发表机构
Yonsei University; Innovative & Intelligent Computational Science Institute (IN2CSI)（延世大学; 创新与智能计算科学研究所（IN2CSI））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出结合神经消融预测模型与遗传算法的数字孪生框架，可快速实现患者特异性微波消融自动规划，提升消融效率、减少器官损伤且规划速度快约420倍
AI 中文摘要
微波消融（MWA）是一种有前景的肝肿瘤微创治疗手段，但其治疗效果高度依赖针对患者的天线插入轨迹、功率及治疗时长的规划。准确的数值模拟可提供物理上可靠的消融预测，但其高昂的计算成本限制了其在需重复正向评估的基于优化的规划中的应用。为解决该问题，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相结合。该模型在由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模拟数据上训练，在规划过程中作为快速正向模型使用。该预测模型的Dice系数达95.1%，可实现基于深度学习的准确优化。在13个未见过的规划案例中，与临床医生定义的规划相比，所提方法将消融效率提升54.3%，器官损伤减少55.0%，同时将插入路径长度略微缩短3.3%。大多数生成的规划也被MWA专家判定为临床适用。此外，该框架的规划速度比基于数值模拟的规划快约420倍，证明其作为定量个性化MWA治疗规划的快速数字孪生的潜力。代码可在该网址获取。
英文摘要
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
CommentsAccepted at the Digital Twin for Healthcare (DT4H 2026) workshop at MICCAI 2026; 10 pages, 2 figures
