correlation between Standardized Uptake Value of 68Ga-DOTA-NOC Positron Emission Tomography/Computed Tomography and Pathological Classification of Neuroendocrine Tumors.

    Science.gov (United States)

    Kaewput, Chalermrat; Suppiah, Subapriya; Vinjamuri, Sobhan

    2018-01-01

    The aim of our study was to correlate tumor uptake of 68 Ga-DOTA-NOC positron emission tomography/computed tomography (PET/CT) with the pathological grade of neuroendocrine tumors (NETs). 68 Ga-DOTA-NOC PET/CT examinations in 41 patients with histopathologically proven NETs were included in the study. Maximum standardized uptake value (SUV max ) and averaged SUV SUV mean of "main tumor lesions" were calculated for quantitative analyses after background subtraction. Uptake on main tumor lesions was compared and correlated with the tumor histological grade based on Ki-67 index and pathological differentiation. Classification was performed into three grades according to Ki-67 levels; low grade: Ki-67 20. Pathological differentiation was graded into well- and poorly differentiated groups. The values were compared and evaluated for correlation and agreement between the two parameters was performed. Our study revealed negatively fair agreement between SUV max of tumor and Ki-67 index ( r = -0.241) and negatively poor agreement between SUV mean of tumor and Ki-67 index ( r = -0.094). SUV max of low-grade, intermediate-grade, and high-grade Ki-67 index is 26.18 ± 14.56, 30.71 ± 24.44, and 6.60 ± 4.59, respectively. Meanwhile, SUV mean of low-grade, intermediate-grade, and high-grade Ki-67 is 8.92 ± 7.15, 9.09 ± 5.18, and 3.00 ± 1.38, respectively. As expected, there was statistically significant decreased SUV max and SUV mean in high-grade tumors (poorly differentiated NETs) as compared with low- and intermediate-grade tumors (well-differentiated NETs). SUV of 68 Ga-DOTA-NOC PET/CT is not correlated with histological grade of NETs. However, there was statistically significant decreased tumor uptake of 68 Ga-DOTA-NOC in poorly differentiated NETs as compared with the well-differentiated group. As a result of this pilot study, we confirm that the lower tumor uptake of 68 Ga-DOTA-NOC may be associated with aggressive behavior and may, therefore, result in poor prognosis.

  10. An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors.

    Science.gov (United States)

    Karageorgis, Anastassia; Dufort, Sandrine; Sancey, Lucie; Henry, Maxime; Hirsjärvi, Samuli; Passirani, Catherine; Benoit, Jean-Pierre; Gravier, Julien; Texier, Isabelle; Montigon, Olivier; Benmerad, Mériem; Siroux, Valérie; Barbier, Emmanuel L; Coll, Jean-Luc

    2016-02-19

    Nanoparticles are useful tools in oncology because of their capacity to passively accumulate in tumors in particular via the enhanced permeability and retention (EPR) effect. However, the importance and reliability of this effect remains controversial and quite often unpredictable. In this preclinical study, we used optical imaging to detect the accumulation of three types of fluorescent nanoparticles in eight different subcutaneous and orthotopic tumor models, and dynamic contrast-enhanced and vessel size index Magnetic Resonance Imaging (MRI) to measure the functional parameters of these tumors. The results demonstrate that the permeability and blood volume fraction determined by MRI are useful parameters for predicting the capacity of a tumor to accumulate nanoparticles. Translated to a clinical situation, this strategy could help anticipate the EPR effect of a particular tumor and thus its accessibility to nanomedicines.

  11. Profiling of microRNAs in tumor interstitial fluid of breast tumors – a novel resource to identify biomarkers for prognostic classification and detection of cancer

    DEFF Research Database (Denmark)

    Halvorsen, Ann Rita; Helland, Åslaug; Gromov, Pavel

    2017-01-01

    and to elucidate the cross-talk that exists among cells in a tumor microenvironment. Matched tumor interstitial fluid samples (TIF, n = 60), normal interstitial fluid samples (NIF, n = 51), corresponding tumor tissue specimens (n = 54), and serum samples (n = 27) were collected from patients with breast cancer......, and detectable microRNAs were analyzed and compared. In addition, serum data from 32 patients with breast cancer and 22 healthy controls were obtained for a validation study. To identify potential serum biomarkers of breast cancer, first the microRNA profiles of TIF and NIF samples were compared. A total of 266...... microRNAs were present at higher level in the TIF samples as compared to normal counterparts. Sixty-one of these microRNAs were present in > 75% of the serum samples and were subsequently tested in a validation set. Seven of the 61 microRNAs were associated with poor survival, while 23 were associated...

  12. Intra-axial brain tumors in adults. On the basis of the 2016 WHO classification; Intraaxiale Hirntumoren des Erwachsenenalters. Auf der Basis der WHO-Klassifizierung 2016

    Energy Technology Data Exchange (ETDEWEB)

    Peitgen, N.; Papanagiotou, P. [Klinikum Bremen-Mitte/Bremen-Ost, Klinik fuer Diagnostische und Interventionelle Neuroradiologie, Bremen (Germany)

    2017-09-15

    The influence of the World Health Organization (WHO) classification from 2016 on the radiological diagnosis for tumors of the central nervous system (CNS) in adults. Computed tomography (CT), magnetic resonance imaging (MRI) and MR spectroscopy. In order to come as close as possible to the correct diagnosis of CNS tumors, MRI is the long-standing accepted method of choice that can in some cases be supported by the use of CT to demonstrate calcification or bone destruction. In individual cases MRI spectroscopy can be helpful for the differentiation between neoplasms and inflammatory lesions or surveillance of tumor therapy, just as perfusion, which is not discussed in this article. (orig.) [German] Einfluss der WHO-Klassifikation von 2016 fuer ZNS-Tumoren auf die radiologische Befunderstellung fuer ZNS-Tumoren des Erwachsenenalters. CT, MRT, MR-Spektroskopie. Um sich der korrekten Diagnose bzgl. eines ZNS-Tumors bestmoeglich zu naehern, ist die MRT das akzeptierte Standardverfahren, die in Einzelfaellen durch eine CT ergaenzt werden kann, wenn Verkalkungen oder knoecherne Arrosionen dargestellt werden sollen. Eine MR-Spektroskopie kann in Einzelfaellen zur Differenzierung von Neoplasien und entzuendlichen Veraenderungen oder Tumorverlaufskontrollen unter Therapie hilfreich sein, ebenso wie die Perfusion, auf die in diesem Artikel nicht eingegangen wird. (orig.)

  13. A multifactorial likelihood model for MMR gene variant classification incorporating probabilities based on sequence bioinformatics and tumor characteristics: a report from the Colon Cancer Family Registry.

    Science.gov (United States)

    Thompson, Bryony A; Goldgar, David E; Paterson, Carol; Clendenning, Mark; Walters, Rhiannon; Arnold, Sven; Parsons, Michael T; Michael D, Walsh; Gallinger, Steven; Haile, Robert W; Hopper, John L; Jenkins, Mark A; Lemarchand, Loic; Lindor, Noralane M; Newcomb, Polly A; Thibodeau, Stephen N; Young, Joanne P; Buchanan, Daniel D; Tavtigian, Sean V; Spurdle, Amanda B

    2013-01-01

    Mismatch repair (MMR) gene sequence variants of uncertain clinical significance are often identified in suspected Lynch syndrome families, and this constitutes a challenge for both researchers and clinicians. Multifactorial likelihood model approaches provide a quantitative measure of MMR variant pathogenicity, but first require input of likelihood ratios (LRs) for different MMR variation-associated characteristics from appropriate, well-characterized reference datasets. Microsatellite instability (MSI) and somatic BRAF tumor data for unselected colorectal cancer probands of known pathogenic variant status were used to derive LRs for tumor characteristics using the Colon Cancer Family Registry (CFR) resource. These tumor LRs were combined with variant segregation within families, and estimates of prior probability of pathogenicity based on sequence conservation and position, to analyze 44 unclassified variants identified initially in Australasian Colon CFR families. In addition, in vitro splicing analyses were conducted on the subset of variants based on bioinformatic splicing predictions. The LR in favor of pathogenicity was estimated to be ~12-fold for a colorectal tumor with a BRAF mutation-negative MSI-H phenotype. For 31 of the 44 variants, the posterior probabilities of pathogenicity were such that altered clinical management would be indicated. Our findings provide a working multifactorial likelihood model for classification that carefully considers mode of ascertainment for gene testing. © 2012 Wiley Periodicals, Inc.

  14. A novel algorithm of super-resolution image reconstruction based on multi-class dictionaries for natural scene

    Science.gov (United States)

    Wu, Wei; Zhao, Dewei; Zhang, Huan

    2015-12-01

    Super-resolution image reconstruction is an effective method to improve the image quality. It has important research significance in the field of image processing. However, the choice of the dictionary directly affects the efficiency of image reconstruction. A sparse representation theory is introduced into the problem of the nearest neighbor selection. Based on the sparse representation of super-resolution image reconstruction method, a super-resolution image reconstruction algorithm based on multi-class dictionary is analyzed. This method avoids the redundancy problem of only training a hyper complete dictionary, and makes the sub-dictionary more representatives, and then replaces the traditional Euclidean distance computing method to improve the quality of the whole image reconstruction. In addition, the ill-posed problem is introduced into non-local self-similarity regularization. Experimental results show that the algorithm is much better results than state-of-the-art algorithm in terms of both PSNR and visual perception.

  15. Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio

    Directory of Open Access Journals (Sweden)

    Sana Ullah Jan

    2018-03-01

    Full Text Available A framework of spectrum sensing with a multi-class hypothesis is proposed to maximize the achievable throughput in cognitive radio networks. The energy range of a sensing signal under the hypothesis that the primary user is absent (in a conventional two-class hypothesis is further divided into quantized regions, whereas the hypothesis that the primary user is present is conserved. The non-radio frequency energy harvesting-equiped secondary user transmits, when the primary user is absent, with transmission power based on the hypothesis result (the energy level of the sensed signal and the residual energy in the battery: the lower the energy of the received signal, the higher the transmission power, and vice versa. Conversely, the lower is the residual energy in the node, the lower is the transmission power. This technique increases the throughput of a secondary link by providing a higher number of transmission events, compared to the conventional two-class hypothesis. Furthermore, transmission with low power for higher energy levels in the sensed signal reduces the probability of interference with primary users if, for instance, detection was missed. The familiar machine learning algorithm known as a support vector machine (SVM is used in a one-versus-rest approach to classify the input signal into predefined classes. The input signal to the SVM is composed of three statistical features extracted from the sensed signal and a number ranging from 0 to 100 representing the percentage of residual energy in the node’s battery. To increase the generalization of the classifier, k-fold cross-validation is utilized in the training phase. The experimental results show that an SVM with the given features performs satisfactorily for all kernels, but an SVM with a polynomial kernel outperforms linear and radial-basis function kernels in terms of accuracy. Furthermore, the proposed multi-class hypothesis achieves higher throughput compared to the

  16. MIDAS: Mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data.

    Science.gov (United States)

    Lee, Sangseon; Park, Youngjune; Kim, Sun

    2017-07-15

    Pathway based analysis of high throughput transcriptome data is a widely used approach to investigate biological mechanisms. Since a pathway consists of multiple functions, the recent approach is to determine condition specific sub-pathways or subpaths. However, there are several challenges. First, few existing methods utilize explicit gene expression information from RNA-seq. More importantly, subpath activity is usually an average of statistical scores, e.g., correlations, of edges in a candidate subpath, which fails to reflect gene expression quantity information. In addition, none of existing methods can handle multiple phenotypes. To address these technical problems, we designed and implemented an algorithm, MIDAS, that determines condition specific subpaths, each of which has different activities across multiple phenotypes. MIDAS utilizes gene expression quantity information fully and the network centrality information to determine condition specific subpaths. To test performance of our tool, we used TCGA breast cancer RNA-seq gene expression profiles with five molecular subtypes. 36 differentially activate subpaths were determined. The utility of our method, MIDAS, was demonstrated in four ways. All 36 subpaths are well supported by the literature information. Subsequently, we showed that these subpaths had a good discriminant power for five cancer subtype classification and also had a prognostic power in terms of survival analysis. Finally, in a performance comparison of MIDAS to a recent subpath prediction method, PATHOME, our method identified more subpaths and much more genes that are well supported by the literature information. http://biohealth.snu.ac.kr/software/MIDAS/. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  17. Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach

    OpenAIRE

    Yamamoto, Yoichiro; Saito, Akira; Tateishi, Ayako; Shimojo, Hisashi; Kanno, Hiroyuki; Tsuchiya, Shinichi; Ito, Ken-ichi; Cosatto, Eric; Graf, Hans Peter; Moraleda, Rodrigo R.; Eils, Roland; Grabe, Niels

    2017-01-01

    Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade du...

  18. Hypoxia in tumors: pathogenesis-related classification, characterization of hypoxia subtypes, and associated biological and clinical implications.

    Science.gov (United States)

    Vaupel, Peter; Mayer, Arnulf

    2014-01-01

    Hypoxia is a hallmark of tumors leading to (mal-)adaptive processes, development of aggressive phenotypes and treatment resistance. Based on underlying mechanisms and their duration, two main types of hypoxia have been identified, coexisting with complex spatial and temporal heterogeneities. Chronic hypoxia is mainly caused by diffusion limitations due to enlarged diffusion distances and adverse diffusion geometries (e.g., concurrent vs. countercurrent microvessels, Krogh- vs. Hill-type diffusion geometry) and, to a lesser extent, by hypoxemia (e.g., in anemic patients, HbCO formation in heavy smokers), and a compromised perfusion or flow stop (e.g., due to disturbed Starling forces or intratumor solid stress). Acute hypoxia mainly results from transient disruptions in perfusion (e.g., vascular occlusion by cell aggregates), fluctuating red blood cell fluxes or short-term contractions of the interstitial matrix. In each of these hypoxia subtypes oxygen supply is critically reduced, but perfusion-dependent nutrient supply, waste removal, delivery of anticancer or diagnostic agents, and repair competence can be impaired or may not be affected. This detailed differentiation of tumor hypoxia may impact on our understanding of tumor biology and may aid in the development of novel treatment strategies, tumor detection by imaging and tumor targeting, and is thus of great clinical relevance.

  19. Leukemia and colon tumor detection based on microarray data classification using momentum backpropagation and genetic algorithm as a feature selection method

    Science.gov (United States)

    Wisesty, Untari N.; Warastri, Riris S.; Puspitasari, Shinta Y.

    2018-03-01

    Cancer is one of the major causes of mordibility and mortality problems in the worldwide. Therefore, the need of a system that can analyze and identify a person suffering from a cancer by using microarray data derived from the patient’s Deoxyribonucleic Acid (DNA). But on microarray data has thousands of attributes, thus making the challenges in data processing. This is often referred to as the curse of dimensionality. Therefore, in this study built a system capable of detecting a patient whether contracted cancer or not. The algorithm used is Genetic Algorithm as feature selection and Momentum Backpropagation Neural Network as a classification method, with data used from the Kent Ridge Bio-medical Dataset. Based on system testing that has been done, the system can detect Leukemia and Colon Tumor with best accuracy equal to 98.33% for colon tumor data and 100% for leukimia data. Genetic Algorithm as feature selection algorithm can improve system accuracy, which is from 64.52% to 98.33% for colon tumor data and 65.28% to 100% for leukemia data, and the use of momentum parameters can accelerate the convergence of the system in the training process of Neural Network.

  20. Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks

    Directory of Open Access Journals (Sweden)

    Martin Alberto JM

    2009-01-01

    Full Text Available Abstract Background Prediction of protein structures from their sequences is still one of the open grand challenges of computational biology. Some approaches to protein structure prediction, especially ab initio ones, rely to some extent on the prediction of residue contact maps. Residue contact map predictions have been assessed at the CASP competition for several years now. Although it has been shown that exact contact maps generally yield correct three-dimensional structures, this is true only at a relatively low resolution (3–4 Å from the native structure. Another known weakness of contact maps is that they are generally predicted ab initio, that is not exploiting information about potential homologues of known structure. Results We introduce a new class of distance restraints for protein structures: multi-class distance maps. We show that Cα trace reconstructions based on 4-class native maps are significantly better than those from residue contact maps. We then build two predictors of 4-class maps based on recursive neural networks: one ab initio, or relying on the sequence and on evolutionary information; one template-based, or in which homology information to known structures is provided as a further input. We show that virtually any level of sequence similarity to structural templates (down to less than 10% yields more accurate 4-class maps than the ab initio predictor. We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates. We also extract binary residue contact maps at an 8 Å threshold (as per CASP assessment from the 4-class predictors and show that the template-based version is also more accurate than the best template and consistently better than the ab initio one, down to very low levels of sequence identity to structural templates. Furthermore, we test both ab-initio and template-based 8

  1. Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer

    NARCIS (Netherlands)

    Hoadley, Katherine A.; Yau, Christina; Hinoue, Toshinori; Wolf, Denise M.; Lazar, Alexander J.; Drill, Esther; Shen, Ronglai; Taylor, Alison M.; Cherniack, Andrew D.; Thorsson, Vésteinn; Akbani, Rehan; Bowlby, Reanne; Wong, Christopher K.; Wiznerowicz, Maciej; Sanchez-Vega, Francisco; Robertson, A. Gordon; Schneider, Barbara G.; Lawrence, Michael S.; Noushmehr, Houtan; Malta, Tathiane M.; Caesar-Johnson, Samantha J.; Demchok, John A.; Felau, Ina; Kasapi, Melpomeni; Ferguson, Martin L.; Hutter, Carolyn M.; Sofia, Heidi J.; Tarnuzzer, Roy; Wang, Zhining; Yang, Liming; Zenklusen, Jean C.; Zhang, Jiashan (Julia); Chudamani, Sudha; Liu, Jia; Lolla, Laxmi; Naresh, Rashi; Pihl, Todd; Sun, Qiang; Wan, Yunhu; Wu, Ye; Cho, Juok; DeFreitas, Timothy; Frazer, Scott; Gehlenborg, Nils; Getz, Gad; Heiman, David I.; Kim, Jaegil; Lawrence, Michael S.; Lin, Pei; Meier, Sam; Noble, Michael S.; Saksena, Gordon; Voet, Doug; Zhang, Hailei; Bernard, Brady; Chambwe, Nyasha; Dhankani, Varsha; Knijnenburg, Theo; Kramer, Roger; Leinonen, Kalle; Liu, Yuexin; Miller, Michael; Reynolds, Sheila; Shmulevich, Ilya; Thorsson, Vesteinn; Zhang, Wei; Akbani, Rehan; Broom, Bradley M.; Hegde, Apurva M.; Ju, Zhenlin; Kanchi, Rupa S.; Korkut, Anil; Li, Jun; Liang, Han; Ling, Shiyun; Liu, Wenbin; Lu, Yiling; Mills, Gordon B.; Ng, Kwok Shing; Rao, Arvind; Ryan, Michael; Wang, Jing; Weinstein, John N.; Zhang, Jiexin; Abeshouse, Adam; Armenia, Joshua; Chakravarty, Debyani; Chatila, Walid K.; de Bruijn, Ino; Gao, Jianjiong; Gross, Benjamin E.; Heins, Zachary J.; Kundra, Ritika; La, Konnor; Ladanyi, Marc; Luna, Augustin; Nissan, Moriah G.; Ochoa, Angelica; Phillips, Sarah M.; Reznik, Ed; Sanchez-Vega, Francisco; Sander, Chris; Schultz, Nikolaus; Sheridan, Robert; Sumer, S. Onur; Sun, Yichao; Taylor, Barry S.; Wang, Jioajiao; Zhang, Hongxin; Anur, Pavana; Peto, Myron; Spellman, Paul; Benz, Christopher C.; Stuart, Joshua M.; Wong, Christopher K.; Yau, Christina; Hayes, D. Neil; Parker, Joel S.; Wilkerson, Matthew D.; Ally, Adrian; Balasundaram, Miruna; Bowlby, Reanne; Brooks, Denise; Carlsen, Rebecca; Chuah, Eric; Dhalla, Noreen; Holt, Robert; Jones, Steven J.M.; Kasaian, Katayoon; Lee, Darlene; Ma, Yussanne; Marra, Marco A.; Mayo, Michael; Moore, Richard A.; Mungall, Andrew J.; Mungall, Karen; Robertson, A. Gordon; Sadeghi, Sara; Schein, Jacqueline E.; Sipahimalani, Payal; Tam, Angela; Thiessen, Nina; Tse, Kane; Wong, Tina; Berger, Ashton C.; Beroukhim, Rameen; Cherniack, Andrew D.; Cibulskis, Carrie; Gabriel, Stacey B.; Gao, Galen F.; Ha, Gavin; Meyerson, Matthew; Schumacher, Steven E.; Shih, Juliann; Kucherlapati, Melanie H.; Kucherlapati, Raju S.; Baylin, Stephen; Cope, Leslie; Danilova, Ludmila; Bootwalla, Moiz S.; Lai, Phillip H.; Maglinte, Dennis T.; Van Den Berg, David J.; Weisenberger, Daniel J.; Auman, J. Todd; Balu, Saianand; Bodenheimer, Tom; Fan, Cheng; Hoadley, Katherine A.; Hoyle, Alan P.; Jefferys, Stuart R.; Jones, Corbin D.; Meng, Shaowu; Mieczkowski, Piotr A.; Mose, Lisle E.; Perou, Amy H.; Perou, Charles M.; Roach, Jeffrey; Shi, Yan; Simons, Janae V.; Skelly, Tara; Soloway, Matthew G.; Tan, Donghui; Veluvolu, Umadevi; Fan, Huihui; Hinoue, Toshinori; Laird, Peter W.; Shen, Hui; Zhou, Wanding; Bellair, Michelle; Chang, Kyle; Covington, Kyle; Creighton, Chad J.; Dinh, Huyen; Doddapaneni, Harsha Vardhan; Donehower, Lawrence A.; Drummond, Jennifer; Gibbs, Richard A.; Glenn, Robert; Hale, Walker; Han, Yi; Hu, Jianhong; Korchina, Viktoriya; Lee, Sandra; Lewis, Lora; Li, Wei; Liu, Xiuping; Morgan, Margaret; Morton, Donna; Muzny, Donna; Santibanez, Jireh; Sheth, Margi; Shinbrot, Eve; Wang, Linghua; Wang, Min; Wheeler, David A.; Xi, Liu; Zhao, Fengmei; Hess, Julian; Appelbaum, Elizabeth L.; Bailey, Matthew; Cordes, Matthew G.; Ding, Li; Fronick, Catrina C.; Fulton, Lucinda A.; Fulton, Robert S.; Kandoth, Cyriac; Mardis, Elaine R.; McLellan, Michael D.; Miller, Christopher A.; Schmidt, Heather K.; Wilson, Richard K.; Crain, Daniel; Curley, Erin; Gardner, Johanna; Lau, Kevin; Mallery, David; Morris, Scott; Paulauskis, Joseph; Penny, Robert; Shelton, Candace; Shelton, Troy; Sherman, Mark; Thompson, Eric; Yena, Peggy; Bowen, Jay; Gastier-Foster, Julie M.; Gerken, Mark; Leraas, Kristen M.; Lichtenberg, Tara M.; Ramirez, Nilsa C.; Wise, Lisa; Zmuda, Erik; Corcoran, Niall; Costello, Tony; Hovens, Christopher; Carvalho, Andre L.; de Carvalho, Ana C.; Fregnani, José H.; Longatto-Filho, Adhemar; Reis, Rui M.; Scapulatempo-Neto, Cristovam; Silveira, Henrique C.S.; Vidal, Daniel O.; Burnette, Andrew; Eschbacher, Jennifer; Hermes, Beth; Noss, Ardene; Singh, Rosy; Anderson, Matthew L.; Castro, Patricia D.; Ittmann, Michael; Huntsman, David; Kohl, Bernard; Le, Xuan; Thorp, Richard; Andry, Chris; Duffy, Elizabeth R.; Lyadov, Vladimir; Paklina, Oxana; Setdikova, Galiya; Shabunin, Alexey; Tavobilov, Mikhail; McPherson, Christopher; Warnick, Ronald; Berkowitz, Ross; Cramer, Daniel; Feltmate, Colleen; Horowitz, Neil; Kibel, Adam; Muto, Michael; Raut, Chandrajit P.; Malykh, Andrei; Barnholtz-Sloan, Jill S.; Barrett, Wendi; Devine, Karen; Fulop, Jordonna; Ostrom, Quinn T.; Shimmel, Kristen; Wolinsky, Yingli; Sloan, Andrew E.; De Rose, Agostino; Giuliante, Felice; Goodman, Marc; Karlan, Beth Y.; Hagedorn, Curt H.; Eckman, John; Harr, Jodi; Myers, Jerome; Tucker, Kelinda; Zach, Leigh Anne; Deyarmin, Brenda; Hu, Hai; Kvecher, Leonid; Larson, Caroline; Mural, Richard J.; Somiari, Stella; Vicha, Ales; Zelinka, Tomas; Bennett, Joseph; Iacocca, Mary; Rabeno, Brenda; Swanson, Patricia; Latour, Mathieu; Lacombe, Louis; Têtu, Bernard; Bergeron, Alain; McGraw, Mary; Staugaitis, Susan M.; Chabot, John; Hibshoosh, Hanina; Sepulveda, Antonia; Su, Tao; Wang, Timothy; Potapova, Olga; Voronina, Olga; Desjardins, Laurence; Mariani, Odette; Roman-Roman, Sergio; Sastre, Xavier; Stern, Marc Henri; Cheng, Feixiong; Signoretti, Sabina; Berchuck, Andrew; Bigner, Darell; Lipp, Eric; Marks, Jeffrey; McCall, Shannon; McLendon, Roger; Secord, Angeles; Sharp, Alexis; Behera, Madhusmita; Brat, Daniel J.; Chen, Amy; Delman, Keith; Force, Seth; Khuri, Fadlo; Magliocca, Kelly; Maithel, Shishir; Olson, Jeffrey J.; Owonikoko, Taofeek; Pickens, Alan; Ramalingam, Suresh; Shin, Dong M.; Sica, Gabriel; Van Meir, Erwin G.; Zhang, Hongzheng; Eijckenboom, Wil; Gillis, Ad; Korpershoek, Esther; Looijenga, Leendert; Oosterhuis, Wolter; Stoop, Hans; van Kessel, Kim E.; Zwarthoff, Ellen C.; Calatozzolo, Chiara; Cuppini, Lucia; Cuzzubbo, Stefania; DiMeco, Francesco; Finocchiaro, Gaetano; Mattei, Luca; Perin, Alessandro; Pollo, Bianca; Chen, Chu; Houck, John; Lohavanichbutr, Pawadee; Hartmann, Arndt; Stoehr, Christine; Stoehr, Robert; Taubert, Helge; Wach, Sven; Wullich, Bernd; Kycler, Witold; Murawa, Dawid; Wiznerowicz, Maciej; Chung, Ki; Edenfield, W. Jeffrey; Martin, Julie; Baudin, Eric; Bubley, Glenn; Bueno, Raphael; De Rienzo, Assunta; Richards, William G.; Kalkanis, Steven; Mikkelsen, Tom; Noushmehr, Houtan; Scarpace, Lisa; Girard, Nicolas; Aymerich, Marta; Campo, Elias; Giné, Eva; Guillermo, Armando López; Van Bang, Nguyen; Hanh, Phan Thi; Phu, Bui Duc; Tang, Yufang; Colman, Howard; Evason, Kimberley; Dottino, Peter R.; Martignetti, John A.; Gabra, Hani; Juhl, Hartmut; Akeredolu, Teniola; Stepa, Serghei; Hoon, Dave; Ahn, Keunsoo; Kang, Koo Jeong; Beuschlein, Felix; Breggia, Anne; Birrer, Michael; Bell, Debra; Borad, Mitesh; Bryce, Alan H.; Castle, Erik; Chandan, Vishal; Cheville, John; Copland, John A.; Farnell, Michael; Flotte, Thomas; Giama, Nasra; Ho, Thai; Kendrick, Michael; Kocher, Jean Pierre; Kopp, Karla; Moser, Catherine; Nagorney, David; O'Brien, Daniel; O'Neill, Brian Patrick; Patel, Tushar; Petersen, Gloria; Que, Florencia; Rivera, Michael; Roberts, Lewis; Smallridge, Robert; Smyrk, Thomas; Stanton, Melissa; Thompson, R. Houston; Torbenson, Michael; Yang, Ju Dong; Zhang, Lizhi; Brimo, Fadi; Ajani, Jaffer A.; Gonzalez, Ana Maria Angulo; Behrens, Carmen; Bondaruk, olanta; Broaddus, Russell; Czerniak, Bogdan; Esmaeli, Bita; Fujimoto, Junya; Gershenwald, Jeffrey; Guo, Charles; Lazar, Alexander J.; Logothetis, Christopher; Meric-Bernstam, Funda; Moran, Cesar; Ramondetta, Lois; Rice, David; Sood, Anil; Tamboli, Pheroze; Thompson, Timothy; Troncoso, Patricia; Tsao, Anne; Wistuba, Ignacio; Carter, Candace; Haydu, Lauren; Hersey, Peter; Jakrot, Valerie; Kakavand, Hojabr; Kefford, Richard; Lee, Kenneth; Long, Georgina; Mann, Graham; Quinn, Michael; Saw, Robyn; Scolyer, Richard; Shannon, Kerwin; Spillane, Andrew; Stretch, Jonathan; Synott, Maria; Thompson, John; Wilmott, James; Al-Ahmadie, Hikmat; Chan, Timothy A.; Ghossein, Ronald; Gopalan, Anuradha; Levine, Douglas A.; Reuter, Victor; Singer, Samuel; Singh, Bhuvanesh; Tien, Nguyen Viet; Broudy, Thomas; Mirsaidi, Cyrus; Nair, Praveen; Drwiega, Paul; Miller, Judy; Smith, Jennifer; Zaren, Howard; Park, Joong Won; Hung, Nguyen Phi; Kebebew, Electron; Linehan, W. Marston; Metwalli, Adam R.; Pacak, Karel; Pinto, Peter A.; Schiffman, Mark; Schmidt, Laura S.; Vocke, Cathy D.; Wentzensen, Nicolas; Worrell, Robert; Yang, Hannah; Moncrieff, Marc; Goparaju, Chandra; Melamed, Jonathan; Pass, Harvey; Botnariuc, Natalia; Caraman, Irina; Cernat, Mircea; Chemencedji, Inga; Clipca, Adrian; Doruc, Serghei; Gorincioi, Ghenadie; Mura, Sergiu; Pirtac, Maria; Stancul, Irina; Tcaciuc, Diana; Albert, Monique; Alexopoulou, Iakovina; Arnaout, Angel; Bartlett, John; Engel, Jay; Gilbert, Sebastien; Parfitt, Jeremy; Sekhon, Harman; Thomas, George; Rassl, Doris M.; Rintoul, Robert C.; Bifulco, Carlo; Tamakawa, Raina; Urba, Walter; Hayward, Nicholas; Timmers, Henri; Antenucci, Anna; Facciolo, Francesco; Grazi, Gianluca; Marino, Mirella; Merola, Roberta; de Krijger, Ronald; Gimenez-Roqueplo, Anne Paule; Piché, Alain; Chevalier, Simone; McKercher, Ginette; Birsoy, Kivanc; Barnett, Gene; Brewer, Cathy; Farver, Carol; Naska, Theresa; Pennell, Nathan A.; Raymond, Daniel; Schilero, Cathy; Smolenski, Kathy; Williams, Felicia; Morrison, Carl; Borgia, Jeffrey A.; Liptay, Michael J.; Pool, Mark; Seder, Christopher W.; Junker, Kerstin; Omberg, Larsson; Dinkin, Mikhail; Manikhas, George; Alvaro, Domenico; Bragazzi, Maria Consiglia; Cardinale, Vincenzo; Carpino, Guido; Gaudio, Eugenio; Chesla, David; Cottingham, Sandra; Dubina, Michael; Moiseenko, Fedor; Dhanasekaran, Renumathy; Becker, Karl Friedrich; Janssen, Klaus Peter; Slotta-Huspenina, Julia; Abdel-Rahman, Mohamed H.; Aziz, Dina; Bell, Sue; Cebulla, Colleen M.; Davis, Amy; Duell, Rebecca; Elder, J. Bradley; Hilty, Joe; Kumar, Bahavna; Lang, James; Lehman, Norman L.; Mandt, Randy; Nguyen, Phuong; Pilarski, Robert; Rai, Karan; Schoenfield, Lynn; Senecal, Kelly; Wakely, Paul; Hansen, Paul; Lechan, Ronald; Powers, James; Tischler, Arthur; Grizzle, William E.; Sexton, Katherine C.; Kastl, Alison; Henderson, Joel; Porten, Sima; Waldmann, Jens; Fassnacht, Martin; Asa, Sylvia L.; Schadendorf, Dirk; Couce, Marta; Graefen, Markus; Huland, Hartwig; Sauter, Guido; Schlomm, Thorsten; Simon, Ronald; Tennstedt, Pierre; Olabode, Oluwole; Nelson, Mark; Bathe, Oliver; Carroll, Peter R.; Chan, June M.; Disaia, Philip; Glenn, Pat; Kelley, Robin K.; Landen, Charles N.; Phillips, Joanna; Prados, Michael; Simko, Jeffry; Smith-McCune, Karen; VandenBerg, Scott; Roggin, Kevin; Fehrenbach, Ashley; Kendler, Ady; Sifri, Suzanne; Steele, Ruth; Jimeno, Antonio; Carey, Francis; Forgie, Ian; Mannelli, Massimo; Carney, Michael; Hernandez, Brenda; Campos, Benito; Herold-Mende, Christel; Jungk, Christin; Unterberg, Andreas; von Deimling, Andreas; Bossler, Aaron; Galbraith, Joseph; Jacobus, Laura; Knudson, Michael; Knutson, Tina; Ma, Deqin; Milhem, Mohammed; Sigmund, Rita; Godwin, Andrew K.; Madan, Rashna; Rosenthal, Howard G.; Adebamowo, Clement; Adebamowo, Sally N.; Boussioutas, Alex; Beer, David; Giordano, Thomas; Mes-Masson, Anne Marie; Saad, Fred; Bocklage, Therese; Landrum, Lisa; Mannel, Robert; Moore, Kathleen; Moxley, Katherine; Postier, Russel; Walker, Joan; Zuna, Rosemary; Feldman, Michael; Valdivieso, Federico; Dhir, Rajiv; Luketich, James; Pinero, Edna M.Mora; Quintero-Aguilo, Mario; Carlotti, Carlos Gilberto; Dos Santos, Jose Sebastião; Kemp, Rafael; Sankarankuty, Ajith; Tirapelli, Daniela; Catto, James; Agnew, Kathy; Swisher, Elizabeth; Creaney, Jenette; Robinson, Bruce; Shelley, Carl Simon; Godwin, Eryn M.; Kendall, Sara; Shipman, Cassaundra; Bradford, Carol; Carey, Thomas; Haddad, Andrea; Moyer, Jeffey; Peterson, Lisa; Prince, Mark; Rozek, Laura; Wolf, Gregory; Bowman, Rayleen; Fong, Kwun M.; Yang, Ian; Korst, Robert; Rathmell, W. Kimryn; Fantacone-Campbell, J. Leigh; Hooke, Jeffrey A.; Kovatich, Albert J.; Shriver, Craig D.; DiPersio, John; Drake, Bettina; Govindan, Ramaswamy; Heath, Sharon; Ley, Timothy; Van Tine, Brian; Westervelt, Peter; Rubin, Mark A.; Lee, Jung Il; Aredes, Natália D.; Mariamidze, Armaz; Stuart, Joshua M.; Benz, Christopher C.; Laird, Peter W.

    2018-01-01

    We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA

  2. Direct immersion single drop micro-extraction method for multi-class pesticides analysis in mango using GC-MS.

    Science.gov (United States)

    Pano-Farias, Norma S; Ceballos-Magaña, Silvia G; Muñiz-Valencia, Roberto; Jurado, Jose M; Alcázar, Ángela; Aguayo-Villarreal, Ismael A

    2017-12-15

    Due the negative effects of pesticides on environment and human health, more efficient and environmentally friendly methods are needed. In this sense, a simple, fast, free from memory effects and economical direct-immersion single drop micro-extraction (SDME) method and GC-MS for multi-class pesticides determination in mango samples was developed. Sample pre-treatment using ultrasound-assisted solvent extraction and factors affecting the SDME procedure (extractant solvent, drop volume, stirring rate, ionic strength, time, pH and temperature) were optimized using factorial experimental design. This method presented high sensitive (LOD: 0.14-169.20μgkg -1 ), acceptable precision (RSD: 0.7-19.1%), satisfactory recovery (69-119%) and high enrichment factors (20-722). Several obtained LOQs are below the MRLs established by the European Commission; therefore, the method could be applied for pesticides determination in routing analysis and custom laboratories. Moreover, this method has shown to be suitable for determination of some of the studied pesticides in lime, melon, papaya, banana, tomato, and lettuce. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach.

    Science.gov (United States)

    Yamamoto, Yoichiro; Saito, Akira; Tateishi, Ayako; Shimojo, Hisashi; Kanno, Hiroyuki; Tsuchiya, Shinichi; Ito, Ken-Ichi; Cosatto, Eric; Graf, Hans Peter; Moraleda, Rodrigo R; Eils, Roland; Grabe, Niels

    2017-04-25

    Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade ductal carcinoma in situ (DCIS). Using a machine learning system, we succeeded in classifying the four histological types with 90.9% accuracy. Electron microscopy observations suggested that the activity of typical myoepithelial cells in DCIS was lowered. Through these observations as well as meta-analytic database analyses, we developed a paracrine cross-talk-based biological mechanism of DCIS progressing to invasive cancer. Our observations support novel approaches in clinical computational diagnostics as well as in therapy development against progression.

  4. Value of the BI-RADS classification in MR-mammography for diagnosis of benign and malignant breast tumors

    International Nuclear Information System (INIS)

    Sohns, Christian; Scherrer, Martin; Staab, Wieland; Obenauer, Silvia

    2011-01-01

    To assess whether the BI-RADS classification in MR-Mammography (MRM) can distinguish between benign and malignant lesions. 207 MRM investigations were categorised according to BI-RADS. The results were compared to histology. All MRM studies were interpreted by two examiners. Statistical significance for the accuracy of MRM was calculated. A significant correlation between specific histology and MRM-tumour-morphology could not be reported. Mass (68%) was significant for malignancy. Significance raised with irregular shape (88%), spiculated margin (97%), rim enhancement (98%), fast initial increase (90%), post initial plateau (65%), and intermediate T2 result (82%). Highly significant for benignity was an oval mass (79%), slow initial increase (94%) and a hyperintense T2 result (77%), also an inconspicuous MRM result (77%) was often seen in benign histology. Symmetry (90%) and further post initial increase (90%) were significant, whereas a regional distribution (74%) was lowly significant for benignity. On basis of the BI-RADS classification an objective comparability and statement of diagnosis could be made highly significant. Due to the fact of false-negative and false-positive MRM-results, histology is necessary. (orig.)

  5. Classification of 27 Tumor-Associated Antigens by Histochemical Analysis of 36 Freshly Resected Lung Cancer Tissues

    Directory of Open Access Journals (Sweden)

    Gene Kurosawa

    2016-11-01

    Full Text Available In previous studies, we identified 29 tumor-associated antigens (TAAs and isolated 488 human monoclonal antibodies (mAbs that specifically bind to one of the 29 TAAs. In the present study, we performed histochemical analysis of 36 freshly resected lung cancer tissues by using 60 mAbs against 27 TAAs. Comparison of the staining patterns of tumor cells, bronchial epithelial cells, and normal pulmonary alveolus cells and interalveolar septum allowed us to determine the type and location of cells that express target molecules, as well as the degree of expression. The patterns were classified into 7 categories. While multiple Abs were used against certain TAAs, the differences observed among them should be derived from differences in the binding activity and/or the epitope. Thus, such data indicate the versatility of respective clones as anti-cancer drugs. Although the information obtained was limited to the lung and bronchial tube, bronchial epithelial cells represent normal growing cells, and therefore, the data are informative. The results indicate that 9 of the 27 TAAs are suitable targets for therapeutic Abs. These 9 Ags include EGFR, HER2, TfR, and integrin α6β4. Based on our findings, a pharmaceutical company has started to develop anti-cancer drugs by using Abs to TfR and integrin α6β4. HGFR, PTP-LAR, CD147, CDCP1, and integrin αvβ3 are also appropriate targets for therapeutic purposes.

  6. Trace analysis of multi-class pesticide residues in Chinese medicinal health wines using gas chromatography with electron capture detection

    Science.gov (United States)

    Kong, Wei-Jun; Liu, Qiu-Tao; Kong, Dan-Dan; Liu, Qian-Zhen; Ma, Xin-Ping; Yang, Mei-Hua

    2016-02-01

    A method is described for multi-residue, high-throughput determination of trace levels of 22 organochlorine pesticides (OCPs) and 5 pyrethroid pesticides (PYPs) in Chinese medicinal (CM) health wines using a QuEChERS (quick, easy, cheap, effective, rugged, and safe) based extraction method and gas chromatography-electron capture detection (GC-ECD). Several parameters were optimized to improve preparation and separation time while still maintaining high sensitivity. Validation tests of spiked samples showed good linearities for 27 pesticides (R = 0.9909-0.9996) over wide concentration ranges. Limits of detection (LODs) and quantification (LOQs) were measured at ng/L levels, 0.06-2 ng/L and 0.2-6 ng/L for OCPs and 0.02-3 ng/L and 0.06-7 ng/L for PYPs, respectively. Inter- and intra-day precision tests showed variations of 0.65-9.89% for OCPs and 0.98-13.99% for PYPs, respectively. Average recoveries were in the range of 47.74-120.31%, with relative standard deviations below 20%. The developed method was then applied to analyze 80 CM wine samples. Beta-BHC (Benzene hexachloride) was the most frequently detected pesticide at concentration levels of 5.67-31.55 mg/L, followed by delta-BHC, trans-chlordane, gamma-BHC, and alpha-BHC. The validated method is simple and economical, with adequate sensitivity for trace levels of multi-class pesticides. It could be adopted by laboratories for this and other types of complex matrices analysis.

  7. Spinal tumors

    International Nuclear Information System (INIS)

    Goethem, J.W.M. van; Hauwe, L. van den; Oezsarlak, Oe.; Schepper, A.M.A. de; Parizel, P.M.

    2004-01-01

    Spinal tumors are uncommon lesions but may cause significant morbidity in terms of limb dysfunction. In establishing the differential diagnosis for a spinal lesion, location is the most important feature, but the clinical presentation and the patient's age and gender are also important. Magnetic resonance (MR) imaging plays a central role in the imaging of spinal tumors, easily allowing tumors to be classified as extradural, intradural-extramedullary or intramedullary, which is very useful in tumor characterization. In the evaluation of lesions of the osseous spine both computed tomography (CT) and MR are important. We describe the most common spinal tumors in detail. In general, extradural lesions are the most common with metastasis being the most frequent. Intradural tumors are rare, and the majority is extramedullary, with meningiomas and nerve sheath tumors being the most frequent. Intramedullary tumors are uncommon spinal tumors. Astrocytomas and ependymomas comprise the majority of the intramedullary tumors. The most important tumors are documented with appropriate high quality CT or MR images and the characteristics of these tumors are also summarized in a comprehensive table. Finally we illustrate the use of the new World Health Organization (WHO) classification of neoplasms affecting the central nervous system

  8. Automatic classification and detection of clinically relevant images for diabetic retinopathy

    Science.gov (United States)

    Xu, Xinyu; Li, Baoxin

    2008-03-01

    We proposed a novel approach to automatic classification of Diabetic Retinopathy (DR) images and retrieval of clinically-relevant DR images from a database. Given a query image, our approach first classifies the image into one of the three categories: microaneurysm (MA), neovascularization (NV) and normal, and then it retrieves DR images that are clinically-relevant to the query image from an archival image database. In the classification stage, the query DR images are classified by the Multi-class Multiple-Instance Learning (McMIL) approach, where images are viewed as bags, each of which contains a number of instances corresponding to non-overlapping blocks, and each block is characterized by low-level features including color, texture, histogram of edge directions, and shape. McMIL first learns a collection of instance prototypes for each class that maximizes the Diverse Density function using Expectation- Maximization algorithm. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new multi-class bag feature space. Finally a multi-class Support Vector Machine is trained in the multi-class bag feature space. In the retrieval stage, we retrieve images from the archival database who bear the same label with the query image, and who are the top K nearest neighbors of the query image in terms of similarity in the multi-class bag feature space. The classification approach achieves high classification accuracy, and the retrieval of clinically-relevant images not only facilitates utilization of the vast amount of hidden diagnostic knowledge in the database, but also improves the efficiency and accuracy of DR lesion diagnosis and assessment.

  9. Tumor Size Evaluation according to the T Component of the Seventh Edition of the International Association for the Study of Lung Cancer's TNM Classification: Interobserver Agreement between Radiologists and Computer-Aided Diagnosis System in Patients with Lung Cancer

    International Nuclear Information System (INIS)

    Kim, Jin Kyoung; Chong, Se Min; Seo, Jae Seung; Lee, Sun Jin; Han, Heon

    2011-01-01

    To assess the interobserver agreement for tumor size evaluation between radiologists and the computer-aided diagnosis (CAD) system based on the 7th edition of the TNM classification by the International Association for the Study of Lung Cancer in patients with lung cancer. We evaluated 20 patients who underwent a lobectomy or pneumonectomy for primary lung cancer. The maximum diameter of each primary tumor was measured by two radiologists and a CAD system on CT, and was staged based on the 7th edition of the TNM classification. The CT size and T-staging of the primary tumors was compared with the pathologic size and staging and the variability in the sizes and T stages of primary tumors was statistically analyzed between each radiologist's measurement or CAD estimation and the pathologic results. There was no statistically significant interobserver difference for the CT size among the two radiologists, between pathologic and CT size estimated by the radiologists, and between pathologic and CT staging by the radiologists and CAD system. However, there was a statistically significant interobserver difference between pathologic size and the CT size estimated by the CAD system (p = 0.003). No significant differences were found in the measurement of tumor size among radiologists or in the assessment of T-staging by radiologists and the CAD system.

  10. Eco-friendly LC-MS/MS method for analysis of multi-class micropollutants in tap, fountain, and well water from northern Portugal.

    Science.gov (United States)

    Barbosa, Marta O; Ribeiro, Ana R; Pereira, Manuel F R; Silva, Adrián M T

    2016-11-01

    Organic micropollutants present in drinking water (DW) may cause adverse effects for public health, and so reliable analytical methods are required to detect these pollutants at trace levels in DW. This work describes the first green analytical methodology for multi-class determination of 21 pollutants in DW: seven pesticides, an industrial compound, 12 pharmaceuticals, and a metabolite (some included in Directive 2013/39/EU or Decision 2015/495/EU). A solid-phase extraction procedure followed by ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (offline SPE-UHPLC-MS/MS) method was optimized using eco-friendly solvents, achieving detection limits below 0.20 ng L -1 . The validated analytical method was successfully applied to DW samples from different sources (tap, fountain, and well waters) from different locations in the north of Portugal, as well as before and after bench-scale UV and ozonation experiments in spiked tap water samples. Thirteen compounds were detected, many of them not regulated yet, in the following order of frequency: diclofenac > norfluoxetine > atrazine > simazine > warfarin > metoprolol > alachlor > chlorfenvinphos > trimethoprim > clarithromycin ≈ carbamazepine ≈ PFOS > citalopram. Hazard quotients were also estimated for the quantified substances and suggested no adverse effects to humans. Graphical Abstract Occurrence and removal of multi-class micropollutants in drinking water, analyzed by an eco-friendly LC-MS/MS method.

  11. Use of Multi-class Empirical Orthogonal Function for Identification of Hydrogeological Parameters and Spatiotemporal Pattern of Multiple Recharges in Groundwater Modeling

    Science.gov (United States)

    Huang, C. L.; Hsu, N. S.; Yeh, W. W. G.; Hsieh, I. H.

    2017-12-01

    This study develops an innovative calibration method for regional groundwater modeling by using multi-class empirical orthogonal functions (EOFs). The developed method is an iterative approach. Prior to carrying out the iterative procedures, the groundwater storage hydrographs associated with the observation wells are calculated. The combined multi-class EOF amplitudes and EOF expansion coefficients of the storage hydrographs are then used to compute the initial gauss of the temporal and spatial pattern of multiple recharges. The initial guess of the hydrogeological parameters are also assigned according to in-situ pumping experiment. The recharges include net rainfall recharge and boundary recharge, and the hydrogeological parameters are riverbed leakage conductivity, horizontal hydraulic conductivity, vertical hydraulic conductivity, storage coefficient, and specific yield. The first step of the iterative algorithm is to conduct the numerical model (i.e. MODFLOW) by the initial guess / adjusted values of the recharges and parameters. Second, in order to determine the best EOF combination of the error storage hydrographs for determining the correction vectors, the objective function is devised as minimizing the root mean square error (RMSE) of the simulated storage hydrographs. The error storage hydrograph are the differences between the storage hydrographs computed from observed and simulated groundwater level fluctuations. Third, adjust the values of recharges and parameters and repeat the iterative procedures until the stopping criterion is reached. The established methodology was applied to the groundwater system of Ming-Chu Basin, Taiwan. The study period is from January 1st to December 2ed in 2012. Results showed that the optimal EOF combination for the multiple recharges and hydrogeological parameters can decrease the RMSE of the simulated storage hydrographs dramatically within three calibration iterations. It represents that the iterative approach that

  12. Discovery of dominant and dormant genes from expression data using a novel generalization of SNR for multi-class problems

    Directory of Open Access Journals (Sweden)

    Chung I-Fang

    2008-10-01

    Full Text Available Abstract Background The Signal-to-Noise-Ratio (SNR is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs. These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems. Results and discussion To evaluate the effectiveness of the GDIs, we have used four multiclass cancer data sets (Small Round Blue Cell Tumors, Leukemia, Central Nervous System Tumors, and Lung Cancer. For each data set we demonstrate that the new indices can find biologically meaningful genes that can act as biomarkers. We then use six machine learning tools, Nearest Neighbor Classifier (NNC, Nearest Mean Classifier (NMC, Support Vector Machine (SVM classifier with linear kernel, and SVM classifier with Gaussian kernel, where both SVMs are used in conjunction with one-vs-all (OVA and one-vs-one (OVO strategies. We found GDIs to be very effective in identifying biomarkers with strong class specific signatures. With all six tools and for all data sets we could achieve better or comparable prediction accuracies usually with fewer marker genes than results reported in the literature using the

  13. Integrated genomic classification of melanocytic tumors of the central nervous system using mutation analysis, copy number alterations and DNA methylation profiling.

    Science.gov (United States)

    Griewank, Klaus; Koelsche, Christian; van de Nes, Johannes A P; Schrimpf, Daniel; Gessi, Marco; Möller, Inga; Sucker, Antje; Scolyer, Richard A; Buckland, Michael E; Murali, Rajmohan; Pietsch, Torsten; von Deimling, Andreas; Schadendorf, Dirk

    2018-06-11

    In the central nervous system, distinguishing primary leptomeningeal melanocytic tumors from melanoma metastases and predicting their biological behavior solely using histopathologic criteria can be challenging. We aimed to assess the diagnostic and prognostic value of integrated molecular analysis. Targeted next-generation-sequencing, array-based genome-wide methylation analysis and BAP1 immunohistochemistry was performed on the largest cohort of central nervous system melanocytic tumors analyzed to date, incl. 47 primary tumors of the central nervous system, 16 uveal melanomas. 13 cutaneous melanoma metastasis and 2 blue nevus-like melanomas. Gene mutation, DNA-methylation and copy-number profiles were correlated with clinicopathological features. Combining mutation, copy-number and DNA-methylation profiles clearly distinguished cutaneous melanoma metastases from other melanocytic tumors. Primary leptomeningeal melanocytic tumors, uveal melanomas and blue nevus-like melanoma showed common DNA-methylation, copy-number alteration and gene mutation signatures. Notably, tumors demonstrating chromosome 3 monosomy and BAP1 alterations formed a homogeneous subset within this group. Integrated molecular profiling aids in distinguishing primary from metastatic melanocytic tumors of the central nervous system. Primary leptomeningeal melanocytic tumors, uveal melanoma and blue nevus-like melanoma share molecular similarity with chromosome 3 and BAP1 alterations markers of poor prognosis. Copyright ©2018, American Association for Cancer Research.

  14. Glial tumors with neuronal differentiation.

    Science.gov (United States)

    Park, Chul-Kee; Phi, Ji Hoon; Park, Sung-Hye

    2015-01-01

    Immunohistochemical studies for neuronal differentiation in glial tumors revealed subsets of tumors having both characteristics of glial and neuronal lineages. Glial tumors with neuronal differentiation can be observed with diverse phenotypes and histologic grades. The rosette-forming glioneuronal tumor of the fourth ventricle and papillary glioneuronal tumor have been newly classified as distinct disease entities. There are other candidates for classification, such as the glioneuronal tumor without pseudopapillary architecture, glioneuronal tumor with neuropil-like islands, and the malignant glioneuronal tumor. The clinical significance of these previously unclassified tumors should be confirmed. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. Renal tumors in infancy

    International Nuclear Information System (INIS)

    Lucaya, J.; Garcia, P.

    1997-01-01

    The classification of childhood renal masses in updated, including the clinical signs and imaging techniques currently employed to confirm their presence and type them. Several bening and malignant childhood tumors are described in substantial detail. (Author) 24 refs

  16. Classifying Classifications

    DEFF Research Database (Denmark)

    Debus, Michael S.

    2017-01-01

    This paper critically analyzes seventeen game classifications. The classifications were chosen on the basis of diversity, ranging from pre-digital classification (e.g. Murray 1952), over game studies classifications (e.g. Elverdam & Aarseth 2007) to classifications of drinking games (e.g. LaBrie et...... al. 2013). The analysis aims at three goals: The classifications’ internal consistency, the abstraction of classification criteria and the identification of differences in classification across fields and/or time. Especially the abstraction of classification criteria can be used in future endeavors...... into the topic of game classifications....

  17. Evaluation of Diagnostic Tests Using Information Theory for Multi-Class Diagnostic Problems and its Application for the Detection of Occlusal Caries Lesions

    Directory of Open Access Journals (Sweden)

    Umut Arslan

    2014-09-01

    Full Text Available Background: Several methods are available to evaluate the performance of the tests when the purpose of the diagnostic test is to discriminate between two possible disease states. However multi-class diagnostic problems frequently appear in many areas of medical science. Hence, there is a need for methods which will enable us to characterize the accuracy of diagnostic tests when there are more than two possible disease states. Aims: To show that two information theory measures, information content (IC and proportional reduction in diagnostic uncertainty (PRDU, can be used for the evaluation of the performance of diagnostic tests for multi-class diagnostic problems that may appear in different areas of medical science. Study Design: Diagnostic accuracy study. Methods: Sixty freshly extracted permanent human molar and premolar teeth suspected to have occlusal caries lesions were selected for the study and were assessed by two experienced examiners. Each examiner performed two evaluations. Histological examination was used as the gold standard. The scores of the histological examination were defined as sound (n=11, enamel caries (n=22 and dentin caries (n=27. Diagnostic performance of i visual inspection, ii radiography, iii laser fluorescence (LF and iv micro-computed tomography (M-CT caries detection methods was evaluated by calculating IC and PRDU. Results: Micro-computed tomography examination was the best method among the diagnostic techniques for the diagnosis of occlusal caries in terms of both IC and PRDU. M-CT examination supplied the maximum diagnostic information about the diagnosis of occlusal caries in the first (IC: 1.056; p<0.05, (PRDU: 70.5% and second evaluation (IC: 1.105; p<0.05, (PRDU: 73.8% for the first examiner. M-CT examination was the best method among the diagnostic techniques for the second examiner in both the first (IC:1.105; p<0.05, (PRDU:73.8% and second evaluation (IC:1.061; p<0.05, (PRDU:70.8%. IC and PRDU were

  18. Pathogenesis and progression of fibroepithelial breast tumors

    NARCIS (Netherlands)

    Kuijper, Arno

    2006-01-01

    Fibroadenoma and phyllodes tumor are fibroepithelial breast tumors. These tumors are biphasic, i.e. they are composed of stroma and epithelium. The behavior of fibroadenomas is benign, whereas phyllodes tumors can recur and even metastasize. Classification criteria for both tumors show considerable

  19. Development of a Multi-class Steroid Hormone Screening Method using Liquid Chromatography/Tandem Mass Spectrometry (LC-MS/MS)

    Science.gov (United States)

    Boggs, Ashley S. P.; Bowden, John A.; Galligan, Thomas M.; Guillette, Louis J.; Kucklick, John R.

    2016-01-01

    Monitoring complex endocrine pathways is often limited by indirect measurement or measurement of a single hormone class per analysis. There is a burgeoning need to develop specific direct-detection methods capable of providing simultaneous measurement of biologically relevant concentrations of multiple classes of hormones (estrogens, androgens, progestogens, and corticosteroids). The objectives of this study were to develop a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for multi-class steroid hormone detection using biologically relevant concentrations, then test limits of detection (LOD) in a high-background matrix by spiking charcoal-stripped fetal bovine serum (FBS) extract. Accuracy was tested with National Institute of Standards and Technology Standard Reference Materials (SRMs) with certified concentrations of cortisol, testosterone, and progesterone. 11-Deoxycorticosterone, 11-deoxycortisol, 17-hydroxypregnenolone, 17-hydroxyprogesterone, adrenosterone, androstenedione, cortisol, corticosterone, dehydroepiandrosterone, dihydrotestosterone, estradiol, estriol, estrone, equilin, pregnenolone, progesterone, and testosterone were also measured using isotopic dilution. Dansyl chloride (DC) derivatization was investigated maintaining the same method to improve and expedite estrogen analysis. Biologically relevant LODs were determined for 15 hormones. DC derivatization improved estrogen response two- to eight-fold, and improved chromatographic separation. All measurements had an accuracy ≤ 14 % difference from certified values (not accounting for uncertainty) and relative standard deviation ≤ 14 %. This method chromatographically separated and quantified biologically relevant concentrations of four hormone classes using highly specific fragmentation patterns and measured certified values of hormones that were previously split into three separate chromatographic methods. PMID:27039201

  20. A Theoretical Methodology and Prototype Implementation for Detection Segmentation Classification of Digital Mammogram Tumor by Machine Learning and Problem Solving Approach

    OpenAIRE

    Raman Valliappan; Sumari Putra; Rajeswari Mandava

    2010-01-01

    Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. The CAD systems can provide such help and they are important and necessary for breast cancer control. Microcalcifications and masses are the two most important indicators of malignancy, and their automated detection is very valuable for early breast cancer diagnosis. The main objective of this paper is to detect, segment and classify the tumor from ...

  1. Modeling nearshore dispersal of river-derived multi-class suspended sediments and radionuclides during a flood event around the mouth of Niida River, Fukushima, Japan

    Science.gov (United States)

    Uchiyama, Y.; Yamanishi, T.; Iwasaki, T.; Shimizu, Y.; Tsumune, D.; Misumi, K.; Onda, Y.

    2016-12-01

    A quadruple nested synoptic oceanic downscale modeling based on ROMS was carried out to investigate hydrodynamics, multi-class non-cohesive sediment transport and associated dispersal of suspended radionuclides (cesium-137; 137Cs) originated from the nuclear accident occurred at the Fukushima Dai-ichi Power Plant in March 2011. The innermost model has horizontal grid resolution of 50 m to marginally resolve the topography around the river mouth including the surf zone. The model is forced by the JCOPE2 oceanic reanalysis as the outermost boundary conditions, the GPV-MSM atmospheric reanalysis, and an in-house SWAN spectral wave hindcast embedded in the operational GPV-CWM wave reanalysis. A particular attention is paid to nearshore behaviors and inventory of the nuclides attached to terrestrial minerals with grain sizes ranging from 5 to 79 micrometers that have been occasionally discharged out to the coastal ocean through hydrological processes within the river basin even after several years since the accident. We examine oceanic dispersal of sediment and suspended 137Cs influxes from Niida River, Fukushima, evaluated with the iRIC-Nays2DH river model. Our focus is on the first flood event in late May of 2011 after the accident. Alongshore asymmetry in transport of suspended sediments and 137Cs is exhibited, comprising storm-driven southward transport confined in the shallow area due to shoreward Ekman transport associated with strong northerly wind, followed by northwestward wide-spread transport under mild southerly wind condition. About 70 % of the Niida River-derived suspended 137Cs remains near the mouth for 20 days after the flood event. Nevertheless, our model results as well as an observation suggest that the area is dominated by erosion as for high bed shear stress all the time, thus suspended radionuclides are redistributed to dissipate away in long term.

  2. Multi-class multi-residue analysis of veterinary drugs in meat using enhanced matrix removal lipid cleanup and liquid chromatography-tandem mass spectrometry.

    Science.gov (United States)

    Zhao, Limian; Lucas, Derick; Long, David; Richter, Bruce; Stevens, Joan

    2018-05-11

    This study presents the development and validation of a quantitation method for the analysis of multi-class, multi-residue veterinary drugs using lipid removal cleanup cartridges, enhanced matrix removal lipid (EMR-Lipid), for different meat matrices by liquid chromatography tandem mass spectrometry detection. Meat samples were extracted using a two-step solid-liquid extraction followed by pass-through sample cleanup. The method was optimized based on the buffer and solvent composition, solvent additive additions, and EMR-Lipid cartridge cleanup. The developed method was then validated in five meat matrices, porcine muscle, bovine muscle, bovine liver, bovine kidney and chicken liver to evaluate the method performance characteristics, such as absolute recoveries and precision at three spiking levels, calibration curve linearity, limit of quantitation (LOQ) and matrix effect. The results showed that >90% of veterinary drug analytes achieved satisfactory recovery results of 60-120%. Over 97% analytes achieved excellent reproducibility results (relative standard deviation (RSD) meat matrices. The matrix co-extractive removal efficiency by weight provided by EMR-lipid cartridge cleanup was 42-58% in samples. The post column infusion study showed that the matrix ion suppression was reduced for samples with the EMR-Lipid cartridge cleanup. The reduced matrix ion suppression effect was also confirmed with 30%) for all tested veterinary drugs in all of meat matrices. The results showed that the two-step solid-liquid extraction provides efficient extraction for the entire spectrum of veterinary drugs, including the difficult classes such as tetracyclines, beta-lactams etc. EMR-Lipid cartridges after extraction provided efficient sample cleanup with easy streamlined protocol and minimal impacts on analytes recovery, improving method reliability and consistency. Copyright © 2018 Elsevier B.V. All rights reserved.

  3. Classificação dos tumores da mama: atualização baseada na nova classificação da Organização Mundial da Saúde de 2012 Classification of tumours of the breast: an update based on the new 2012 World Health Organization Classification

    Directory of Open Access Journals (Sweden)

    Helenice Gobbi

    2012-12-01

    the Classification of Breast Tumors in July 2012. This review summarizes the principal changes that were introduced in the new classification with emphasis on diagnostic and therapeutic implications. The major changes were: (i the new edition is entirely dedicated to breast tumors; (ii the epithelial tumors were sorted differently, recognizing nine special types and variants, and eleven rare types of breast tumors apart from invasive ductal carcinoma of no special type. New codes were included for the lobular, medullary, and metaplastic subtypes; (iii new scores were suggested for the immunohistochemical evaluation of hormone receptor (> 1% positive cells and human epidermal growth factor receptor 2 (HER2 (> 30% highly positive cells surrounding the whole membrane; (iv a new approach to molecular and genomic classification of breast cancer was presented including predictive and prognostic tests using gene expression profile; (v the traditional terminology of intraductal proliferative lesions was maintained and the terminology ductal intraepithelial neoplasia was not recommended; (vi the prognostic importance of distinguishing atypical lobular hyperplasia and lobular carcinoma in situ (LCIS within the spectrum of lobular neoplasia was acknowledged; (vii the columnar cell lesions (columnar cell change and columnar cell hyperplasia without atypia were excluded from the flat epithelial atypia group, whose biological behavior is still unknown. It is expected that the widespread use of the new classification by pathologists and oncologists will benefit patients by improving diagnostic and therapeutic decisions.

  4. Predicting Assignment Submissions in a Multiclass Classification Problem

    Directory of Open Access Journals (Sweden)

    Bogdan Drăgulescu

    2015-08-01

    Full Text Available Predicting student failure is an important task that can empower educators to counteract the factors that affect student performance. In this paper, a part of the bigger problem of predicting student failure is addressed: predicting the students that do not complete their assignment tasks. For solving this problem, real data collected by our university’s educational platform was used. Because the problem consisted of predicting one of three possible classes (multi-class classification, the appropriate algorithms and methods were selected. Several experiments were carried out to find the best approach for this prediction problem and the used data set. An approach of time segmentation is proposed in order to facilitate the prediction from early on. Methods that address the problems of high dimensionality and imbalanced data were also evaluated. The outcome of each approach is shown and compared in order to select the best performing classification algorithm for the problem at hand.

  5. Imaging of pancreatic tumors

    International Nuclear Information System (INIS)

    Brambs, Hans-Juergen; Juchems, Markus

    2010-01-01

    Ductal adenocarcinoma is the most frequent solid tumor of the pancreas. This tumor has distinct features including early obstruction of the pancreatic duct, diminished enhancement after administration of contrast material due to desmoplastic growth, high propensity to infiltrate adjacent structures and to metastasize into the liver and the peritoneum. Hormone active endocrine tumors cause specific clinical symptoms. Imaging is aimed at localization of these hypervascular tumors. Non hormone active tumors are most frequently malignant and demonstrate very varying features. Cystic pancreatic tumors are increasingly detected by means of cross sectional imaging. Exact classification can be achieved with knowledge of the macropathology and considering clinical presentation as well as age and gender of the patients. (orig.)

  6. Predicting difficult laryngoscopy in acromegalic patients undergoing surgery for excision of pituitary tumors: A comparison of extended Mallampati score with modified Mallampati classification

    Directory of Open Access Journals (Sweden)

    Ashish Bindra

    2013-01-01

    Full Text Available Background: There are numerous reports of difficult laryngoscopy and intubation in patients with acromegaly. To date, no study has assessed the application of extended Mallampati score (EMS for predicting difficult intubation in acromegalics. The primary aim of this study was to compare EMS with modified Mallampati classification (MMP in predicting difficult laryngoscopy in acromegalic patients. We hypothesized that since EMS has been reported to be more specific and better predictor than MMP, it may be superior to the MMP to predict difficult laryngoscopy in acromegalic patients. Materials and Methods: For this prospective cohort study with matched controls, acromegalic patients scheduled to undergo pituitary surgery over a period of 3 years (January 2008-December 2010 were enrolled. Preoperative airway assessment was performed by experienced anesthesiologists and involved a MMP and the EMS. Under anesthesia, laryngoscopic view was assessed using Cormack-Lehane (CL grading. MMP and CL grades of I and II were defined "easy" and III and IV as "difficult". EMS grade of I and II were defined "easy" and III as "difficult". Data were used to determine the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of MMP and EMS in predicting difficult laryngoscopy. Results: Seventy eight patients participated in the study (39 patients in each group. Both MMP and EMS failed to detect difficult laryngoscopy in seven patients. Only one laryngoscopy was predicted to be difficult by both tests which was in fact, difficult. Conclusion: We found that addition of neck extension did not improve the predictive value of MMP.

  7. Bone tumor

    Science.gov (United States)

    Tumor - bone; Bone cancer; Primary bone tumor; Secondary bone tumor; Bone tumor - benign ... The cause of bone tumors is unknown. They often occur in areas of the bone that grow rapidly. Possible causes include: Genetic defects ...

  8. Comparison of the Agilent, ROMA/NimbleGen and Illumina platforms for classification of copy number alterations in human breast tumors

    Directory of Open Access Journals (Sweden)

    Naume B

    2008-08-01

    Full Text Available Abstract Background Microarray Comparative Genomic Hybridization (array CGH provides a means to examine DNA copy number aberrations. Various platforms, brands and underlying technologies are available, facing the user with many choices regarding platform sensitivity and number, localization, and density distribution of probes. Results We evaluate three different platforms presenting different nature and arrangement of the probes: The Agilent Human Genome CGH Microarray 44 k, the ROMA/NimbleGen Representational Oligonucleotide Microarray 82 k, and the Illumina Human-1 Genotyping 109 k BeadChip, with Agilent being gene oriented, ROMA/NimbleGen being genome oriented, and Illumina being genotyping oriented. We investigated copy number changes in 20 human breast tumor samples representing different gene expression subclasses, using a suite of graphical and statistical methods designed to work across platforms. Despite substantial differences in the composition and spatial distribution of probes, the comparison revealed high overall concordance. Notably however, some short amplifications and deletions of potential biological importance were not detected by all platforms. Both correlation and cluster analysis indicate a somewhat higher similarity between ROMA/NimbleGen and Illumina than between Agilent and the other two platforms. The programs developed for the analysis are available from http://www.ifi.uio.no/bioinf/Projects/. Conclusion We conclude that platforms based on different technology principles reveal similar aberration patterns, although we observed some unique amplification or deletion peaks at various locations, only detected by one of the platforms. The correct platform choice for a particular study is dependent on whether the appointed research intention is gene, genome, or genotype oriented.

  9. Bone tumors

    International Nuclear Information System (INIS)

    Unni, K.K.

    1988-01-01

    This book contains the proceedings on bone tumors. Topics covered include: Bone tumor imaging: Contribution of CT and MRI, staging of bone tumors, perind cell tumors of bone, and metastatic bone disease

  10. A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

    Science.gov (United States)

    Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

    2013-01-01

    In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

  11. Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

    Science.gov (United States)

    Sørensen, Lauge; Nielsen, Mads

    2018-05-15

    The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

  12. Interval prediction for graded multi-label classification

    CERN Document Server

    Lastra, Gerardo; Bahamonde, Antonio

    2014-01-01

    Multi-label was introduced as an extension of multi-class classification. The aim is to predict a set of classes (called labels in this context) instead of a single one, namely the set of relevant labels. If membership to the set of relevant labels is defined to a certain degree, the learning task is called graded multi-label classification. These learning tasks can be seen as a set of ordinal classifications. Hence, recommender systems can be considered as multi-label classification tasks. In this paper, we present a new type of nondeterministic learner that, for each instance, tries to predict at the same time the true grade for each label. When the classification is uncertain for a label, however, the hypotheses predict a set of consecutive grades, i.e., an interval. The goal is to keep the set of predicted grades as small as possible; while still containing the true grade. We shall see that these classifiers take advantage of the interrelations of labels. The result is that, with quite narrow intervals, i...

  13. A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

    Directory of Open Access Journals (Sweden)

    Cuihong Wen

    Full Text Available Optical Music Recognition (OMR has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM. The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM, which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs and Neural Networks (NNs.

  14. A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

    Science.gov (United States)

    Wen, Cuihong; Zhang, Jing; Rebelo, Ana; Cheng, Fanyong

    2016-01-01

    Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs).

  15. Brain tumor classification using Probabilistic Neural Network

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... Baghdad, Iraq. 1sami.hasan@coie.nahrainuniv.edu.iq ... The Human brain is the most amazing and complex thing known in the world [1]. ... achieved using gray level co-occurrence matrix (GLCM). This work is aimed to ...

  16. GASTROINTESTINAL STROMAL TUMOR (GIST

    Directory of Open Access Journals (Sweden)

    Luigi eTornillo

    2014-11-01

    Full Text Available Gastrointestinal stromal tumors are the most frequent mesenchymal tumors of the gastrointestinal tract. The discovery that these tumors, formerly thought of smooth muscle origin, are indeed better characterized by specific activating mutation in genes coding for the receptor tyrosine kinases CKIT and PDGFRA and that these mutations are strongly predictive for the response to targeted therapy with receptor tyrosine kinase inhibitors has made GISTs the typical example of the integration of basic molecular knowledge in the daily clinical activity. The information on the mutational status of these tumors is essential to predict (and subsequently to plan the therapy. As resistant cases are frequently wild-type, other possible oncogenic events, defining other entities, have been discovered (e.g. succinil dehydrogenase mutation/dysregulation, insuline growth factor expression, mutations in the RAS-RAF-MAPK pathway. The classification of disease must nowadays rely on the integration of the clinico-morphological characteristics with the molecular data.

  17. Tissue Classification

    DEFF Research Database (Denmark)

    Van Leemput, Koen; Puonti, Oula

    2015-01-01

    Computational methods for automatically segmenting magnetic resonance images of the brain have seen tremendous advances in recent years. So-called tissue classification techniques, aimed at extracting the three main brain tissue classes (white matter, gray matter, and cerebrospinal fluid), are now...... well established. In their simplest form, these methods classify voxels independently based on their intensity alone, although much more sophisticated models are typically used in practice. This article aims to give an overview of often-used computational techniques for brain tissue classification...

  18. Salivary gland tumors in Uganda: clinical pathological study ...

    African Journals Online (AJOL)

    African Health Sciences ... salivary gland tumors as defined by WHO classification (1991), is accepted world-wide but little is available in the literature ... Objective: To outline the clinicopathological features of salivary gland tumors in Uganda.

  19. Malignant tumors of gastrointestinal tract

    International Nuclear Information System (INIS)

    Anon.

    1989-01-01

    International histological classification and classification according to TNM systems, domestic clinical classification according to stages of carcinoma of stomach, large intestine and rectum are presented. Diagnosis of tumoral processes of the given localizations should be based on complex application of diagnostic methods: clinical, ultrasonic, radiological and others. Surgical method and variants of surgical method with preoperative radiotherapy play a leading role in treatment of mentioned tumors. Combined method of treatment-surgical intervention with postoperation intravenous injection of colloid 198 Au - is applied for preventing propagation of stomach cancer metastases. Advisability of combining operations with radiological and antitumoral medicamentous therapy is shown. Reliable results of treatment of malignant tumors of gastrointestinal tract are presented

  20. 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)...

  1. From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

    Directory of Open Access Journals (Sweden)

    Dawyndt Peter

    2010-01-01

    Full Text Available Abstract Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the

  2. From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification.

    Science.gov (United States)

    Slabbinck, Bram; Waegeman, Willem; Dawyndt, Peter; De Vos, Paul; De Baets, Bernard

    2010-01-30

    Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial

  3. From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

    Science.gov (United States)

    2010-01-01

    Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for

  4. Primary cardiac and pericardial tumors, imaging approach

    Energy Technology Data Exchange (ETDEWEB)

    Yu-Qing Liu, M D [Chinese Academy of Medical Sciences, Beijing, BJ (China). Dept. of Radiology, Fu Wai Hospital and Cardiovascular Inst.

    1996-12-31

    The incidence of cardiac tumor and its classification was discussed. Imaging study i.e. conventional radiology, echocardiagoaphy (echo), magnetic resonance imaging (MRI), angiography and computed tomography (CT) used also discussed briefly. (8 refs.).

  5. Primary cardiac and pericardial tumors, imaging approach

    International Nuclear Information System (INIS)

    Yu-Qing Liu, M.D.

    1995-01-01

    The incidence of cardiac tumor and its classification was discussed. Imaging study i.e. conventional radiology, echocardiagoaphy (echo), magnetic resonance imaging (MRI), angiography and computed tomography (CT) used also discussed briefly. (8 refs.)

  6. Neuroendocrine tumors and smoking

    Directory of Open Access Journals (Sweden)

    Tanja Miličević

    2016-12-01

    Full Text Available Neuroendocrine cells are dispersed around the body and can be found within the gastrointestinal system, lungs, larynx, thymus, thyroid, adrenal, gonads, skin and other tissues. These cells form the so-called ''diffuse neuroendocrine system'' and tumors arising from them are defined as neuroendocrine tumors (NETs. The traditional classification of NETs based on their embryonic origin includes foregut tumors (lung, thymus, stomach, pancreas and duodenum, midgut tumors (beyond the ligament of Treitz of the duodenum to the proximal transverse colon and hindgut tumors (distal colon and rectum. NETs at each site are biologically and clinically distinct from their counterparts at other sites. Symptoms in patients with early disease are often insidious in onset, leading to a delay in diagnosis. The majority of these tumors are thus diagnosed at a stage at which the only curative treatment, radical surgical intervention, is no longer an option. Due to the increasing incidence and mortality, many studies have been conducted in order to identify risk factors for the development of NETs. Still, little is known especially when it comes to preventable risk factors such as smoking. This review will focus on smoking and its contribution to the development of different subtypes of NETs.

  7. Overfitting Reduction of Text Classification Based on AdaBELM

    Directory of Open Access Journals (Sweden)

    Xiaoyue Feng

    2017-07-01

    Full Text Available Overfitting is an important problem in machine learning. Several algorithms, such as the extreme learning machine (ELM, suffer from this issue when facing high-dimensional sparse data, e.g., in text classification. One common issue is that the extent of overfitting is not well quantified. In this paper, we propose a quantitative measure of overfitting referred to as the rate of overfitting (RO and a novel model, named AdaBELM, to reduce the overfitting. With RO, the overfitting problem can be quantitatively measured and identified. The newly proposed model can achieve high performance on multi-class text classification. To evaluate the generalizability of the new model, we designed experiments based on three datasets, i.e., the 20 Newsgroups, Reuters-21578, and BioMed corpora, which represent balanced, unbalanced, and real application data, respectively. Experiment results demonstrate that AdaBELM can reduce overfitting and outperform classical ELM, decision tree, random forests, and AdaBoost on all three text-classification datasets; for example, it can achieve 62.2% higher accuracy than ELM. Therefore, the proposed model has a good generalizability.

  8. adabag: An R Package for Classification with Boosting and Bagging

    Directory of Open Access Journals (Sweden)

    Esteban Alfaro

    2013-09-01

    Full Text Available Boosting and bagging are two widely used ensemble methods for classification. Their common goal is to improve the accuracy of a classifier combining single classifiers which are slightly better than random guessing. Among the family of boosting algorithms, AdaBoost (adaptive boosting is the best known, although it is suitable only for dichotomous tasks. AdaBoost.M1 and SAMME (stagewise additive modeling using a multi-class exponential loss function are two easy and natural extensions to the general case of two or more classes. In this paper, the adabag R package is introduced. This version implements AdaBoost.M1, SAMME and bagging algorithms with classification trees as base classifiers. Once the ensembles have been trained, they can be used to predict the class of new samples. The accuracy of these classifiers can be estimated in a separated data set or through cross validation. Moreover, the evolution of the error as the ensemble grows can be analysed and the ensemble can be pruned. In addition, the margin in the class prediction and the probability of each class for the observations can be calculated. Finally, several classic examples in classification literature are shown to illustrate the use of this package.

  9. Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    PANIGRAHY, P. S.

    2018-02-01

    Full Text Available Data driven approach for multi-class fault diagnosis of induction motor using MCSA at steady state condition is a complex pattern classification problem. This investigation has exploited the built-in ensemble process of non-iterative classifiers to resolve the most challenging issues in this area, including bearing and stator fault detection. Non-iterative techniques exhibit with an average 15% of increased fault classification accuracy against their iterative counterparts. Particularly RF has shown outstanding performance even at less number of training samples and noisy feature space because of its distributive feature model. The robustness of the results, backed by the experimental verification shows that the non-iterative individual classifiers like RF is the optimum choice in the area of automatic fault diagnosis of induction motor.

  10. Risk factors and classifications of hilar cholangiocarcinoma.

    Science.gov (United States)

    Suarez-Munoz, Miguel Angel; Fernandez-Aguilar, Jose Luis; Sanchez-Perez, Belinda; Perez-Daga, Jose Antonio; Garcia-Albiach, Beatriz; Pulido-Roa, Ysabel; Marin-Camero, Naiara; Santoyo-Santoyo, Julio

    2013-07-15

    Cholangiocarcinoma is the second most common primary malignant tumor of the liver. Perihilar cholangiocarcinoma or Klatskin tumor represents more than 50% of all biliary tract cholangiocarcinomas. A wide range of risk factors have been identified among patients with Perihilar cholangiocarcinoma including advanced age, male gender, primary sclerosing cholangitis, choledochal cysts, cholelithiasis, cholecystitis, parasitic infection (Opisthorchis viverrini and Clonorchis sinensis), inflammatory bowel disease, alcoholic cirrhosis, nonalcoholic cirrhosis, chronic pancreatitis and metabolic syndrome. Various classifications have been used to describe the pathologic and radiologic appearance of cholangiocarcinoma. The three systems most commonly used to evaluate Perihilar cholangiocarcinoma are the Bismuth-Corlette (BC) system, the Memorial Sloan-Kettering Cancer Center and the TNM classification. The BC classification provides preoperative assessment of local spread. The Memorial Sloan-Kettering cancer center proposes a staging system according to three factors related to local tumor extent: the location and extent of bile duct involvement, the presence or absence of portal venous invasion, and the presence or absence of hepatic lobar atrophy. The TNM classification, besides the usual descriptors, tumor, node and metastases, provides additional information concerning the possibility for the residual tumor (R) and the histological grade (G). Recently, in 2011, a new consensus classification for the Perihilar cholangiocarcinoma had been published. The consensus was organised by the European Hepato-Pancreato-Biliary Association which identified the need for a new staging system for this type of tumors. The classification includes information concerning biliary or vascular (portal or arterial) involvement, lymph node status or metastases, but also other essential aspects related to the surgical risk, such as remnant hepatic volume or the possibility of underlying disease.

  11. 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. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data.

    Science.gov (United States)

    Guo, Yang; Liu, Shuhui; Li, Zhanhuai; Shang, Xuequn

    2018-04-11

    The classification of cancer subtypes is of great importance to cancer disease diagnosis and therapy. Many supervised learning approaches have been applied to cancer subtype classification in the past few years, especially of deep learning based approaches. Recently, the deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble decision trees. It has been proved that the deep forest model has competitive or even better performance than deep neural networks in some extent. However, the standard deep forest model may face overfitting and ensemble diversity challenges when dealing with small sample size and high-dimensional biology data. In this paper, we propose a deep learning model, so-called BCDForest, to address cancer subtype classification on small-scale biology datasets, which can be viewed as a modification of the standard deep forest model. The BCDForest distinguishes from the standard deep forest model with the following two main contributions: First, a named multi-class-grained scanning method is proposed to train multiple binary classifiers to encourage diversity of ensemble. Meanwhile, the fitting quality of each classifier is considered in representation learning. Second, we propose a boosting strategy to emphasize more important features in cascade forests, thus to propagate the benefits of discriminative features among cascade layers to improve the classification performance. Systematic comparison experiments on both microarray and RNA-Seq gene expression datasets demonstrate that our method consistently outperforms the state-of-the-art methods in application of cancer subtype classification. The multi-class-grained scanning and boosting strategy in our model provide an effective solution to ease the overfitting challenge and improve the robustness of deep forest model working on small-scale data. Our model provides a useful approach to the classification of cancer subtypes

  13. Neuroendocrine Tumors of the Lung

    Energy Technology Data Exchange (ETDEWEB)

    Fisseler-Eckhoff, Annette, E-mail: Annette.Fisseler-Eckhoff@hsk-wiesbaden.de; Demes, Melanie [Department of Pathology und Cytology, Dr. Horst-Schmidt-Kliniken (HSK), Wiesbaden 65199 (Germany)

    2012-07-31

    Neuroendocrine tumors may develop throughout the human body with the majority being found in the gastrointestinal tract and bronchopulmonary system. Neuroendocrine tumors are classified according to the grade of biological aggressiveness (G1–G3) and the extent of differentiation (well-differentiated/poorly-differentiated). The well-differentiated neoplasms comprise typical (G1) and atypical (G2) carcinoids. Large cell neuroendocrine carcinomas as well as small cell carcinomas (G3) are poorly-differentiated. The identification and differentiation of atypical from typical carcinoids or large cell neuroendocrine carcinomas and small cell carcinomas is essential for treatment options and prognosis. Pulmonary neuroendocrine tumors are characterized according to the proportion of necrosis, the mitotic activity, palisading, rosette-like structure, trabecular pattern and organoid nesting. The given information about the histopathological assessment, classification, prognosis, genetic aberration as well as treatment options of pulmonary neuroendocrine tumors are based on own experiences and reviewing the current literature available. Most disagreements among the classification of neuroendocrine tumor entities exist in the identification of typical versus atypical carcinoids, atypical versus large cell neuroendocrine carcinomas and large cell neuroendocrine carcinomas versus small cell carcinomas. Additionally, the classification is restricted in terms of limited specificity of immunohistochemical markers and possible artifacts in small biopsies which can be compressed in cytological specimens. Until now, pulmonary neuroendocrine tumors have been increasing in incidence. As compared to NSCLCs, only little research has been done with respect to new molecular targets as well as improving the classification and differential diagnosis of neuroendocrine tumors of the lung.

  14. Adrenocortical tumors in children

    Directory of Open Access Journals (Sweden)

    R.C. Ribeiro

    2000-10-01

    Full Text Available Childhood adrenocortical tumors (ACT are rare. In the USA, only about 25 new cases occur each year. In Southern Brazil, however, approximately 10 times that many cases are diagnosed each year. Most cases occur in the contiguous states of São Paulo and Paraná. The cause of this higher rate has not been identified. Familial genetic predisposition to cancer (p53 mutations and selected genetic syndromes (Beckwith-Wiedemann syndrome have been associated with childhood ACT in general but not with the Brazilian counterpart. Most of the affected children are young girls with classic endocrine syndromes (virilizing and/or Cushing. Levels of urinary 17-ketosteroids and plasma dehydroepiandrosterone sulfate (DHEA-S, which are abnormal in approximately 90% of the cases, provide the pivotal clue to a diagnosis of ACT. Typical imaging findings of pediatric ACT consist of a large, well-defined suprarenal tumor containing calcifications with a thin capsule and central necrosis or hemorrhage. The pathologic classification of pediatric ACT is troublesome. Even an experienced pathologist can find it difficult to differentiate carcinoma from adenoma. Surgery is the single most important procedure in the successful treatment of ACT. The role of chemotherapy in the management of childhood ACT has not been established although occasional tumors are responsive to mitotane or cisplatin-containing regimens. Because of the heterogeneity and rarity of the disease, prognostic factors have been difficult to establish in pediatric ACT. Patients with incomplete tumor resection or with metastatic disease at diagnosis have a dismal prognosis. In patients with localized and completely resected tumors, the size of the tumor has predictive value. Patients with large tumors have a much higher relapse rate than those with small tumors.

  15. Sinus Tumors

    Science.gov (United States)

    ... RESOURCES Medical Societies Patient Education About this Website Font Size + - Home > CONDITIONS > Sinus Tumors Adult Sinusitis Pediatric ... and they vary greatly in location, size and type. Care for these tumors is individualized to each ...

  16. Tumors markers

    International Nuclear Information System (INIS)

    Yamaguchi-Mizumoto, N.H.

    1989-01-01

    In order to study blood and cell components alterations (named tumor markers) that may indicate the presence of a tumor, several methods are presented. Aspects as diagnostic, prognostic, therapeutic value and clinical evaluation are discussed. (M.A.C.)

  17. Wilms tumor

    Science.gov (United States)

    ... suggested. Alternative Names Nephroblastoma; Kidney tumor - Wilms Images Kidney anatomy Wilms tumor References Babaian KN, Delacroix SE, Wood CG, Jonasch E. Kidney cancer. In: Skorecki K, Chertow GM, Marsden PA, ...

  18. Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.

    Science.gov (United States)

    Faust, Kevin; Xie, Quin; Han, Dominick; Goyle, Kartikay; Volynskaya, Zoya; Djuric, Ugljesa; Diamandis, Phedias

    2018-05-16

    There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered.

  19. Brain Tumors

    Science.gov (United States)

    A brain tumor is a growth of abnormal cells in the tissues of the brain. Brain tumors can be benign, with no cancer cells, ... cancer cells that grow quickly. Some are primary brain tumors, which start in the brain. Others are ...

  20. Urogenital tumors

    Energy Technology Data Exchange (ETDEWEB)

    Weller, R.E.

    1994-03-01

    An overview is provided for veterinary care of urogenital tumors in companion animals, especially the dog. Neoplasms discussed include tumors of the kidney, urinary bladder, prostate, testis, ovary, vagina, vulva and the canine transmissible venereal tumor. Topics addressed include description, diagnosis and treatment.

  1. [The pathology of salivary glands. Tumors of the salivary glands].

    Science.gov (United States)

    Mahy, P; Reychler, H

    2006-01-01

    The management of benign and malignant neoplasms of the salivary glands requires precise knowledge of tumor histogenesis and classification as well as surgical skills. Pleomorphic adenoma and Whartin's tumor are the most frequent tumors in parotid glands while the probability for malignant tumors is higher in other glands, especially in sublingual and minor salivary glands. Those malignant salivary glands tumors are rare and necessitate multidisciplinar staging and management in close collaboration with the pathologist and the radiation oncologist.

  2. Classification in context

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper surveys classification research literature, discusses various classification theories, and shows that the focus has traditionally been on establishing a scientific foundation for classification research. This paper argues that a shift has taken place, and suggests that contemporary...... classification research focus on contextual information as the guide for the design and construction of classification schemes....

  3. Classification of the web

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper discusses the challenges faced by investigations into the classification of the Web and outlines inquiries that are needed to use principles for bibliographic classification to construct classifications of the Web. This paper suggests that the classification of the Web meets challenges...... that call for inquiries into the theoretical foundation of bibliographic classification theory....

  4. Biological pathways involved in the aggressive behavior of the keratocystic odontogenic tumor and possible implications for molecular oriented treatment - An overview

    NARCIS (Netherlands)

    Mendes, R.A.; Carvalho, J.F.C.; van der Waal, I.

    2010-01-01

    In the classification of Head and Neck Tumors, published in 2005 by the World Health Organization Classification, the odontogenic keratocyst has been reclassified as a benign intraosseous neoplasm, calling it "keratocystic odontogenic tumor" (KCOT). Significant differences on the molecular level

  5. Tumor immunology

    International Nuclear Information System (INIS)

    Otter, W. den

    1987-01-01

    Tumor immunology, the use of immunological techniques for tumor diagnosis and approaches to immunotherapy of cancer are topics covered in this multi-author volume. Part A, 'Tumor Immunology', deals with present views on tumor-associated antigens, the initiation of immune reactions of tumor cells, effector cell killing, tumor cells and suppression of antitumor immunity, and one chapter dealing with the application of mathematical models in tumor immunology. Part B, 'Tumor Diagnosis and Imaging', concerns the use of markers to locate the tumor in vivo, for the histological diagnosis, and for the monitoring of tumor growth. In Part C, 'Immunotherapy', various experimental approaches to immunotherapy are described, such as the use of monoclonal antibodies to target drugs, the use of interleukin-2 and the use of drugs inhibiting suppression. In the final section, the evaluation, a pathologist and a clinician evaluate the possibilities and limitations of tumor immunology and the extent to which it is useful for diagnosis and therapy. refs.; figs.; tabs

  6. What is a pediatric tumor?

    Directory of Open Access Journals (Sweden)

    Mora J

    2012-11-01

    Full Text Available Jaume Mora1,21Department of Oncology, 2Developmental Tumor Biology Laboratory, Hospital Sant Joan de Deu, Fundacio Sant Joan de Deu, Barcelona, SpainAbstract: Working together with medical oncologists, the question of whether a Ewing sarcoma in a 25-year-old is a pediatric tumor comes up repeatedly. Like Ewing's, some tumors present characteristically at ages that cross over what has been set as the definition of pediatrics (15 years, 18 years, or 21 years?. Pediatric oncology textbooks, surprisingly, do not address the subject of defining a pediatric tumor. They all begin with an epidemiology chapter defining the types of tumors appearing at distinct stages of childhood, adolescence, and young adulthood. Describing the epidemiology of tumors in relation to age, it becomes clear that the disease is related to the phenomenon of aging. The question, however, remains: is there a biological definition of what pediatric age is? And if so, will tumors occurring during this period of life have anything to do with such biological definition? With the aim of finding an objective definition, the fundamental concepts of what defines "pediatrics" was reviewed and then the major features of tumors arising during development were analyzed. The tumors were explored from the perspective of a host immersed in the normal process of growth and development. This physiological process, from pluripotential and undifferentiated cells, makes possible the differentiation, maturation, organization, and function of tissues, organs, and apparatus. A biological definition of pediatric tumors and the infancy–childhood–puberty classification of developmental tumors according to the infancy–childhood–puberty model of normal human development are proposed.Keywords: growth and development, pediatric tumor, infant, childhood and adolescence, pubertal tumors

  7. Hazard classification methodology

    International Nuclear Information System (INIS)

    Brereton, S.J.

    1996-01-01

    This document outlines the hazard classification methodology used to determine the hazard classification of the NIF LTAB, OAB, and the support facilities on the basis of radionuclides and chemicals. The hazard classification determines the safety analysis requirements for a facility

  8. Discrimination of Breast Tumors in Ultrasonic Images by Classifier Ensemble Trained with AdaBoost

    Science.gov (United States)

    Takemura, Atsushi; Shimizu, Akinobu; Hamamoto, Kazuhiko

    In this paper, we propose a novel method for acurate automated discrimination of breast tumors (carcinoma, fibroadenoma, and cyst). We defined 199 features related to diagnositic observations noticed when a doctor judges breast tumors, such as internal echo, shape, and boundary echo. These features included novel features based on a parameter of log-compressed K distribution, which reflect physical characteristics of ultrasonic B-mode imaging. Furthermore, we propose a discrimination method of breast tumors by using an ensemble classifier based on the multi-class AdaBoost algorithm with effective features selection. Verification by analyzing 200 carcinomas, 30 fibroadenomas and 30 cycts showed the usefulness of the newly defined features and the effectiveness of the discrimination by using an ensemble classifier trained by AdaBoost.

  9. Modern classification of neoplasms: reconciling differences between morphologic and molecular approaches

    International Nuclear Information System (INIS)

    Berman, Jules

    2005-01-01

    For over 150 years, pathologists have relied on histomorphology to classify and diagnose neoplasms. Their success has been stunning, permitting the accurate diagnosis of thousands of different types of neoplasms using only a microscope and a trained eye. In the past two decades, cancer genomics has challenged the supremacy of histomorphology by identifying genetic alterations shared by morphologically diverse tumors and by finding genetic features that distinguish subgroups of morphologically homogeneous tumors. The Developmental Lineage Classification and Taxonomy of Neoplasms groups neoplasms by their embryologic origin. The putative value of this classification is based on the expectation that tumors of a common developmental lineage will share common metabolic pathways and common responses to drugs that target these pathways. The purpose of this manuscript is to show that grouping tumors according to their developmental lineage can reconcile certain fundamental discrepancies resulting from morphologic and molecular approaches to neoplasm classification. In this study, six issues in tumor classification are described that exemplify the growing rift between morphologic and molecular approaches to tumor classification: 1) the morphologic separation between epithelial and non-epithelial tumors; 2) the grouping of tumors based on shared cellular functions; 3) the distinction between germ cell tumors and pluripotent tumors of non-germ cell origin; 4) the distinction between tumors that have lost their differentiation and tumors that arise from uncommitted stem cells; 5) the molecular properties shared by morphologically disparate tumors that have a common developmental lineage, and 6) the problem of re-classifying morphologically identical but clinically distinct subsets of tumors. The discussion of these issues in the context of describing different methods of tumor classification is intended to underscore the clinical value of a robust tumor classification. A

  10. Lagrangian Multi-Class Traffic State Estimation

    NARCIS (Netherlands)

    Yuan, Y.

    2013-01-01

    Road traffic is important to everybody in the world. People travel and commute everyday. For those who travel by cars (or other types of road vehicles), traffic congestion is a daily experience. One essential goal of traffic researchers is to reduce traffic congestion and to improve the whole

  11. Conjunctival Melanocytic Tumors-New Developments

    Directory of Open Access Journals (Sweden)

    Hülya Gökmen Soysal

    2014-09-01

    Full Text Available Melanocytic lesions of the conjunctiva represent a wide spectrum of tumors that include benign, premalignant, and malignant tumors. There are many ongoing arguments about the definition, classification, and therapeutic options of the conjunctival melanocytic tumors with many different suggestions. Conjunctival nevi are the most common melanocytic tumors and their risk of malignant transformation is less than1%. Primary acquired melanosis (PAM histopathologically includes various types of lesions from increased melanin pigmentation without melanocyte proliferation to melanoma in situ and is accepted as a clinical definition, so that a new classification is recommended which is based on more objective criteria than before. Although conjunctival melanoma is seen rarely, it is associated with a high mortality rate. Management of these tumors mainly involves surgery and adjuvant topical chemotherapy, cryotherapy, and radiation therapy that help improving the survival, however, new options are being investigated related to genetic and molecular researches. (Turk J Ophthalmol 2014; 44: Supplement 15-21

  12. Tumoral tracers

    International Nuclear Information System (INIS)

    Camargo, E.E.

    1979-01-01

    Direct tumor tracers are subdivided in the following categories:metabolite tracers, antitumoral tracers, radioactive proteins and cations. Use of 67 Ga-citrate as a clinically important tumoral tracer is emphasized and gallium-67 whole-body scintigraphy is discussed in detail. (M.A.) [pt

  13. Animal tumors

    International Nuclear Information System (INIS)

    Gillette, E.L.

    1983-01-01

    There are few trained veterinary radiation oncologists and the expense of facilities has limited the extent to which this modality is used. In recent years, a few cobalt teletherapy units and megavoltage x-ray units have been employed in larger veterinary institutions. In addition, some radiation oncologists of human medical institutions are interested and willing to cooperate with veterinarians in the treatment of animal tumors. Carefully designed studies of the response of animal tumors to new modalities serve two valuable purposes. First, these studies may lead to improved tumor control in companion animals. Second, these studies may have important implications to the improvement of therapy of human tumors. Much remains to be learned of animal tumor biology so that appropriate model systems can be described for such studies. Many of the latter studies can be sponsored by agencies interested in the improvement of cancer management

  14. Supervised remote sensing image classification: An example of a ...

    African Journals Online (AJOL)

    These conventional multi-class classifiers/algorithms are usually written in programming languages such as C, C++, and python. The objective of this research is to experiment the use of a binary classifier/algorithm for multi-class remote sensing task, implemented in MATLAB. MATLAB is a programming language just like C ...

  15. Pathogenesis of Testicular Germ Cell Tumors from a Developmental Point of View

    NARCIS (Netherlands)

    K. Biermann (Katharina)

    2010-01-01

    textabstractCurrent classification systems of human germ cell tumors (GCTs) are based on histological composition. In the group of nonseminomas, different variants of teratoma (somatic differentiation), yolk sac tumor and choriocarcinoma (extra-embryonic differentiation), are recognized, as well

  16. A rare case report of an adenomatoid odontogenic tumor associated with odontoma in the maxilla

    Directory of Open Access Journals (Sweden)

    Agnes Assao

    2017-01-01

    Conclusions: Therefore, it is necessary similar cases to be published to increase the knowledge about the clinical behavior and evolution of this tumor, to enable such lesions to be more clearly defined in the next classification of odontogenic tumors.

  17. SAW Classification Algorithm for Chinese Text Classification

    OpenAIRE

    Xiaoli Guo; Huiyu Sun; Tiehua Zhou; Ling Wang; Zhaoyang Qu; Jiannan Zang

    2015-01-01

    Considering the explosive growth of data, the increased amount of text data’s effect on the performance of text categorization forward the need for higher requirements, such that the existing classification method cannot be satisfied. Based on the study of existing text classification technology and semantics, this paper puts forward a kind of Chinese text classification oriented SAW (Structural Auxiliary Word) algorithm. The algorithm uses the special space effect of Chinese text where words...

  18. CHONDROID SKULL BASE TUMORS (A REVIEW OF LITERATURE

    Directory of Open Access Journals (Sweden)

    T. G. Gasparyan

    2012-01-01

    Full Text Available Chondroid skull base tumors are a rare and little studied pathology; many problems of their classification, diagnosis and treatment remain to be solved. This group of neoplasms is referred to as bone tumors arising from the cartilaginous tissue of the skull base bones, particularly from the bones formed during chondral osteogenesis. The paper details the clinical picture, X-ray and morphological diagnosis of chondroid tumors. Particular attention is given to surgery and radiotherapy for this category of tumors.

  19. Progress in the diagnosis and classification of pituitary adenomas

    Directory of Open Access Journals (Sweden)

    Luis V Syro

    2015-06-01

    Full Text Available Pituitary adenomas are common neoplasms. Their classification is based upon size, invasion of adjacent structures, sporadic or familial cases, biochemical activity, clinical manifestations, morphological characteristics, response to treatment and recurrence. Although they are considered benign tumors, some of them are difficult to treat due to their tendency to recur, despite standardized treatment. Functional tumors present other challenges for normalizing their biochemical activity. Novel approaches for early diagnosis as well as different perspectives on classification may help to identify subgroups of patients with similar characteristics, creating opportunities to match each patient with the best personalized treatment option. In this paper we present the progress in the diagnosis and classification of different subgroups of patients with pituitary tumors that may be managed with specific considerations according to their tumor subtype.

  20. Training echo state networks for rotation-invariant bone marrow cell classification.

    Science.gov (United States)

    Kainz, Philipp; Burgsteiner, Harald; Asslaber, Martin; Ahammer, Helmut

    2017-01-01

    The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.

  1. Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

    Science.gov (United States)

    Ganguly, S.; Kumar, U.; Nemani, R. R.; Kalia, S.; Michaelis, A.

    2017-12-01

    In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS - national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91% was achieved, which is a 6% improvement in unmixing based classification relative to per-pixel based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

  2. Pituitary Tumors

    Science.gov (United States)

    ... Association (ABTA) International RadioSurgery Association National Brain Tumor Society National Institute of Child Health and Human Development ... Definition The pituitary is a small, bean-sized gland ...

  3. Hypothalamic tumor

    Science.gov (United States)

    ... in the brain to reduce spinal fluid pressure. Risks of radiation therapy include damage to healthy brain cells when tumor cells are destroyed. Common side effects from chemotherapy include loss of appetite, nausea and vomiting, and fatigue.

  4. Multi-class, multi-residue analysis of pesticides, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, polybrominated diphenyl ethers and novel flame retardants in fish using fast, low-pressure gas chromatography–tandem mass spectrometry

    International Nuclear Information System (INIS)

    Sapozhnikova, Yelena; Lehotay, Steven J.

    2013-01-01

    Highlights: ► A method for analysis of POPs and novel flame retardants in catfish was developed. ► The method is based on a QuEChERS extraction, d-SPE clean-up and low pressure GC/MS–MS. ► The method validation demonstrated good recoveries and low detection limits. ► The method was successfully applied for analysis of catfish samples from the market. - Abstract: A multi-class, multi-residue method for the analysis of 13 novel flame retardants, 18 representative pesticides, 14 polychlorinated biphenyl (PCB) congeners, 16 polycyclic aromatic hydrocarbons (PAHs), and 7 polybrominated diphenyl ether (PBDE) congeners in catfish muscle was developed and evaluated using fast low pressure gas chromatography triple quadrupole tandem mass spectrometry (LP-GC/MS–MS). The method was based on a QuEChERS (quick, easy, cheap, effective, rugged, safe) extraction with acetonitrile and dispersive solid-phase extraction (d-SPE) clean-up with zirconium-based sorbent prior to LP-GC/MS–MS analysis. The developed method was evaluated at 4 spiking levels and further validated by analysis of NIST Standard Reference Materials (SRMs) 1974B and 1947. Sample preparation for a batch of 10 homogenized samples took about 1 h/analyst, and LP-GC/MS–MS analysis provided fast separation of multiple analytes within 9 min achieving high throughput. With the use of isotopically labeled internal standards, recoveries of all but one analyte were between 70 and 120% with relative standard deviations less than 20% (n = 5). The measured values for both SRMs agreed with certified/reference values (72–119% accuracy) for the majority of analytes. The detection limits were 0.1–0.5 ng g −1 for PCBs, 0.5–10 ng g −1 for PBDEs, 0.5–5 ng g −1 for select pesticides and PAHs and 1–10 ng g −1 for flame retardants. The developed method was successfully applied for analysis of catfish samples from the market.

  5. Neuroendocrine Tumor, diagnostic difficulties

    Directory of Open Access Journals (Sweden)

    Pedro Oliveira

    2017-06-01

    Full Text Available Ectopic adrenocorticotropic hormone (ACTH secretion is a rare disease. A 51 years old woman, with a Cushing syndrome secondary to ectopic ACTH secretion, diagnosed in 2009, with mediastinal lymphadenopathy, whose biopsy was compatible with lung small cell carcinoma, staged as IIIB using TNM classification. No other lesions were found in patient study. The patient was submitted to chemotherapy, associated to ketoconazole 200 mg twice daily, with partial remission of both conditions. Three years later was admitted with an aggravation of Cushing syndrome. There was no evidence of progression of pulmonary disease. A cystic lesion in the pancreatic uncinated process was found by abdominal CT scan and with avid uptake by DOTANOC PET discreet in anterior mediastinal lymphadenopathy. Biopsy of pancreatic mass revealed a neuroendocrine tumor. Pulmonary masses were biopsied again and was in favor of neuroendocrine tumor. It was assumed the diagnosis of pancreatic neuroendocrine tumor with mediastinal metastasis. The patient initiated lanreotid (120 mg, monthly, subcutaneous in association with ketoconazole. After 5 months of therapy, patient died with sepsis secondary to pneumonia. Neuroendocrine tumours are rare, difficult to diagnose and with poor prognosis when associated with ectopic ACTH secreting Cushing syndrome.

  6. Asteroid taxonomic classifications

    International Nuclear Information System (INIS)

    Tholen, D.J.

    1989-01-01

    This paper reports on three taxonomic classification schemes developed and applied to the body of available color and albedo data. Asteroid taxonomic classifications according to two of these schemes are reproduced

  7. Tumor Types: Understanding Brain Tumors

    Science.gov (United States)

    ... May cause excessive secretion of hormones Common among men and women in their 50s-80s Accounts for about 13 percent of all brain tumors Symptoms Headache Depression Vision loss Nausea or vomiting Behavioral and cognitive ...

  8. Clinicopathological Spectrum of Ovarian Tumors: A 5‑Year ...

    African Journals Online (AJOL)

    of ovarian cancer and breast cancer,[4] and mutation of. BRCA1 and/or BRCA2. ... Among these, mucinous cystadenoma (42 cases/138 cases,. 30.4%) was the ..... Tavassoli FA, Devillee P. World Health Organisation Classification of. Tumors.

  9. Hand eczema classification

    DEFF Research Database (Denmark)

    Diepgen, T L; Andersen, Klaus Ejner; Brandao, F M

    2008-01-01

    of the disease is rarely evidence based, and a classification system for different subdiagnoses of hand eczema is not agreed upon. Randomized controlled trials investigating the treatment of hand eczema are called for. For this, as well as for clinical purposes, a generally accepted classification system...... A classification system for hand eczema is proposed. Conclusions It is suggested that this classification be used in clinical work and in clinical trials....

  10. Brainstem tumors: Current management and future directions

    Directory of Open Access Journals (Sweden)

    Pablo F Recinos

    2012-01-01

    Full Text Available Tumors arising in the brainstem comprise 10-20% of all pediatric central nervous system (CNS tumors and account for a small percentage in adults. The prognosis for these tumors was considered uniformly poor prior to the era of modern neuroimaging and the location was fraught with disaster being considered a ′no man′s land′ for neurosurgeons. Following the introduction of advanced imaging modalities and neurophysiological monitoring, striking progress has occurred in the management of these lesions. Brainstem tumors are presently classified based on their anatomic location, focality, and histopathology. This article reviews the current classification of brainstem tumors, current management options, and future directions in the treatment for these rare tumors.

  11. Classification with support hyperplanes

    NARCIS (Netherlands)

    G.I. Nalbantov (Georgi); J.C. Bioch (Cor); P.J.F. Groenen (Patrick)

    2006-01-01

    textabstractA new classification method is proposed, called Support Hy- perplanes (SHs). To solve the binary classification task, SHs consider the set of all hyperplanes that do not make classification mistakes, referred to as semi-consistent hyperplanes. A test object is classified using

  12. Standard classification: Physics

    International Nuclear Information System (INIS)

    1977-01-01

    This is a draft standard classification of physics. The conception is based on the physics part of the systematic catalogue of the Bayerische Staatsbibliothek and on the classification given in standard textbooks. The ICSU-AB classification now used worldwide by physics information services was not taken into account. (BJ) [de

  13. Tumor immunology.

    Science.gov (United States)

    Mocellin, Simone; Lise, Mario; Nitti, Donato

    2007-01-01

    Advances in tumor immunology are supporting the clinical implementation of several immunological approaches to cancer in the clinical setting. However, the alternate success of current immunotherapeutic regimens underscores the fact that the molecular mechanisms underlying immune-mediated tumor rejection are still poorly understood. Given the complexity of the immune system network and the multidimensionality of tumor/host interactions, the comprehension of tumor immunology might greatly benefit from high-throughput microarray analysis, which can portrait the molecular kinetics of immune response on a genome-wide scale, thus accelerating the discovery pace and ultimately catalyzing the development of new hypotheses in cell biology. Although in its infancy, the implementation of microarray technology in tumor immunology studies has already provided investigators with novel data and intriguing new hypotheses on the molecular cascade leading to an effective immune response against cancer. Although the general principles of microarray-based gene profiling have rapidly spread in the scientific community, the need for mastering this technique to produce meaningful data and correctly interpret the enormous output of information generated by this technology is critical and represents a tremendous challenge for investigators, as outlined in the first section of this book. In the present Chapter, we report on some of the most significant results obtained with the application of DNA microarray in this oncology field.

  14. Pancreatic islet cell tumor

    Science.gov (United States)

    ... cell tumors; Islet of Langerhans tumor; Neuroendocrine tumors; Peptic ulcer - islet cell tumor; Hypoglycemia - islet cell tumor ... stomach acid. Symptoms may include: Abdominal pain Diarrhea ... and small bowel Vomiting blood (occasionally) Glucagonomas make ...

  15. Classification of refrigerants; Classification des fluides frigorigenes

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2001-07-01

    This document was made from the US standard ANSI/ASHRAE 34 published in 2001 and entitled 'designation and safety classification of refrigerants'. This classification allows to clearly organize in an international way the overall refrigerants used in the world thanks to a codification of the refrigerants in correspondence with their chemical composition. This note explains this codification: prefix, suffixes (hydrocarbons and derived fluids, azeotropic and non-azeotropic mixtures, various organic compounds, non-organic compounds), safety classification (toxicity, flammability, case of mixtures). (J.S.)

  16. Identification of high-risk cutaneous melanoma tumors is improved when combining the online American Joint Committee on Cancer Individualized Melanoma Patient Outcome Prediction Tool with a 31-gene expression profile-based classification.

    Science.gov (United States)

    Ferris, Laura K; Farberg, Aaron S; Middlebrook, Brooke; Johnson, Clare E; Lassen, Natalie; Oelschlager, Kristen M; Maetzold, Derek J; Cook, Robert W; Rigel, Darrell S; Gerami, Pedram

    2017-05-01

    A significant proportion of patients with American Joint Committee on Cancer (AJCC)-defined early-stage cutaneous melanoma have disease recurrence and die. A 31-gene expression profile (GEP) that accurately assesses metastatic risk associated with primary cutaneous melanomas has been described. We sought to compare accuracy of the GEP in combination with risk determined using the web-based AJCC Individualized Melanoma Patient Outcome Prediction Tool. GEP results from 205 stage I/II cutaneous melanomas with sufficient clinical data for prognostication using the AJCC tool were classified as low (class 1) or high (class 2) risk. Two 5-year overall survival cutoffs (AJCC 79% and 68%), reflecting survival for patients with stage IIA or IIB disease, respectively, were assigned for binary AJCC risk. Cox univariate analysis revealed significant risk classification of distant metastasis-free and overall survival (hazard ratio range 3.2-9.4, P risk by GEP but low risk by AJCC. Specimens reflect tertiary care center referrals; more effective therapies have been approved for clinical use after accrual. The GEP provides valuable prognostic information and improves identification of high-risk melanomas when used together with the AJCC online prediction tool. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

  17. Imaging of brain tumors

    International Nuclear Information System (INIS)

    Gaensler, E.H.L.

    1995-01-01

    The contents are diagnostic approaches, general features of tumors -hydrocephalus, edema, attenuation and/or intensity value, hemorrhage, fat, contrast enhancement, intra-axial supratentorial tumors - tumors of glial origin, oligodendrogliomas, ependymomas, subependymomas, subependymal giant cell astrocytomas, choroid plexus papilloma; midline tumors - colloid cysts, craniopharyngiomas; pineal region tumors and miscellaneous tumors i.e. primary intracerebral lymphoma, primitive neuroectodermal tumors, hemangioblastomas; extraaxial tumors - meningiomas; nerve sheath tumors -schwannomas, epidermoids, dermoids, lipomas, arachnoid cysts; metastatic tumors (8 refs.)

  18. Imaging of brain tumors

    Energy Technology Data Exchange (ETDEWEB)

    Gaensler, E H.L. [California Univ., San Francisco, CA (United States). Dept. of Radiology

    1996-12-31

    The contents are diagnostic approaches, general features of tumors -hydrocephalus, edema, attenuation and/or intensity value, hemorrhage, fat, contrast enhancement, intra-axial supratentorial tumors - tumors of glial origin, oligodendrogliomas, ependymomas, subependymomas, subependymal giant cell astrocytomas, choroid plexus papilloma; midline tumors - colloid cysts, craniopharyngiomas; pineal region tumors and miscellaneous tumors i.e. primary intracerebral lymphoma, primitive neuroectodermal tumors, hemangioblastomas; extraaxial tumors - meningiomas; nerve sheath tumors -schwannomas, epidermoids, dermoids, lipomas, arachnoid cysts; metastatic tumors (8 refs.).

  19. Classification, disease, and diagnosis.

    Science.gov (United States)

    Jutel, Annemarie

    2011-01-01

    Classification shapes medicine and guides its practice. Understanding classification must be part of the quest to better understand the social context and implications of diagnosis. Classifications are part of the human work that provides a foundation for the recognition and study of illness: deciding how the vast expanse of nature can be partitioned into meaningful chunks, stabilizing and structuring what is otherwise disordered. This article explores the aims of classification, their embodiment in medical diagnosis, and the historical traditions of medical classification. It provides a brief overview of the aims and principles of classification and their relevance to contemporary medicine. It also demonstrates how classifications operate as social framing devices that enable and disable communication, assert and refute authority, and are important items for sociological study.

  20. Bone tumors

    International Nuclear Information System (INIS)

    Moylan, D.J.; Yelovich, R.M.

    1991-01-01

    Primary bone malignancies are relatively rare with less than 4,000 new cases per year. Multiple myeloma (more correctly a hematologic malignancy) accounts for 40%; osteosarcomas, 28%; chondrosarcomas, 13%; fibrosarcomas arising in bone, 4%; and Ewing's sarcoma, 7%. The authors discuss various treatments for bone tumors, including radiotherapy, chemotherapy and surgery

  1. Wilms Tumor

    Science.gov (United States)

    ... a child's general health and to detect any adverse side effects (such as low red or white blood cell ... medicine needed, which helps reduce long-term side effects. The most common ... can be completely removed by surgery. About 41% of all Wilms tumors are stage ...

  2. Nephrogenic tumors

    International Nuclear Information System (INIS)

    Wiesbauer, P.

    2008-01-01

    Nephroblastomas are the most common malignant renal tumors in childhood. According to the guidelines of the SIOP (Societe Internationale d'Oncologie Pediatrique) and GPOH (Gesellschaft fuer Paediatrische Onkologie und Haematologie) pre-operative chemotherapy can be started without histological confirmation and thus initial imaging studies, in particular ultrasound, play an outstanding role for diagnostic purposes

  3. Exploring diversity in ensemble classification: Applications in large area land cover mapping

    Science.gov (United States)

    Mellor, Andrew; Boukir, Samia

    2017-07-01

    Ensemble classifiers, such as random forests, are now commonly applied in the field of remote sensing, and have been shown to perform better than single classifier systems, resulting in reduced generalisation error. Diversity across the members of ensemble classifiers is known to have a strong influence on classification performance - whereby classifier errors are uncorrelated and more uniformly distributed across ensemble members. The relationship between ensemble diversity and classification performance has not yet been fully explored in the fields of information science and machine learning and has never been examined in the field of remote sensing. This study is a novel exploration of ensemble diversity and its link to classification performance, applied to a multi-class canopy cover classification problem using random forests and multisource remote sensing and ancillary GIS data, across seven million hectares of diverse dry-sclerophyll dominated public forests in Victoria Australia. A particular emphasis is placed on analysing the relationship between ensemble diversity and ensemble margin - two key concepts in ensemble learning. The main novelty of our work is on boosting diversity by emphasizing the contribution of lower margin instances used in the learning process. Exploring the influence of tree pruning on diversity is also a new empirical analysis that contributes to a better understanding of ensemble performance. Results reveal insights into the trade-off between ensemble classification accuracy and diversity, and through the ensemble margin, demonstrate how inducing diversity by targeting lower margin training samples is a means of achieving better classifier performance for more difficult or rarer classes and reducing information redundancy in classification problems. Our findings inform strategies for collecting training data and designing and parameterising ensemble classifiers, such as random forests. This is particularly important in large area

  4. Security classification of information

    Energy Technology Data Exchange (ETDEWEB)

    Quist, A.S.

    1993-04-01

    This document is the second of a planned four-volume work that comprehensively discusses the security classification of information. The main focus of Volume 2 is on the principles for classification of information. Included herein are descriptions of the two major types of information that governments classify for national security reasons (subjective and objective information), guidance to use when determining whether information under consideration for classification is controlled by the government (a necessary requirement for classification to be effective), information disclosure risks and benefits (the benefits and costs of classification), standards to use when balancing information disclosure risks and benefits, guidance for assigning classification levels (Top Secret, Secret, or Confidential) to classified information, guidance for determining how long information should be classified (classification duration), classification of associations of information, classification of compilations of information, and principles for declassifying and downgrading information. Rules or principles of certain areas of our legal system (e.g., trade secret law) are sometimes mentioned to .provide added support to some of those classification principles.

  5. A hierarchical anatomical classification schema for prediction of phenotypic side effects.

    Science.gov (United States)

    Wadhwa, Somin; Gupta, Aishwarya; Dokania, Shubham; Kanji, Rakesh; Bagler, Ganesh

    2018-01-01

    Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects. We highlight the intrinsic issue with SIDER data and methodological flaws in relying on performance measures such as AUC while attempting to predict side effects.We argue for the use of metrics that are robust to class imbalance for evaluation of classifiers. Importantly, we present a 'hierarchical anatomical classification schema' which aggregates side effects into organs, sub-systems, and systems. With the help of a weighted performance measure, using 5-fold cross-validation we show that this strategy facilitates biologically meaningful side effects prediction at different levels of anatomical hierarchy. By implementing various machine learning classifiers we show that Random Forest model yields best classification accuracy at each level of coarse-graining. The manually curated, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects.

  6. The World Health Organization Classification of dontogenic Lesions: A Summary of the Changes of the 2017 (4th Edition

    Directory of Open Access Journals (Sweden)

    Merva SOLUK-TEKKEŞİN

    2018-01-01

    Full Text Available The 4th edition of the World Health Organization (WHO Classification of Head and Neck Tumors was published in January 2017. The edition serves to provide an updated classification scheme, and extended genetic and molecular data that are useful as diagnostic tools for the lesions of the head and neck region. This review focuses on the most current update of odontogenic cysts and tumors based on the 2017 WHO edition. The updated classification has some important differences from the 3rd edition (2005, including a new classification of odontogenic cysts, ‘reclassified’ odontogenic tumors, and some new entities.

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

    Science.gov (United States)

    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

  8. Classification of Flotation Frothers

    Directory of Open Access Journals (Sweden)

    Jan Drzymala

    2018-02-01

    Full Text Available In this paper, a scheme of flotation frothers classification is presented. The scheme first indicates the physical system in which a frother is present and four of them i.e., pure state, aqueous solution, aqueous solution/gas system and aqueous solution/gas/solid system are distinguished. As a result, there are numerous classifications of flotation frothers. The classifications can be organized into a scheme described in detail in this paper. The frother can be present in one of four physical systems, that is pure state, aqueous solution, aqueous solution/gas and aqueous solution/gas/solid system. It results from the paper that a meaningful classification of frothers relies on choosing the physical system and next feature, trend, parameter or parameters according to which the classification is performed. The proposed classification can play a useful role in characterizing and evaluation of flotation frothers.

  9. "Cancer tumor".

    Science.gov (United States)

    Bronshtehn, V. A.

    The title is a phrase borrowed from a speech by a Leningrad pressman, V. E. Lvov, who called upon those attending a theoretical conference on ideological issues in astronomy held by the Leningrad Branch of the All-Union Astronomic and Geodetic Society (13 - 4 December 1948), "to make a more radical emphasis on the negative role of relativistic cosmology which is a cancer tumor disintegrating the contemporary astronomy theory, and a major ideological enemy of a materialist astronomy".

  10. Brain's tumor image processing using shearlet transform

    Science.gov (United States)

    Cadena, Luis; Espinosa, Nikolai; Cadena, Franklin; Korneeva, Anna; Kruglyakov, Alexey; Legalov, Alexander; Romanenko, Alexey; Zotin, Alexander

    2017-09-01

    Brain tumor detection is well known research area for medical and computer scientists. In last decades there has been much research done on tumor detection, segmentation, and classification. Medical imaging plays a central role in the diagnosis of brain tumors and nowadays uses methods non-invasive, high-resolution techniques, especially magnetic resonance imaging and computed tomography scans. Edge detection is a fundamental tool in image processing, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image has discontinuities. Shearlets is the most successful frameworks for the efficient representation of multidimensional data, capturing edges and other anisotropic features which frequently dominate multidimensional phenomena. The paper proposes an improved brain tumor detection method by automatically detecting tumor location in MR images, its features are extracted by new shearlet transform.

  11. Apocrine hidradenocarcinoma of the scalp: a classification conundrum.

    Science.gov (United States)

    Cohen, Marc; Cassarino, David S; Shih, Hubert B; Abemayor, Elliot; St John, Maie

    2009-03-01

    The classification of malignant sweat gland lesions is complex. Traditionally, cutaneous sweat gland tumors have been classified by either eccrine or apocrine features. A case report of a 33-year-old Hispanic man with a left scalp mass diagnosed as a malignancy of adnexal origin preoperatively is discussed. After presentation at our multidisciplinary tumor board, excision with ipsilateral neck dissection was undertaken. Final pathology revealed an apocrine hidradenocarcinoma. The classification and behavior of this entity are discussed in this report. Apocrine hidradenocarcinoma can be viewed as an aggressive malignant lesion of cutaneous sweat glands on a spectrum that involves both eccrine and apoeccrine lesions.

  12. Ontologies vs. Classification Systems

    DEFF Research Database (Denmark)

    Madsen, Bodil Nistrup; Erdman Thomsen, Hanne

    2009-01-01

    What is an ontology compared to a classification system? Is a taxonomy a kind of classification system or a kind of ontology? These are questions that we meet when working with people from industry and public authorities, who need methods and tools for concept clarification, for developing meta...... data sets or for obtaining advanced search facilities. In this paper we will present an attempt at answering these questions. We will give a presentation of various types of ontologies and briefly introduce terminological ontologies. Furthermore we will argue that classification systems, e.g. product...... classification systems and meta data taxonomies, should be based on ontologies....

  13. Clinicopathological characteristics of duodenal epithelial neoplasms: Focus on tumors with a gastric mucin phenotype (pyloric gland-type tumors.

    Directory of Open Access Journals (Sweden)

    Takehiro Mitsuishi

    Full Text Available Epithelial tumors less commonly occur in the duodenum than in the stomach or large intestine. The clinicopathological characteristics of duodenal epithelial tumors remain a matter of debate. We therefore studied resected specimens to investigate the clinicopathological characteristics of duodenal epithelial tumors.Among duodenal epithelial tumors resected endoscopically or surgically in our hospital, we studied the clinicopathological characteristics of 110 adenomas or intramucosal carcinomas. The grade of atypia of all tumors was classified into 3 groups according to the World Health Organization (WHO 2010 classification. The tumors were immunohistochemically evaluated to determine the frequency of differentiation toward fundic glands.As for patient characteristics, there were 76 men (75.2% and 25 women (24.8%, with a median age of 65 years (range, 34 to 84. The tumors most commonly arose in the first to second part of the duodenum. Many lesions were flat, and the median tumor diameter was 8.0 mm. The lesions were classified into 2 types according to mucin phenotype: intestinal-type tumors (98 lesions, 89.1% and gastric-type tumors (12 lesions, 10.9%. Intestinal-type tumors were subdivided into 2 groups: tubular-type tumors (91 lesions, 82.7% and tubulovillous-type tumors (7 lesions, 6.4%. Gastric-type tumors were classified into 2 types: foveolar type (3 lesions, 2.7% and pyloric gland-type (PG tumors (9 lesions, 8.2%. The grade of atypia was significantly higher in gastric-type tumors (p<0.01. PG tumors were gastric-type tumors characterized by pyloric glands and findings suggesting differentiation toward fundic glands.About 10% of the duodenal tumors had a gastric-type mucin phenotype. Gastric-type tumors showed high-grade atypia. In particular, PG tumors showed similarities to PG tumors of the stomach, such as differentiation toward fundic glands.

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

    Directory of Open Access Journals (Sweden)

    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. Understanding Brain Tumors

    Science.gov (United States)

    ... to Know About Brain Tumors . What is a Brain Tumor? A brain tumor is an abnormal growth
 ... Tumors” from Frankly Speaking Frankly Speaking About Cancer: Brain Tumors Download the full book Questions to ask ...

  16. Brain tumor - primary - adults

    Science.gov (United States)

    ... Vestibular schwannoma (acoustic neuroma) - adults; Meningioma - adults; Cancer - brain tumor (adults) ... Primary brain tumors include any tumor that starts in the brain. Primary brain tumors can start from brain cells, ...

  17. Brain tumor - children

    Science.gov (United States)

    ... children; Neuroglioma - children; Oligodendroglioma - children; Meningioma - children; Cancer - brain tumor (children) ... The cause of primary brain tumors is unknown. Primary brain tumors may ... (spread to nearby areas) Cancerous (malignant) Brain tumors ...

  18. Adrenal Gland Tumors: Statistics

    Science.gov (United States)

    ... Gland Tumor: Statistics Request Permissions Adrenal Gland Tumor: Statistics Approved by the Cancer.Net Editorial Board , 03/ ... primary adrenal gland tumor is very uncommon. Exact statistics are not available for this type of tumor ...

  19. Pediatric brain tumors

    Energy Technology Data Exchange (ETDEWEB)

    Poussaint, Tina Y. [Department of Radiology, Boston, MA (United States); Panigrahy, Ashok [Children' s Hospital of Pittsburgh of University of Pittsburgh Medical Center, Department of Radiology, Pittsburgh, PA (United States); Huisman, Thierry A.G.M. [Charlotte R. Bloomberg Children' s Center, Johns Hopkins Hospital, Division of Pediatric Radiology and Pediatric Neuroradiology, Baltimore, MD (United States)

    2015-09-15

    Among all causes of death in children from solid tumors, pediatric brain tumors are the most common. This article includes an overview of a subset of infratentorial and supratentorial tumors with a focus on tumor imaging features and molecular advances and treatments of these tumors. Key to understanding the imaging features of brain tumors is a firm grasp of other disease processes that can mimic tumor on imaging. We also review imaging features of a common subset of tumor mimics. (orig.)

  20. Apocrine Hidradenocarcinoma of the Scalp: A Classification Conundrum

    OpenAIRE

    Cohen, Marc; Cassarino, David S.; Shih, Hubert B.; Abemayor, Elliot; John, Maie St.

    2008-01-01

    Introduction The classification of malignant sweat gland lesions is complex. Traditionally, cutaneous sweat gland tumors have been classified by either eccrine or apocrine features. Methods A case report of a 33-year-old Hispanic man with a left scalp mass diagnosed as a malignancy of adnexal origin preoperatively is discussed. After presentation at our multidisciplinary tumor board, excision with ipsilateral neck dissection was undertaken. Results Final pathology revealed an apocrine hidrade...

  1. Classification of radiological procedures

    International Nuclear Information System (INIS)

    1989-01-01

    A classification for departments in Danish hospitals which use radiological procedures. The classification codes consist of 4 digits, where the first 2 are the codes for the main groups. The first digit represents the procedure's topographical object and the second the techniques. The last 2 digits describe individual procedures. (CLS)

  2. Colombia: Territorial classification

    International Nuclear Information System (INIS)

    Mendoza Morales, Alberto

    1998-01-01

    The article is about the approaches of territorial classification, thematic axes, handling principles and territorial occupation, politician and administrative units and administration regions among other topics. Understanding as Territorial Classification the space distribution on the territory of the country, of the geographical configurations, the human communities, the political-administrative units and the uses of the soil, urban and rural, existent and proposed

  3. Munitions Classification Library

    Science.gov (United States)

    2016-04-04

    members of the community to make their own additions to any, or all, of the classification libraries . The next phase entailed data collection over less......Include area code) 04/04/2016 Final Report August 2014 - August 2015 MUNITIONS CLASSIFICATION LIBRARY Mr. Craig Murray, Parsons Dr. Thomas H. Bell, Leidos

  4. Recursive automatic classification algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Bauman, E V; Dorofeyuk, A A

    1982-03-01

    A variational statement of the automatic classification problem is given. The dependence of the form of the optimal partition surface on the form of the classification objective functional is investigated. A recursive algorithm is proposed for maximising a functional of reasonably general form. The convergence problem is analysed in connection with the proposed algorithm. 8 references.

  5. Library Classification 2020

    Science.gov (United States)

    Harris, Christopher

    2013-01-01

    In this article the author explores how a new library classification system might be designed using some aspects of the Dewey Decimal Classification (DDC) and ideas from other systems to create something that works for school libraries in the year 2020. By examining what works well with the Dewey Decimal System, what features should be carried…

  6. Spectroscopic classification of transients

    DEFF Research Database (Denmark)

    Stritzinger, M. D.; Fraser, M.; Hummelmose, N. N.

    2017-01-01

    We report the spectroscopic classification of several transients based on observations taken with the Nordic Optical Telescope (NOT) equipped with ALFOSC, over the nights 23-25 August 2017.......We report the spectroscopic classification of several transients based on observations taken with the Nordic Optical Telescope (NOT) equipped with ALFOSC, over the nights 23-25 August 2017....

  7. Testis tumors

    International Nuclear Information System (INIS)

    White, R.L.; Maier, J.G.

    1987-01-01

    Clinical trials are evaluating new combinations of drugs with the goal of diminishing the toxicity associated with the current regimens while not compromising the chance for cure. The evolution of information and staging studies such as tumor markers, CT scanning and MR scanning has made possible the detection of residual metastatic disease while obviating the need for surgical staging procedures. This has made less treatment possible for a large number of patients. The regularity of follow-up studies has made early detection of recurrences a possibility, so that effective and curative treatment is generally possible

  8. DOE LLW classification rationale

    International Nuclear Information System (INIS)

    Flores, A.Y.

    1991-01-01

    This report was about the rationale which the US Department of Energy had with low-level radioactive waste (LLW) classification. It is based on the Nuclear Regulatory Commission's classification system. DOE site operators met to review the qualifications and characteristics of the classification systems. They evaluated performance objectives, developed waste classification tables, and compiled dose limits on the waste. A goal of the LLW classification system was to allow each disposal site the freedom to develop limits to radionuclide inventories and concentrations according to its own site-specific characteristics. This goal was achieved with the adoption of a performance objectives system based on a performance assessment, with site-specific environmental conditions and engineered disposal systems

  9. Constructing criticality by classification

    DEFF Research Database (Denmark)

    Machacek, Erika

    2017-01-01

    " in the bureaucratic practice of classification: Experts construct material criticality in assessments as they allot information on the materials to the parameters of the assessment framework. In so doing, they ascribe a new set of connotations to the materials, namely supply risk, and their importance to clean energy......, legitimizing a criticality discourse.Specifically, the paper introduces a typology delineating the inferences made by the experts from their produced recommendations in the classification of rare earth element criticality. The paper argues that the classification is a specific process of constructing risk....... It proposes that the expert bureaucratic practice of classification legitimizes (i) the valorisation that was made in the drafting of the assessment framework for the classification, and (ii) political operationalization when enacted that might have (non-)distributive implications for the allocation of public...

  10. Teratoid Wilms′ tumor - A rare renal tumor

    Directory of Open Access Journals (Sweden)

    Biswanath Mukhopadhyay

    2011-01-01

    Full Text Available Teratoid Wilms′ tumor is an extremely rare renal tumor. We report a case of unilateral teratoid Wilms′ tumor in a 4-year-old girl. The patient was admitted with a right-sided abdominal mass. The mass was arising from the right kidney. Radical nephrectomy was done and the patient had an uneventful recovery. Histopathology report showed teratoid Wilms′ tumor.

  11. Classification across gene expression microarray studies

    Directory of Open Access Journals (Sweden)

    Kuner Ruprecht

    2009-12-01

    Full Text Available Abstract Background The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive and histological grade (low/high of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM, predictive analysis of microarrays (PAM, random forest (RF and k-top scoring pairs (kTSP. Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing. Results For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In

  12. Automatic classification of endogenous seismic sources within a landslide body using random forest algorithm

    Science.gov (United States)

    Provost, Floriane; Hibert, Clément; Malet, Jean-Philippe; Stumpf, André; Doubre, Cécile

    2016-04-01

    Different studies have shown the presence of microseismic activity in soft-rock landslides. The seismic signals exhibit significantly different features in the time and frequency domains which allow their classification and interpretation. Most of the classes could be associated with different mechanisms of deformation occurring within and at the surface (e.g. rockfall, slide-quake, fissure opening, fluid circulation). However, some signals remain not fully understood and some classes contain few examples that prevent any interpretation. To move toward a more complete interpretation of the links between the dynamics of soft-rock landslides and the physical processes controlling their behaviour, a complete catalog of the endogeneous seismicity is needed. We propose a multi-class detection method based on the random forests algorithm to automatically classify the source of seismic signals. Random forests is a supervised machine learning technique that is based on the computation of a large number of decision trees. The multiple decision trees are constructed from training sets including each of the target classes. In the case of seismic signals, these attributes may encompass spectral features but also waveform characteristics, multi-stations observations and other relevant information. The Random Forest classifier is used because it provides state-of-the-art performance when compared with other machine learning techniques (e.g. SVM, Neural Networks) and requires no fine tuning. Furthermore it is relatively fast, robust, easy to parallelize, and inherently suitable for multi-class problems. In this work, we present the first results of the classification method applied to the seismicity recorded at the Super-Sauze landslide between 2013 and 2015. We selected a dozen of seismic signal features that characterize precisely its spectral content (e.g. central frequency, spectrum width, energy in several frequency bands, spectrogram shape, spectrum local and global maxima

  13. Tumor Macroenvironment and Metabolism

    OpenAIRE

    Al-Zhoughbi, Wael; Huang, Jianfeng; Paramasivan, Ganapathy S.; Till, Holger; Pichler, Martin; Guertl-Lackner, Barbara; Hoefler, Gerald

    2014-01-01

    In this review we introduce the concept of the tumor macroenvironment and explore it in the context of metabolism. Tumor cells interact with the tumor microenvironment including immune cells. Blood and lymph vessels are the critical components that deliver nutrients to the tumor and also connect the tumor to the macroenvironment. Several factors are then released from the tumor itself but potentially also from the tumor microenvironment, influencing the metabolism of distant tissues and organ...

  14. Classification of movement disorders.

    Science.gov (United States)

    Fahn, Stanley

    2011-05-01

    The classification of movement disorders has evolved. Even the terminology has shifted, from an anatomical one of extrapyramidal disorders to a phenomenological one of movement disorders. The history of how this shift came about is described. The history of both the definitions and the classifications of the various neurologic conditions is then reviewed. First is a review of movement disorders as a group; then, the evolving classifications for 3 of them--parkinsonism, dystonia, and tremor--are covered in detail. Copyright © 2011 Movement Disorder Society.

  15. Imaging of urinary bladder tumors

    International Nuclear Information System (INIS)

    Hadjidekov, G.

    2015-01-01

    Full text: Primary bladder neoplasms account for 2%-6% of all tumors, with urinary bladder cancer ranked as the fourth most common cancer in males. Transitional cell carcinoma (TCC) is the most common subtype of urothelial tumour accounting for approximately 90% of all urothelial cancers. It is typically observed in men aged 50-70 years with history of smoking or occupational exposure to carcinogens. Most urothelial neoplasms are low-grade papillary tumors, with high incidence of recurrence, requires rigorous follow-up but have a relatively good prognosis. Other bladder neoplasm include squamous cell carcinoma accounts for 2%-15% mainly according to geographic location; adenocarcinoma - less than 2% /both occurring in the context of chronic bladder infection and irritation/; mesenchymal tumors in 5%, with the most common examples being rhabdomyosarcoma in children and leiomyosarcoma in adults. More rare mesenchymal tumors include paraganglioma, lymphoma, leiomyoma and solitary fibrous tumor which have no specific typical imaging findings to be differentiated. Multidetector computed tomography urography is an efficient tool for diagnosis and follow-up in patients with transitional cell carcinoma and it can be considered the primary radiologic method for detection, staging and assessment of the entire urothelium regarding the multicentric nature of TCC. MRI is rapidly expanding modality of choice especially in locally staging the tumor and in controversies. Accurate TNM staging is primordial in choosing treatment and prognosis for patients with bladder carcinoma. Correct interpretation and classification of the tumour is helpful for the urologists to determine further management in these cases. The learning objectives of the presentation are: to illustrate the spectrum of CT and MRI findings and to assess their clinical value in patients with transitional cell carcinoma and some other bladder neoplasm; to discuss the TNM staging based on the imaging findings; to be

  16. Prevalence profile of odontogenic cysts and tumors on Brazilian sample after the reclassification of odontogenic keratocyst.

    Science.gov (United States)

    Jaeger, Filipe; de Noronha, Mariana Saturnino; Silva, Maiza Luiza Vieira; Amaral, Márcio Bruno Figueiredo; Grossmann, Soraya de Mattos Carmago; Horta, Martinho Campolina Rebello; de Souza, Paulo Eduardo Alencar; de Aguiar, Maria Cássia Ferreira; Mesquita, Ricardo Alves

    2017-02-01

    The aim of this study was to evaluate the impact of the reclassification of odontogenic keratocyst (OKC) as a tumor on the prevalence profile of odontogenic cysts (OCs) and odontogenic tumors (OTs). Two referral Oral and Maxillofacial Pathology services in Brazil were evaluated. All cases diagnosed as OCs or OTs were selected and classified according to the 1992 WHO-classification (cases before 2005 WHO classification of tumors excluding OKC) and the 2005 WHO classification of tumors, going forward including cases of odontogenic keratocyst tumor (KCOT). The frequency and prevalence of OCs and OTs were compared before and after the reclassification. Among 27,854 oral biopsies, 4920 (17.66%) were OCs and 992 (3.56%) were OTs. The prevalence of OTs before 2005 WHO classification of tumors was 2.04%, while the prevalence after 2005 WHO classification was 11.51% (p < 0.0001). Before 2006, the most frequent tumor diagnosed was odontoma with 194 cases (39.67%), and after 2005 WHO classification of tumors the KCOT was the most frequent with 207 cases (41.07%). The increase in the prevalence of OTs after 2005 WHO is related to the improvement of pathology services and to the inclusion of KCOT in the OTs group. Copyright © 2016 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

  17. Structural knowledge learning from maps for supervised land cover/use classification: Application to the monitoring of land cover/use maps in French Guiana

    Science.gov (United States)

    Bayoudh, Meriam; Roux, Emmanuel; Richard, Gilles; Nock, Richard

    2015-03-01

    The number of satellites and sensors devoted to Earth observation has become increasingly elevated, delivering extensive data, especially images. At the same time, the access to such data and the tools needed to process them has considerably improved. In the presence of such data flow, we need automatic image interpretation methods, especially when it comes to the monitoring and prediction of environmental and societal changes in highly dynamic socio-environmental contexts. This could be accomplished via artificial intelligence. The concept described here relies on the induction of classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system, combined with a multi-class classification procedure. This methodology was used to monitor changes in land cover/use of the French Guiana coastline. One hundred and fifty-eight classification rules were induced from 3 diachronic land cover/use maps including 38 classes. These rules were expressed in first order logic language, which makes them easily understandable by non-experts. A 10-fold cross-validation gave significant average values of 84.62%, 99.57% and 77.22% for classification accuracy, specificity and sensitivity, respectively. Our methodology could be beneficial to automatically classify new objects and to facilitate object-based classification procedures.

  18. Update on diabetes classification.

    Science.gov (United States)

    Thomas, Celeste C; Philipson, Louis H

    2015-01-01

    This article highlights the difficulties in creating a definitive classification of diabetes mellitus in the absence of a complete understanding of the pathogenesis of the major forms. This brief review shows the evolving nature of the classification of diabetes mellitus. No classification scheme is ideal, and all have some overlap and inconsistencies. The only diabetes in which it is possible to accurately diagnose by DNA sequencing, monogenic diabetes, remains undiagnosed in more than 90% of the individuals who have diabetes caused by one of the known gene mutations. The point of classification, or taxonomy, of disease, should be to give insight into both pathogenesis and treatment. It remains a source of frustration that all schemes of diabetes mellitus continue to fall short of this goal. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Learning Apache Mahout classification

    CERN Document Server

    Gupta, Ashish

    2015-01-01

    If you are a data scientist who has some experience with the Hadoop ecosystem and machine learning methods and want to try out classification on large datasets using Mahout, this book is ideal for you. Knowledge of Java is essential.

  20. CLASSIFICATION OF VIRUSES

    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. CLASSIFICATION OF VIRUSES. On basis of morphology. On basis of chemical composition. On basis of structure of genome. On basis of mode of replication. Notes:

  1. Pitch Based Sound Classification

    DEFF Research Database (Denmark)

    Nielsen, Andreas Brinch; Hansen, Lars Kai; Kjems, U

    2006-01-01

    A sound classification model is presented that can classify signals into music, noise and speech. The model extracts the pitch of the signal using the harmonic product spectrum. Based on the pitch estimate and a pitch error measure, features are created and used in a probabilistic model with soft......-max output function. Both linear and quadratic inputs are used. The model is trained on 2 hours of sound and tested on publicly available data. A test classification error below 0.05 with 1 s classification windows is achieved. Further more it is shown that linear input performs as well as a quadratic......, and that even though classification gets marginally better, not much is achieved by increasing the window size beyond 1 s....

  2. A Structural and Functional Acromegaly Classification

    Science.gov (United States)

    Cuevas-Ramos, Daniel; Carmichael, John D.; Cooper, Odelia; Bonert, Vivien S.; Gertych, Arkadiusz; Mamelak, Adam N.

    2015-01-01

    Context: GH-secreting pituitary adenomas exhibit heterogeneous natural history ranging from small tumors to large aggressive adenomas. Objective: To rigorously classify an acromegaly patient cohort defined by clinical, radiological, histopathological, and outcome characteristics. Design: Cross-sectional study. Setting: Tertiary referral pituitary center. Patients: Subjects were selected from a pituitary tumor research registry that includes 1178 patients with pituitary disease. Cluster analysis was performed on 338 acromegaly patients. Interventions: None. Main Outcome Measures: Biochemically active disease with elevated IGF-1 levels at follow-up. Results: Cluster analysis of all patients yielded 292 who were rigorously classified to three acromegaly types. Type 1 (50%) comprised older patients with the longest follow-up and most favorable outcomes, characterized by densely granulated, nonaggressive microadenomas and macroadenomas. Type 1 tumors extend to the sphenoid sinus more frequently than suprasellar extension (concave tumor image) and express abundant immunoreactive p21 and somatostatin receptor 2. Type 2 (19%) comprised noninvasive, densely or sparsely granulated macroadenomas, without significant extension (flat tumor image), with intermediate biochemical outcome. Type 3 (31%) was characterized by sparsely granulated aggressive macroadenomas and comprised patients with adverse therapeutic outcomes, despite receiving more treatments. These tumors extend to both the sphenoid sinus and suprasellar regions with commonly encountered optic chiasm compression (“peanut” magnetic resonance image), with low tumor p21 and somatostatin receptor 2 expression. Conclusions: After validation, this classification may be useful to accurately identify acromegaly patients with distinctive patterns of disease aggressiveness and outcome, as well as to provide an accurate tool for selection criteria in clinical studies. PMID:25250634

  3. [Tumors of the central nervous system].

    Science.gov (United States)

    Alegría-Loyola, Marco Antonio; Galnares-Olalde, Javier Andrés; Mercado, Moisés

    2017-01-01

    Central nervous system (CNS) tumors constitute a heterogeneous group of neoplasms that share a considerable morbidity and mortality rate. Recent advances in the underlying oncogenic mechanisms of these tumors have led to new classification systems, which, in turn, allow for a better diagnostic approach and therapeutic planning. Most of these neoplasms occur sporadically and several risk factors have been found to be associated with their development, such as exposure to ionizing radiation or electromagnetic fields and the concomitant presence of conditions like diabetes, hypertension and Parkinson's disease. A relatively minor proportion of primary CNS tumors occur in the context of hereditary syndromes. The purpose of this review is to analyze the etiopathogenesis, clinical presentation, diagnosis and therapy of CNS tumors with particular emphasis in the putative risk factors mentioned above.

  4. Gastroenteropancreatic neuroendocrine tumors (GEP-NETS)

    International Nuclear Information System (INIS)

    Vargas Martinez, Cristian Camilo; Castano Llano, Rodrigo

    2010-01-01

    Gastroenteropancreatic neuroendocrine tumors (GEP-NETS) are rare neoplasms which can occur anywhere in the gastrointestinal tract. Their particular characteristics include uptake of silver salts, neuroendocrine cell marker expression and hormonal secretory granules. Depending on their size, anatomical location and upon whether or not metastasis has occurred, these tumors can show different clinical patterns and have different prognoses. Early diagnosis is essential for treating these lesions and improving the patients' prognoses, but it requires a high degree of suspicion and confirmation by special testing. Surgical treatment is the first choice, but other medical therapy can be helpful for patients who have unresectable disease. This review presents the most relevant aspects of classification, morphology, methods of locating tumors, diagnosis and treatment of GEP-NETS. It presents only the Colombian experience in the epidemiology and management of these tumors.

  5. Towards secondary fingerprint classification

    CSIR Research Space (South Africa)

    Msiza, IS

    2011-07-01

    Full Text Available an accuracy figure of 76.8%. This small difference between the two figures is indicative of the validity of the proposed secondary classification module. Keywords?fingerprint core; fingerprint delta; primary classifi- cation; secondary classification I..., namely, the fingerprint core and the fingerprint delta. Forensically, a fingerprint core is defined as the innermost turning point where the fingerprint ridges form a loop, while the fingerprint delta is defined as the point where these ridges form a...

  6. Expected Classification Accuracy

    Directory of Open Access Journals (Sweden)

    Lawrence M. Rudner

    2005-08-01

    Full Text Available Every time we make a classification based on a test score, we should expect some number..of misclassifications. Some examinees whose true ability is within a score range will have..observed scores outside of that range. A procedure for providing a classification table of..true and expected scores is developed for polytomously scored items under item response..theory and applied to state assessment data. A simplified procedure for estimating the..table entries is also presented.

  7. Latent classification models

    DEFF Research Database (Denmark)

    Langseth, Helge; Nielsen, Thomas Dyhre

    2005-01-01

    parametric family ofdistributions.  In this paper we propose a new set of models forclassification in continuous domains, termed latent classificationmodels. The latent classification model can roughly be seen ascombining the \\NB model with a mixture of factor analyzers,thereby relaxing the assumptions...... classification model, and wedemonstrate empirically that the accuracy of the proposed model issignificantly higher than the accuracy of other probabilisticclassifiers....

  8. 78 FR 68983 - Cotton Futures Classification: Optional Classification Procedure

    Science.gov (United States)

    2013-11-18

    ...-AD33 Cotton Futures Classification: Optional Classification Procedure AGENCY: Agricultural Marketing... regulations to allow for the addition of an optional cotton futures classification procedure--identified and... response to requests from the U.S. cotton industry and ICE, AMS will offer a futures classification option...

  9. High Dimensional Classification Using Features Annealed Independence Rules.

    Science.gov (United States)

    Fan, Jianqing; Fan, Yingying

    2008-01-01

    Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel and Levina (2004) show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even for the independence classification rule, classification using all the features can be as bad as the random guessing due to noise accumulation in estimating population centroids in high-dimensional feature space. In fact, we demonstrate further that almost all linear discriminants can perform as bad as the random guessing. Thus, it is paramountly important to select a subset of important features for high-dimensional classification, resulting in Features Annealed Independence Rules (FAIR). The conditions under which all the important features can be selected by the two-sample t-statistic are established. The choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure.

  10. Supernova Photometric Lightcurve Classification

    Science.gov (United States)

    Zaidi, Tayeb; Narayan, Gautham

    2016-01-01

    This is a preliminary report on photometric supernova classification. We first explore the properties of supernova light curves, and attempt to restructure the unevenly sampled and sparse data from assorted datasets to allow for processing and classification. The data was primarily drawn from the Dark Energy Survey (DES) simulated data, created for the Supernova Photometric Classification Challenge. This poster shows a method for producing a non-parametric representation of the light curve data, and applying a Random Forest classifier algorithm to distinguish between supernovae types. We examine the impact of Principal Component Analysis to reduce the dimensionality of the dataset, for future classification work. The classification code will be used in a stage of the ANTARES pipeline, created for use on the Large Synoptic Survey Telescope alert data and other wide-field surveys. The final figure-of-merit for the DES data in the r band was 60% for binary classification (Type I vs II).Zaidi was supported by the NOAO/KPNO Research Experiences for Undergraduates (REU) Program which is funded by the National Science Foundation Research Experiences for Undergraduates Program (AST-1262829).

  11. Image Denoising And Segmentation Approchto Detect Tumor From BRAINMRI Images

    Directory of Open Access Journals (Sweden)

    Shanta Rangaswamy

    2018-04-01

    Full Text Available The detection of the Brain Tumor is a challenging problem, due to the structure of the Tumor cells in the brain. This project presents a systematic method that enhances the detection of brain tumor cells and to analyze functional structures by training and classification of the samples in SVM and tumor cell segmentation of the sample using DWT algorithm. From the input MRI Images collected, first noise is removed from MRI images by applying wiener filtering technique. In image enhancement phase, all the color components of MRI Images will be converted into gray scale image and make the edges clear in the image to get better identification and improvised quality of the image. In the segmentation phase, DWT on MRI Image to segment the grey-scale image is performed. During the post-processing, classification of tumor is performed by using SVM classifier. Wiener Filter, DWT, SVM Segmentation strategies were used to find and group the tumor position in the MRI filtered picture respectively. An essential perception in this work is that multi arrange approach utilizes various leveled classification strategy which supports execution altogether. This technique diminishes the computational complexity quality in time and memory. This classification strategy works accurately on all images and have achieved the accuracy of 93%.

  12. Indications for surgical resection of benign pancreatic tumors

    International Nuclear Information System (INIS)

    Isenmann, R.; Henne-Bruns, D.

    2008-01-01

    Benign pancreatic tumors should undergo surgical resection when they are symptomatic or - in the case of incidental discovery - bear malignant potential. This is the case for the majority of benign pancreatic tumors, especially for intraductal papillary mucinous neoplasms or mucinous cystic adenomas. In addition, resection is indicated for all tumors where preoperative diagnostic fails to provide an exact classification. Several different operative techniques are available. The treatment of choice depends on the localization of the tumor, its size and on whether there is evidence of malignant transformation. Partial duodenopancreatectomy is the oncological treatment of choice for tumors of the pancreatic head whereas for tumors of the pancreatic tail a left-sided pancreatectomy is appropriate. Middle pancreatectomy or duodenum-preserving resection of the pancreatic head is not a radical oncologic procedure. They should only be performed in cases of tumors without malignant potential. (orig.) [de

  13. Prediction and classification of respiratory motion

    CERN Document Server

    Lee, Suk Jin

    2014-01-01

    This book describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. This book presents a customized prediction of respiratory motion with clustering from multiple patient interactions. The proposed method contributes to the improvement of patient treatments by considering breathing pattern for the accurate dose calculation in radiotherapy systems. Real-time tumor-tracking, where the prediction of irregularities becomes relevant, has yet to be clinically established. The statistical quantitative modeling for irregular breathing classification, in which commercial respiration traces are retrospectively categorized into several classes based on breathing pattern are discussed as well. The proposed statistical classification may provide clinical advantages to adjust the dose rate before and during the external beam radiotherapy for minimizing the safety margin. In the first chapter following the Introduction  to this book, we...

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

    Directory of Open Access Journals (Sweden)

    Gabriel Kocevar

    2016-10-01

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

  15. Mesenchymal Tumors of the Breast: Imaging and the Histopathologic Correlation

    International Nuclear Information System (INIS)

    Kim, Bo Mi; Kim, Eun Kyung; You, Jae Kyoung; Kim, Yee Jeong

    2011-01-01

    Various benign and malignant mesenchymal tumors can occur in the breast. Most radiologists are unfamiliar with the imaging features of these tumors and the imaging features have not been described in the radiologic literature. It is important that radiologists should be familiar with the broad spectrum of imaging features of rare mesenchymal breast tumors. In this pictorial review, we demonstrate the sonographic findings and the corresponding pathologic findings of various mesenchymal tumors of the breast as defined by the World Health Organization classification system

  16. SEMIAUTOMATIC DETECTION OF TUMORAL ZONE

    Directory of Open Access Journals (Sweden)

    Ezzeddine Zagrouba

    2011-05-01

    Full Text Available This paper describes a robust method based on the cooperation of fuzzy classification and regions segmentation algorithms, in order to detect the tumoral zone in the brain Magnetic Resonance Imaging (MRI. On one hand, the classification in fuzzy sets is done by the Fuzzy C-Means algorithm (FCM, where a study of its different parameters and its complexity has been previously realised, which led us to improve it. On the other hand, the segmentation in regions is obtained by an hierarchical method through adaptive thresholding. Then, an operator expert selects a germ in the tumoral zone, and the class containing the sick zone is localised in return for the FCM algorithm. Finally, the superposition of the two partitions of the image will determine the sick zone. The originality of our approach is the parallel exploitation of different types of information in the image by the cooperation of two complementary approaches. This allows us to carry out a pertinent approach for the detection of sick zone in MRI images.

  17. A New Classification Approach Based on Multiple Classification Rules

    OpenAIRE

    Zhongmei Zhou

    2014-01-01

    A good classifier can correctly predict new data for which the class label is unknown, so it is important to construct a high accuracy classifier. Hence, classification techniques are much useful in ubiquitous computing. Associative classification achieves higher classification accuracy than some traditional rule-based classification approaches. However, the approach also has two major deficiencies. First, it generates a very large number of association classification rules, especially when t...

  18. Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

    Science.gov (United States)

    Uddin, M B; Chow, C M; Su, S W

    2018-03-26

    Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

  19. Pediatric Brain Tumor Foundation

    Science.gov (United States)

    ... navigate their brain tumor diagnosis. WATCH AND SHARE Brain tumors and their treatment can be deadly so ... Pediatric Central Nervous System Cancers Read more >> Pediatric Brain Tumor Foundation 302 Ridgefield Court, Asheville, NC 28806 ...

  20. Brain Tumors (For Parents)

    Science.gov (United States)

    ... Staying Safe Videos for Educators Search English Español Brain Tumors KidsHealth / For Parents / Brain Tumors What's in ... radiation therapy or chemotherapy, or both. Types of Brain Tumors There are many different types of brain ...

  1. Childhood Brain Tumors

    Science.gov (United States)

    Brain tumors are abnormal growths inside the skull. They are among the most common types of childhood ... still be serious. Malignant tumors are cancerous. Childhood brain and spinal cord tumors can cause headaches and ...

  2. Malignant bone tumors

    International Nuclear Information System (INIS)

    Zedgenidze, G.A.; Kishkovskij, A.N.; Elashov, Yu.G.

    1984-01-01

    Clinicoroentgenologic semiotics of malignant bone tumors as well as metastatic bone tumors are presented. Diagnosis of malignant and metastatic bone tumors should be always complex, representing a result of cooperation of a physician, roentgenologist, pathoanatomist

  3. Tumors and Pregnancy

    Science.gov (United States)

    Tumors during pregnancy are rare, but they can happen. Tumors can be either benign or malignant. Benign tumors aren't cancer. Malignant ones are. The most common cancers in pregnancy are breast cancer, cervical cancer, lymphoma, and melanoma. ...

  4. Neuroendocrine Tumor: Statistics

    Science.gov (United States)

    ... Tumor > Neuroendocrine Tumor: Statistics Request Permissions Neuroendocrine Tumor: Statistics Approved by the Cancer.Net Editorial Board , 01/ ... the body. It is important to remember that statistics on the survival rates for people with a ...

  5. Cancer classification in the genomic era: five contemporary problems.

    Science.gov (United States)

    Song, Qingxuan; Merajver, Sofia D; Li, Jun Z

    2015-10-19

    Classification is an everyday instinct as well as a full-fledged scientific discipline. Throughout the history of medicine, disease classification is central to how we develop knowledge, make diagnosis, and assign treatment. Here, we discuss the classification of cancer and the process of categorizing cancer subtypes based on their observed clinical and biological features. Traditionally, cancer nomenclature is primarily based on organ location, e.g., "lung cancer" designates a tumor originating in lung structures. Within each organ-specific major type, finer subgroups can be defined based on patient age, cell type, histological grades, and sometimes molecular markers, e.g., hormonal receptor status in breast cancer or microsatellite instability in colorectal cancer. In the past 15+ years, high-throughput technologies have generated rich new data regarding somatic variations in DNA, RNA, protein, or epigenomic features for many cancers. These data, collected for increasingly large tumor cohorts, have provided not only new insights into the biological diversity of human cancers but also exciting opportunities to discover previously unrecognized cancer subtypes. Meanwhile, the unprecedented volume and complexity of these data pose significant challenges for biostatisticians, cancer biologists, and clinicians alike. Here, we review five related issues that represent contemporary problems in cancer taxonomy and interpretation. (1) How many cancer subtypes are there? (2) How can we evaluate the robustness of a new classification system? (3) How are classification systems affected by intratumor heterogeneity and tumor evolution? (4) How should we interpret cancer subtypes? (5) Can multiple classification systems co-exist? While related issues have existed for a long time, we will focus on those aspects that have been magnified by the recent influx of complex multi-omics data. Exploration of these problems is essential for data-driven refinement of cancer classification

  6. A Rare Tumor of Nasal Cavity: Glomangiopericytoma

    Directory of Open Access Journals (Sweden)

    Aysegul Verim

    2014-01-01

    Full Text Available Glomangiopericytoma is a rare vascular neoplasm characterized by a pattern of prominent perivascular growth. A 72-year-old woman was admitted to our clinic complaining of nasal obstruction, frequent epistaxis, and facial pain. A reddish tumor filling the left nasal cavity was observed on endoscopy and treated with endoscopic excision. Microscopically, closely packed cells interspersed with numerous thin-walled, branching staghorn vessels were seen. Glomangiopericytoma is categorized as a borderline low malignancy tumor by WHO classification. Long-term follow-up with systemic examination is necessary due to high risk of recurrence.

  7. Peripheral epithelial odontogenic tumor

    International Nuclear Information System (INIS)

    Carzoglio, J.; Tancredi, N.; Capurro, S.; Ravecca, T.; Scarrone, P.

    2006-01-01

    A new case of peripheral epithelial odontogenic tumor (Pindborg tumor) is reported. It is localized in the superior right gingival region, a less frequent site, and has the histopathological features previously reported. Immunochemical studies were performed, revealing a differential positive stain to cytokeratins in tumor cells deeply seated in the tumor mass, probably related to tumoral cell heterogeneity.Interestingly, in this particular case S-100 protein positive reactivity was also detected in arborescent cells intermingled with tumoral cells, resembling Langerhans cells. Even though referred in the literature in central Pindborg tumors, no references were found about their presence in peripheral tumors, like the one that is presented here

  8. Odontogenic tumors: A review of 675 cases in Eastern Libya

    Directory of Open Access Journals (Sweden)

    Saravana HL Goteti

    2016-01-01

    Full Text Available Aims: The aim of this study was to determine the relative frequency of odontogenic tumors (OTs in an Eastern Libyan population based on the 2005 World Health Organization (WHO classification, and also to compare the actual data with previous studies. Materials and Methods: We retrieved and analyzed 85 OTs from a total of 675 tumors and tumor-like lesions of the oral and perioral structures, for gender, age, tumor site, and frequency. The diagnosis was based on the most recent WHO (2005 classification of OTs. Results: OTs constituted 12.6% of all oral/jaw tumors and tumor-like lesions. Ameloblastoma (28.2% was the most common type, followed by keratocystic odontogenic tumor (25.2% and odontoma (19.9%. The male: female ratio was 1.2:1, and maxilla: mandible ratio 1:2. The mean age of occurrence of tumors was 29 years with a peak incidence between 10 and 40 years. Conclusions: OTs are relatively common lesion in this Libyan Population, but the incidence of tumors is neither similar to Caucasians nor Sub-Saharan population.

  9. Childhood brain tumors: epidemiology, current management and future directions.

    Science.gov (United States)

    Pollack, Ian F; Jakacki, Regina I

    2011-07-26

    Brain tumors are the most common solid tumors in children. With the increasingly widespread availability of MRI, the incidence of childhood brain tumors seemed to rise in the 1980s, but has subsequently remained relatively stable. However, management of brain tumors in children has evolved substantially during this time, reflecting refinements in classification of tumors, delineation of risk groups within histological subsets of tumors, and incorporation of molecular techniques to further define tumor subgroups. Although considerable progress has been made in the outcomes of certain tumors, prognosis in other childhood brain tumor types is poor. Among the tumor groups with more-favorable outcomes, attention has been focused on reducing long-term morbidity without sacrificing survival rates. Studies for high-risk groups have examined the use of intensive therapy or novel, molecularly targeted approaches to improve disease control rates. In addition to reviewing the literature and providing an overview of the complexities in diagnosing childhood brain tumors, we will discuss advances in the treatment and categorization of several tumor types in which progress has been most apparent, as well as those in which improvements have been lacking. The latest insights from molecular correlative studies that hold potential for future refinements in therapy will also be discussed.

  10. Molecular Classification of Melanoma

    Science.gov (United States)

    Tissue-based analyses of precursors, melanoma tumors and metastases within existing study populations to further understanding of the heterogeneity of melanoma and determine a predictive pattern of progression for dysplastic nevi.

  11. Cellular image classification

    CERN Document Server

    Xu, Xiang; Lin, Feng

    2017-01-01

    This book introduces new techniques for cellular image feature extraction, pattern recognition and classification. The authors use the antinuclear antibodies (ANAs) in patient serum as the subjects and the Indirect Immunofluorescence (IIF) technique as the imaging protocol to illustrate the applications of the described methods. Throughout the book, the authors provide evaluations for the proposed methods on two publicly available human epithelial (HEp-2) cell datasets: ICPR2012 dataset from the ICPR'12 HEp-2 cell classification contest and ICIP2013 training dataset from the ICIP'13 Competition on cells classification by fluorescent image analysis. First, the reading of imaging results is significantly influenced by one’s qualification and reading systems, causing high intra- and inter-laboratory variance. The authors present a low-order LP21 fiber mode for optical single cell manipulation and imaging staining patterns of HEp-2 cells. A focused four-lobed mode distribution is stable and effective in optical...

  12. Bosniak classification system

    DEFF Research Database (Denmark)

    Graumann, Ole; Osther, Susanne Sloth; Karstoft, Jens

    2016-01-01

    BACKGROUND: The Bosniak classification was originally based on computed tomographic (CT) findings. Magnetic resonance (MR) and contrast-enhanced ultrasonography (CEUS) imaging may demonstrate findings that are not depicted at CT, and there may not always be a clear correlation between the findings...... at MR and CEUS imaging and those at CT. PURPOSE: To compare diagnostic accuracy of MR, CEUS, and CT when categorizing complex renal cystic masses according to the Bosniak classification. MATERIAL AND METHODS: From February 2011 to June 2012, 46 complex renal cysts were prospectively evaluated by three...... readers. Each mass was categorized according to the Bosniak classification and CT was chosen as gold standard. Kappa was calculated for diagnostic accuracy and data was compared with pathological results. RESULTS: CT images found 27 BII, six BIIF, seven BIII, and six BIV. Forty-three cysts could...

  13. Bosniak Classification system

    DEFF Research Database (Denmark)

    Graumann, Ole; Osther, Susanne Sloth; Karstoft, Jens

    2014-01-01

    Background: The Bosniak classification is a diagnostic tool for the differentiation of cystic changes in the kidney. The process of categorizing renal cysts may be challenging, involving a series of decisions that may affect the final diagnosis and clinical outcome such as surgical management....... Purpose: To investigate the inter- and intra-observer agreement among experienced uroradiologists when categorizing complex renal cysts according to the Bosniak classification. Material and Methods: The original categories of 100 cystic renal masses were chosen as “Gold Standard” (GS), established...... to the calculated weighted κ all readers performed “very good” for both inter-observer and intra-observer variation. Most variation was seen in cysts catagorized as Bosniak II, IIF, and III. These results show that radiologists who evaluate complex renal cysts routinely may apply the Bosniak classification...

  14. Acoustic classification of dwellings

    DEFF Research Database (Denmark)

    Berardi, Umberto; Rasmussen, Birgit

    2014-01-01

    insulation performance, national schemes for sound classification of dwellings have been developed in several European countries. These schemes define acoustic classes according to different levels of sound insulation. Due to the lack of coordination among countries, a significant diversity in terms...... exchanging experiences about constructions fulfilling different classes, reducing trade barriers, and finally increasing the sound insulation of dwellings.......Schemes for the classification of dwellings according to different building performances have been proposed in the last years worldwide. The general idea behind these schemes relates to the positive impact a higher label, and thus a better performance, should have. In particular, focusing on sound...

  15. Minimum Error Entropy Classification

    CERN Document Server

    Marques de Sá, Joaquim P; Santos, Jorge M F; Alexandre, Luís A

    2013-01-01

    This book explains the minimum error entropy (MEE) concept applied to data classification machines. Theoretical results on the inner workings of the MEE concept, in its application to solving a variety of classification problems, are presented in the wider realm of risk functionals. Researchers and practitioners also find in the book a detailed presentation of practical data classifiers using MEE. These include multi‐layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE‐like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions.

  16. Classification of iconic images

    OpenAIRE

    Zrianina, Mariia; Kopf, Stephan

    2016-01-01

    Iconic images represent an abstract topic and use a presentation that is intuitively understood within a certain cultural context. For example, the abstract topic “global warming” may be represented by a polar bear standing alone on an ice floe. Such images are widely used in media and their automatic classification can help to identify high-level semantic concepts. This paper presents a system for the classification of iconic images. It uses a variation of the Bag of Visual Words approach wi...

  17. Casemix classification systems.

    Science.gov (United States)

    Fetter, R B

    1999-01-01

    The idea of using casemix classification to manage hospital services is not new, but has been limited by available technology. It was not until after the introduction of Medicare in the United States in 1965 that serious attempts were made to measure hospital production in order to contain spiralling costs. This resulted in a system of casemix classification known as diagnosis related groups (DRGs). This paper traces the development of DRGs and their evolution from the initial version to the All Patient Refined DRGs developed in 1991.

  18. Normed kernel function-based fuzzy possibilistic C-means (NKFPCM) algorithm for high-dimensional breast cancer database classification with feature selection is based on Laplacian Score

    Science.gov (United States)

    Lestari, A. W.; Rustam, Z.

    2017-07-01

    In the last decade, breast cancer has become the focus of world attention as this disease is one of the primary leading cause of death for women. Therefore, it is necessary to have the correct precautions and treatment. In previous studies, Fuzzy Kennel K-Medoid algorithm has been used for multi-class data. This paper proposes an algorithm to classify the high dimensional data of breast cancer using Fuzzy Possibilistic C-means (FPCM) and a new method based on clustering analysis using Normed Kernel Function-Based Fuzzy Possibilistic C-Means (NKFPCM). The objective of this paper is to obtain the best accuracy in classification of breast cancer data. In order to improve the accuracy of the two methods, the features candidates are evaluated using feature selection, where Laplacian Score is used. The results show the comparison accuracy and running time of FPCM and NKFPCM with and without feature selection.

  19. Investigating the KLF4 Gene Expression as a New Molecular Marker in Breast Tumors

    Directory of Open Access Journals (Sweden)

    MA Hosseinpour Feizi

    2013-12-01

    Results: The results showed that: 1 KLF4 is over expressed in Breast tumors rather than adjacent normal tissues. 2 KLF4 is an oncogene in breast tumors (at least in IDC type. 3 The KLF4 expression levels are related significantly with nature of malignant breast tumors. Conclusion: Findings do not confirm KLF4 as a diagnostic marker in classification and identification of tumoral tissues from non-tumoral ones in breast, but we can use this marker to identify at least 50% of invasive Ductal Carcinoma in breast and utilize it as a potential predictive factor to demonstrate severity degree in various tumors.

  20. Identifying colon cancer risk modules with better classification performance based on human signaling network.

    Science.gov (United States)

    Qu, Xiaoli; Xie, Ruiqiang; Chen, Lina; Feng, Chenchen; Zhou, Yanyan; Li, Wan; Huang, Hao; Jia, Xu; Lv, Junjie; He, Yuehan; Du, Youwen; Li, Weiguo; Shi, Yuchen; He, Weiming

    2014-10-01

    Identifying differences between normal and tumor samples from a modular perspective may help to improve our understanding of the mechanisms responsible for colon cancer. Many cancer studies have shown that signaling transduction and biological pathways are disturbed in disease states, and expression profiles can distinguish variations in diseases. In this study, we integrated a weighted human signaling network and gene expression profiles to select risk modules associated with tumor conditions. Risk modules as classification features by our method had a better classification performance than other methods, and one risk module for colon cancer had a good classification performance for distinguishing between normal/tumor samples and between tumor stages. All genes in the module were annotated to the biological process of positive regulation of cell proliferation, and were highly associated with colon cancer. These results suggested that these genes might be the potential risk genes for colon cancer. Copyright © 2013. Published by Elsevier Inc.

  1. Information gathering for CLP classification

    Directory of Open Access Journals (Sweden)

    Ida Marcello

    2011-01-01

    Full Text Available Regulation 1272/2008 includes provisions for two types of classification: harmonised classification and self-classification. The harmonised classification of substances is decided at Community level and a list of harmonised classifications is included in the Annex VI of the classification, labelling and packaging Regulation (CLP. If a chemical substance is not included in the harmonised classification list it must be self-classified, based on available information, according to the requirements of Annex I of the CLP Regulation. CLP appoints that the harmonised classification will be performed for carcinogenic, mutagenic or toxic to reproduction substances (CMR substances and for respiratory sensitisers category 1 and for other hazard classes on a case-by-case basis. The first step of classification is the gathering of available and relevant information. This paper presents the procedure for gathering information and to obtain data. The data quality is also discussed.

  2. The paradox of atheoretical classification

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2016-01-01

    A distinction can be made between “artificial classifications” and “natural classifications,” where artificial classifications may adequately serve some limited purposes, but natural classifications are overall most fruitful by allowing inference and thus many different purposes. There is strong...... support for the view that a natural classification should be based on a theory (and, of course, that the most fruitful theory provides the most fruitful classification). Nevertheless, atheoretical (or “descriptive”) classifications are often produced. Paradoxically, atheoretical classifications may...... be very successful. The best example of a successful “atheoretical” classification is probably the prestigious Diagnostic and Statistical Manual of Mental Disorders (DSM) since its third edition from 1980. Based on such successes one may ask: Should the claim that classifications ideally are natural...

  3. Liver Tumors (For Parents)

    Science.gov (United States)

    ... Staying Safe Videos for Educators Search English Español Liver Tumors KidsHealth / For Parents / Liver Tumors What's in this article? Types of Tumors ... Cancerous) Tumors Symptoms Diagnosis Treatment Coping Print The liver is the body's largest solid organ. Lying next ...

  4. MicroRNAs in the Tumor Biology of Soft Tissue Sarcomas

    NARCIS (Netherlands)

    C.M.M. Gits (Caroline)

    2013-01-01

    markdownabstract__Abstract__ Soft tissue sarcomas represent a rare, heterogeneous group of mesenchymal tumors. In sarcomas, histological classification, prediction of clinical behaviour and prognosis, and targeted treatment is often a challenge. A better understanding of the biology of soft

  5. Endocrine tumors other than thyroid tumors

    International Nuclear Information System (INIS)

    Takeichi, Norio; Dohi, Kiyohiko

    1992-01-01

    This paper discusses the tendency for the occurrence of tumors in the endocrine glands, other than the thyroid gland, in A-bomb survivors using both autopsy and clinical data. ABCC-RERF sample data using 4136 autopsy cases (1961-1977) revealed parathyroid tumors in 13 A-bomb survivors, including 3 with the associated hyperparathyroidism, with the suggestion of dose-dependent increase in the occurrence of tumors. Based on clinical data from Hiroshima University, 7 (46.7%) of 15 parathyroid tumors cases were A-bomb survivors. Data (1974-1987) from the Tumor Registry Committee (TRC) in Hiroshima Prefecture revealed that a relative risk of parathyroid tumors was 5.6 times higher in the entire group of A-bomb survivors and 16.2 times higher in the group of heavily exposed A-bomb survivors, suggesting the dose-dependent increase in their occurrence. Adrenal tumors were detected in 47 of 123 cases from the TRC data, and 15 (31.5%) of these 47 were A-bomb survivors. Particularly, 11 cases of adrenal tumors associated with Cushing syndrome included 6 A-bomb survivors (54.5%). The incidence of multiple endocrine gonadial tumors (MEGT) tended to be higher with increasing exposure doses; and the 1-9 rad group, the 10-99 rad group, and the 100 or more rad group had a risk of developing MEGT of 4.1, 5.7, and 7.1, respectively, relative to both the not-in the city group and the 0 rad group. These findings suggested that there is a correlation between A-bomb radiation and the occurrence of parathyroid tumors (including hyperparathyroidism), adrenal tumors associated with Cushing syndrome and MEGT (especially, the combined thyroid and ovarian tumors and the combined thyroid and parathyroid tumors). (N.K.)

  6. Colorectal cancer: genetic abnormalities, tumor progression, tumor heterogeneity, clonal evolution and tumor-initiating cells.

    Science.gov (United States)

    Testa, Ugo; Pelosi, Elvira; Castelli, Germana

    2018-04-13

    Colon cancer is the third most common cancer worldwide. Most colorectal cancer occurrences are sporadic, not related to genetic predisposition or family history; however, 20-30% of patients with colorectal cancer have a family history of colorectal cancer and 5% of these tumors arise in the setting of a Mendelian inheritance syndrome. In many patients, the development of a colorectal cancer is preceded by a benign neoplastic lesion: either an adenomatous polyp or a serrated polyp. Studies carried out in the last years have characterized the main molecular alterations occurring in colorectal cancers, showing that the tumor of each patient displays from two to eight driver mutations. The ensemble of molecular studies, including gene expression studies, has led to two proposed classifications of colorectal cancers, with the identification of four/five non-overlapping groups. The homeostasis of the rapidly renewing intestinal epithelium is ensured by few stem cells present at the level of the base of intestinal crypts. Various experimental evidence suggests that colorectal cancers may derive from the malignant transformation of intestinal stem cells or of intestinal cells that acquire stem cell properties following malignant transformation. Colon cancer stem cells seem to be involved in tumor chemoresistance, radioresistance and relapse.

  7. Management of hemorrhage in gastrointestinal stromal tumors: a review.

    Science.gov (United States)

    Liu, Qi; Kong, Fanmin; Zhou, Jianping; Dong, Ming; Dong, Qi

    2018-01-01

    Gastrointestinal stromal tumors (GISTs) are relatively common mesenchymal tumors. They originate from the wall of hollow viscera and may be found in any part of the digestive tract. The prognosis of patients with stromal tumors depends on various risk factors, including size, location, presence of mitotic figures, and tumor rupture. Emergency surgery is often required for stromal tumors with hemorrhage. The current literature suggests that stromal tumor hemorrhage indicates poor prognosis. Although the optimal treatment options for hemorrhagic GISTs are based on surgical experience, there remains controversy with regard to optimum postoperative management as well as the classification of malignant potential. This article reviews the biological characteristics, diagnostic features, prognostic factors, treatment, and postoperative management of GISTs with hemorrhage.

  8. Rosette-forming glioneuronal tumor of the fourth ventricle.

    Science.gov (United States)

    Preusser, Matthias; Dietrich, Wolfgang; Czech, Thomas; Prayer, Daniela; Budka, Herbert; Hainfellner, Johannes A

    2003-11-01

    Rosette-forming glioneuronal tumor (RGNT) of the fourth ventricle has been reported recently as a novel type of primary CNS neoplasm. We present the case of a 35-year-old male patient with RGNT of the fourth ventricle. The tumor was found incidentally; the patient did not suffer from any neurological symptoms. The tumor mass involved the caudal cerebellar vermis, filled the fourth ventricle and protruded into the caudal part of the mesencephalic aquaeduct. Smaller tumor nodules were visible in the adjacent right cerebellar hemisphere. Histologically, prominent neurocytic rosettes with synaptophysin expression were embedded in a glial tumor component resembling pilocytic astrocytoma. Clinicopathological features of our case closely resemble those reported in the original description. Thus, our case confirms RGNT as a new distinct type of primary CNS neoplasm. Due to its distinct features, adoption of RGNT as a new entity into the WHO classification of tumors should be considered.

  9. Ecosystem classification, Chapter 2

    Science.gov (United States)

    M.J. Robin-Abbott; L.H. Pardo

    2011-01-01

    The ecosystem classification in this report is based on the ecoregions developed through the Commission for Environmental Cooperation (CEC) for North America (CEC 1997). Only ecosystems that occur in the United States are included. CEC ecoregions are described, with slight modifications, below (CEC 1997) and shown in Figures 2.1 and 2.2. We chose this ecosystem...

  10. The classification of phocomelia.

    Science.gov (United States)

    Tytherleigh-Strong, G; Hooper, G

    2003-06-01

    We studied 24 patients with 44 phocomelic upper limbs. Only 11 limbs could be grouped in the classification system of Frantz and O' Rahilly. The non-classifiable limbs were further studied and their characteristics identified. It is confirmed that phocomelia is not an intercalary defect.

  11. Principles for ecological classification

    Science.gov (United States)

    Dennis H. Grossman; Patrick Bourgeron; Wolf-Dieter N. Busch; David T. Cleland; William Platts; G. Ray; C. Robins; Gary Roloff

    1999-01-01

    The principal purpose of any classification is to relate common properties among different entities to facilitate understanding of evolutionary and adaptive processes. In the context of this volume, it is to facilitate ecosystem stewardship, i.e., to help support ecosystem conservation and management objectives.

  12. Mimicking human texture classification

    NARCIS (Netherlands)

    Rogowitz, B.E.; van Rikxoort, Eva M.; van den Broek, Egon; Pappas, T.N.; Schouten, Theo E.; Daly, S.J.

    2005-01-01

    In an attempt to mimic human (colorful) texture classification by a clustering algorithm three lines of research have been encountered, in which as test set 180 texture images (both their color and gray-scale equivalent) were drawn from the OuTex and VisTex databases. First, a k-means algorithm was

  13. Classification, confusion and misclassification

    African Journals Online (AJOL)

    The classification of objects and phenomena in science and nature has fascinated academics since Carl Linnaeus, the Swedish botanist and zoologist, created his binomial description of living things in the 1700s and probably long before in accounts of others in textbooks long since gone. It must have concerned human ...

  14. Classifications in popular music

    NARCIS (Netherlands)

    van Venrooij, A.; Schmutz, V.; Wright, J.D.

    2015-01-01

    The categorical system of popular music, such as genre categories, is a highly differentiated and dynamic classification system. In this article we present work that studies different aspects of these categorical systems in popular music. Following the work of Paul DiMaggio, we focus on four

  15. Shark Teeth Classification

    Science.gov (United States)

    Brown, Tom; Creel, Sally; Lee, Velda

    2009-01-01

    On a recent autumn afternoon at Harmony Leland Elementary in Mableton, Georgia, students in a fifth-grade science class investigated the essential process of classification--the act of putting things into groups according to some common characteristics or attributes. While they may have honed these skills earlier in the week by grouping their own…

  16. Text document classification

    Czech Academy of Sciences Publication Activity Database

    Novovičová, Jana

    č. 62 (2005), s. 53-54 ISSN 0926-4981 R&D Projects: GA AV ČR IAA2075302; GA AV ČR KSK1019101; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : document representation * categorization * classification Subject RIV: BD - Theory of Information

  17. Classification in Medical Imaging

    DEFF Research Database (Denmark)

    Chen, Chen

    Classification is extensively used in the context of medical image analysis for the purpose of diagnosis or prognosis. In order to classify image content correctly, one needs to extract efficient features with discriminative properties and build classifiers based on these features. In addition...... on characterizing human faces and emphysema disease in lung CT images....

  18. Improving Student Question Classification

    Science.gov (United States)

    Heiner, Cecily; Zachary, Joseph L.

    2009-01-01

    Students in introductory programming classes often articulate their questions and information needs incompletely. Consequently, the automatic classification of student questions to provide automated tutorial responses is a challenging problem. This paper analyzes 411 questions from an introductory Java programming course by reducing the natural…

  19. NOUN CLASSIFICATION IN ESAHIE

    African Journals Online (AJOL)

    The present work deals with noun classification in Esahie (Kwa, Niger ... phonological information influences the noun (form) class system of Esahie. ... between noun classes and (grammatical) Gender is interrogated (in the light of ..... the (A) argument6 precedes the verb and the (P) argument7 follows the verb in a simple.

  20. Dynamic Latent Classification Model

    DEFF Research Database (Denmark)

    Zhong, Shengtong; Martínez, Ana M.; Nielsen, Thomas Dyhre

    as possible. Motivated by this problem setting, we propose a generative model for dynamic classification in continuous domains. At each time point the model can be seen as combining a naive Bayes model with a mixture of factor analyzers (FA). The latent variables of the FA are used to capture the dynamics...

  1. Classification of myocardial infarction

    DEFF Research Database (Denmark)

    Saaby, Lotte; Poulsen, Tina Svenstrup; Hosbond, Susanne Elisabeth

    2013-01-01

    The classification of myocardial infarction into 5 types was introduced in 2007 as an important component of the universal definition. In contrast to the plaque rupture-related type 1 myocardial infarction, type 2 myocardial infarction is considered to be caused by an imbalance between demand...

  2. Event Classification using Concepts

    NARCIS (Netherlands)

    Boer, M.H.T. de; Schutte, K.; Kraaij, W.

    2013-01-01

    The semantic gap is one of the challenges in the GOOSE project. In this paper a Semantic Event Classification (SEC) system is proposed as an initial step in tackling the semantic gap challenge in the GOOSE project. This system uses semantic text analysis, multiple feature detectors using the BoW

  3. Improved Correlation of the Neuropathologic Classification According to Adapted World Health Organization Classification and Outcome After Radiotherapy in Patients With Atypical and Anaplastic Meningiomas

    International Nuclear Information System (INIS)

    Combs, Stephanie E.; Schulz-Ertner, Daniela; Debus, Jürgen; Deimling, Andreas von; Hartmann, Christian

    2011-01-01

    Purpose: To evaluate the correlation between the 1993 and 2000/2007 World Health Organization (WHO) classification with the outcome in patients with high-grade meningiomas. Patients and Methods: Between 1985 and 2004, 73 patients diagnosed with atypical or anaplastic meningiomas were treated with radiotherapy. Sections from the paraffin-embedded tumor material from 66 patients (90%) from 13 different pathology departments were re-evaluated according to the first revised WHO classification from 1993 and the revised classifications from 2000/2007. In 4 cases, the initial diagnosis meningioma was not reproducible (5%). Therefore, 62 patients with meningiomas were analyzed. Results: All 62 tumors were reclassified according to the 1993 and 2000/2007 WHO classification systems. Using the 1993 system, 7 patients were diagnosed with WHO grade I meningioma (11%), 23 with WHO grade II (37%), and 32 with WHO grade III meningioma (52%). After scoring using the 2000/2007 system, we found 17 WHO grade I meningiomas (27%), 32 WHO grade II meningiomas (52%), and 13 WHO grade III meningiomas (21%). According to the 1993 classification, the difference in overall survival was not statistically significant among the histologic subgroups (p = .96). Using the 2000/2007 WHO classifications, the difference in overall survival became significant (p = .02). Of the 62 reclassified patients 29 developed tumor progression (47%). No difference in progression-free survival was observed among the histologic subgroups (p = .44). After grading according to the 2000/2007 WHO classifications, significant differences in progression-free survival were observed among the three histologic groups (p = .005). Conclusion: The new 2000/2007 WHO classification for meningiomas showed an improved correlation between the histologic grade and outcome. This classification therefore provides a useful basis to determine the postoperative indication for radiotherapy. According to our results, a comparison of the

  4. Improved Correlation of the Neuropathologic Classification According to Adapted World Health Organization Classification and Outcome After Radiotherapy in Patients With Atypical and Anaplastic Meningiomas

    Energy Technology Data Exchange (ETDEWEB)

    Combs, Stephanie E., E-mail: Stephanie.Combs@med.uni-heidelberg.de [Department of Radiation Oncology, University Hospital of Heidelberg, Heidelberg (Germany); Schulz-Ertner, Daniela [Radiologisches Institut, Markuskrankenhaus Frankfurt, Frankfurt am Main (Germany); Debus, Juergen [Department of Radiation Oncology, University Hospital of Heidelberg, Heidelberg (Germany); Deimling, Andreas von; Hartmann, Christian [Department of Neuropathology, Institute for Pathology, University Hospital of Heidelberg, Heidelberg (Germany); Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg (Germany)

    2011-12-01

    Purpose: To evaluate the correlation between the 1993 and 2000/2007 World Health Organization (WHO) classification with the outcome in patients with high-grade meningiomas. Patients and Methods: Between 1985 and 2004, 73 patients diagnosed with atypical or anaplastic meningiomas were treated with radiotherapy. Sections from the paraffin-embedded tumor material from 66 patients (90%) from 13 different pathology departments were re-evaluated according to the first revised WHO classification from 1993 and the revised classifications from 2000/2007. In 4 cases, the initial diagnosis meningioma was not reproducible (5%). Therefore, 62 patients with meningiomas were analyzed. Results: All 62 tumors were reclassified according to the 1993 and 2000/2007 WHO classification systems. Using the 1993 system, 7 patients were diagnosed with WHO grade I meningioma (11%), 23 with WHO grade II (37%), and 32 with WHO grade III meningioma (52%). After scoring using the 2000/2007 system, we found 17 WHO grade I meningiomas (27%), 32 WHO grade II meningiomas (52%), and 13 WHO grade III meningiomas (21%). According to the 1993 classification, the difference in overall survival was not statistically significant among the histologic subgroups (p = .96). Using the 2000/2007 WHO classifications, the difference in overall survival became significant (p = .02). Of the 62 reclassified patients 29 developed tumor progression (47%). No difference in progression-free survival was observed among the histologic subgroups (p = .44). After grading according to the 2000/2007 WHO classifications, significant differences in progression-free survival were observed among the three histologic groups (p = .005). Conclusion: The new 2000/2007 WHO classification for meningiomas showed an improved correlation between the histologic grade and outcome. This classification therefore provides a useful basis to determine the postoperative indication for radiotherapy. According to our results, a comparison of the

  5. CT of abdominal tumor

    International Nuclear Information System (INIS)

    Endo, Satoshi; Yamada, Kenji; Ito, Masatoshi; Ito, Hisao; Yamaura, Harutsugu

    1981-01-01

    CT findings in 33 patients who had an abdominal tumor were evaluated. CT revealed a tumor in 31 cases. The organ from which the tumor originated was correctly diagnosed in 18 patients. Whether the tumor was solid or cystic was correctly predicted in 28 patients. The diagnosis malignant or benign nature of tumor was correct, incorrect and impossible, in 23, 3, and five patiens, respectively. (Kondo, M.)

  6. Automatic feed phase identification in multivariate bioprocess profiles by sequential binary classification.

    Science.gov (United States)

    Nikzad-Langerodi, Ramin; Lughofer, Edwin; Saminger-Platz, Susanne; Zahel, Thomas; Sagmeister, Patrick; Herwig, Christoph

    2017-08-22

    In this paper, we propose a new strategy for retrospective identification of feed phases from online sensor-data enriched feed profiles of an Escherichia Coli (E. coli) fed-batch fermentation process. In contrast to conventional (static), data-driven multi-class machine learning (ML), we exploit process knowledge in order to constrain our classification system yielding more parsimonious models compared to static ML approaches. In particular, we enforce unidirectionality on a set of binary, multivariate classifiers trained to discriminate between adjacent feed phases by linking the classifiers through a one-way switch. The switch is activated when the actual classifier output changes. As a consequence, the next binary classifier in the classifier chain is used for the discrimination between the next feed phase pair etc. We allow activation of the switch only after a predefined number of consecutive predictions of a transition event in order to prevent premature activation of the switch and undertake a sensitivity analysis regarding the optimal choice of the (time) lag parameter. From a complexity/parsimony perspective the benefit of our approach is three-fold: i) The multi-class learning task is broken down into binary subproblems which usually have simpler decision surfaces and tend to be less susceptible to the class-imbalance problem. ii) We exploit the fact that the process follows a rigid feed cycle structure (i.e. batch-feed-batch-feed) which allows us to focus on the subproblems involving phase transitions as they occur during the process while discarding off-transition classifiers and iii) only one binary classifier is active at the time which keeps effective model complexity low. We further use a combination of logistic regression and Lasso (i.e. regularized logistic regression, RLR) as a wrapper to extract the most relevant features for individual subproblems from the whole set of high-dimensional sensor data. We train different soft computing classifiers

  7. NEW CLASSIFICATION OF ECOPOLICES

    Directory of Open Access Journals (Sweden)

    VOROBYOV V. V.

    2016-09-01

    Full Text Available Problem statement. Ecopolices are the newest stage of the urban planning. They have to be consideredsuchas material and energy informational structures, included to the dynamic-evolutionary matrix netsofex change processes in the ecosystems. However, there are not made the ecopolice classifications, developing on suchapproaches basis. And this determined the topicality of the article. Analysis of publications on theoretical and applied aspects of the ecopolices formation showed, that the work on them is managed mainly in the context of the latest scientific and technological achievements in the various knowledge fields. These settlements are technocratic. They are connected with the morphology of space, network structures of regional and local natural ecosystems, without independent stability, can not exist without continuous man support. Another words, they do not work in with an ecopolices idea. It is come to a head for objective, symbiotic searching of ecopolices concept with the development of their classifications. Purpose statement is to develop the objective evidence for ecopolices and to propose their new classification. Conclusion. On the base of the ecopolices classification have to lie an elements correlation idea of their general plans and men activity type according with natural mechanism of accepting, reworking and transmission of material, energy and information between geo-ecosystems, planet, man, ecopolices material part and Cosmos. New ecopolices classification should be based on the principles of multi-dimensional, time-spaced symbiotic clarity with exchange ecosystem networks. The ecopolice function with this approach comes not from the subjective anthropocentric economy but from the holistic objective of Genesis paradigm. Or, otherwise - not from the Consequence, but from the Cause.

  8. Efficient Fingercode Classification

    Science.gov (United States)

    Sun, Hong-Wei; Law, Kwok-Yan; Gollmann, Dieter; Chung, Siu-Leung; Li, Jian-Bin; Sun, Jia-Guang

    In this paper, we present an efficient fingerprint classification algorithm which is an essential component in many critical security application systems e. g. systems in the e-government and e-finance domains. Fingerprint identification is one of the most important security requirements in homeland security systems such as personnel screening and anti-money laundering. The problem of fingerprint identification involves searching (matching) the fingerprint of a person against each of the fingerprints of all registered persons. To enhance performance and reliability, a common approach is to reduce the search space by firstly classifying the fingerprints and then performing the search in the respective class. Jain et al. proposed a fingerprint classification algorithm based on a two-stage classifier, which uses a K-nearest neighbor classifier in its first stage. The fingerprint classification algorithm is based on the fingercode representation which is an encoding of fingerprints that has been demonstrated to be an effective fingerprint biometric scheme because of its ability to capture both local and global details in a fingerprint image. We enhance this approach by improving the efficiency of the K-nearest neighbor classifier for fingercode-based fingerprint classification. Our research firstly investigates the various fast search algorithms in vector quantization (VQ) and the potential application in fingerprint classification, and then proposes two efficient algorithms based on the pyramid-based search algorithms in VQ. Experimental results on DB1 of FVC 2004 demonstrate that our algorithms can outperform the full search algorithm and the original pyramid-based search algorithms in terms of computational efficiency without sacrificing accuracy.

  9. Differential Classification of Dementia

    Directory of Open Access Journals (Sweden)

    E. Mohr

    1995-01-01

    Full Text Available In the absence of biological markers, dementia classification remains complex both in terms of characterization as well as early detection of the presence or absence of dementing symptoms, particularly in diseases with possible secondary dementia. An empirical, statistical approach using neuropsychological measures was therefore developed to distinguish demented from non-demented patients and to identify differential patterns of cognitive dysfunction in neurodegenerative disease. Age-scaled neurobehavioral test results (Wechsler Adult Intelligence Scale—Revised and Wechsler Memory Scale from Alzheimer's (AD and Huntington's (HD patients, matched for intellectual disability, as well as normal controls were used to derive a classification formula. Stepwise discriminant analysis accurately (99% correct distinguished controls from demented patients, and separated the two patient groups (79% correct. Variables discriminating between HD and AD patient groups consisted of complex psychomotor tasks, visuospatial function, attention and memory. The reliability of the classification formula was demonstrated with a new, independent sample of AD and HD patients which yielded virtually identical results (classification accuracy for dementia: 96%; AD versus HD: 78%. To validate the formula, the discriminant function was applied to Parkinson's (PD patients, 38% of whom were classified as demented. The validity of the classification was demonstrated by significant PD subgroup differences on measures of dementia not included in the discriminant function. Moreover, a majority of demented PD patients (65% were classified as having an HD-like pattern of cognitive deficits, in line with previous reports of the subcortical nature of PD dementia. This approach may thus be useful in classifying presence or absence of dementia and in discriminating between dementia subtypes in cases of secondary or coincidental dementia.

  10. Plexiform neurofibroma tissue classification

    Science.gov (United States)

    Weizman, L.; Hoch, L.; Ben Sira, L.; Joskowicz, L.; Pratt, L.; Constantini, S.; Ben Bashat, D.

    2011-03-01

    Plexiform Neurofibroma (PN) is a major complication of NeuroFibromatosis-1 (NF1), a common genetic disease that involving the nervous system. PNs are peripheral nerve sheath tumors extending along the length of the nerve in various parts of the body. Treatment decision is based on tumor volume assessment using MRI, which is currently time consuming and error prone, with limited semi-automatic segmentation support. We present in this paper a new method for the segmentation and tumor mass quantification of PN from STIR MRI scans. The method starts with a user-based delineation of the tumor area in a single slice and automatically detects the PN lesions in the entire image based on the tumor connectivity. Experimental results on seven datasets yield a mean volume overlap difference of 25% as compared to manual segmentation by expert radiologist with a mean computation and interaction time of 12 minutes vs. over an hour for manual annotation. Since the user interaction in the segmentation process is minimal, our method has the potential to successfully become part of the clinical workflow.

  11. 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

  12. 78 FR 54970 - Cotton Futures Classification: Optional Classification Procedure

    Science.gov (United States)

    2013-09-09

    ... Service 7 CFR Part 27 [AMS-CN-13-0043] RIN 0581-AD33 Cotton Futures Classification: Optional Classification Procedure AGENCY: Agricultural Marketing Service, USDA. ACTION: Proposed rule. SUMMARY: The... optional cotton futures classification procedure--identified and known as ``registration'' by the U.S...

  13. A neural network-based optimal spatial filter design method for motor imagery classification.

    Directory of Open Access Journals (Sweden)

    Ayhan Yuksel

    Full Text Available In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy.

  14. 32 CFR 2700.22 - Classification guides.

    Science.gov (United States)

    2010-07-01

    ... SECURITY INFORMATION REGULATIONS Derivative Classification § 2700.22 Classification guides. OMSN shall... direct derivative classification, shall identify the information to be protected in specific and uniform...

  15. Rare, but challenging tumors: NET

    International Nuclear Information System (INIS)

    Ivanova, D.; Balev, B.

    2013-01-01

    Full text: Introduction: Gastroenteropancreatic Neuroendocrine Tumors (GEP - NET) are a heterogeneous group of tumors with different locations and many different clinical, histological, and imaging performance. In a part of them a secretion of various organic substances is present. The morbidity of GEP - NET in the EU is growing, and this leads to increase the attention to them. What you will learn: Imaging methods used for localization and staging of GEP - NET, characteristics of the study’s protocols; Classification of GEP - NET; Demonstration of typical and atypical imaging features of GEP - NET in patients registered at the NET Center at University Hospital ‘St. Marina’, Varna; Features of metastatic NET, The role of imaging in the evaluation of treatment response and follow-up of the patients. Discussion: The image semiotics analysis is based on 19 cases of GEP - NET registered NET Center at University Hospital ‘St. Marina’. The main imaging method is multidetector CT (MDCT), and magnetic resonance imaging (MRI ) has advantages in the evaluation of liver lesions and the local prevalence of anorectal tumors. In patients with advanced disease and liver lesions the assessment of skeletal involvement (MRI/ nuclear medical method) is mandatory. The majority of GEP - NET have not any specific imaging findings. Therefore it is extremely important proper planning and conducting of the study (MDCT and MR enterography; accurate assessment phase of scanning, positive and negative contrast). Conclusion: GEP - NET is a major diagnostic challenge due to the absence of typical imaging characteristics and often an overlap with those of the tumors of different origin can be observed. Therefore, a good knowledge of clinical and imaging changes occurring at different locations is needed. MDCT is the basis for the diagnosis, staging and follow-up of these neoplasms

  16. Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification.

    Science.gov (United States)

    Hariharan, M; Sindhu, R; Vijean, Vikneswaran; Yazid, Haniza; Nadarajaw, Thiyagar; Yaacob, Sazali; Polat, Kemal

    2018-03-01

    Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. The experimental

  17. MRI findings of inflammatory myofibroblastic tumor of the soft tissue

    International Nuclear Information System (INIS)

    Deng Demao; Meng Quanfei; Chen Yinming; Zhou Chunxiang; Gao Zhenhua; Yang Zheng; Wang Liantang

    2008-01-01

    Objective: To describe MR findings in inflammatory myofibroblastic tumor (IMT) of the soft tissue. Methods: MR manifestations of 11 cases of IMT of the soft tissue were retrospectively analyzed, and the MR findings were correlated with surgical and histological results. Results: The pathological classification of the tumors was type I in 1 case, type II in 4 cases, mainly type II admixed with type I in 3 cases, and mainly type II admixed with type III in 3 eases. In 4 cases with primary tumor, the tumors were spheroid in shape, with well-defined margin and pseudocapsule. In 2 eases with primary axillary tumor and 5 cases with recurrent tumor, the tumors were irregular in shape, with ill-defined margin and invasion of adjacent structures. The tumors were mainly isointensive in T 1 -weighted images. Tumors of different pathological classifications had different signal intensities in T 2 -weighted images: 1 case of type I tumor was bright; 4 cases of type II tumor and 3 cases of type II tumor admixed with type I tumor were slightly bright; 3 cases of type II tumor admixed with type III were isointense or slightly hypointense in signal. All of the 11 cases in the study exhibited 'pitaya cross-section sign' in T 2 -weighted sequence, which referred to discrete punctuate foci of relatively hypointensity in the background of hyperintensity, slightly hypointensity or isointensity. All of the 11 cases exhibited inhomogeneously significant enhancement after gadolinium administration. In the follow-up of the 6 eases of primary tumor, 4 cases had recurrence, 1 case had no recurrence, and 1 case was lost in the follow-up process. In the follow-up of the 5 cases of recurrent tumor, 4 cases showed recurrence again, and 3 cases were lost in the follow-up process. Conclusions: The IMT of the soft tissue has characteristic MR features. The signal intensity of the tumor on T2-weighted sequence could reflect the pathological type of the tumor' to some extent. 'pitaya cross

  18. Tumor macroenvironment and metabolism.

    Science.gov (United States)

    Al-Zoughbi, Wael; Al-Zhoughbi, Wael; Huang, Jianfeng; Paramasivan, Ganapathy S; Till, Holger; Pichler, Martin; Guertl-Lackner, Barbara; Hoefler, Gerald

    2014-04-01

    In this review we introduce the concept of the tumor macroenvironment and explore it in the context of metabolism. Tumor cells interact with the tumor microenvironment including immune cells. Blood and lymph vessels are the critical components that deliver nutrients to the tumor and also connect the tumor to the macroenvironment. Several factors are then released from the tumor itself but potentially also from the tumor microenvironment, influencing the metabolism of distant tissues and organs. Amino acids, and distinct lipid and lipoprotein species can be essential for further tumor growth. The role of glucose in tumor metabolism has been studied extensively. Cancer-associated cachexia is the most important tumor-associated systemic syndrome and not only affects the quality of life of patients with various malignancies but is estimated to be the cause of death in 15%-20% of all cancer patients. On the other hand, systemic metabolic diseases such as obesity and diabetes are known to influence tumor development. Furthermore, the clinical implications of the tumor macroenvironment are explored in the context of the patient's outcome with special consideration for pediatric tumors. Finally, ways to target the tumor macroenvironment that will provide new approaches for therapeutic concepts are described. Copyright © 2014 Elsevier Inc. All rights reserved.

  19. IAEA Classification of Uranium Deposits

    International Nuclear Information System (INIS)

    Bruneton, Patrice

    2014-01-01

    Classifications of uranium deposits follow two general approaches, focusing on: • descriptive features such as the geotectonic position, the host rock type, the orebody morphology, …… : « geologic classification »; • or on genetic aspects: « genetic classification »

  20. Classification of Osteogenesis Imperfecta revisited

    NARCIS (Netherlands)

    van Dijk, F. S.; Pals, G.; van Rijn, R. R.; Nikkels, P. G. J.; Cobben, J. M.

    2010-01-01

    In 1979 Sillence proposed a classification of Osteogenesis Imperfecta (OI) in OI types I, II, III and IV. In 2004 and 2007 this classification was expanded with OI types V-VIII because of distinct clinical features and/or different causative gene mutations. We propose a revised classification of OI

  1. The future of general classification

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2013-01-01

    Discusses problems related to accessing multiple collections using a single retrieval language. Surveys the concepts of interoperability and switching language. Finds that mapping between more indexing languages always will be an approximation. Surveys the issues related to general classification...... and contrasts that to special classifications. Argues for the use of general classifications to provide access to collections nationally and internationally....

  2. Molecular Classification of Lobular Carcinoma of the Breast

    Science.gov (United States)

    Fu, Denggang; Zuo, Qi; Huang, Qi; Su, Li; Ring, Huijun Z.; Ring, Brian Z.

    2017-01-01

    The morphology of breast tumors is complicated and diagnosis can be difficult. We present here a novel diagnostic model which we validate on both array-based and RNA sequencing platforms which reliably distinguishes this tumor type across multiple cohorts. We also examine how this molecular classification predicts sensitivity to common chemotherapeutics in cell-line based assays. A total of 1845 invasive breast cancer cases in six cohorts were collected, split into discovery and validation cohorts, and a classifier was created and compared to pathological diagnosis, grade and survival. In the validation cohorts the concordance of predicted diagnosis with a pathological diagnosis was 92%, and 97% when inconclusively classified cases were excluded. Tumor-derived cell lines were classified with the model as having predominantly ductal or lobular-like molecular physiologies, and sensitivity of these lines to relevant compounds was analyzed. A diagnostic tool can be created that reliably distinguishes lobular from ductal carcinoma and allows the classification of cell lines on the basis of molecular profiles associated with these tumor types. This tool may assist in improved diagnosis and aid in explorations of the response of lobular type breast tumor models to different compounds. PMID:28303886

  3. [Headache: classification and diagnosis].

    Science.gov (United States)

    Carbaat, P A T; Couturier, E G M

    2016-11-01

    There are many types of headache and, moreover, many people have different types of headache at the same time. Adequate treatment is possible only on the basis of the correct diagnosis. Technically and in terms of content the current diagnostics process for headache is based on the 'International Classification of Headache Disorders' (ICHD-3-beta) that was produced under the auspices of the International Headache Society. This classification is based on a distinction between primary and secondary headaches. The most common primary headache types are the tension type headache, migraine and the cluster headache. Application of uniform diagnostic concepts is essential to come to the most appropriate treatment of the various types of headache.

  4. Classification of hand eczema

    DEFF Research Database (Denmark)

    Agner, T; Aalto-Korte, K; Andersen, K E

    2015-01-01

    BACKGROUND: Classification of hand eczema (HE) is mandatory in epidemiological and clinical studies, and also important in clinical work. OBJECTIVES: The aim was to test a recently proposed classification system of HE in clinical practice in a prospective multicentre study. METHODS: Patients were...... recruited from nine different tertiary referral centres. All patients underwent examination by specialists in dermatology and were checked using relevant allergy testing. Patients were classified into one of the six diagnostic subgroups of HE: allergic contact dermatitis, irritant contact dermatitis, atopic...... system investigated in the present study was useful, being able to give an appropriate main diagnosis for 89% of HE patients, and for another 7% when using two main diagnoses. The fact that more than half of the patients had one or more additional diagnoses illustrates that HE is a multifactorial disease....

  5. Sound classification of dwellings

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    2012-01-01

    National schemes for sound classification of dwellings exist in more than ten countries in Europe, typically published as national standards. The schemes define quality classes reflecting different levels of acoustical comfort. Main criteria concern airborne and impact sound insulation between...... dwellings, facade sound insulation and installation noise. The schemes have been developed, implemented and revised gradually since the early 1990s. However, due to lack of coordination between countries, there are significant discrepancies, and new standards and revisions continue to increase the diversity...... is needed, and a European COST Action TU0901 "Integrating and Harmonizing Sound Insulation Aspects in Sustainable Urban Housing Constructions", has been established and runs 2009-2013, one of the main objectives being to prepare a proposal for a European sound classification scheme with a number of quality...

  6. Granular loess classification based

    International Nuclear Information System (INIS)

    Browzin, B.S.

    1985-01-01

    This paper discusses how loess might be identified by two index properties: the granulometric composition and the dry unit weight. These two indices are necessary but not always sufficient for identification of loess. On the basis of analyses of samples from three continents, it was concluded that the 0.01-0.5-mm fraction deserves the name loessial fraction. Based on the loessial fraction concept, a granulometric classification of loess is proposed. A triangular chart is used to classify loess

  7. Classification and regression trees

    CERN Document Server

    Breiman, Leo; Olshen, Richard A; Stone, Charles J

    1984-01-01

    The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

  8. CLASSIFICATION OF CRIMINAL GROUPS

    OpenAIRE

    Natalia Romanova

    2013-01-01

    New types of criminal groups are emerging in modern society.  These types have their special criminal subculture. The research objective is to develop new parameters of classification of modern criminal groups, create a new typology of criminal groups and identify some features of their subculture. Research methodology is based on the system approach that includes using the method of analysis of documentary sources (materials of a criminal case), method of conversations with themembers of the...

  9. Decimal Classification Editions

    Directory of Open Access Journals (Sweden)

    Zenovia Niculescu

    2009-01-01

    Full Text Available The study approaches the evolution of Dewey Decimal Classification editions from the perspective of updating the terminology, reallocating and expanding the main and auxilary structure of Dewey indexing language. The comparative analysis of DDC editions emphasizes the efficiency of Dewey scheme from the point of view of improving the informational offer, through basic index terms, revised and developed, as well as valuing the auxilary notations.

  10. Decimal Classification Editions

    OpenAIRE

    Zenovia Niculescu

    2009-01-01

    The study approaches the evolution of Dewey Decimal Classification editions from the perspective of updating the terminology, reallocating and expanding the main and auxilary structure of Dewey indexing language. The comparative analysis of DDC editions emphasizes the efficiency of Dewey scheme from the point of view of improving the informational offer, through basic index terms, revised and developed, as well as valuing the auxilary notations.

  11. Bronchial carcinoid tumors: A rare malignant tumor

    African Journals Online (AJOL)

    2015-02-03

    Feb 3, 2015 ... Nigerian Journal of Clinical Practice • Sep-Oct 2015 • Vol 18 • Issue 5. Abstract. Bronchial carcinoid tumors (BCTs) are an uncommon group of lung tumors. They commonly affect the young adults and the middle aged, the same age group affected by other more common chronic lung conditions such as ...

  12. Classifications of track structures

    International Nuclear Information System (INIS)

    Paretzke, H.G.

    1984-01-01

    When ionizing particles interact with matter they produce random topological structures of primary activations which represent the initial boundary conditions for all subsequent physical, chemical and/or biological reactions. There are two important aspects of research on such track structures, namely their experimental or theoretical determination on one hand and the quantitative classification of these complex structures which is a basic pre-requisite for the understanding of mechanisms of radiation actions. This paper deals only with the latter topic, i.e. the problems encountered in and possible approaches to quantitative ordering and grouping of these multidimensional objects by their degrees of similarity with respect to their efficiency in producing certain final radiation effects, i.e. to their ''radiation quality.'' Various attempts of taxonometric classification with respect to radiation efficiency have been made in basic and applied radiation research including macro- and microdosimetric concepts as well as track entities and stopping power based theories. In this paper no review of those well-known approaches is given but rather an outline and discussion of alternative methods new to this field of radiation research which have some very promising features and which could possibly solve at least some major classification problems

  13. Neuromuscular disease classification system

    Science.gov (United States)

    Sáez, Aurora; Acha, Begoña; Montero-Sánchez, Adoración; Rivas, Eloy; Escudero, Luis M.; Serrano, Carmen

    2013-06-01

    Diagnosis of neuromuscular diseases is based on subjective visual assessment of biopsies from patients by the pathologist specialist. A system for objective analysis and classification of muscular dystrophies and neurogenic atrophies through muscle biopsy images of fluorescence microscopy is presented. The procedure starts with an accurate segmentation of the muscle fibers using mathematical morphology and a watershed transform. A feature extraction step is carried out in two parts: 24 features that pathologists take into account to diagnose the diseases and 58 structural features that the human eye cannot see, based on the assumption that the biopsy is considered as a graph, where the nodes are represented by each fiber, and two nodes are connected if two fibers are adjacent. A feature selection using sequential forward selection and sequential backward selection methods, a classification using a Fuzzy ARTMAP neural network, and a study of grading the severity are performed on these two sets of features. A database consisting of 91 images was used: 71 images for the training step and 20 as the test. A classification error of 0% was obtained. It is concluded that the addition of features undetectable by the human visual inspection improves the categorization of atrophic patterns.

  14. An automated cirrus classification

    Science.gov (United States)

    Gryspeerdt, Edward; Quaas, Johannes; Goren, Tom; Klocke, Daniel; Brueck, Matthias

    2018-05-01

    Cirrus clouds play an important role in determining the radiation budget of the earth, but many of their properties remain uncertain, particularly their response to aerosol variations and to warming. Part of the reason for this uncertainty is the dependence of cirrus cloud properties on the cloud formation mechanism, which itself is strongly dependent on the local meteorological conditions. In this work, a classification system (Identification and Classification of Cirrus or IC-CIR) is introduced to identify cirrus clouds by the cloud formation mechanism. Using reanalysis and satellite data, cirrus clouds are separated into four main types: orographic, frontal, convective and synoptic. Through a comparison to convection-permitting model simulations and back-trajectory-based analysis, it is shown that these observation-based regimes can provide extra information on the cloud-scale updraughts and the frequency of occurrence of liquid-origin ice, with the convective regime having higher updraughts and a greater occurrence of liquid-origin ice compared to the synoptic regimes. Despite having different cloud formation mechanisms, the radiative properties of the regimes are not distinct, indicating that retrieved cloud properties alone are insufficient to completely describe them. This classification is designed to be easily implemented in GCMs, helping improve future model-observation comparisons and leading to improved parametrisations of cirrus cloud processes.

  15. Detecting brain tumor in pathological slides using hyperspectral imaging.

    Science.gov (United States)

    Ortega, Samuel; Fabelo, Himar; Camacho, Rafael; de la Luz Plaza, María; Callicó, Gustavo M; Sarmiento, Roberto

    2018-02-01

    Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnosed with high-grade glioma. Based on the diagnosis provided by pathologists, a spectral library of normal and tumor tissues was created and processed using three different supervised classification algorithms. Results prove that HSI is a suitable technique to automatically detect high-grade tumors from pathological slides.

  16. Reliable classification of moving waste materials with LIBS in concrete recycling.

    Science.gov (United States)

    Xia, Han; Bakker, M C M

    2014-03-01

    Effective discrimination between different waste materials is of paramount importance for inline quality inspection of recycle concrete aggregates from demolished buildings. The moving targeted materials in the concrete waste stream are wood, PVC, gypsum block, glass, brick, steel rebar, aggregate and cement paste. For each material, up to three different types were considered, while thirty particles of each material were selected. Proposed is a reliable classification methodology based on integration of the LIBS spectral emissions in a fixed time window, starting from the deployment of the laser shot. PLS-DA (multi class) and the hybrid combination PCA-Adaboost (binary class) were investigated as efficient classifiers. In addition, mean centre and auto scaling approaches were compared for both classifiers. Using 72 training spectra and 18 test spectra per material, each averaged by ten shots, only PLS-DA achieved full discrimination, and the mean centre approach made it slightly more robust. Continuing with PLS-DA, the relation between data averaging and convergence to 0.3% average error was investigated using 9-fold cross-validations. Single-shot PLS-DA presented the highest challenge and most desirable methodology, which converged with 59 PC. The degree of success in practical testing will depend on the quality of the training set and the implications of the possibly remaining false positives. © 2013 Published by Elsevier B.V.

  17. Maximum mutual information regularized classification

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-09-07

    In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

  18. Maximum mutual information regularized classification

    KAUST Repository

    Wang, Jim Jing-Yan; Wang, Yi; Zhao, Shiguang; Gao, Xin

    2014-01-01

    In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

  19. Odontogenic Tumors: A Review of 675 Cases in Eastern Libya

    African Journals Online (AJOL)

    Aims: The aim of this study was to determine the relative frequency of odontogenic tumors (OTs) in an Eastern Libyan population based on the 2005 World Health Organization (WHO) classification, and also to compare the actual data with previous studies. Materials and Methods: We retrieved and analyzed 85 OTs from a ...

  20. The IASLC/ITMIG Thymic Epithelial Tumors Staging Project

    DEFF Research Database (Denmark)

    Kondo, Kazuya; Van Schil, Paul; Detterbeck, Frank C

    2014-01-01

    Stage classification is an important underpinning of management of patients with cancer, and rests on a combination of three components: T for tumor extent, N for nodal involvement, and M for more distant metastases. This article details an initiative to develop proposals for the first official...

  1. Children's Brain Tumor Foundation

    Science.gov (United States)

    ... 2 Family Donate Volunteer Justin's Hope Fund Children’s Brain Tumor Foundation, A non-profit organization, was founded ... and the long term outlook for children with brain and spinal cord tumors through research, support, education, ...

  2. Metaphyseal giant cell tumor

    International Nuclear Information System (INIS)

    Pereira, L.F.; Hemais, P.M.P.G.; Aymore, I.L.; Carmo, M.C.R. do; Cunha, M.E.P.R. da; Resende, C.M.C.

    1986-01-01

    Three cases of metaphyseal giant cell tumor are presented. A review of the literature is done, demostrating the lesion is rare and that there are few articles about it. Age incidence and characteristics of the tumor are discussed. (Author) [pt

  3. Testicular germinal tumors

    International Nuclear Information System (INIS)

    Fresco, R.

    2010-01-01

    This work is about diagnosis, treatment and monitoring of testicular germinal tumors. The presumed diagnosis is based in the anamnesis, clinical examination, testicular ultrasound and tumor markers. The definitive diagnosis is obtained through the inguinal radical orchidectomy

  4. Intra-tumor heterogeneity in breast cancer has limited impact on transcriptomic-based molecular profiling.

    Science.gov (United States)

    Karthik, Govindasamy-Muralidharan; Rantalainen, Mattias; Stålhammar, Gustav; Lövrot, John; Ullah, Ikram; Alkodsi, Amjad; Ma, Ran; Wedlund, Lena; Lindberg, Johan; Frisell, Jan; Bergh, Jonas; Hartman, Johan

    2017-11-29

    Transcriptomic profiling of breast tumors provides opportunity for subtyping and molecular-based patient stratification. In diagnostic applications the specimen profiled should be representative of the expression profile of the whole tumor and ideally capture properties of the most aggressive part of the tumor. However, breast cancers commonly exhibit intra-tumor heterogeneity at molecular, genomic and in phenotypic level, which can arise during tumor evolution. Currently it is not established to what extent a random sampling approach may influence molecular breast cancer diagnostics. In this study we applied RNA-sequencing to quantify gene expression in 43 pieces (2-5 pieces per tumor) from 12 breast tumors (Cohort 1). We determined molecular subtype and transcriptomic grade for all tumor pieces and analysed to what extent pieces originating from the same tumors are concordant or discordant with each other. Additionally, we validated our finding in an independent cohort consisting of 19 pieces (2-6 pieces per tumor) from 6 breast tumors (Cohort 2) profiled using microarray technique. Exome sequencing was also performed on this cohort, to investigate the extent of intra-tumor genomic heterogeneity versus the intra-tumor molecular subtype classifications. Molecular subtyping was consistent in 11 out of 12 tumors and transcriptomic grade assignments were consistent in 11 out of 12 tumors as well. Molecular subtype predictions revealed consistent subtypes in four out of six patients in this cohort 2. Interestingly, we observed extensive intra-tumor genomic heterogeneity in these tumor pieces but not in their molecular subtype classifications. Our results suggest that macroscopic intra-tumoral transcriptomic heterogeneity is limited and unlikely to have an impact on molecular diagnostics for most patients.

  5. Correlation of primary tumor FDG uptake with histopathologic features of advanced gastric cancer

    International Nuclear Information System (INIS)

    Kim, Hae Won; Won, Kyoung Sook; Song, Bong Il; Kang, Yu Na

    2015-01-01

    Histopathologic features could affect the FDG uptake of primary gastric cancer and detection rate on FDG PET/CT. The aim of this study was to evaluate the FDG uptake of primary gastric cancer by correlating it with the histopathologic features of the tumors. Fifty patients with locally advanced gastric adenocarcinoma who were referred for preoperative FDG-PET/CT scans were enrolled in this study. The detection rate of PET/CT and maximum standardized uptake values (SUV max ) of the primary tumor were compared using the WHO, Lauren, Ming and Borrmann classifications and tumor size and location. In 45 of the 50 patients (90 %), the primary gastric tumors were detected by FDG PET/CT. On comparison using the WHO classification, the detection rate and SUV max of the tubular type were significantly higher than those of the poorly cohesive type. On comparison using the Lauren and Ming classifications, the SUV maxs of the intestinal type and expanding type were significantly higher than those of the diffuse and infiltrative type, respectively. On comparison using the Borrmann classification and tumor size and location, there was no significant difference in the detection rate and SUV max of primary gastric tumors. This study demonstrates that the poorly cohesive type according to the WHO classification, diffuse type according to the Lauren classification and infiltrative type according to the Ming classification have low FDG uptake in patients with locally advanced gastric carcinoma. Understanding the relationship between primary tumor FDG uptake and histopathologic features would be helpful in detecting the primary tumor by FDG PET/CT in patients with gastric cancer

  6. Pulsed terahertz imaging of breast cancer in freshly excised murine tumors

    Science.gov (United States)

    Bowman, Tyler; Chavez, Tanny; Khan, Kamrul; Wu, Jingxian; Chakraborty, Avishek; Rajaram, Narasimhan; Bailey, Keith; El-Shenawee, Magda

    2018-02-01

    This paper investigates terahertz (THz) imaging and classification of freshly excised murine xenograft breast cancer tumors. These tumors are grown via injection of E0771 breast adenocarcinoma cells into the flank of mice maintained on high-fat diet. Within 1 h of excision, the tumor and adjacent tissues are imaged using a pulsed THz system in the reflection mode. The THz images are classified using a statistical Bayesian mixture model with unsupervised and supervised approaches. Correlation with digitized pathology images is conducted using classification images assigned by a modal class decision rule. The corresponding receiver operating characteristic curves are obtained based on the classification results. A total of 13 tumor samples obtained from 9 tumors are investigated. The results show good correlation of THz images with pathology results in all samples of cancer and fat tissues. For tumor samples of cancer, fat, and muscle tissues, THz images show reasonable correlation with pathology where the primary challenge lies in the overlapping dielectric properties of cancer and muscle tissues. The use of a supervised regression approach shows improvement in the classification images although not consistently in all tissue regions. Advancing THz imaging of breast tumors from mice and the development of accurate statistical models will ultimately progress the technique for the assessment of human breast tumor margins.

  7. [Risk factors for malignant evolution of gastrointestinal stromal tumors].

    Science.gov (United States)

    Andrei, S; Andrei, Adriana; Tonea, A; Andronesi, D; Becheanu, G; Dumbravă, Mona; Pechianu, C; Herlea, V; Popescu, I

    2007-01-01

    Gastrointestinal stromal tumors are the most frequent non-epithelial digestive tumors, being classified in the group of primitive mesenchymal tumors of the digestive tract. These tumors have a non predictable evolution and where stratified regarding the risk for malignant behavior in 4 categories: very low risk, low risk, intermediate risk and high risk. We performed a retrospective non randomised study including the patients with gastrointestinal stromal tumors treated in the Department of General Surgery and Liver Transplantation of Fundeni Clinical Institute in the period January 2002 - June 2007, to define the epidemiological, clinico-paraclinical, histological and especially evolutive features of the gastrointestinal stromal tumors from this group, with a special regard to the risk factors for their malignant behavior. The most important risk factors in gastrointestinal stromal tumors are the tumor size and the mitotic index, based on them being realised the classification of Fletcher in the 4 risk categories mentioned above. In our group all the local advanced or metastatic gastrointestinal stromal tumors, regardless of their location, were classified in the group of high risk for the malignant behavior. The gastric location and the epithelioid type were positive prognostic factors, and the complete resection of the tumor, an other important positive prognostic feature, was possible in about 80% of the cases, probably because the gastrointestinal stromal tumors in our study were diagnosed in less advanced evolutive situations, only about one third being metastatic and about 14% being locally advanced at the time of diagnose. The association with other neoplasias was in our cases insignificant, only 5% of the patients presenting concomitant malignant digestive tumors and 7.6% intraabdominal benign tumors. Gastrointestinal stromal tumors remain a challenge for the medical staff, regarding their diagnose and therapeutical management, the stratification of the

  8. Tissue engineered tumor models.

    Science.gov (United States)

    Ingram, M; Techy, G B; Ward, B R; Imam, S A; Atkinson, R; Ho, H; Taylor, C R

    2010-08-01

    Many research programs use well-characterized tumor cell lines as tumor models for in vitro studies. Because tumor cells grown as three-dimensional (3-D) structures have been shown to behave more like tumors in vivo than do cells growing in monolayer culture, a growing number of investigators now use tumor cell spheroids as models. Single cell type spheroids, however, do not model the stromal-epithelial interactions that have an important role in controlling tumor growth and development in vivo. We describe here a method for generating, reproducibly, more realistic 3-D tumor models that contain both stromal and malignant epithelial cells with an architecture that closely resembles that of tumor microlesions in vivo. Because they are so tissue-like we refer to them as tumor histoids. They can be generated reproducibly in substantial quantities. The bioreactor developed to generate histoid constructs is described and illustrated. It accommodates disposable culture chambers that have filled volumes of either 10 or 64 ml, each culture yielding on the order of 100 or 600 histoid particles, respectively. Each particle is a few tenths of a millimeter in diameter. Examples of histological sections of tumor histoids representing cancers of breast, prostate, colon, pancreas and urinary bladder are presented. Potential applications of tumor histoids include, but are not limited to, use as surrogate tumors for pre-screening anti-solid tumor pharmaceutical agents, as reference specimens for immunostaining in the surgical pathology laboratory and use in studies of invasive properties of cells or other aspects of tumor development and progression. Histoids containing nonmalignant cells also may have potential as "seeds" in tissue engineering. For drug testing, histoids probably will have to meet certain criteria of size and tumor cell content. Using a COPAS Plus flow cytometer, histoids containing fluorescent tumor cells were analyzed successfully and sorted using such criteria.

  9. Current concepts in non-gastrointestinal stromal tumor soft tissue sarcomas: A primer for radiologists

    Energy Technology Data Exchange (ETDEWEB)

    Baheti, Akahay D. [Dept. of Radiology, Tata Memorial Centre, Mumbai (India); Tirumani, Harika [Dept. of Radiology, University of Arkansas for Medical Sciences, Little Rock (United States); O' Neill, Alibhe; Jagannathan, Jyothi P. [Dept. of Imaging, Dana-Farber Cancer Institute, Boston (United States)

    2017-01-15

    Non-gastrointestinal stromal tumor (GIST) soft tissue sarcomas (STSs) are a heterogeneous group of neoplasms whose classification and management continues to evolve with better understanding of their biologic behavior. The 2013 World Health Organization (WHO) has revised their classification based on new immunohistochemical and cytogenetic data. In this article, we will provide a brief overview of the revised WHO classification of soft tissue tumors, discuss in detail the radiology and management of the two most common adult non-GIST STS, namely liposarcoma and leiomyosarcoma, and review some of the emerging histology-driven targeted therapies in non-GIST STS, focusing on the role of the radiologist.

  10. Current concepts in non-gastrointestinal stromal tumor soft tissue sarcomas: A primer for radiologists

    International Nuclear Information System (INIS)

    Baheti, Akahay D.; Tirumani, Harika; O'Neill, Alibhe; Jagannathan, Jyothi P.

    2017-01-01

    Non-gastrointestinal stromal tumor (GIST) soft tissue sarcomas (STSs) are a heterogeneous group of neoplasms whose classification and management continues to evolve with better understanding of their biologic behavior. The 2013 World Health Organization (WHO) has revised their classification based on new immunohistochemical and cytogenetic data. In this article, we will provide a brief overview of the revised WHO classification of soft tissue tumors, discuss in detail the radiology and management of the two most common adult non-GIST STS, namely liposarcoma and leiomyosarcoma, and review some of the emerging histology-driven targeted therapies in non-GIST STS, focusing on the role of the radiologist

  11. Tumor interstitial fluid

    DEFF Research Database (Denmark)

    Gromov, Pavel; Gromova, Irina; Olsen, Charlotta J.

    2013-01-01

    Tumor interstitial fluid (TIF) is a proximal fluid that, in addition to the set of blood soluble phase-borne proteins, holds a subset of aberrantly externalized components, mainly proteins, released by tumor cells and tumor microenvironment through various mechanisms, which include classical...

  12. Gene discovery in glioma in the context of molecular reclassification of tumors

    Directory of Open Access Journals (Sweden)

    Khushboo Irshad

    2015-12-01

    Full Text Available Conventional classification of tumors, especially in terms of staging and grading is of immense importance for both prognostication as well as management strategies. However it is not a perfect system and there are many instances where tumor behaviour does not correspond to what is expected. In addition, with the onset of targeted therapy, the identification of the distinct molecular target in a subset of tumors becomes a marker of tumor behaviour as well as a target of therapy. This leads to the concept of molecular subclassification of tumors where molecular markers further refine and in some cases, alter conventional classification. We would be presenting this concept in relation to glial tumors, especially in the context of molecular markers discovered in our laboratory.

  13. Dependence of radiotherapeutic results on tumor size in patients with cervix uteri carcinoma

    International Nuclear Information System (INIS)

    Gabelov, A.A.; Zharinov, G.M.

    1981-01-01

    A method is suggested that permits specifying the primary tumor size on the basis of clinical examination of patients with cervix uteri carcinoma. The values of tumor size have been correlated with long-term results of concomitant radiotherapy in 1358 patients with cervix uteri carcinoma. The data obtained have shown that the primary tumor size is a factor that determines to a large extent radiotherapeutic results in patients with cervix uteri carcinoma. The specification of tumor size values makes it possible to considerably lessen prognostic uncertainty of present-day staging classifications. The structure of radiotherapeutic failures also turned out to be closely associated with the primary tumor size

  14. Using fuzzy association rule mining in cancer classification

    International Nuclear Information System (INIS)

    Mahmoodian, Hamid; Marhaban, M.H.; Abdulrahim, Raha; Rosli, Rozita; Saripan, Iqbal

    2011-01-01

    Full text: The classification of the cancer tumors based on gene expression profiles has been extensively studied in numbers of studies. A wide variety of cancer datasets have been implemented by the various methods of gene selec tion and classification to identify the behavior of the genes in tumors and find the relationships between them and outcome of diseases. Interpretability of the model, which is developed by fuzzy rules and linguistic variables in this study, has been rarely considered. In addition, creating a fuzzy classifier with high performance in classification that uses a subset of significant genes which have been selected by different types of gene selection methods is another goal of this study. A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. At first, different subset of genes which have been selected by different methods, were used to generate primary fuzzy classifiers separately and then proposed algorithm was implemented to mix the genes which have been associated in the primary classifiers and generate a new classifier. The results show that fuzzy classifier can classify the tumors with high performance while presenting the relationships between the genes by linguistic variables

  15. Contemporary Management of Benign and Malignant Parotid Tumors.

    Science.gov (United States)

    Thielker, Jovanna; Grosheva, Maria; Ihrler, Stephan; Wittig, Andrea; Guntinas-Lichius, Orlando

    2018-01-01

    To report the standard of care, interesting new findings and controversies about the treatment of parotid tumors. Relevant and actual studies were searched in PubMed and reviewed for diagnostics, treatment and outcome of both benign and malignant tumors. Prospective trials are lacking due to rarity of the disease and high variety of tumor subtypes. The establishment of reliable non-invasive diagnostics tools for the differentiation between benign and malignant tumors is desirable. Prospective studies clarifying the association between different surgical techniques for benign parotid tumors and morbidity are needed. The role of adjuvant or definitive radiotherapy in securing loco-regional control and improving survival in malignant disease is established. Prospective clinical trials addressing the role of chemotherapy/molecular targeted therapy for parotid cancer are needed. An international consensus on the classification of parotid surgery techniques would facilitate the comparison of different trials. Such efforts should lead into a clinical guideline.

  16. Numeric pathologic lymph node classification shows prognostic superiority to topographic pN classification in esophageal squamous cell carcinoma.

    Science.gov (United States)

    Sugawara, Kotaro; Yamashita, Hiroharu; Uemura, Yukari; Mitsui, Takashi; Yagi, Koichi; Nishida, Masato; Aikou, Susumu; Mori, Kazuhiko; Nomura, Sachiyo; Seto, Yasuyuki

    2017-10-01

    The current eighth tumor node metastasis lymph node category pathologic lymph node staging system for esophageal squamous cell carcinoma is based solely on the number of metastatic nodes and does not consider anatomic distribution. We aimed to assess the prognostic capability of the eighth tumor node metastasis pathologic lymph node staging system (numeric-based) compared with the 11th Japan Esophageal Society (topography-based) pathologic lymph node staging system in patients with esophageal squamous cell carcinoma. We retrospectively reviewed the clinical records of 289 patients with esophageal squamous cell carcinoma who underwent esophagectomy with extended lymph node dissection during the period from January 2006 through June 2016. We compared discrimination abilities for overall survival, recurrence-free survival, and cancer-specific survival between these 2 staging systems using C-statistics. The median number of dissected and metastatic nodes was 61 (25% to 75% quartile range, 45 to 79) and 1 (25% to 75% quartile range, 0 to 3), respectively. The eighth tumor node metastasis pathologic lymph node staging system had a greater ability to accurately determine overall survival (C-statistics: tumor node metastasis classification, 0.69, 95% confidence interval, 0.62-0.76; Japan Esophageal Society classification; 0.65, 95% confidence interval, 0.58-0.71; P = .014) and cancer-specific survival (C-statistics: tumor node metastasis classification, 0.78, 95% confidence interval, 0.70-0.87; Japan Esophageal Society classification; 0.72, 95% confidence interval, 0.64-0.80; P = .018). Rates of total recurrence rose as the eighth tumor node metastasis pathologic lymph node stage increased, while stratification of patients according to the topography-based node classification system was not feasible. Numeric nodal staging is an essential tool for stratifying the oncologic outcomes of patients with esophageal squamous cell carcinoma even in the cohort in which adequate

  17. Molecular Classification of Medulloblastoma

    OpenAIRE

    KIJIMA, Noriyuki; KANEMURA, Yonehiro

    2015-01-01

    Medulloblastoma (MB) is one of the most frequent malignant brain tumors in children. The current standard treatment regimen consists of surgical resection, craniospinal irradiation, and adjuvant chemotherapy. Although these treatments have the potential to increase the survival of 70–80% of patients with MB, they are also associated with serious treatment-induced morbidity. The current risk stratification of MB is based on clinical factors, including age at presentation, metastatic status, an...

  18. Current Trends in the Molecular Classification of Renal Neoplasms

    Directory of Open Access Journals (Sweden)

    Andrew N. Young

    2006-01-01

    Full Text Available Renal cell carcinoma (RCC is the most common form of kidney cancer in adults. RCC is a significant challenge for pathologic diagnosis and clinical management. The primary approach to diagnosis is by light microscopy, using the World Health Organization (WHO classification system, which defines histopathologic tumor subtypes with distinct clinical behavior and underlying genetic mutations. However, light microscopic diagnosis of RCC subtypes is often difficult due to variable histology. In addition, the clinical behavior of RCC is highly variable and therapeutic response rates are poor. Few clinical assays are available to predict outcome in RCC or correlate behavior with histology. Therefore, novel RCC classification systems based on gene expression should be useful for diagnosis, prognosis, and treatment. Recent microarray studies have shown that renal tumors are characterized by distinct gene expression profiles, which can be used to discover novel diagnostic and prognostic biomarkers. Here, we review clinical features of kidney cancer, the WHO classification system, and the growing role of molecular classification for diagnosis, prognosis, and therapy of this disease.

  19. 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.

  20. Classification of IRAS asteroids

    International Nuclear Information System (INIS)

    Tedesco, E.F.; Matson, D.L.; Veeder, G.J.

    1989-01-01

    Albedos and spectral reflectances are essential for classifying asteroids. For example, classes E, M and P are indistinguishable without albedo data. Colorometric data are available for about 1000 asteroids but, prior to IRAS, albedo data was available for only about 200. IRAS broke this bottleneck by providing albedo data on nearly 2000 asteroids. Hence, excepting absolute magnitudes, the albedo and size are now the most common asteroid physical parameters known. In this chapter the authors present the results of analyses of IRAS-derived asteroid albedos, discuss their application to asteroid classification, and mention several studies which might be done to exploit further this data set

  1. SPORT FOOD ADDITIVE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    I. P. Prokopenko

    2015-01-01

    Full Text Available Correctly organized nutritive and pharmacological support is an important component of an athlete's preparation for competitions, an optimal shape maintenance, fast recovery and rehabilitation after traumas and defatigation. Special products of enhanced biological value (BAS for athletes nutrition are used with this purpose. Easy-to-use energy sources are administered into athlete's organism, yielded materials and biologically active substances which regulate and activate exchange reactions which proceed with difficulties during certain physical trainings. The article presents sport supplements classification which can be used before warm-up and trainings, after trainings and in competitions breaks.

  2. Radioactive facilities classification criteria

    International Nuclear Information System (INIS)

    Briso C, H.A.; Riesle W, J.

    1992-01-01

    Appropriate classification of radioactive facilities into groups of comparable risk constitutes one of the problems faced by most Regulatory Bodies. Regarding the radiological risk, the main facts to be considered are the radioactive inventory and the processes to which these radionuclides are subjected. Normally, operations are ruled by strict safety procedures. Thus, the total activity of the radionuclides existing in a given facility is the varying feature that defines its risk. In order to rely on a quantitative criterion and, considering that the Annual Limits of Intake are widely accepted references, an index based on these limits, to support decisions related to radioactive facilities, is proposed. (author)

  3. PET and endocrine tumors

    International Nuclear Information System (INIS)

    Rigo, P.; Belhocine, T.; Hustinx, R.; Foidart-Willems, J.

    2000-01-01

    The authors review the main indications of PET examination, and specifically of 18 FDG, in the assessment of endocrine tumors: of the thyroid, of the parathyroid, of the adrenal and of the pituitary glands. Neuroendocrine tumors, gastro-entero-pancreatic or carcinoid tumors are also under the scope. Usually, the most differentiated tumors show only poor uptake of the FDG as they have a weak metabolic and proliferative activity. In the assessment of endocrine tumors, FDG-PET should be used only after most specific nuclear examinations been performed. (author)

  4. Tumor penetrating peptides

    Directory of Open Access Journals (Sweden)

    Tambet eTeesalu

    2013-08-01

    Full Text Available Tumor-homing peptides can be used to deliver drugs into tumors. Phage library screening in live mice has recently identified homing peptides that specifically recognize the endothelium of tumor vessels, extravasate, and penetrate deep into the extravascular tumor tissue. The prototypic peptide of this class, iRGD (CRGDKGPDC, contains the integrin-binding RGD motif. RGD mediates tumor homing through binding to αv integrins, which are selectively expressed on various cells in tumors, including tumor endothelial cells. The tumor-penetrating properties of iRGD are mediated by a second sequence motif, R/KXXR/K. This C-end Rule (or CendR motif is active only when the second basic residue is exposed at the C-terminus of the peptide. Proteolytic processing of iRGD in tumors activates the cryptic CendR motif, which then binds to neuropilin-1 activating an endocytic bulk transport pathway through tumor tissue. Phage screening has also yielded tumor-penetrating peptides that function like iRGD in activating the CendR pathway, but bind to a different primary receptor. Moreover, novel tumor-homing peptides can be constructed from tumor-homing motifs, CendR elements and protease cleavage sites. Pathologies other than tumors can be targeted with tissue-penetrating peptides, and the primary receptor can also be a vascular zip code of a normal tissue. The CendR technology provides a solution to a major problem in tumor therapy, poor penetration of drugs into tumors. The tumor-penetrating peptides are capable of taking a payload deep into tumor tissue in mice, and they also penetrate into human tumors ex vivo. Targeting with these peptides specifically increases the accumulation in tumors of a variety of drugs and contrast agents, such as doxorubicin, antibodies and nanoparticle-based compounds. Remarkably the drug to be targeted does not have to be coupled to the peptide; the bulk transport system activated by the peptide sweeps along any compound that is

  5. Development of a Prognostic Marker for Lung Cancer Using Analysis of Tumor Evolution

    Science.gov (United States)

    2017-08-01

    AWARD NUMBER: W81XWH-15-1-0243 TITLE: Development of a Prognostic Marker for Lung Cancer Using Analysis of Tumor Evolution PRINCIPAL...SUBTITLE 5a. CONTRACT NUMBER Development of a Prognostic Marker for Lung Cancer Using Analysis of Tumor Evolution 5b. GRANT NUMBER 5c. PROGRAM...derive a prognostic classifier. 15. SUBJECT TERMS NSCLC; tumor evolution ; whole exome sequencing 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF

  6. Epilepsy and Brain Tumors

    Institute of Scientific and Technical Information of China (English)

    Zhi-yi Sha

    2009-01-01

    @@ Epidemiology It is estimated 61,414 new cases of primary brain tumors are expected to be diagnosed in 2009 in the U.S. The incidence statistic of 61,414 persons diagnosed per year includes both malignant (22,738) and non-malignant (38,677) brain tumors. (Data from American Brain Tumor Association). During the years 2004-2005, approximately 359,000 people in the United States were living with the diagnosis of a primary brain or central nervous system tumor. Specifically, more than 81,000 persons were living with a malignant tumor, more than 267,000 persons with a benign tumor. For every 100,000 people in the United States, approximately 131 are living following the diagnosis of a brain tumor. This represents a prevalence rate of 130.8 per 100,000 person years[1].

  7. Supply chain planning classification

    Science.gov (United States)

    Hvolby, Hans-Henrik; Trienekens, Jacques; Bonde, Hans

    2001-10-01

    Industry experience a need to shift in focus from internal production planning towards planning in the supply network. In this respect customer oriented thinking becomes almost a common good amongst companies in the supply network. An increase in the use of information technology is needed to enable companies to better tune their production planning with customers and suppliers. Information technology opportunities and supply chain planning systems facilitate companies to monitor and control their supplier network. In spite if these developments, most links in today's supply chains make individual plans, because the real demand information is not available throughout the chain. The current systems and processes of the supply chains are not designed to meet the requirements now placed upon them. For long term relationships with suppliers and customers, an integrated decision-making process is needed in order to obtain a satisfactory result for all parties. Especially when customized production and short lead-time is in focus. An effective value chain makes inventory available and visible among the value chain members, minimizes response time and optimizes total inventory value held throughout the chain. In this paper a supply chain planning classification grid is presented based current manufacturing classifications and supply chain planning initiatives.

  8. Waste classification sampling plan

    International Nuclear Information System (INIS)

    Landsman, S.D.

    1998-01-01

    The purpose of this sampling is to explain the method used to collect and analyze data necessary to verify and/or determine the radionuclide content of the B-Cell decontamination and decommissioning waste stream so that the correct waste classification for the waste stream can be made, and to collect samples for studies of decontamination methods that could be used to remove fixed contamination present on the waste. The scope of this plan is to establish the technical basis for collecting samples and compiling quantitative data on the radioactive constituents present in waste generated during deactivation activities in B-Cell. Sampling and radioisotopic analysis will be performed on the fixed layers of contamination present on structural material and internal surfaces of process piping and tanks. In addition, dose rate measurements on existing waste material will be performed to determine the fraction of dose rate attributable to both removable and fixed contamination. Samples will also be collected to support studies of decontamination methods that are effective in removing the fixed contamination present on the waste. Sampling performed under this plan will meet criteria established in BNF-2596, Data Quality Objectives for the B-Cell Waste Stream Classification Sampling, J. M. Barnett, May 1998

  9. Cluster Based Text Classification Model

    DEFF Research Database (Denmark)

    Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

    2011-01-01

    We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases...... the accuracy at the same time. The test example is classified using simpler and smaller model. The training examples in a particular cluster share the common vocabulary. At the time of clustering, we do not take into account the labels of the training examples. After the clusters have been created......, the classifier is trained on each cluster having reduced dimensionality and less number of examples. The experimental results show that the proposed model outperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups...

  10. Classification of smooth Fano polytopes

    DEFF Research Database (Denmark)

    Øbro, Mikkel

    A simplicial lattice polytope containing the origin in the interior is called a smooth Fano polytope, if the vertices of every facet is a basis of the lattice. The study of smooth Fano polytopes is motivated by their connection to toric varieties. The thesis concerns the classification of smooth...... Fano polytopes up to isomorphism. A smooth Fano -polytope can have at most vertices. In case of vertices an explicit classification is known. The thesis contains the classification in case of vertices. Classifications of smooth Fano -polytopes for fixed exist only for . In the thesis an algorithm...... for the classification of smooth Fano -polytopes for any given is presented. The algorithm has been implemented and used to obtain the complete classification for ....

  11. Small-scale classification schemes

    DEFF Research Database (Denmark)

    Hertzum, Morten

    2004-01-01

    Small-scale classification schemes are used extensively in the coordination of cooperative work. This study investigates the creation and use of a classification scheme for handling the system requirements during the redevelopment of a nation-wide information system. This requirements...... classification inherited a lot of its structure from the existing system and rendered requirements that transcended the framework laid out by the existing system almost invisible. As a result, the requirements classification became a defining element of the requirements-engineering process, though its main...... effects remained largely implicit. The requirements classification contributed to constraining the requirements-engineering process by supporting the software engineers in maintaining some level of control over the process. This way, the requirements classification provided the software engineers...

  12. Active Learning for Text Classification

    OpenAIRE

    Hu, Rong

    2011-01-01

    Text classification approaches are used extensively to solve real-world challenges. The success or failure of text classification systems hangs on the datasets used to train them, without a good dataset it is impossible to build a quality system. This thesis examines the applicability of active learning in text classification for the rapid and economical creation of labelled training data. Four main contributions are made in this thesis. First, we present two novel selection strategies to cho...

  13. Unsupervised Classification Using Immune Algorithm

    OpenAIRE

    Al-Muallim, M. T.; El-Kouatly, R.

    2012-01-01

    Unsupervised classification algorithm based on clonal selection principle named Unsupervised Clonal Selection Classification (UCSC) is proposed in this paper. The new proposed algorithm is data driven and self-adaptive, it adjusts its parameters to the data to make the classification operation as fast as possible. The performance of UCSC is evaluated by comparing it with the well known K-means algorithm using several artificial and real-life data sets. The experiments show that the proposed U...

  14. Automatic computer aided analysis algorithms and system for adrenal tumors on CT images.

    Science.gov (United States)

    Chai, Hanchao; Guo, Yi; Wang, Yuanyuan; Zhou, Guohui

    2017-12-04

    The adrenal tumor will disturb the secreting function of adrenocortical cells, leading to many diseases. Different kinds of adrenal tumors require different therapeutic schedules. In the practical diagnosis, it highly relies on the doctor's experience to judge the tumor type by reading the hundreds of CT images. This paper proposed an automatic computer aided analysis method for adrenal tumors detection and classification. It consisted of the automatic segmentation algorithms, the feature extraction and the classification algorithms. These algorithms were then integrated into a system and conducted on the graphic interface by using MATLAB Graphic user interface (GUI). The accuracy of the automatic computer aided segmentation and classification reached 90% on 436 CT images. The experiments proved the stability and reliability of this automatic computer aided analytic system.

  15. Cutaneous Leiomyoma: Novel Histologic Findings for Classification and Diagnosis

    Directory of Open Access Journals (Sweden)

    Kambiz Kamyab Hesari

    2013-01-01

    Full Text Available Smooth muscle tumors rather benign or malignant can arise wherever the muscular tissue presents but cutaneous leiomyoma is one of the rare benign tumors of the which even the diagnostic criteria from the malignant type of the tumor is still in doubt. This study was aimed to compare the subtypes of cutaneous leiomyoma from different histologic aspects in order to find unique criteria for better classification and diagnosis. The six year data base of our center was reviewed and 25 patients with cutaneous leiomyoma were included in this study. Of 25 patients, 5 were female and 20 were male. 5 patients had angioleiomyoma (ALM and 20 had pilar leiomyoma (PLM. ALM had following characteristics: dilated vascular canals intermingled with compact smooth muscle bundles; well circumscribe counter and myxoid and hyaline changes through the tumor. In contrast, PLMs had following histologic features: poor defined outline, entrapped hair follicles and eccrine glands, acanthosis and elongated rete ridges with hyperpigmentation and smooth muscle bundles which are interdigitated with elongated rete ridges. Here we introduced some distinct histological features for each subtype of the cutaneous leiomyoma which can lead to create novel criteria for classification and diagnosis of the lesion.

  16. Application of higher order spectral features and support vector machines for bearing faults classification.

    Science.gov (United States)

    Saidi, Lotfi; Ben Ali, Jaouher; Fnaiech, Farhat

    2015-01-01

    Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals. Copyright © 2014 ISA

  17. A kernel-based multivariate feature selection method for microarray data classification.

    Directory of Open Access Journals (Sweden)

    Shiquan Sun

    Full Text Available High dimensionality and small sample sizes, and their inherent risk of overfitting, pose great challenges for constructing efficient classifiers in microarray data classification. Therefore a feature selection technique should be conducted prior to data classification to enhance prediction performance. In general, filter methods can be considered as principal or auxiliary selection mechanism because of their simplicity, scalability, and low computational complexity. However, a series of trivial examples show that filter methods result in less accurate performance because they ignore the dependencies of features. Although few publications have devoted their attention to reveal the relationship of features by multivariate-based methods, these methods describe relationships among features only by linear methods. While simple linear combination relationship restrict the improvement in performance. In this paper, we used kernel method to discover inherent nonlinear correlations among features as well as between feature and target. Moreover, the number of orthogonal components was determined by kernel Fishers linear discriminant analysis (FLDA in a self-adaptive manner rather than by manual parameter settings. In order to reveal the effectiveness of our method we performed several experiments and compared the results between our method and other competitive multivariate-based features selectors. In our comparison, we used two classifiers (support vector machine, [Formula: see text]-nearest neighbor on two group datasets, namely two-class and multi-class datasets. Experimental results demonstrate that the performance of our method is better than others, especially on three hard-classify datasets, namely Wang's Breast Cancer, Gordon's Lung Adenocarcinoma and Pomeroy's Medulloblastoma.

  18. An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.

    Science.gov (United States)

    Liang, Ying; Liao, Bo; Zhu, Wen

    2017-01-01

    Tumourigenesis is a mutation accumulation process, which is likely to start with a mutated founder cell. The evolutionary nature of tumor development makes phylogenetic models suitable for inferring tumor evolution through genetic variation data. Copy number variation (CNV) is the major genetic marker of the genome with more genes, disease loci, and functional elements involved. Fluorescence in situ hybridization (FISH) accurately measures multiple gene copy number of hundreds of single cells. We propose an improved binary differential evolution algorithm, BDEP, to infer tumor phylogenetic tree based on FISH platform. The topology analysis of tumor progression tree shows that the pathway of tumor subcell expansion varies greatly during different stages of tumor formation. And the classification experiment shows that tree-based features are better than data-based features in distinguishing tumor. The constructed phylogenetic trees have great performance in characterizing tumor development process, which outperforms other similar algorithms.

  19. Reliability of Oronasal Fistula Classification.

    Science.gov (United States)

    Sitzman, Thomas J; Allori, Alexander C; Matic, Damir B; Beals, Stephen P; Fisher, David M; Samson, Thomas D; Marcus, Jeffrey R; Tse, Raymond W

    2018-01-01

    Objective Oronasal fistula is an important complication of cleft palate repair that is frequently used to evaluate surgical quality, yet reliability of fistula classification has never been examined. The objective of this study was to determine the reliability of oronasal fistula classification both within individual surgeons and between multiple surgeons. Design Using intraoral photographs of children with repaired cleft palate, surgeons rated the location of palatal fistulae using the Pittsburgh Fistula Classification System. Intrarater and interrater reliability scores were calculated for each region of the palate. Participants Eight cleft surgeons rated photographs obtained from 29 children. Results Within individual surgeons reliability for each region of the Pittsburgh classification ranged from moderate to almost perfect (κ = .60-.96). By contrast, reliability between surgeons was lower, ranging from fair to substantial (κ = .23-.70). Between-surgeon reliability was lowest for the junction of the soft and hard palates (κ = .23). Within-surgeon and between-surgeon reliability were almost perfect for the more general classification of fistula in the secondary palate (κ = .95 and κ = .83, respectively). Conclusions This is the first reliability study of fistula classification. We show that the Pittsburgh Fistula Classification System is reliable when used by an individual surgeon, but less reliable when used among multiple surgeons. Comparisons of fistula occurrence among surgeons may be subject to less bias if they use the more general classification of "presence or absence of fistula of the secondary palate" rather than the Pittsburgh Fistula Classification System.

  20. Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images

    Directory of Open Access Journals (Sweden)

    Soheila Gheisari

    2018-01-01

    Full Text Available Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.

  1. Soft Tissue Tumor Immunohistochemistry Update: Illustrative Examples of Diagnostic Pearls to Avoid Pitfalls.

    Science.gov (United States)

    Wei, Shi; Henderson-Jackson, Evita; Qian, Xiaohua; Bui, Marilyn M

    2017-08-01

    - Current 2013 World Health Organization classification of tumors of soft tissue arranges these tumors into 12 groups according to their histogenesis. Tumor behavior is classified as benign, intermediate (locally aggressive), intermediate (rarely metastasizing), and malignant. In our practice, a general approach to reaching a definitive diagnosis of soft tissue tumors is to first evaluate clinicoradiologic, histomorphologic, and cytomorphologic features of the tumor to generate some pertinent differential diagnoses. These include the potential line of histogenesis and whether the tumor is benign or malignant, and low or high grade. Although molecular/genetic testing is increasingly finding its applications in characterizing soft tissue tumors, currently immunohistochemistry still not only plays an indispensable role in defining tumor histogenesis, but also serves as a surrogate for underlining molecular/genetic alterations. Objective- To provide an overview focusing on the current concepts in the classification and diagnosis of soft tissue tumors, incorporating immunohistochemistry. This article uses examples to discuss how to use the traditional and new immunohistochemical markers for the diagnosis of soft tissue tumors. Practical diagnostic pearls, summary tables, and figures are used to show how to avoid diagnostic pitfalls. - Data were obtained from pertinent peer-reviewed English-language literature and the authors' first-hand experience as bone and soft tissue pathologists. - -The ultimate goal for a pathologist is to render a specific diagnosis that provides diagnostic, prognostic, and therapeutic information to guide patient care. Immunohistochemistry is integral to the diagnosis and management of soft tissue tumors.

  2. Modeling self-consistent multi-class dynamic traffic flow

    Science.gov (United States)

    Cho, Hsun-Jung; Lo, Shih-Ching

    2002-09-01

    In this study, we present a systematic self-consistent multiclass multilane traffic model derived from the vehicular Boltzmann equation and the traffic dispersion model. The multilane domain is considered as a two-dimensional space and the interaction among vehicles in the domain is described by a dispersion model. The reason we consider a multilane domain as a two-dimensional space is that the driving behavior of road users may not be restricted by lanes, especially motorcyclists. The dispersion model, which is a nonlinear Poisson equation, is derived from the car-following theory and the equilibrium assumption. Under the concept that all kinds of users share the finite section, the density is distributed on a road by the dispersion model. In addition, the dynamic evolution of the traffic flow is determined by the systematic gas-kinetic model derived from the Boltzmann equation. Multiplying Boltzmann equation by the zeroth, first- and second-order moment functions, integrating both side of the equation and using chain rules, we can derive continuity, motion and variance equation, respectively. However, the second-order moment function, which is the square of the individual velocity, is employed by previous researches does not have physical meaning in traffic flow. Although the second-order expansion results in the velocity variance equation, additional terms may be generated. The velocity variance equation we propose is derived from multiplying Boltzmann equation by the individual velocity variance. It modifies the previous model and presents a new gas-kinetic traffic flow model. By coupling the gas-kinetic model and the dispersion model, a self-consistent system is presented.

  3. Insights into the classification of small GTPases

    Directory of Open Access Journals (Sweden)

    Dominik Heider

    2010-05-01

    Full Text Available Dominik Heider1, Sascha Hauke3, Martin Pyka4, Daniel Kessler21Department of Bioinformatics, Center for Medical Biotechnology, 2Institute of Cell Biology (Cancer Research, University of Duisburg-Essen, Essen, Germany; 3Institute of Computer Science, University of Münster, Münster, Germany; 4Interdisciplinary Center for Clinical Research, University Hospital of Münster, Münster, GermanyAbstract: In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases to specific subgroups. Small GTPases represent an important functional group of proteins that serve as molecular switches in a wide range of fundamental cellular processes, including intracellular transport, movement and signaling events. These proteins have further gained a special emphasis in cancer research, because within the last decades a huge variety of small GTPases from different subgroups could be related to the development of all types of tumors. Using a random forest approach, we were able to identify the most important amino acid positions for the classification process within the small GTPases superfamily and its subgroups. These positions are in line with the results of earlier studies and have been shown to be the essential elements for the different functionalities of the GTPase families. Furthermore, we provide an accurate and reliable software tool (GTPasePred to identify potential novel GTPases and demonstrate its application to genome sequences.Keywords: cancer, machine learning, classification, Random Forests, proteins

  4. Classification and treatment of radiation maculopathy.

    LENUS (Irish Health Repository)

    Horgan, Noel

    2012-02-01

    PURPOSE OF REVIEW: Radiation maculopathy is a sight-limiting consequence of radiotherapy in the management of uveal melanoma and other intraocular tumors. In this review, we consider clinical, fluorescein angiographic and optical coherence tomographic findings, propose a classification for radiation maculopathy and discuss the management of this condition. RECENT FINDINGS: Radiation macular edema (RME) can be classified by optical coherence tomography into noncystoid or cystoid edema, with foveolar or extrafoveolar involvement. Optical coherence tomographic grading of RME has been found to correlate with visual acuity. Focal argon laser might have some limited benefit in the treatment of RME. Intravitreal triamcinolone and intravitreal antivascular endothelial growth factor agents can be of short-term benefit in the treatment of RME. In a randomized controlled trial, periocular triamcinolone significantly reduced rates of RME and vision loss up to 18 months following plaque radiotherapy for uveal melanoma. SUMMARY: Currently, there is no proven treatment for established RME, though periocular triamcinolone has been shown to have a preventive benefit. An accepted classification system for radiation maculopathy would be of benefit in planning and comparing future treatment trials.

  5. Classification of radioactive waste

    International Nuclear Information System (INIS)

    1994-01-01

    Radioactive wastes are generated in a number of different kinds of facilities and arise in a wide range of concentrations of radioactive materials and in a variety of physical and chemical forms. To simplify their management, a number of schemes have evolved for classifying radioactive waste according to the physical, chemical and radiological properties of significance to those facilities managing this waste. These schemes have led to a variety of terminologies, differing from country to country and even between facilities in the same country. This situation makes it difficult for those concerned to communicate with one another regarding waste management practices. This document revises and updates earlier IAEA references on radioactive waste classification systems given in IAEA Technical Reports Series and Safety Series. Guidance regarding exemption of materials from regulatory control is consistent with IAEA Safety Series and the RADWASS documents published under IAEA Safety Series. 11 refs, 2 figs, 2 tab

  6. Nonlinear estimation and classification

    CERN Document Server

    Hansen, Mark; Holmes, Christopher; Mallick, Bani; Yu, Bin

    2003-01-01

    Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data This is due in part to recent advances in data collection and computing technologies As a result, fundamental statistical research is being undertaken in a variety of different fields Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future

  7. Automatic diabetic retinopathy classification

    Science.gov (United States)

    Bravo, María. A.; Arbeláez, Pablo A.

    2017-11-01

    Diabetic retinopathy (DR) is a disease in which the retina is damaged due to augmentation in the blood pressure of small vessels. DR is the major cause of blindness for diabetics. It has been shown that early diagnosis can play a major role in prevention of visual loss and blindness. This work proposes a computer based approach for the detection of DR in back-of-the-eye images based on the use of convolutional neural networks (CNNs). Our CNN uses deep architectures to classify Back-of-the-eye Retinal Photographs (BRP) in 5 stages of DR. Our method combines several preprocessing images of BRP to obtain an ACA score of 50.5%. Furthermore, we explore subproblems by training a larger CNN of our main classification task.

  8. [Immune system and tumors].

    Science.gov (United States)

    Terme, Magali; Tanchot, Corinne

    2017-02-01

    Despite having been much debated, it is now well established that the immune system plays an essential role in the fight against cancer. In this article, we will highlight the implication of the immune system in the control of tumor growth and describe the major components of the immune system involved in the antitumoral immune response. The immune system, while exerting pressure on tumor cells, also will play a pro-tumoral role by sculpting the immunogenicity of tumors cells as they develop. Finally, we will illustrate the numerous mechanisms of immune suppression that take place within the tumoral microenvironment which allow tumor cells to escape control from the immune system. The increasingly precise knowledge of the brakes to an effective antitumor immune response allows the development of immunotherapy strategies more and more innovating and promising of hope. Copyright © 2016. Published by Elsevier Masson SAS.

  9. An NRG Oncology/GOG study of molecular classification for risk prediction in endometrioid endometrial cancer.

    Science.gov (United States)

    Cosgrove, Casey M; Tritchler, David L; Cohn, David E; Mutch, David G; Rush, Craig M; Lankes, Heather A; Creasman, William T; Miller, David S; Ramirez, Nilsa C; Geller, Melissa A; Powell, Matthew A; Backes, Floor J; Landrum, Lisa M; Timmers, Cynthia; Suarez, Adrian A; Zaino, Richard J; Pearl, Michael L; DiSilvestro, Paul A; Lele, Shashikant B; Goodfellow, Paul J

    2018-01-01

    The purpose of this study was to assess the prognostic significance of a simplified, clinically accessible classification system for endometrioid endometrial cancers combining Lynch syndrome screening and molecular risk stratification. Tumors from NRG/GOG GOG210 were evaluated for mismatch repair defects (MSI, MMR IHC, and MLH1 methylation), POLE mutations, and loss of heterozygosity. TP53 was evaluated in a subset of cases. Tumors were assigned to four molecular classes. Relationships between molecular classes and clinicopathologic variables were assessed using contingency tests and Cox proportional methods. Molecular classification was successful for 982 tumors. Based on the NCI consensus MSI panel assessing MSI and loss of heterozygosity combined with POLE testing, 49% of tumors were classified copy number stable (CNS), 39% MMR deficient, 8% copy number altered (CNA) and 4% POLE mutant. Cancer-specific mortality occurred in 5% of patients with CNS tumors; 2.6% with POLE tumors; 7.6% with MMR deficient tumors and 19% with CNA tumors. The CNA group had worse progression-free (HR 2.31, 95%CI 1.53-3.49) and cancer-specific survival (HR 3.95; 95%CI 2.10-7.44). The POLE group had improved outcomes, but the differences were not statistically significant. CNA class remained significant for cancer-specific survival (HR 2.11; 95%CI 1.04-4.26) in multivariable analysis. The CNA molecular class was associated with TP53 mutation and expression status. A simple molecular classification for endometrioid endometrial cancers that can be easily combined with Lynch syndrome screening provides important prognostic information. These findings support prospective clinical validation and further studies on the predictive value of a simplified molecular classification system. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. Artificial neural net system for interactive tissue classification with MR imaging and image segmentation

    International Nuclear Information System (INIS)

    Clarke, L.P.; Silbiger, M.; Naylor, C.; Brown, K.

    1990-01-01

    This paper reports on the development of interactive methods for MR tissue classification that permit mathematically rigorous methods for three-dimensional image segmentation and automatic organ/tumor contouring, as required for surgical and RTP planning. The authors investigate a number of image-intensity based tissue- classification methods that make no implicit assumptions on the MR parameters and hence are not limited by image data set. Similarly, we have trained artificial neural net (ANN) systems for both supervised and unsupervised tissue classification

  11. Perineural spread in head and neck tumors.

    Science.gov (United States)

    Brea Álvarez, B; Tuñón Gómez, M

    2014-01-01

    Perineural spread is the dissemination of some types of head and neck tumors along nervous structures. Perineural spread has negative repercussions on treatment because it requires more extensive resection and larger fields of irradiation. Moreover, perineural spread is associated with increased local recurrence, and it is considered an independent indicator of poor prognosis in the TNM classification for tumor staging. However, perineural spread often goes undetected on imaging studies. In this update, we review the concept of perineural spread, its pathogenesis, and the main pathways and connections among the facial nerves, which are essential to understand this process. Furthermore, we discuss the appropriate techniques for imaging studies, and we describe and illustrate the typical imaging signs that help identify perineural spread on CT and MRI. Finally, we discuss the differential diagnosis with other entities. Copyright © 2013 SERAM. Published by Elsevier Espana. All rights reserved.

  12. Hazard classification or risk assessment

    DEFF Research Database (Denmark)

    Hass, Ulla

    2013-01-01

    The EU classification of substances for e.g. reproductive toxicants is hazard based and does not to address the risk suchsubstances may pose through normal, or extreme, use. Such hazard classification complies with the consumer's right to know. It is also an incentive to careful use and storage...

  13. Seismic texture classification. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Vinther, R.

    1997-12-31

    The seismic texture classification method, is a seismic attribute that can both recognize the general reflectivity styles and locate variations from these. The seismic texture classification performs a statistic analysis for the seismic section (or volume) aiming at describing the reflectivity. Based on a set of reference reflectivities the seismic textures are classified. The result of the seismic texture classification is a display of seismic texture categories showing both the styles of reflectivity from the reference set and interpolations and extrapolations from these. The display is interpreted as statistical variations in the seismic data. The seismic texture classification is applied to seismic sections and volumes from the Danish North Sea representing both horizontal stratifications and salt diapers. The attribute succeeded in recognizing both general structure of successions and variations from these. Also, the seismic texture classification is not only able to display variations in prospective areas (1-7 sec. TWT) but can also be applied to deep seismic sections. The seismic texture classification is tested on a deep reflection seismic section (13-18 sec. TWT) from the Baltic Sea. Applied to this section the seismic texture classification succeeded in locating the Moho, which could not be located using conventional interpretation tools. The seismic texture classification is a seismic attribute which can display general reflectivity styles and deviations from these and enhance variations not found by conventional interpretation tools. (LN)

  14. Efficient AUC optimization for classification

    NARCIS (Netherlands)

    Calders, T.; Jaroszewicz, S.; Kok, J.N.; Koronacki, J.; Lopez de Mantaras, R.; Matwin, S.; Mladenic, D.; Skowron, A.

    2007-01-01

    In this paper we show an efficient method for inducing classifiers that directly optimize the area under the ROC curve. Recently, AUC gained importance in the classification community as a mean to compare the performance of classifiers. Because most classification methods do not optimize this

  15. Dewey Decimal Classification: A Quagmire.

    Science.gov (United States)

    Gamaluddin, Ahmad Fouad

    1980-01-01

    A survey of 660 Pennsylvania school librarians indicates that, though there is limited professional interest in the Library of Congress Classification system, Dewey Decimal Classification (DDC) appears to be firmly entrenched. This article also discusses the relative merits of DDC, the need for a uniform system, librarianship preparation, and…

  16. Latent class models for classification

    NARCIS (Netherlands)

    Vermunt, J.K.; Magidson, J.

    2003-01-01

    An overview is provided of recent developments in the use of latent class (LC) and other types of finite mixture models for classification purposes. Several extensions of existing models are presented. Two basic types of LC models for classification are defined: supervised and unsupervised

  17. Central nervous system tumors

    International Nuclear Information System (INIS)

    Curran, W.J. Jr.

    1991-01-01

    Intrinsic tumors of the central nervous system (CNS) pose a particularly challenging problem to practicing oncologists. These tumors rarely metastasize outside the CNS, yet even histologically benign tumors can be life-threatening due to their local invasiveness and strategic location. The surrounding normal tissues of the nervous system is often incapable of full functional regeneration, therefore prohibiting aggressive attempts to use either complete surgical resection or high doses of irradiation. Despite these limitations, notable achievements have recently been recorded in the management of these tumors

  18. Management of CNS tumors

    International Nuclear Information System (INIS)

    Griem, M.L.

    1987-01-01

    The treatment of tumors of the CNS has undergone a number of changes based on the impact of CT. The use of intraoperative US for the establishment of tumor location and tumor histology is demonstrated. MR imaging also is beginning to make an impact on the diagnosis and treatment of tumors of the CNS. Examples of MR images are shown. The authors then discuss the important aspects of tumor histology as it affects management and newer concepts in surgery, radiation, and chemotherapy on tumor treatment. The role of intraoperative placement of radioactive sources, the utilization of heavy particle radiation therapy, and the potential role of other experimental radiation therapy techniques are discussed. The role of hyperfractionated radiation and of neutrons and x-ray in a mixed-beam treatment are discussed in perspective with standard radiation therapy. Current chemotherapy techniques, including intraarterial chemotherapy, are discussed. The complications of radiation therapy alone and in combination with chemotherapy in the management of primary brain tumors, brain metastases, and leukemia are reviewed. A summary of the current management of pituitary tumors, including secreting pituitary adenomas and chromophobe adenomas, are discussed. The treatment with heavy particle radiation, transsphenoidal microsurgical removal, and combined radiotherapeutic and surgical management are considered. Tumor metastasis management of lesions of the brain and spinal cord are considered

  19. 45 CFR 601.5 - Derivative classification.

    Science.gov (United States)

    2010-10-01

    ... CLASSIFICATION AND DECLASSIFICATION OF NATIONAL SECURITY INFORMATION § 601.5 Derivative classification. Distinct... 45 Public Welfare 3 2010-10-01 2010-10-01 false Derivative classification. 601.5 Section 601.5... classification guide, need not possess original classification authority. (a) If a person who applies derivative...

  20. 12 CFR 403.4 - Derivative classification.

    Science.gov (United States)

    2010-01-01

    ... SAFEGUARDING OF NATIONAL SECURITY INFORMATION § 403.4 Derivative classification. (a) Use of derivative classification. (1) Unlike original classification which is an initial determination, derivative classification... 12 Banks and Banking 4 2010-01-01 2010-01-01 false Derivative classification. 403.4 Section 403.4...

  1. 32 CFR 2001.15 - Classification guides.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 6 2010-07-01 2010-07-01 false Classification guides. 2001.15 Section 2001.15..., NATIONAL ARCHIVES AND RECORDS ADMINISTRATION CLASSIFIED NATIONAL SECURITY INFORMATION Classification § 2001.15 Classification guides. (a) Preparation of classification guides. Originators of classification...

  2. 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.

  3. Vietnamese Document Representation and Classification

    Science.gov (United States)

    Nguyen, Giang-Son; Gao, Xiaoying; Andreae, Peter

    Vietnamese is very different from English and little research has been done on Vietnamese document classification, or indeed, on any kind of Vietnamese language processing, and only a few small corpora are available for research. We created a large Vietnamese text corpus with about 18000 documents, and manually classified them based on different criteria such as topics and styles, giving several classification tasks of different difficulty levels. This paper introduces a new syllable-based document representation at the morphological level of the language for efficient classification. We tested the representation on our corpus with different classification tasks using six classification algorithms and two feature selection techniques. Our experiments show that the new representation is effective for Vietnamese categorization, and suggest that best performance can be achieved using syllable-pair document representation, an SVM with a polynomial kernel as the learning algorithm, and using Information gain and an external dictionary for feature selection.

  4. Tumor carcinoide apendicular Appendiceal carcinoid tumor

    Directory of Open Access Journals (Sweden)

    Julio Vázquez Palanco

    2008-12-01

    Full Text Available El objetivo de este trabajo fue dar a conocer un interesante caso de tumor carcinoide que se presentó con cuadro clínico de apendicitis aguda. El paciente fue un varón de 8 años de edad, al cual se realizó apendicectomía a causa de una apendicitis aguda. El resultado anatomopatológico confirmó un tumor de células endocrinas (argentafinoma, tumor carcinoide en el tercio distal del órgano, que infiltraba hasta la serosa, y apendicitis aguda supurada. El paciente fue enviado a un servicio de oncohematología para tratamiento oncoespecífico. Por lo inusual de estos tumores en edades tempranas y por lo que puede representar para el niño una conducta no consecuente, decidimos presentar este caso a la comunidad científica nacional e internacional. Es extremadamente importante el seguimiento de los pacientes con apendicitis aguda y de las conclusiones del examen histológico, por lo que puede representar para el niño una conducta inadecuada en una situación como esta.The objective of this paper was to make known an interesting case of carcinoid tumor that presented a clinical picture of acute appendicitis.The patient was an eight-year-old boy that underwent appendectomy due to an acute appendicitis. The anatomopathological report confirmed an endocrine cell tumor (argentaffinoma, carcinoid tumor in the distal third of the organ that infiltrated up to the serosa, and acute suppurative appendicitis. The patient was referred to an oncohematology service for oncospecific treatment. As it is a rare tumor at early ages, and taking into account what a inconsequent behavior may represent for the child, it was decided to present this case to the national and international scientific community. The follow-up of the patients with acute appendicitis and of the conclusions of the histological examination is extremely important considering what an inadequate conduct may represent for the child in a situation like this.

  5. An exceptional collision tumor: gastric calcified stromal tumor and ...

    African Journals Online (AJOL)

    The authors report an exceptional case of collision tumor comprised of a gastric calcified stromal tumor and a pancreatic adenocarcinoma. The pancreatic tumor was detected fortuitously on the histological exam of resection specimen. Key words: Collision tumor, stromal tumor, adenocarcinoma ...

  6. Pituitary gland tumors

    International Nuclear Information System (INIS)

    Jesser, J.; Schlamp, K.; Bendszus, M.

    2014-01-01

    This article gives an overview of the most common tumors of the pituitary gland and the differential diagnostics with special emphasis on radiological diagnostic criteria. A selective search of the literature in PubMed was carried out. Pituitary adenomas constitute 10-15 % of all intracranial tumors and are the most common tumors of the sellar region. Tumors smaller than 1 cm in diameter are called microadenomas while those larger than 1 cm in diameter are called macroadenomas. Approximately 65 % of pituitary gland adenomas secrete hormones whereby approximately 50 % secrete prolactin, 10 % secrete growth hormone (somatotropin) and 6 % secrete corticotropin. Other tumors located in the sella turcica can also cause endocrinological symptoms, such as an oversecretion of pituitary hormone or pituitary insufficiency by impinging on the pituitary gland or its stalk. When tumors spread into the space cranial to the sella turcica, they can impinge on the optic chiasm and cause visual disorders. A common differential diagnosis of a sellar tumor is a craniopharyngeoma. In children up to 10 % of all intracranial tumors are craniopharyngeomas. Other differential diagnoses for sellar tumors are metastases, meningiomas, epidermoids and in rare cases astrocytomas, germinomas or Rathke cleft cysts As these tumors are located in an anatomically complex region of the skull base and are often very small, a highly focused imaging protocol is required. The currently favored modality is magnetic resonance imaging (MRI) with the administration of a contrast agent. The sellar region should be mapped in thin slices. In cases of suspected microadenoma the imaging protocol should also contain a sequence with dynamic contrast administration in order to assess the specific enhancement characteristics of the tumor and the pituitary gland. (orig.) [de

  7. [Pituitary gland tumors].

    Science.gov (United States)

    Jesser, J; Schlamp, K; Bendszus, M

    2014-10-01

    This article gives an overview of the most common tumors of the pituitary gland and the differential diagnostics with special emphasis on radiological diagnostic criteria. A selective search of the literature in PubMed was carried out. Pituitary adenomas constitute 10-15% of all intracranial tumors and are the most common tumors of the sellar region. Tumors smaller than 1 cm in diameter are called microadenomas while those larger than 1 cm in diameter are called macroadenomas. Approximately 65% of pituitary gland adenomas secrete hormones whereby approximately 50% secrete prolactin, 10% secrete growth hormone (somatotropin) and 6% secrete corticotropin. Other tumors located in the sella turcica can also cause endocrinological symptoms, such as an oversecretion of pituitary hormone or pituitary insufficiency by impinging on the pituitary gland or its stalk. When tumors spread into the space cranial to the sella turcica, they can impinge on the optic chiasm and cause visual disorders. A common differential diagnosis of a sellar tumor is a craniopharyngeoma. In children up to 10% of all intracranial tumors are craniopharyngeomas. Other differential diagnoses for sellar tumors are metastases, meningiomas, epidermoids and in rare cases astrocytomas, germinomas or Rathke cleft cysts As these tumors are located in an anatomically complex region of the skull base and are often very small, a highly focused imaging protocol is required. The currently favored modality is magnetic resonance imaging (MRI) with the administration of a contrast agent. The sellar region should be mapped in thin slices. In cases of suspected microadenoma the imaging protocol should also contain a sequence with dynamic contrast administration in order to assess the specific enhancement characteristics of the tumor and the pituitary gland.

  8. Ewing tumors in infants

    NARCIS (Netherlands)

    van den Berg, Henk; Dirksen, Uta; Ranft, Andreas; Jürgens, Heribert

    2008-01-01

    Malignancies in infancy are extremely rare. Ewing tumors are hardly ever noted in these children. Since it is generally assumed that malignancies in infancy have an extremely poor outcome, we wanted to investigate whether this was also the case in Ewing tumors. We identified in the Munster data

  9. GASTROENTEROPANCREATIC NEUROENDOCRINE TUMORS ...

    African Journals Online (AJOL)

    Pavel M.E., Baum U., Hahn E.G., Hensen J. Doxorubucin and streptozocin after failed biotherapy of Neuroendocrine tumors. Int J. Gastrointest Cancer 2005; 35 179-185. 33. Yao J.C., Phan A., Hoff P.M., et al. Targeting vas- cular endothelial growth factor in advanced carci- noid tumors: a random assignment phase II study.

  10. Atypically localized glomus tumors

    Directory of Open Access Journals (Sweden)

    Meric Ugurlar

    2016-12-01

    Conclusion: When a painful mass is found in the body, glomus tumors should be kept in mind. The consideration of symptoms, including pain, temperature sensitivity, point tenderness, and discoloration, common characteristics of glomus tumors, may aid diagnosis. [Hand Microsurg 2016; 5(3.000: 112-117

  11. Renal inflammatory myofibroblastic tumor

    DEFF Research Database (Denmark)

    Heerwagen, S T; Jensen, C; Bagi, P

    2007-01-01

    Renal inflammatory myofibroblastic tumor (IMT) is a rare soft-tissue tumor of controversial etiology with a potential for local recurrence after incomplete surgical resection. The radiological findings in renal IMT are not well described. We report two cases in adults with a renal mass treated...

  12. Pseudoanaplastic tumors of bone

    Energy Technology Data Exchange (ETDEWEB)

    Bahk, Won-Jong [Uijongbu St. Mary Hospital, The Catholic University of Korea, Department of Orthopaedic Surgery, Gyunggido, 480-821 (Korea); Mirra, Joseph M. [Orthopaedic Hospital, Orthopedic Oncology, Los Angeles, California (United States)

    2004-11-01

    To discuss the concept of pseudoanaplastic tumors of bone, which pathologically show hyperchromatism and marked pleomorphism with quite enlarged, pleomorphic nuclei, but with no to extremely rare, typical mitoses, and to propose guidelines for their diagnosis. From a database of 4,262 bone tumors covering from 1971 to 2001, 15 cases of pseudoanaplastic bone tumors (0.35% of total) were retrieved for clinical, radiographic and pathologic review. Postoperative follow-up after surgical treatment was at least 3 years and a maximum of 7 years. There were eight male and seven female patients. Their ages ranged from 10 to 64 years with average of 29.7 years. Pathologic diagnoses of pseudoanaplastic variants of benign bone tumors included: osteoblastoma (4 cases), giant cell tumor (4 cases), chondromyxoid fibroma (3 cases), fibrous dysplasia (2 cases), fibrous cortical defect (1 case) and aneurysmal bone cyst (1 case). Radiography of all cases showed features of a benign bone lesion. Six cases, one case each of osteoblastoma, fibrous dysplasia, aneurysmal bone cyst, chondromyxoid fibroma, giant cell tumor and osteoblastoma, were initially misdiagnosed as osteosarcoma. The remaining cases were referred for a second opinion to rule out sarcoma. Despite the presence of significant cytologic aberrations, none of our cases showed malignant behavior following simple curettage or removal of bony lesions. Our observation justifies the concept of pseudoanaplasia in some benign bone tumors as in benign soft tissue tumors, especially in their late evolutionary stage when bizarre cytologic alterations strongly mimic a sarcoma. (orig.)

  13. Vanishing tumor in pregnancy

    Directory of Open Access Journals (Sweden)

    M V Vimal

    2012-01-01

    Full Text Available A patient with microprolactinoma, who had two successful pregnancies, is described for management issues. First pregnancy was uneventful. During the second pregnancy, the tumor enlarged to macroprolactinoma with headache and blurring of vision which was managed successfully with bromocriptine. Post delivery, complete disappearance of the tumor was documented.

  14. Vanishing tumor in pregnancy

    Science.gov (United States)

    Vimal, M. V.; Budyal, Sweta; Kasliwal, Rajeev; Jagtap, Varsha S.; Lila, Anurag R.; Bandgar, Tushar; Menon, Padmavathy; Shah, Nalini S.

    2012-01-01

    A patient with microprolactinoma, who had two successful pregnancies, is described for management issues. First pregnancy was uneventful. During the second pregnancy, the tumor enlarged to macroprolactinoma with headache and blurring of vision which was managed successfully with bromocriptine. Post delivery, complete disappearance of the tumor was documented. PMID:23226664

  15. Combined Scintigraphy and Tumor Marker Analysis Predicts Unfavorable Histopathology of Neuroblastic Tumors with High Accuracy.

    Directory of Open Access Journals (Sweden)

    Wolfgang Peter Fendler

    Full Text Available Our aim was to improve the prediction of unfavorable histopathology (UH in neuroblastic tumors through combined imaging and biochemical parameters.123I-MIBG SPECT and MRI was performed before surgical resection or biopsy in 47 consecutive pediatric patients with neuroblastic tumor. Semi-quantitative tumor-to-liver count-rate ratio (TLCRR, MRI tumor size and margins, urine catecholamine and NSE blood levels of neuron specific enolase (NSE were recorded. Accuracy of single and combined variables for prediction of UH was tested by ROC analysis with Bonferroni correction.34 of 47 patients had UH based on the International Neuroblastoma Pathology Classification (INPC. TLCRR and serum NSE both predicted UH with moderate accuracy. Optimal cut-off for TLCRR was 2.0, resulting in 68% sensitivity and 100% specificity (AUC-ROC 0.86, p < 0.001. Optimal cut-off for NSE was 25.8 ng/ml, resulting in 74% sensitivity and 85% specificity (AUC-ROC 0.81, p = 0.001. Combination of TLCRR/NSE criteria reduced false negative findings from 11/9 to only five, with improved sensitivity and specificity of 85% (AUC-ROC 0.85, p < 0.001.Strong 123I-MIBG uptake and high serum level of NSE were each predictive of UH. Combined analysis of both parameters improved the prediction of UH in patients with neuroblastic tumor. MRI parameters and urine catecholamine levels did not predict UH.

  16. Tumorous interstitial lung disease

    International Nuclear Information System (INIS)

    Dinkel, E.; Meyer, E.; Mundinger, A.; Helwig, A.; Blum, U.; Wuertemberger, G.

    1990-01-01

    The radiological findings in pulmonary lymphangitic carcinomatosis and in leukemic pulmonary infiltrates mirror the tumor-dependent monomorphic interstitial pathology of lung parenchyma. It is a proven fact that pulmonary lymphangitic carcinomatosis is caused by hematogenous tumor embolization to the lungs; pathogenesis by contiguous lymphangitic spread is the exception. High-resolution CT performed as a supplement to the radiological work-up improves the sensitivity for pulmonary infiltrates in general and thus makes the differential diagnosis decided easier. Radiological criteria cannot discriminate the different forms of leukemia. Plain chest X-ray allows the diagnosis of pulmonary involvement in leukemia due to tumorous infiltrates and of tumor- or therapy-induced complications. It is essential that the radiological findings be interpreted with reference to the stage of tumor disease and the clinical parameters to make the radiological differential diagnosis of opportunistic infections more reliable. (orig.) [de

  17. Tumors of peripheral nerves

    International Nuclear Information System (INIS)

    Ho, Michael; Lutz, Amelie M.

    2017-01-01

    Differentiation between malignant and benign tumors of peripheral nerves in the early stages is challenging; however, due to the unfavorable prognosis of malignant tumors early identification is required. To show the possibilities for detection, differential diagnosis and clinical management of peripheral nerve tumors by imaging appearance in magnetic resonance (MR) neurography. Review of current literature available in PubMed and MEDLINE, supplemented by the authors' own observations in clinical practice. Although not pathognomonic, several imaging features have been reported for a differentiation between distinct peripheral nerve tumors. The use of MR neurography enables detection and initial differential diagnosis in tumors of peripheral nerves. Furthermore, it plays an important role in clinical follow-up, targeted biopsy and surgical planning. (orig.) [de

  18. Wilm's tumor in adulthood

    International Nuclear Information System (INIS)

    Matveev, B.P.; Bukharkin, B.V.; Gotsadze, D.T.

    1984-01-01

    Wilms' tumor occurs extremely rarely in adults. There is no consensus in the literature on the problems of clinical manifestations, diagnosis and treatment of the diseasa. Ten adult patients (aged 16-29) with Wilms' tumor formed the study group. They made up 0.9 per cent of the total number of kidney tumor patients. The peculiarities of the clinical course that distinguish adult nephroblastoma from renal cancer and Wilms' tumor of the infancy were analysed. The latent period appeared to be long. Problems of diagnosis are discussed. Angiography proved to be of the highest diagnostic value. Complex treatment including transperitoneal nephrectory, radiation and chemotherapy was carried out in 7 cases, palliative radiation treatmenchemotherapy andn 3. Unlike pediatric nephroblastomt - i Wilms' tumor in adults was resistant to radiation. Treatment results still remained unsatisfactory: 6 patients died 7-19 months after the beginning of treatment

  19. Radiotherapy of pineal tumors

    International Nuclear Information System (INIS)

    Danoff, B.; Sheline, G.E.

    1984-01-01

    Radiotherapy has universally been used in the treatment of pineal tumors and suprasellar germinomas. Recently however, major technical advances related to the use of the operating microscope and development of microsurgical techniques have prompted a renewed interest in the direct surgical approach for biopsy and/or excision. This interest has resulted in a controversy regarding the role of surgery prior to radiotherapy. Because of the heterogeneity of tumors occurring in the pineal region (i.e., germ cell tumors, pineal parenchymal tumors, glial tumors, and cysts) and their differing biological behavior, controversy also surrounds aspects of radiotherapy such as: the optimal radiation dose, the volume to be irradiated, and indications for prophylactic spinal irradiation. A review of the available data is presented in an attempt to answer these questions

  20. Classification of titanium dioxide

    International Nuclear Information System (INIS)

    Macias B, L.R.; Garcia C, R.M.; Maya M, M.E.; Ita T, A. De; Palacios G, J.

    2002-01-01

    In this work the X-ray diffraction (XRD), Scanning Electron Microscopy (Sem) and the X-ray Dispersive Energy Spectroscopy techniques are used with the purpose to achieve a complete identification of phases and mixture of phases of a crystalline material as titanium dioxide. The problem for solving consists of being able to distinguish a sample of titanium dioxide being different than a titanium dioxide pigment. A standard sample of titanium dioxide with NIST certificate is used, which indicates a purity of 99.74% for the TiO 2 . The following way is recommended to proceed: a)To make an analysis by means of X-ray diffraction technique to the sample of titanium dioxide pigment and on the standard of titanium dioxide waiting not find differences. b) To make a chemical analysis by the X-ray Dispersive Energy Spectroscopy via in a microscope, taking advantage of the high vacuum since it is oxygen which is analysed and if it is concluded that the aluminium oxide appears in a greater proportion to 1% it is established that is a titanium dioxide pigment, but if it is lesser then it will be only titanium dioxide. This type of analysis is an application of the nuclear techniques useful for the tariff classification of merchandise which is considered as of difficult recognition. (Author)

  1. Classification of new particles

    International Nuclear Information System (INIS)

    Karl, G.

    1976-01-01

    A classification of the new particles is proposed. Hadrons are constructed from quarks corresponding to several different representations of an SU 3 color group, with confined color. The new family of resonances, related to psi/J, is assigned to color-antisextet quarks Q. These new quarks Q do not form mixed mesons q-barQ with old antiquarks but can form mixed baryons Qqq. We speculate on the relation between color and mass. High-mass recurrences of the psi/J family are expected to have associated large changes in the cross section for electron-positron annihilation (ΔR > 4). A conjectured mass formula, which relates the masses of psi/J and ω, predicts the masses of possible recurrences of the psi/J particle. Other experimental implications at presently available energies are discussed, especially the necessity for an isovector partner for psi/J, and for pseudoscalar mesons at 1.8--2.2 GeV, some of which can decay into two photons

  2. Parallel evolution of tumor subclones mimics diversity between tumors

    DEFF Research Database (Denmark)

    Martinez, Pierre; Birkbak, Nicolai Juul; Gerlinger, Marco

    2013-01-01

    Intratumor heterogeneity (ITH) may foster tumor adaptation and compromise the efficacy of personalized medicines approaches. The scale of heterogeneity within a tumor (intratumor heterogeneity) relative to genetic differences between tumors (intertumor heterogeneity) is unknown. To address this, ...

  3. A real-time classification algorithm for EEG-based BCI driven by self-induced emotions.

    Science.gov (United States)

    Iacoviello, Daniela; Petracca, Andrea; Spezialetti, Matteo; Placidi, Giuseppe

    2015-12-01

    The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. The obtained

  4. Music classification with MPEG-7

    Science.gov (United States)

    Crysandt, Holger; Wellhausen, Jens

    2003-01-01

    Driven by increasing amount of music available electronically the need and possibility of automatic classification systems for music becomes more and more important. Currently most search engines for music are based on textual descriptions like artist or/and title. This paper presents a system for automatic music description, classification and visualization for a set of songs. The system is designed to extract significant features of a piece of music in order to find songs of similar genre or a similar sound characteristics. The description is done with the help of MPEG-7 only. The classification and visualization is done with the self organizing map algorithm.

  5. Long-term Behavior of Serous Borderline Tumors Subdivided Into Atypical Proliferative Tumors and Noninvasive Low-grade Carcinomas

    DEFF Research Database (Denmark)

    Vang, Russell; Hannibal, Charlotte G; Junge, Jette

    2017-01-01

    Ovarian serous borderline tumors (SBTs) have been the subject of considerable controversy, particularly with regard to terminology and behavior. It has been proposed that they constitute a heterogenous group of tumors composed, for the most part, of typical SBTs that are benign and designated...... "atypical proliferative serous tumor (APST)" and a small subset of SBTs with micropapillary architecture that have a poor outcome and are designated "noninvasive low-grade serous carcinoma (niLGSC)". It also has been argued that the difference in behavior between the 2 groups is not due to the subtype...... of the primary tumor but rather the presence of extraovarian disease, specifically invasive implants. According to the terminology of the 2014 WHO Classification, typical SBTs are equivalent to APSTs and SBTs displaying micropapillary architecture are synonymous with niLGSC. In addition, "invasive implants" were...

  6. Systema Naturae. Classification of living things.

    OpenAIRE

    Alexey Shipunov

    2007-01-01

    Original classification of living organisms containing four kingdoms (Monera, Protista, Vegetabilia and Animalia), 60 phyla and 254 classes, is presented. The classification is based on latest available information.

  7. Tumor control probability after a radiation of animal tumors

    International Nuclear Information System (INIS)

    Urano, Muneyasu; Ando, Koichi; Koike, Sachiko; Nesumi, Naofumi

    1975-01-01

    Tumor control and regrowth probability of animal tumors irradiated with a single x-ray dose were determined, using a spontaneous C3H mouse mammary carcinoma. Cellular radiation sensitivity of tumor cells and tumor control probability of the tumor were examined by the TD 50 and TCD 50 assays respectively. Tumor growth kinetics were measured by counting the percentage of labelled mitosis and by measuring the growth curve. A mathematical analysis of tumor control probability was made from these results. A formula proposed, accounted for cell population kinetics or division probability model, cell sensitivity to radiation and number of tumor cells. (auth.)

  8. The Tumor Macroenvironment: Cancer-Promoting Networks Beyond Tumor Beds.

    Science.gov (United States)

    Rutkowski, Melanie R; Svoronos, Nikolaos; Perales-Puchalt, Alfredo; Conejo-Garcia, Jose R

    2015-01-01

    During tumor progression, alterations within the systemic tumor environment, or macroenvironment, result in the promotion of tumor growth, tumor invasion to distal organs, and eventual metastatic disease. Distally produced hormones, commensal microbiota residing within mucosal surfaces, myeloid cells and even the bone marrow impact the systemic immune system, tumor growth, and metastatic spread. Understanding the reciprocal interactions between the cells and soluble factors within the macroenvironment and the primary tumor will enable the design of specific therapies that have the potential to prevent dissemination and metastatic spread. This chapter will summarize recent findings detailing how the primary tumor and systemic tumor macroenvironment coordinate malignant progression. © 2015 Elsevier Inc. All rights reserved.

  9. Unclassified sarcomas : a study to improve classification in a cohort of Golden Retriever dogs

    NARCIS (Netherlands)

    Boerkamp, Kim M; Hellmén, Eva; Willén, Helena; Grinwis, Guy C M; Teske, Erik; Rutteman, Gerard R

    2016-01-01

    Morphologically, canine soft-tissue sarcomas (STSs) resemble human STSs. In humans, proper classification of STSs is considered essential to improve insight in the biology of these tumors, and to optimize diagnosis and therapy. To date, there is a paucity of data published on the significance of

  10. How to Fill the Cavity after Curettage of Giant Cell Tumors around the Knee? A Multicenter Analysis

    Directory of Open Access Journals (Sweden)

    Kai Zheng

    2017-01-01

    Conclusions: Parameters including patients' age, gender, tumor location, and radiological classification did not affect surgeons' treatments in cavity filling after GCT curettage. Cementation should be recommended because of easy usage, the similar postoperative knee function with bone grafting, and the better local tumor control than bone grafting.

  11. Progression in nuclear classification

    International Nuclear Information System (INIS)

    Wang Yuying

    1999-01-01

    In this book, summarize the author's achievements of nuclear classification by new method in latest 30 years, new foundational law of nuclear layer in matter world is found. It is explained with a hypothesis of a nucleus which it is made up of two nucleon's clusters with deuteron and triton. Its concrete content is: to advance a new method which analyze data of nuclei with natural abundance using relationship between the numbers of proton and neutron. The relationship of each nucleus increases to 4 sets: S+H=Z H+Z=N Z+N=A and S-H=K. To expand the similarity between proton and neutron to the similarity among p,n, deuteron, triton, and He-5 clusters. According to the distribution law of same kind of nuclei, it obtains that the upper limits of stable region both should be '44s'. New foundational law of nuclear system is 1,2,4,8,16,8,4,2,1. In order to explain new law, a hypothesis which nucleus is made up of deuteron and triton is developing and nuclear field of whole number is built up. And it relates that unity of matter motion, which is the most foundational form atomic nuclear systematic is similar to the most first-class form chromosome numbers of mankind. These achievements will shake the foundations of traditional nuclear science. These achievements will supply new tasks in developing nuclear theory. And shake the ground of which magic number is the basic of nuclear science. It opens up a new field on foundational research. The book will supply new knowledge for researcher, teachers and students in universities and polytechnic schools. Scientific workers read in works of research and technical exploit. It can be stored up for library and laboratory of society and universities. In nowadays of prosperity our nation by science and education, the book is readable for workers of scientific technology and amateurs of natural science

  12. Classification and clinical assessment

    Directory of Open Access Journals (Sweden)

    F. Cantini

    2012-06-01

    Full Text Available There are at least nine classification criteria for psoriatic arthritis (PsA that have been proposed and used in clinical studies. With the exception of the ESSG and Bennett rules, all of the other criteria sets have a good performance in identifying PsA patients. As the CASPAR criteria are based on a robust study methodology, they are considered the current reference standard. However, if there seems to be no doubt that they are very good to classify PsA patients (very high specificity, they might be not sensitive enough to diagnose patients with unknown early PsA. The vast clinical heterogeneity of PsA makes its assessment very challenging. Peripheral joint involvement is measured by 78/76 joint counts, spine involvement by the instruments used for ankylosing spondylitis (AS, dactylitis by involved digit count or by the Leeds dactylitis index, enthesitis by the number of affected entheses (several indices available and psoriasis by the Psoriasis Area and Severity Index (PASI. Peripheral joint damage can be assessed by a modified van der Heijde-Sharp scoring system and axial damage by the methods used for AS or by the Psoriatic Arthritis Spondylitis Radiology Index (PASRI. As in other arthritides, global evaluation of disease activity and severity by patient and physician and assessment of disability and quality of life are widely used. Finally, composite indices that capture several clinical manifestations of PsA have been proposed and a new instrument, the Psoriatic ARthritis Disease Activity Score (PASDAS, is currently being developed.

  13. The classification of easement

    Directory of Open Access Journals (Sweden)

    Popov Danica D.

    2015-01-01

    Full Text Available Easement means, a right enjoyed by the owner of land over the lands of another: such as rights of way, right of light, rights of support, rights to a flow of air or water etc. The dominant tenement is the land owned by the possessor of the easement, and the servient tenement is the land over which the right is enjoyed. An easement must exist for the accommodation and better enjoyment to which it is annexed, otherwise it may amount to mere licence. An easement benefits and binds the land itself and therefore countinious despite any change of ownership of either dominant or servient tenement, although it will be extinguished if the two tenemants come into common ownership. An easement can only be enjoyed in respect of land. This means two parcels of land. First there must be a 'dominant tenement' and a 'servient tenement'. Dominant tenement to which the benefit of the easement attaches, and another (servient tenement which bears the burden of the easement. A positive easement consist of a right to do something on the land of another; a negative easement restrict the use of owner of the serviant tenement may make of his land. An easement may be on land or on the house made on land. The next classification is on easement on the ground, and the other one under the ground. An easement shall be done in accordance with the principle of restrictions. This means that the less burden the servient tenement. When there is doubt about the extent of the actual easement shall take what easier the servient tenement. The new needs of the dominant estate does not result in the expansion of servitude. In the article is made comparison between The Draft Code of property and other real estate, and The Draft of Civil Code of Serbia.

  14. Classification of malignant lymphomas

    International Nuclear Information System (INIS)

    Schneider, M.; Thyss, A.

    1986-01-01

    Malignant lymphomas, primary tumors of the lymphoid tissues, were first described in 1832 by Thomas Hodgkin. The histological characteristics were later defined by Sternberg and Reed, and Virchow introduced the concept of lymphosarcoma in 1863. Today, these pathologies are grouped together under the synonymous terms hematosarcoma or malignant lymphoma, which are in turn divided into Hodgkin's disease (HD) and non-Hodgkin's malignant lymphomas (NHL). The therapy of lymphomas is controversial. The validity of treatment for asymptomatic patients is questioned, owing to the indolent course of many lymphomas. Results for histologically unfavorable forms are highly disparate. Exclusive radiotherapy has occasionally produced up to 78% disease-free survival at 5 years for truly localized stages. Today, however, use of chemotherapy/radiotherapy combinations is almost universal, with chemotherapy occasionally being used alone and providing 90% disease-free survival at 5 years. Chemotherapy is the main treatment for disseminated forms; the major associations include doxorubicin hydrochloride (Adriamycin), cyclophosphamide, vincristine sulfate, methotrexate, and prednisone. Radiotherapy is used more for adjuvant purposes. Synthesis of recent studies allows us to reasonably expect 40% relapse-free survival at 10 years and the establishment of a cure plateau in the near future

  15. Radiofrequency Ablation of Lung Tumors

    Science.gov (United States)

    ... News Physician Resources Professions Site Index A-Z Radiofrequency Ablation (RFA) / Microwave Ablation (MWA) of Lung Tumors ... and Microwave Ablation of Lung Tumors? What are Radiofrequency and Microwave Ablation of Lung Tumors? Radiofrequency ablation, ...

  16. The PCa Tumor Microenvironment.

    Science.gov (United States)

    Sottnik, Joseph L; Zhang, Jian; Macoska, Jill A; Keller, Evan T

    2011-12-01

    The tumor microenvironment (TME) is a very complex niche that consists of multiple cell types, supportive matrix and soluble factors. Cells in the TME consist of both host cells that are present at tumor site at the onset of tumor growth and cells that are recruited in either response to tumor- or host-derived factors. PCa (PCa) thrives on crosstalk between tumor cells and the TME. Crosstalk results in an orchestrated evolution of both the tumor and microenvironment as the tumor progresses. The TME reacts to PCa-produced soluble factors as well as direct interaction with PCa cells. In return, the TME produces soluble factors, structural support and direct contact interactions that influence the establishment and progression of PCa. In this review, we focus on the host side of the equation to provide a foundation for understanding how different aspects of the TME contribute to PCa progression. We discuss immune effector cells, specialized niches, such as the vascular and bone marrow, and several key protein factors that mediate host effects on PCa. This discussion highlights the concept that the TME offers a potentially very fertile target for PCa therapy.

  17. Epilepsy and brain tumors

    Science.gov (United States)

    ENGLOT, DARIO J.; CHANG, EDWARD F.; VECHT, CHARLES J.

    2016-01-01

    Seizures are common in patients with brain tumors, and epilepsy can significantly impact patient quality of life. Therefore, a thorough understanding of rates and predictors of seizures, and the likelihood of seizure freedom after resection, is critical in the treatment of brain tumors. Among all tumor types, seizures are most common with glioneuronal tumors (70–80%), particularly in patients with frontotemporal or insular lesions. Seizures are also common in individuals with glioma, with the highest rates of epilepsy (60–75%) observed in patients with low-grade gliomas located in superficial cortical or insular regions. Approximately 20–50% of patients with meningioma and 20–35% of those with brain metastases also suffer from seizures. After tumor resection, approximately 60–90% are rendered seizure-free, with most favorable seizure outcomes seen in individuals with glioneuronal tumors. Gross total resection, earlier surgical therapy, and a lack of generalized seizures are common predictors of a favorable seizure outcome. With regard to anticonvulsant medication selection, evidence-based guidelines for the treatment of focal epilepsy should be followed, and individual patient factors should also be considered, including patient age, sex, organ dysfunction, comorbidity, or cotherapy. As concomitant chemotherapy commonly forms an essential part of glioma treatment, enzyme-inducing anticonvulsants should be avoided when possible. Seizure freedom is the ultimate goal in the treatment of brain tumor patients with epilepsy, given the adverse effects of seizures on quality of life. PMID:26948360

  18. CNS tumors: postoperative evaluation

    International Nuclear Information System (INIS)

    Dayanir, Y.

    2012-01-01

    Full text: Imaging assessment of brain tumors following surgery is complex and depends upon several factors, including the location of the tumor, the surgical procedure and the disease process for which it was performed. Depending upon these factors, one or a combination of complementary imaging modalities may be required to demonstrate any clinically relevant situation, to assist the surgeon in deciding if repeat surgery is necessary. Conventional magnetic resonance imaging (MRI) can show the shape, size, signal intensity, and enhancement of a brain tumor. It has been widely used to diagnose and differentiate brain tumors and to assess the surgery outcomes. Longitudinal MRI scans have also been applied for the assessment of treatment and response to surgery. The newly developed MRI techniques, including diffusion weighted imaging (DWI), perfusion weighted imaging (PWI) and magnetic resonance spectroscopy (MRS), have the potential to provide the molecular, functional and metabolic information of preoperative and postoperative brain tumors. Postoperative diffusion and perfusion magnetic resonance imaging are especially useful in predicting early functional recovery from new deficits after brain tumor surgery.This lecture will stress the principles, applications, and pitfalls of conventional as well as newly developing functional imaging techniques following operation of brain tumors

  19. Tumor cell surface proteins

    International Nuclear Information System (INIS)

    Kennel, S.J.; Braslawsky, G.R.; Flynn, K.; Foote, L.J.; Friedman, E.; Hotchkiss, J.A.; Huang, A.H.L.; Lankford, P.K.

    1982-01-01

    Cell surface proteins mediate interaction between cells and their environment. Unique tumor cell surface proteins are being identified and quantified in several tumor systems to address the following questions: (i) how do tumor-specific proteins arise during cell transformation; (ii) can these proteins be used as markers of tumor cell distribution in vivo; (iii) can cytotoxic drugs be targeted specifically to tumor cells using antibody; and (iv) can solid state radioimmunoassay of these proteins provide a means to quantify transformation frequencies. A tumor surface protein of 180,000 M/sub r/ (TSP-180) has been identified on cells of several lung carcinomas of BALB/c mice. TSP-180 was not detected on normal lung tissue, embryonic tissue, or other epithelial or sarcoma tumors, but it was found on lung carcinomas of other strains of mice. Considerable amino acid sequence homology exists among TSP-180's from several cell sources, indicating that TSP-180 synthesis is directed by normal cellular genes although it is not expressed in normal cells. The regulation of synthesis of TSP-180 and its relationship to normal cell surface proteins are being studied. Monoclonal antibodies (MoAb) to TSP-180 have been developed. The antibodies have been used in immunoaffinity chromatography to isolate TSP-180 from tumor cell sources. This purified tumor antigen was used to immunize rats. Antibody produced by these animals reacted at different sites (epitopes) on the TSP-180 molecule than did the original MoAb. These sera and MoAb from these animals are being used to identify normal cell components related to the TSP-180 molecule

  20. Central nervous system tumors

    International Nuclear Information System (INIS)

    Gavin, P.R.; Fike, J.R.; Hoopes, P.J.

    1995-01-01

    Central nervous system (CNS) tumors are relatively common in veterinary medicine, with most diagnoses occurring in the canine and feline species. Numerous tumor types from various cells or origins have been identified with the most common tumors being meningiomas and glial cell tumors. Radiation therapy is often used as an aid to control the clinical signs associated with these neoplasms. In general, these tumors have a very low metastatic potential, such that local control offers substantial benefit. Experience in veterinary radiation oncology would indicate that many patients benefit from radiation treatment. Current practice indicates the need for computed tomography or magnetic resonance imaging studies. These highly beneficial studies are used for diagnosis, treatment planning, and to monitor treatment response. Improvements in treatment planning and radiation delivered to the tumor, while sparing the normal tissues, should improve local control and decrease potential radiation related problems to the CNS. When possible, multiple fractions of 3 Gy or less should be used. The tolerance dose to the normal tissue with this fractionation schedule is 50 to 55 Gy. The most common and serious complications of radiation for CNS tumors is delayed radiation myelopathy and necrosis. Medical management of the patient during radiation therapy requires careful attention to anesthetic protocols, and medications to reduce intracranial pressure that is often elevated in these patients. Canine brain tumors have served as an experimental model to test numerous new treatments. Increased availability of advanced imaging modalities has spawned increased detection of these neoplasms. Early detection of these tumors with appropriate aggressive therapy should prove beneficial to many patients

  1. Critical Evaluation of Headache Classifications.

    Science.gov (United States)

    Özge, Aynur

    2013-08-01

    Transforming a subjective sense like headache into an objective state and establishing a common language for this complaint which can be both a symptom and a disease all by itself have kept the investigators busy for years. Each recommendation proposed has brought along a set of patients who do not meet the criteria. While almost the most ideal and most comprehensive classification studies continued at this point, this time criticisims about withdrawing from daily practice came to the fore. In this article, the classification adventure of scientists who work in the area of headache will be summarized. More specifically, 2 classifications made by the International Headache Society (IHS) and the point reached in relation with the 3rd classification which is still being worked on will be discussed together with headache subtypes. It has been presented with the wish and belief that it will contribute to the readers and young investigators who are interested in this subject.

  2. The last classification of vasculitis

    NARCIS (Netherlands)

    Kallenberg, Cees G. M.

    2008-01-01

    Systemic vasculitides are a group of diverse conditions characterized by inflammation of the blood vessels. To obtain homogeneity in clinical characteristics, prognosis, and response to treatment, patients with vasculitis should be classified into defined disease categories. Many classification

  3. Radon classification of building ground

    International Nuclear Information System (INIS)

    Slunga, E.

    1988-01-01

    The Laboratories of Building Technology and Soil Mechanics and Foundation Engineering at the Helsinki University of Technology in cooperation with The Ministry of the Environment have proposed a radon classification for building ground. The proposed classification is based on the radon concentration in soil pores and on the permeability of the foundation soil. The classification includes four radon classes: negligible, normal, high and very high. Depending on the radon class the radon-technical solution for structures is chosen. It is proposed that the classification be done in general terms in connection with the site investigations for the planning of land use and in more detail in connection with the site investigations for an individual house. (author)

  4. Deep Learning for ECG Classification

    Science.gov (United States)

    Pyakillya, B.; Kazachenko, N.; Mikhailovsky, N.

    2017-10-01

    The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. Currently, there are many machine learning (ML) solutions which can be used for analyzing and classifying ECG data. However, the main disadvantages of these ML results is use of heuristic hand-crafted or engineered features with shallow feature learning architectures. The problem relies in the possibility not to find most appropriate features which will give high classification accuracy in this ECG problem. One of the proposing solution is to use deep learning architectures where first layers of convolutional neurons behave as feature extractors and in the end some fully-connected (FCN) layers are used for making final decision about ECG classes. In this work the deep learning architecture with 1D convolutional layers and FCN layers for ECG classification is presented and some classification results are showed.

  5. Vehicle classification using mobile sensors.

    Science.gov (United States)

    2013-04-01

    In this research, the feasibility of using mobile traffic sensors for binary vehicle classification on arterial roads is investigated. Features (e.g. : speed related, acceleration/deceleration related, etc.) are extracted from vehicle traces (passeng...

  6. Classification of remotely sensed images

    CSIR Research Space (South Africa)

    Dudeni, N

    2008-10-01

    Full Text Available For this research, the researchers examine various existing image classification algorithms with the aim of demonstrating how these algorithms can be applied to remote sensing images. These algorithms are broadly divided into supervised...

  7. Classification of Building Object Types

    DEFF Research Database (Denmark)

    Jørgensen, Kaj Asbjørn

    2011-01-01

    made. This is certainly the case in the Danish development. Based on the theories about these abstraction mechanisms, the basic principles for classification systems are presented and the observed misconceptions are analyses and explained. Furthermore, it is argued that the purpose of classification...... systems has changed and that new opportunities should be explored. Some proposals for new applications are presented and carefully aligned with IT opportunities. Especially, the use of building modelling will give new benefits and many of the traditional uses of classification systems will instead...... be managed by software applications and on the basis of building models. Classification systems with taxonomies of building object types have many application opportunities but can still be beneficial in data exchange between building construction partners. However, this will be performed by new methods...

  8. VT Biodiversity Project - Bedrock Classification

    Data.gov (United States)

    Vermont Center for Geographic Information — (Link to Metadata) This dataset is a five category, nine sub-category classification of the bedrock units appearing on the Centennial Geologic Map of Vermont. The...

  9. Classification of Cortical Brain Malformations

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2008-03-01

    Full Text Available Clinical, radiological, and genetic classifications of 113 cases of malformations of cortical development (MCD were evaluated at the Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, the Netherlands.

  10. Stochastic models for tumoral growth

    OpenAIRE

    Escudero, Carlos

    2006-01-01

    Strong experimental evidence has indicated that tumor growth belongs to the molecular beam epitaxy universality class. This type of growth is characterized by the constraint of cell proliferation to the tumor border, and surface diffusion of cells at the growing edge. Tumor growth is thus conceived as a competition for space between the tumor and the host, and cell diffusion at the tumor border is an optimal strategy adopted for minimizing the pressure and helping tumor development. Two stoch...

  11. Uterine mesenchymal tumors

    Directory of Open Access Journals (Sweden)

    Nikhil A Sangle

    2011-01-01

    Full Text Available Uterine mesenchymal tumors are a heterogeneous group of neoplasms that can frequently be diagnostically challenging. Differentiation between the benign and malignant counterparts of mesenchymal tumors is significant due to differences in clinical outcome, and the role of the surgical pathologist in making this distinction (especially in the difficult cases cannot be underestimated. Although immunohistochemical stains are supportive toward establishing a final diagnosis, the morphologic features trump all the other ancillary techniques for this group of neoplasms. This review therefore emphasizes the key morphologic features required to diagnose and distinguish uterine mesenchymal tumors from their mimics, with a brief description of the relevant immunohistochemical features.

  12. Targeting the tumor microenvironment

    Energy Technology Data Exchange (ETDEWEB)

    Kenny, P.A.; Lee, G.Y.; Bissell, M.J.

    2006-11-07

    Despite some notable successes cancer remains, for the most part, a seemingly intractable problem. There is, however, a growing appreciation that targeting the tumor epithelium in isolation is not sufficient as there is an intricate mutually sustaining synergy between the tumor epithelial cells and their surrounding stroma. As the details of this dialogue emerge, new therapeutic targets have been proposed. The FDA has already approved drugs targeting microenvironmental components such as VEGF and aromatase and many more agents are in the pipeline. In this article, we describe some of the 'druggable' targets and processes within the tumor microenvironment and review the approaches being taken to disrupt these interactions.

  13. Phylogenetic classification of bony fishes.

    Science.gov (United States)

    Betancur-R, Ricardo; Wiley, Edward O; Arratia, Gloria; Acero, Arturo; Bailly, Nicolas; Miya, Masaki; Lecointre, Guillaume; Ortí, Guillermo

    2017-07-06

    Fish classifications, as those of most other taxonomic groups, are being transformed drastically as new molecular phylogenies provide support for natural groups that were unanticipated by previous studies. A brief review of the main criteria used by ichthyologists to define their classifications during the last 50 years, however, reveals slow progress towards using an explicit phylogenetic framework. Instead, the trend has been to rely, in varying degrees, on deep-rooted anatomical concepts and authority, often mixing taxa with explicit phylogenetic support with arbitrary groupings. Two leading sources in ichthyology frequently used for fish classifications (JS Nelson's volumes of Fishes of the World and W. Eschmeyer's Catalog of Fishes) fail to adopt a global phylogenetic framework despite much recent progress made towards the resolution of the fish Tree of Life. The first explicit phylogenetic classification of bony fishes was published in 2013, based on a comprehensive molecular phylogeny ( www.deepfin.org ). We here update the first version of that classification by incorporating the most recent phylogenetic results. The updated classification presented here is based on phylogenies inferred using molecular and genomic data for nearly 2000 fishes. A total of 72 orders (and 79 suborders) are recognized in this version, compared with 66 orders in version 1. The phylogeny resolves placement of 410 families, or ~80% of the total of 514 families of bony fishes currently recognized. The ordinal status of 30 percomorph families included in this study, however, remains uncertain (incertae sedis in the series Carangaria, Ovalentaria, or Eupercaria). Comments to support taxonomic decisions and comparisons with conflicting taxonomic groups proposed by others are presented. We also highlight cases were morphological support exist for the groups being classified. This version of the phylogenetic classification of bony fishes is substantially improved, providing resolution

  14. A classification of chinese culture

    OpenAIRE

    Fan, Y

    2000-01-01

    This paper presents a classification of Chinese Cultural Values (CCVs). Although there exist great differences between the Mainland China, Hong Kong and Taiwan, it is still possible to identify certain core cultural values that are shared by the Chinese people no matter where they live. Based on the original list by the Chinese Cultural Connection (1987), the paper creates a new list that contains 71 core values against 40 in the old. The implications and limitations of the classification are...

  15. Classification of pyodestructive pulmonary diseases

    International Nuclear Information System (INIS)

    Muromskij, Yu.A.; Semivolkov, V.I.; Shlenova, L.A.

    1993-01-01

    Classification of pyodestructive lungs diseases, thier complications and outcomes is proposed which makes it possible for physioians engaged in studying respiratory organs pathology to orient themselves in problems of diagnosis and treatment tactics. The above classification is developed on the basis of studying the disease anamnesis and its clinical process, as well as on the basis of roentgenological and morphological study results by more than 10000 patients

  16. Quantum computing for pattern classification

    OpenAIRE

    Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco

    2014-01-01

    It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of pattern classification. We introduce a quantum pattern classification algorithm that draws on Trugenberger's proposal for measuring the Hamming di...

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

    Directory of Open Access Journals (Sweden)

    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.

  18. 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.

  19. Information Classification on University Websites

    DEFF Research Database (Denmark)

    Nawaz, Ather; Clemmensen, Torkil; Hertzum, Morten

    2011-01-01

    Websites are increasingly used as a medium for providing information to university students. The quality of a university website depends on how well the students’ information classification fits with the structure of the information on the website. This paper investigates the information classifi......Websites are increasingly used as a medium for providing information to university students. The quality of a university website depends on how well the students’ information classification fits with the structure of the information on the website. This paper investigates the information...... classification of 14 Danish and 14 Pakistani students and compares it with the information classification of their university website. Brainstorming, card sorting, and task exploration activities were used to discover similarities and differences in the participating students’ classification of website...... information and their ability to navigate the websites. The results of the study indicate group differences in user classification and related taskperformance differences. The main implications of the study are that (a) the edit distance appears a useful measure in cross-country HCI research and practice...

  20. Ototoxicity (cochleotoxicity) classifications: A review.

    Science.gov (United States)

    Crundwell, Gemma; Gomersall, Phil; Baguley, David M

    2016-01-01

    Drug-mediated ototoxicity, specifically cochleotoxicity, is a concern for patients receiving medications for the treatment of serious illness. A number of classification schemes exist, most of which are based on pure-tone audiometry, in order to assist non-audiological/non-otological specialists in the identification and monitoring of iatrogenic hearing loss. This review identifies the primary classification systems used in cochleototoxicity monitoring. By bringing together classifications published in discipline-specific literature, the paper aims to increase awareness of their relative strengths and limitations in the assessment and monitoring of ototoxic hearing loss and to indicate how future classification systems may improve upon the status-quo. Literature review. PubMed identified 4878 articles containing the search term ototox*. A systematic search identified 13 key classification systems. Cochleotoxicity classification systems can be divided into those which focus on hearing change from a baseline audiogram and those that focus on the functional impact of the hearing loss. Common weaknesses of these grading scales included a lack of sensitivity to small adverse changes in hearing thresholds, a lack of high-frequency audiometry (>8 kHz), and lack of indication of which changes are likely to be clinically significant for communication and quality of life.