Pre-operative overall survival time prediction for glioblastoma patients using deep learning on both imaging phenotype and genotype

Abstract

Glioblastoma (GBM) is the most common and deadly malignant brain tumor with short yet varied overall survival (OS) time. Per request of personalized treatment, accurate pre-operative prognosis for GBM patients is highly desired. Currently, many machine learning-based studies have been conducted to predict OS time based on pre-operative multimodal MR images of brain tumor patients. However, tumor genotype, such as MGMT and IDH, which has been proven to have strong relationship with OS, is completely not considered in pre-operative prognosis as the genotype information is unavailable until craniotomy. In this paper, we propose a new deep learning based method for OS time prediction. It can derive genotype related features from pre-operative multimodal MR images of brain tumor patients to guide OS time prediction. Particularly, we propose a multi-task convolutional neural network (CNN) to accomplish tumor genotype and OS time prediction tasks. As the network can benefit from learning genotype related features toward genotype prediction, we verify upon a dataset of 120 GBM patients and conclude that the multi-task learning can effectively improve the accuracy of predicting OS time in personalized prognosis.