Classification of MGMT Promoter Methylation Status in Brain Tumor MR Images using Deep Learning

Abstract

Glioblastoma is the most dangerous brain tumor type (worst prognosis), having the least survival rate of less than a year. O6-methylguanine-DNA methyltransferase (MGMT) promoter status “methylation” adds a favorable prognosis and is a reliable indication of cancer's chemosensitivity. Therefore, determining the MGMT status of a brain tumor patient is essential for treatment planning. Surgical tissue sample removal is required to conduct genetic testing for tumors. The genetic characterization of the tumors may then take a few weeks to establish after that. Non-invasive diagnosis using MR images plays a significant role in the early detection of the deadly disease by using Magnetic Resonance Imaging (MRI) to detect the tumorous regions using deep learning techniques. The main requirement for detecting MGMT status is obtaining the sub-regions of brain tumor from MRIs. Previous studies have worked on Brain tumor segmentation and MGMT classification separately, few of the studies have worked on segmentation and classification together but have used selective modalities for MGMT Prediction. Since each modality has its own significance and MGMT promoter status lies in the tumorous region, this study proposed a pipeline of segmentation and classification models using stacked multimodalities of MRI scans of the benchmark dataset BraTS2021. The pipeline is divided into two phases. The first phase uses a stack of multiple MRI modalities to segment the brain tumor into sub-region using a 3D Residual U-Net Architecture, and the second phase uses the segmentation model's output (stacked multimodalities tumor voxel) to determine the MGMT promoter status using a 3D ResNet 10 Classifier. With the help of pipeline, classification model received precise information about the tumorous regions which helped in efficient prediction of the MGMT status present in the brain tumor. The proposed pipeline has therapeutic value because it can assist radiologists in diagnosing brain tumors more efficiently and precisely by reducing the subjectivity and variability of human interpretation. The segmentation and classification pipeline for brain tumors x can potentially increase the consistency and objectivity of diagnoses. Furthermore, it can also assist radiologists in predicting the MGMT status without using surgical equipment and can help in treatment decision making. The segmentation phase of the proposed pipeline yielded promising results with average dice scores of 0.81, 0.84, and 0.80 for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET) on validation. The classification phase achieved ROC-AUC score of 0.66 on validation.