用深度学习加速脑瘤生长模型求解，提升放疗规划效率
Efficient Deep Learning-based Forward Solvers for Brain Tumor Growth Models
- 用神经网络构建可微分的前向求解器，替代传统高耗时计算
- nnU-Net在肿瘤轮廓和细胞浓度预测上误差最小，Dice分数最高
- 适合需要快速个性化治疗模拟的临床研究与医学影像团队
胶质母细胞瘤是一种高度侵袭性的脑瘤，预后差且致残率高。基于偏微分方程的模型可通过模拟个体化肿瘤行为，有望改善放疗规划效果。然而，模型校准因蒙特卡洛采样和进化算法等优化方法计算量大而成为瓶颈。为此，我们此前提出利用基于梯度优化的神经前向求解器，显著降低校准时间，但依赖高精度且完全可微的前向模型。本文对比了三种架构：(i) 增强版 TumorSurrogate，(ii) 改进的 nnU-Net，(iii) 3D Vision Transformer (ViT)。结果表明，nnU-Net整体表现最优，在肿瘤轮廓匹配和体素级细胞浓度预测上均优于其他模型，其细胞浓度预测的均方误差（MSE）最低，且在所有细胞浓度阈值下均取得最高 Dice 分数。
原文摘要 · Abstract (English)
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.
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