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 Imaging
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.