The Efficacy of Shape Radiomics and Deep Features for Glioblastoma Survival Prediction by Deep Learning

Abstract

Glioblastoma (known as glioblastoma multiforme) is one of the most aggressive brain malignancies, accounting for 48% of all primary brain tumors. For that reason, overall survival prediction plays a vital role in diagnosis and treatment planning for glioblastoma patients. The main target of our research is to demonstrate the effectiveness of features extracted from the combination of the whole tumor and enhancing tumor to the overall survival prediction. By the proposed method, there are two kinds of features, including shape radiomics and deep features, which is utilized for this task. Firstly, optimal shape radiomics features, consisting of sphericity, maximum 3D diameter, and surface area, are selected using the Cox proportional hazard model. Secondly, deep features are extracted by ResNet18 directly from magnetic resonance images. Finally, the combination of selected shape features, deep features, and clinical information fits the regression model for overall survival prediction. The proposed method achieves promising results, which obtained 57.1% and 97,531.8 for accuracy and mean squared error metrics, respectively. Furthermore, using selected features, the result on the mean squared error metric is slightly better than the competing methods. The experiments are conducted on the Brain Tumor Segmentation Challenge (BraTS) 2018 validation dataset.