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

2025-01-14

AI Summary

Existing glioblastoma (GBM) growth modeling suffers from low prediction accuracy and inefficient patient-specific parameter calibration. Method: We propose TumorSurrogate, the first end-to-end differentiable neural forward solver for GBM reaction-diffusion models, integrating fully differentiable PDE solving with gradient-based optimization. Our framework synergistically combines an enhanced TumorSurrogate architecture, an improved nnU-Net, and a 3D Vision Transformer to enable multi-scale tumor evolution forecasting and end-to-end differentiable training. Contributions/Results: Experiments demonstrate that TumorSurrogate reduces mean squared error by 50%, achieves state-of-the-art Dice scores across multiple concentration thresholds, and accelerates parameter calibration by an order of magnitude. These improvements significantly enhance the precision and clinical feasibility of radiotherapy planning.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 optimized TumorSurrogate achieved the best overall results, excelling in both tumor outline matching and voxel-level prediction of tumor cell concentration. It halved the MSE relative to the baseline model and achieved 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.

Problem

• Glioblastoma
• Mathematical Models
• Treatment Planning

Innovation

• Neural Forward Solver
• Gradient Optimization
• TumorSurrogate Model