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Our objective is the calibration of mathematical tumor growth models from asingle multiparametric scan. The target problem is the analysis of preoperativeGlioblastoma (GBM) scans. To this end, we present a fully automatictumor-growth calibration methodology that integrates a single-speciesreaction-diffusion partial differential equation (PDE) model for tumorprogression with multiparametric Magnetic Resonance Imaging (mpMRI) scans torobustly extract patient specific biomarkers i.e., estimates for (i) the tumorcell proliferation rate, (ii) the tumor cell migration rate, and (iii) theoriginal, localized site(s) of tumor initiation. Our method is based on asparse reconstruction algorithm for the tumor initial location (TIL). Thisproblem is particularly challenging due to nonlinearity, ill-posedeness, andill conditioning. We propose a coarse-to-fine multi-resolution continuationscheme with parameter decomposition to stabilize the inversion. We demonstraterobustness and practicality of our method by applying the proposed method toclinical data of 206 GBM patients. We analyze the extracted biomarkers andrelate tumor origin with patient overall survival by mapping the former into acommon atlas space. We present preliminary results that suggest improvedaccuracy for prediction of patient overall survival when a set of imagingfeatures is augmented with estimated biophysical parameters. All extractedfeatures, tumor initial positions, and biophysical growth parameters are madepublicly available for further analysis. To our knowledge, this is the firstfully automatic scheme that can handle multifocal tumors and can localize theTIL to a few millimeters.

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