A Learnable Prior Improves Inverse Tumor Growth Modeling
This addresses the computational and robustness limitations in personalized disease treatment modeling, though it appears incremental as it combines existing approaches.
The paper tackles the challenge of inverse tumor growth modeling by combining deep learning with evolutionary sampling, achieving a fivefold convergence acceleration and 95% Dice score for estimating brain tumor cell concentrations from MRI images.
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%.