Residual Convolutional Neural Network for the Determination ofIDHStatus in Low- and High-Grade Gliomas from MR Imaging

Ken Chang, Harrison X. Bai, Hao Zhou, Chang Su, Wenya Linda Bi, Ena Agbodza, Vasileios K. Kavouridis, Joeky T. Senders, Alessandro Boaro, Andrew Beers, Biqi Zhang, Alexandra Capellini (+18 others)
2017 Clinical Cancer Research  
Purpose: Isocitrate dehydrogenase (IDH) mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the IDH status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative radiographic data.Experimental Design: Preoperative imaging was acquired for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Women's Hospital (BWH), and 138 patients from The Cancer
more » ... ng Archive (TCIA) and divided into training, validation, and testing sets. We trained a residual convolutional neural network for each MR sequence (FLAIR, T2, T1 precontrast, and T1 postcontrast) and built a predictive model from the outputs. To increase the size of the training set and prevent overfitting, we augmented the training set images by introducing random rotations, translations, flips, shearing, and zooming.Results: With our neural network model, we achieved IDH prediction accuracies of 82.8% (AUC = 0.90), 83.0% (AUC = 0.93), and 85.7% (AUC = 0.94) within training, validation, and testing sets, respectively. When age at diagnosis was incorporated into the model, the training, validation, and testing accuracies increased to 87.3% (AUC = 0.93), 87.6% (AUC = 0.95), and 89.1% (AUC = 0.95), respectively.Conclusions: We developed a deep learning technique to noninvasively predict IDH genotype in grade II-IV glioma using conventional MR imaging using a multi-institutional data set. Clin Cancer Res; 24(5); 1073-81. ©2017 AACR.
doi:10.1158/1078-0432.ccr-17-2236 pmid:29167275 pmcid:PMC6051535 fatcat:jzzhd5itgbaxfe66tjqr5bd63e