RT Journal Article
SR Electronic
T1 MR Imaging–Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 154
OP 161
DO 10.3174/ajnr.A5899
VO 40
IS 1
A1 M. Iv
A1 M. Zhou
A1 K. Shpanskaya
A1 S. Perreault
A1 Z. Wang
A1 E. Tranvinh
A1 B. Lanzman
A1 S. Vajapeyam
A1 N.A. Vitanza
A1 P.G. Fisher
A1 Y.J. Cho
A1 S. Laughlin
A1 V. Ramaswamy
A1 M.D. Taylor
A1 S.H. Cheshier
A1 G.A. Grant
A1 T. Young Poussaint
A1 O. Gevaert
A1 K.W. Yeom
YR 2019
UL http://www.ajnr.org/content/40/1/154.abstract
AB 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 with medulloblastoma from 3 children's hospitals from January 2001 to January 2014. A computational framework was developed to extract 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 area under the receiver operating characteristic curve to evaluate model performance.RESULTS: Of 590 MR imaging–derived radiomic features, including intensity-based histograms, tumor edge-sharpness, Gabor features, and local area integral invariant features, extracted from imaging-derived tumor segmentations, tumor edge-sharpness was most useful for predicting sonic hedgehog and group 4 tumors. Receiver operating characteristic analysis revealed superior performance of the double 10-fold cross-validation model for predicting sonic hedgehog, group 3, and group 4 tumors when using combined T1- and T2-weighted images (area under the curve = 0.79, 0.70, and 0.83, respectively). With the independent 3-dataset cross-validation strategy, select radiomic features were predictive of sonic hedgehog (area under the curve = 0.70–0.73) and group 4 (area under the curve = 0.76–0.80) medulloblastoma.CONCLUSIONS: This study provides proof-of-concept results for the application of radiomic and machine learning approaches to a multi-institutional dataset for the prediction of medulloblastoma subgroups.AUCarea under the curveLAIIlocal area integral invariantMBmedulloblastomaROCreceiver operating characteristicSHHsonic hedgehogSVMsupport vector machinesWNTwingless type