MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network

Abstract

BACKGROUND: IDH mutation and 1p/19q codeletion status are important prognostic markers for glioma that are currently determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to noninvasively determine molecular alterations from MRI. METHODS: Pre-operative MRI scans of 2648 glioma patients were collected from Washington University School of Medicine (WUSM; RESULTS: For IDH, the best-performing model achieved areas under the receiver operating characteristic (AUROC) of 0.925, 0.874, 0.933 and areas under the precision-recall curves (AUPRC) of 0.899, 0.702, 0.853 on the internal, WUSM, and EGD test sets, respectively. For 1p/19q, the best model achieved AUROCs of 0.782, 0.754, 0.842, and AUPRCs of 0.588, 0.713, 0.782, on those three data-splits, respectively. CONCLUSIONS: The high accuracy of the model on unseen data showcases its generalization capabilities and suggests its potential to perform virtual biopsy for tailoring treatment planning and overall clinical management of gliomas