| Support vector machines in DSC-based glioma imaging: suggestions for optimal characterization. | |
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MedLine Citation:
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PMID: 20564592 Owner: NLM Status: In-Process |
Abstract/OtherAbstract:
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Dynamic susceptibility contrast magnetic resonance perfusion imaging (DSC-MRI) is a useful method to characterize gliomas. Recently, support vector machines (SVMs) have been introduced as means to prospectively characterize new patients based on information from previous patients. Based on features derived from automatically segmented tumor volumes from 101 DSC-MR examinations, four different SVM models were compared. All SVM models achieved high prediction accuracies (>82%) after rebalancing the training data sets to equal amounts of samples per class. Best discrimination was obtained using a SVM model with a radial basis function kernel. A correct prediction of low-grade glioma was obtained at 83% (true positive rate) and for high-grade glioma at 91% (true negative rate) on the independent test data set. In conclusion, the combination of automated tumor segmentation followed by SVM classification is feasible. Thereby, a powerful tool is available to characterize glioma presurgically in patients. |
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Authors:
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Frank G Zöllner; Kyrre E Emblem; Lothar R Schad |
Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine Volume: 64 ISSN: 1522-2594 ISO Abbreviation: Magn Reson Med Publication Date: 2010 Oct |
Date Detail:
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Created Date: 2010-09-27 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8505245 Medline TA: Magn Reson Med Country: United States |
Other Details:
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Languages: eng Pagination: 1230-6 Citation Subset: IM |
Affiliation:
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Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany. frank.zoellner@medma.uni-heidelberg.de |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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