A Lightweight Optimization Framework for Estimating 3D Brain Tumor Infiltration

doi:10.1007/978-3-032-06624-4_1

2026-01-01

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DOI：10.1007/978-3-032-06624-4_1

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胶质母细胞瘤是最具侵袭性的原发性脑肿瘤，由于其弥漫性微观浸润在标准MRI上大多未被检测到，给临床治疗带来严峻挑战。因此，当前放射治疗计划采用围绕切除腔的统一15毫米边缘，无法捕捉患者特定的肿瘤扩散。肿瘤生长建模为揭示这种隐藏浸润提供了有前景的方法。然而，基于偏微分方程或物理信息神经网络的模型往往计算密集或过度受限，限制了其临床适应性。我们提出了一种新颖的框架，通过将浓度拟合到MRI肿瘤分割并强制平滑浓度景观，实现肿瘤生长的精准建模。该框架在两个公开数据集的192例脑肿瘤患者中实现了卓越的肿瘤复发预测性能，优于现有最佳基线方法，并将运行时间从30分钟缩短至不到1分钟。此外，我们展示了该框架的通用性和适应性，证明其能够无缝整合额外的成像模态或物理约束。

Keyword:

Brain Tumor

MRI

Recurrence Prediction

期刊

COMPUTATIONAL MATHEMATICS MODELING IN CANCER ANALYSIS, CMMCA 2025

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technical university of munich

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7.4K

论文数: 2.9K

被引数: 1

imperial college london

学者数:

9.9K

论文数: 4.4K

被引数: 0

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