Abstract
In glioblastoma (GBM), contrast enhanced (CE)-MRI delineates bulk tumor with contrast-enhancement but poorly characterizes invasive tumor in the nonenhancing T2W abnormality. There is extensive literature in both machine-learning (ML) and mechanistic mathematical oncology seeking to accurately predict diffuse tumor invasion from multi-parametric MRI. ML offers strengths of a data-driven iterative approach, while mechanistic (proliferation-invasion, PI) modeling incorporates spatial relationships with expected drop-offs of tumor cell density from central regions of MRI enhancement. In this study, we build and cross-validate a first-of-its-kind hybrid (ML-PI) model. We collected 82 image-guided biopsies from 18 primary GBM patients throughout CE T1W and nonenhancing T2W regions. For each biopsy, we obtained neuropathologist estimates of tumor cell density and spatially matched MRI (CE T1W, T2W) from which we extracted texture features. PI maps of tumor invasion were generated from MRI-based patient-specific estimates of the net rates of invasion and proliferation. Then, the PI maps were incorporated with a ML model that uses texture features to predict cell density to minimize the prediction error on biopsy samples (ML strength) while making sure tumor-wide prediction (biopsied and unbiopsied regions) conformed with glioblastoma biology (PI strength). We optimized this hybrid ML-PI model using leave-one-out-cross-validation, and compared its performance with PI and ML alone. We used Pearson correlation (r) between cross-validated predicted tumor cell density and true tumor cell density. We focused on prediction within the nonenhancing zone (n=32) because accurate estimation of invasive tumor cell density in this zone has important clinical value for radiation and surgical planning. PI-ML showed significantly stronger correlation (r=0.76) compared to PI (r=0.44) and ML (r=0.04) models alone (p value<0.01). We present a first-of-its-kind hybrid model combining mechanistic modeling and machine learning approaches that accurately quantifies tumor cell gradients within the non-enhancing T2W zone of GBM.