  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.
