Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are
important prognostic markers for glioma. Currently, they are determined using
invasive procedures. Our goal was to develop artificial intelligence-based
methods to non-invasively determine these molecular alterations from MRI. For
this purpose, pre-operative MRI scans of 2648 patients with gliomas (grade
II-IV) were collected from Washington University School of Medicine (WUSM; n =
835) and publicly available datasets viz. Brain Tumor Segmentation (BraTS; n =
378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy GAP; n = 41),
The Cancer Genome Atlas (TCGA; n = 461), and the Erasmus Glioma Database (EGD;
n = 774). A 2.5D hybrid convolutional neural network was proposed to
simultaneously localize the tumor and classify its molecular status by
leveraging imaging features from MR scans and prior knowledge features from
clinical records and tumor location. The models were tested on one internal
(TCGA) and two external (WUSM and EGD) test sets. 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. The high
accuracy of the model on unseen data showcases its generalization capabilities
and suggests its potential to perform a 'virtual biopsy' for tailoring
treatment planning and overall clinical management of gliomas
