Efficient Deep Learning-based Forward Solvers for Brain Tumor Growth Models

TL;DR

This paper presents a neural network-based forward solver for brain tumor growth models that significantly accelerates model calibration, improving accuracy and efficiency in simulating tumor behavior for better treatment planning.

Contribution

It introduces and compares multiple neural architectures, with nnU-Net achieving the best performance for tumor outline and cell concentration prediction, advancing fast and accurate model calibration.

Findings

nnU-Net outperformed other architectures in tumor outline matching

Achieved lowest MSE in tumor cell concentration prediction

Highest Dice score across tumor concentration thresholds

Abstract

Glioblastoma, a highly aggressive brain tumor, poses major challenges due to its poor prognosis and high morbidity rates. Partial differential equation-based models offer promising potential to enhance therapeutic outcomes by simulating patient-specific tumor behavior for improved radiotherapy planning. However, model calibration remains a bottleneck due to the high computational demands of optimization methods like Monte Carlo sampling and evolutionary algorithms. To address this, we recently introduced an approach leveraging a neural forward solver with gradient-based optimization to significantly reduce calibration time. This approach requires a highly accurate and fully differentiable forward model. We investigate multiple architectures, including (i) an enhanced TumorSurrogate, (ii) a modified nnU-Net, and (iii) a 3D Vision Transformer (ViT). The nnU-Net achieved the best overall results, excelling in both tumor outline matching and voxel-level prediction of tumor cell concentration. It yielded the lowest MSE in tumor cell concentration compared to ground truth numerical simulation and the highest Dice score across all tumor cell concentration thresholds. Our study demonstrates significant enhancement in forward solver performance and outlines important future research directions.