Automated Neural Network-based Survival Prediction of Glioblastoma Patients Using Pre-operative MRI and Clinical Data

Abstract

In this paper, we proposed a lightweight two-dimensional (2D) methodology to predict the survival time of GBM patients. Firstly, we trained the 2D ResUNet-SEG (Residual UNet for Segmentation) model to perform semantic segmentation on brain tumour subregions. Then, we used the raw and segmented MRI volumes along with clinical data to train the 2D CNN-SP (Convolutional Neural Network for Survival Prediction) model to predict the survival time in days. The experiments showed that our proposed methodology achieved an accuracy of 0.517, Mean Square Error (MSE) of 136,783.42, MSE, Median Square Error (medianSE) of 106,608.6, Standard Deviation Error (stdSE) of 139,210.8, and SpearmanR correlation score of 0.299 on the Multimodal Brain Tumour Segmentation (BraTS) 2020 validation set. The obtained results are competitive compared to the state-of-the-art automated techniques for survival prognosis of GBM patients validated on the same set of patients. Results proved that Deep Learning (DL) based feature learning is better than existing Machine Learning (ML) based techniques with handcrafted radiomics based feature extraction. It eliminates the need for feature selection as well. However, the results achieved are limited due to the unavailability of vast clinical data required to train Convolutional Neural Network (CNN) based deep architectures.