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MR Imaging–Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma

MR Imaging–Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma ORIGINAL RESEARCH PEDIATRICS MR Imaging–Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma X M. Iv, X M. Zhou, X K. Shpanskaya, X S. Perreault, X Z. Wang, X E. Tranvinh, X B. Lanzman, X S. Vajapeyam, X N.A. Vitanza, X P.G. Fisher, X Y.J. Cho, X S. Laughlin, X V. Ramaswamy, X M.D. Taylor, X S.H. Cheshier, X G.A. Grant, X T. Young Poussaint, X O. Gevaert, and X K.W. Yeom ABSTRACT BACKGROUND AND PURPOSE: Distinct molecular subgroups of pediatric medulloblastoma confer important differences in prognosis and therapy. Currently, tissue sampling is the only method to obtain information for classification. Our goal was to develop and validate radiomic and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma. MATERIALS AND METHODS: In this multi-institutional retrospective study, we evaluated MR imaging datasets of 109 pediatric patients withmedulloblastomafrom3children’shospitalsfromJanuary2001toJanuary2014.Acomputationalframeworkwasdevelopedtoextract MR imaging–based radiomic features from tumor segmentations, and we tested 2 predictive models: a double 10-fold cross-validation using a combined dataset consisting of all 3 patient cohorts and a 3-dataset cross-validation, in which training was performed on 2 cohorts and testing was performed on the third independent cohort. We used the Wilcoxon rank sum test for feature selection with assessment of http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Neuroradiology American Journal of Neuroradiology

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Publisher
American Journal of Neuroradiology
Copyright
© 2019 by American Journal of Neuroradiology
ISSN
0195-6108
eISSN
1936-959X
DOI
10.3174/ajnr.A5899
Publisher site
See Article on Publisher Site

Abstract

ORIGINAL RESEARCH PEDIATRICS MR Imaging–Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma X M. Iv, X M. Zhou, X K. Shpanskaya, X S. Perreault, X Z. Wang, X E. Tranvinh, X B. Lanzman, X S. Vajapeyam, X N.A. Vitanza, X P.G. Fisher, X Y.J. Cho, X S. Laughlin, X V. Ramaswamy, X M.D. Taylor, X S.H. Cheshier, X G.A. Grant, X T. Young Poussaint, X O. Gevaert, and X K.W. Yeom ABSTRACT BACKGROUND AND PURPOSE: Distinct molecular subgroups of pediatric medulloblastoma confer important differences in prognosis and therapy. Currently, tissue sampling is the only method to obtain information for classification. Our goal was to develop and validate radiomic and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma. MATERIALS AND METHODS: In this multi-institutional retrospective study, we evaluated MR imaging datasets of 109 pediatric patients withmedulloblastomafrom3children’shospitalsfromJanuary2001toJanuary2014.Acomputationalframeworkwasdevelopedtoextract MR imaging–based radiomic features from tumor segmentations, and we tested 2 predictive models: a double 10-fold cross-validation using a combined dataset consisting of all 3 patient cohorts and a 3-dataset cross-validation, in which training was performed on 2 cohorts and testing was performed on the third independent cohort. We used the Wilcoxon rank sum test for feature selection with assessment of

Journal

American Journal of NeuroradiologyAmerican Journal of Neuroradiology

Published: Jan 1, 2019

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