Overall Survival Time Prediction of Glioblastoma on Preoperative MRI Using Lesion Network Mapping

Abstract

Glioblastoma (GBM) is the most aggressive malignant brain tumor. Its poor survival rate highlights the pressing need to adopt easily accessible, non-invasive neuroimaging techniques to preoperatively predict GBM survival, which can benefit treatment planning and patient care. MRI and MRI-based radiomics, although effective for survival prediction, do not consider brain’s functional alternations caused by tumors, which are clinically significant for guiding therapeutic strategies aimed at inhibiting tumor-brain communication. In this paper, we propose an augmented lesion network mapping (A-LNM) based survival prediction framework, where a novel neuroimaging feature family, called functional lesion network (FLN) maps generated by the A-LNM, is achieved from patients’ structural MRI, and thus are more readily available than functional connections measured with functional MRI of patients. Specifically, for each patient, the A-LNM first estimates functional disconnection (FDC) maps by embedding the lesion (the whole tumor) into an atlas of functional connections in a large cohort of healthy subjects, and many FLN maps are then obtained by averaging subsets of the FDC maps such that we can artificially boost data volume (i.e., FLN maps), which helps to mitigate over-fitting and improve survival prediction performance when learning a deep neural network from a small sized dataset. The augmented FLN maps are finally fed to a 3D ResNet-based backbone followed by the average pooling operation and fully-connected layers for GBM survival prediction. Experimental results on the BraTS 2020 training dataset demonstrate the effectiveness of our proposed framework with the A-LNM derived FLN maps for GBM survival classification. Moreover, we identify the survival-relevant brain regions that can be traced back with biological interpretability.