用AI加速肝肿瘤微波消融规划，420倍提速且更精准。
Automatic Patient-Specific Microwave Ablation Planning Accelerated by a Physics-Guided Deep Learning Model
- 结合神经网络与遗传算法，构建物理引导的快速消融预测模型。
- 在13例新病例中提升疗效54.3%，减少器官损伤55.0%。
- 适合临床医生快速生成个性化、可接受的治疗方案。
微波消融（MWA）是治疗肝肿瘤的微创方法，但其疗效高度依赖于患者特异性的天线路径、功率和治疗时间规划。精确的数值模拟虽能提供可靠的消融预测，但计算成本高，难以用于需反复前向评估的优化规划。为此，我们提出一种基于数字孪生的自动规划框架，将神经消融预测模型与遗传算法结合。模型基于患者特异性肿瘤与血管结构、天线配置及治疗条件生成的多物理场仿真数据训练，作为规划中的快速前向模型。预测模型达到95.1%的Dice分数，实现精准的深度学习优化。在13个未见规划案例中，该方法相比临床医生制定方案，使消融效率提升54.3%，器官损伤减少55.0%，插入路径长度仅缩短3.3%。多数生成方案获消融专家认可为临床可用。此外，该框架使规划速度比基于数值模拟的方法快约420倍，展现出作为定量、个性化MWA治疗规划快速数字孪生的潜力。代码已公开：https://github.com/SeonAengCho/MWA-Planning.git
原文摘要 · Abstract (English)
Microwave ablation (MWA) is a promising minimally invasive treatment for liver tumors, but its therapeutic outcome strongly depends on patient-specific planning of antenna insertion trajectory, power, and treatment duration. Accurate numerical simulation can provide physically reliable ablation predictions; however, its high computational cost limits its use in optimization-based planning, where repeated forward evaluations are required. To address this issue, we propose a digital twin-based automatic planning framework that combines a neural ablation prediction model with a genetic algorithm. The model was trained on multiphysics simulation data generated from patient-specific tumor and vessel structures, antenna configurations, and treatment conditions, and was used as a fast forward model during planning. The prediction model achieved a Dice score of 95.1%, enabling accurate deep learning-based optimization. In 13 unseen planning cases, the proposed method improved ablation efficiency by 54.3% and reduced organ damage by 55.0% compared with clinician-defined planning, while slightly shortening the insertion path length by 3.3%. Most generated plans were also judged clinically applicable by MWA specialists. Furthermore, the framework enabled approximately 420-fold faster planning than numerical-simulation-based planning, demonstrating its potential as a fast digital twin for quantitative and personalized MWA treatment planning. The code is available at: https://github.com/SeonAengCho/MWA-Planning.git
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