Automatic MRI-Driven Model Calibration for Advanced Brain Tumor Progression Analysis

TL;DR

This paper introduces a fully automatic MRI-based method for calibrating tumor growth models in glioblastoma, enabling personalized analysis of tumor progression and potential survival prediction.

Contribution

The authors develop a novel automatic calibration approach combining PDE modeling with multiparametric MRI, capable of handling multifocal tumors and localizing tumor initiation sites with high precision.

Findings

Robust extraction of patient-specific tumor growth parameters.

Improved prediction accuracy of patient survival when combining imaging features with biophysical parameters.

Method successfully applied to clinical data from 206 GBM patients.

Abstract

Our objective is the calibration of mathematical tumor growth models from a single multiparametric scan. The target problem is the analysis of preoperative Glioblastoma (GBM) scans. To this end, we present a fully automatic tumor-growth calibration methodology that integrates a single-species reaction-diffusion partial differential equation (PDE) model for tumor progression with multiparametric Magnetic Resonance Imaging (mpMRI) scans to robustly extract patient specific biomarkers i.e., estimates for (i) the tumor cell proliferation rate, (ii) the tumor cell migration rate, and (iii) the original, localized site(s) of tumor initiation. Our method is based on a sparse reconstruction algorithm for the tumor initial location (TIL). This problem is particularly challenging due to nonlinearity, ill-posedeness, and ill conditioning. We propose a coarse-to-fine multi-resolution continuation scheme with parameter decomposition to stabilize the inversion. We demonstrate robustness and practicality of our method by applying the proposed method to clinical data of 206 GBM patients. We analyze the extracted biomarkers and relate tumor origin with patient overall survival by mapping the former into a common atlas space. We present preliminary results that suggest improved accuracy for prediction of patient overall survival when a set of imaging features is augmented with estimated biophysical parameters. All extracted features, tumor initial positions, and biophysical growth parameters are made publicly available for further analysis. To our knowledge, this is the first fully automatic scheme that can handle multifocal tumors and can localize the TIL to a few millimeters.