Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect

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.Index Terms-tumor growth model personalization, inverse problem, mass effect, glioblastoma (Preprint submitted to IEEE Transactions on Medical Imaging) I. INTRODUCTIONComputational oncology is an emerging field that attempts to integrate biophysical models with imaging with the goal of assisting image analysis in a clinical setting. Typically, the integration is accomplished by calibrating PDE model parameters from images. A significant challenge in brain tumors, and in particular, GBMs, is the single-scan calibration, that becomes even harder in the presence of mass effect. However, modeling provides the capability for automatic quantification of mass effect along with additional biomarkers related to

Modelling glioma progression, mass effect and intracranial pressure in patient anatomy

“…[51,52], who used a nonlinear reaction-advection-diffusion equation to couple the tumour growth with brain tissue deformation, enabling simulations of large brain deformations. The model has been used in several simulation studies as well as medical image-processing tasks [33,[53][54][55][56]. Recently, the model has been extended to account for different tumour components, such as proliferating, invasive and necrotic tumour cells, along with tumour-induced brain oedema, resulting in realistically appearing simulated tumours [57].…”

CLAIRE: Constrained Large Deformation Diffeomorphic Image Registration on Parallel Computing Architectures

“…The GPU version of CLAIRE can solve clinically relevant problems (50 M unknowns) in approximately 5 seconds on a single NVIDIA Tesla V100 ( Brunn et al, 2020 ). CLAIRE has also been applied to hundreds of images in brain tumor imaging studies ( Bakas et al, 2018 ; Mang et al, 2017 ; Scheufele et al, 2021 ), and has been integrated with models for biophysics inversion ( Mang et al, 2018 , 2020 ; Scheufele et al, 2019 , 2021 ; Scheufele, Subramanian, Mang, et al, 2020 ; Subramanian et al, 2020 ) and Alzheimer’s disease progression ( Scheufele, Subramanian, & Biros, 2020 ). CLAIRE uses highly optimized computational kernels and effective, state-of-the-art algorithms for time integration and numerical optimization.…”

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis

“…This is especially true in the field of tumor angiogenesis where the mathematical models demand data that are acquired at both high temporal and spatial resolution to resolve vascular dynamics [22,[26][27][28][29][30]. Furthermore, the models themselves may have a myriad of parameters yielding an ill-posed parameter calibration problem [31][32][33][34]. Still, these models, even if uncalibrated against experimental data, can serve as useful tools to guide the experimental study of tumor-induced angiogenesis.…”