MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks

• DOI: 10.1142/9789813235533_0031
• Publication Date: 2017-11-17T10:27:21Z
• Authors: Lichy Han; Maulik R. Kamdar
• Keywords: 0301 basic medicine; Brain Neoplasms; Tumor Suppressor Proteins; Computational Biology; DNA Methylation; Regulatory Sequences, Nucleic Acid; Magnetic Resonance Imaging; 3. Good health; Dacarbazine; 03 medical and health sciences; DNA Repair Enzymes; Drug Resistance, Neoplasm; Databases, Genetic; Temozolomide; Humans; Neural Networks, Computer; Glioblastoma; Antineoplastic Agents, Alkylating; DNA Modification Methylases

Glioblastoma Multiforme (GBM), a malignant brain tumor, is among the most lethal of all cancers. Temozolomide is the primary chemotherapy treatment for patients diagnosed with GBM. The methylation status of the promoter or the enhancer regions of the O6-methylguanine methyltransferase (MGMT) gene may impact the efficacy and sensitivity of temozolomide, and hence may affect overall patient survival. Microscopic genetic changes may manifest as macroscopic morphological changes in the brain tumors that can be detected using magnetic resonance imaging (MRI), which can serve as noninvasive biomarkers for determining methylation of MGMT regulatory regions. In this research, we use a compendium of brain MRI scans of GBM patients collected from The Cancer Imaging Archive (TCIA) combined with methylation data from The Cancer Genome Atlas (TCGA) to predict the methylation state of the MGMT regulatory regions in these patients. Our approach relies on a bi-directional convolutional recurrent neural network architecture (CRNN) that leverages the spatial aspects of these 3-dimensional MRI scans. Our CRNN obtains an accuracy of 67% on the validation data and 62% on the test data, with precision and recall both at 67%, suggesting the existence of MRI features that may complement existing markers for GBM patient stratification and prognosis. We have additionally presented our model via a novel neural network visualization platform, which we have developed to improve interpretability of deep learning MRI-based classification models.