Geometry-aware neural solver for fast Bayesian calibration of brain tumor models

TL;DR

This paper introduces a geometry-aware neural surrogate model that accelerates Bayesian brain tumor modeling, enabling near-real-time personalized diagnosis while maintaining accuracy comparable to traditional numerical solvers.

Contribution

It presents a novel learnable neural solver that directly maps model parameters to tumor growth outputs, respecting patient-specific geometry, and significantly reduces inference time for clinical use.

Findings

Achieves near-real-time tumor model personalization.

Maintains accuracy comparable to traditional solvers.

Enables practical clinical application of Bayesian tumor modeling.

Abstract

Modeling of brain tumor dynamics has the potential to advance therapeutic planning. Current modeling approaches resort to numerical solvers that simulate the tumor progression according to a given differential equation. Using highly-efficient numerical solvers, a single forward simulation takes up to a few minutes of compute. At the same time, clinical applications of tumor modeling often imply solving an inverse problem, requiring up to tens of thousands forward model evaluations when used for a Bayesian model personalization via sampling. This results in a total inference time prohibitively expensive for clinical translation. While recent data-driven approaches become capable of emulating physics simulation, they tend to fail in generalizing over the variability of the boundary conditions imposed by the patient-specific anatomy. In this paper, we propose a learnable surrogate for simulating tumor growth which maps the biophysical model parameters directly to simulation outputs, i.e. the local tumor cell densities, whilst respecting patient geometry. We test the neural solver on Bayesian tumor model personalization for a cohort of glioma patients. Bayesian inference using the proposed surrogate yields estimates analogous to those obtained by solving the forward model with a regular numerical solver. The near-real-time computation cost renders the proposed method suitable for clinical settings. The code is available at https://github.com/IvanEz/tumor-surrogate.