Ensemble inversion for brain tumor growth models with mass effect

TL;DR

This paper introduces an ensemble inversion method to calibrate a PDE-based brain tumor growth model that accounts for mass effect, enabling extraction of biophysical biomarkers from a single MRI scan and improving tumor characterization and survival prediction.

Contribution

The study presents a novel ensemble inversion scheme using healthy brain templates to calibrate tumor growth models with mass effect from single scans, addressing severe ill-posedness.

Findings

Inclusion of mass effect improves model calibration, increasing dice coefficients by 10%.

The method provides quantitative measures of tumor biophysics and mass effect.

Biophysics-based features derived from the model enhance survival analysis.

Abstract

We propose a method for extracting physics-based biomarkers from a single multiparametric Magnetic Resonance Imaging (mpMRI) scan bearing a glioma tumor. We account for mass effect, the deformation of brain parenchyma due to the growing tumor, which on its own is an important radiographic feature but its automatic quantification remains an open problem. In particular, we calibrate a partial differential equation (PDE) tumor growth model that captures mass effect, parameterized by a single scalar parameter, tumor proliferation, migration, while localizing the tumor initiation site. The single-scan calibration problem is severely ill-posed because the precancerous, healthy, brain anatomy is unknown. To address the ill-posedness, we introduce an ensemble inversion scheme that uses a number of normal subject brain templates as proxies for the healthy precancer subject anatomy. We verify our solver on a synthetic dataset and perform a retrospective analysis on a clinical dataset of 216 glioblastoma (GBM) patients. We analyze the reconstructions using our calibrated biophysical model and demonstrate that our solver provides both global and local quantitative measures of tumor biophysics and mass effect. We further highlight the improved performance in model calibration through the inclusion of mass effect in tumor growth models -- including mass effect in the model leads to 10% increase in average dice coefficients for patients with significant mass effect. We further evaluate our model by introducing novel biophysics-based features and using them for survival analysis. Our preliminary analysis suggests that including such features can improve patient stratification and survival prediction.