用患者专属数字孪生模型，提前预测肺癌放疗副作用。
A Patient-Specific Digital Twin for Adaptive Radiotherapy of Non-Small Cell Lung Cancer
- 构建动态数字孪生系统，融合每周期影像与剂量数据建模组织变化。
- 在毒性出现前数次治疗即能识别风险上升，提前预警。
- 适合关注精准放疗、AI辅助决策的临床与科研人员。
放疗日益精确且数据密集，当前治疗方案产生高频影像与剂量流，非常适合用AI进行时间建模以捕捉正常组织随时间演变的生物过程。在生物引导放疗（BGRT）中，非小细胞肺癌（NSCLC）患者每周期均记录新的代谢、解剖和剂量信息。然而，临床决策仍依赖静态、群体化的正常组织并发症概率（NTCP）模型，忽略了序列数据中编码的个体化生物轨迹。我们开发了COMPASS（综合个性化评估系统），作为时间数字孪生架构，利用每周期的PET、CT、剂量组学、影像组学及累积生物等效剂量（BED）动力学，将正常组织生物学建模为动态时间序列过程。采用GRU自编码器学习器官特异性潜在轨迹，并通过逻辑回归分类预测最终发生CTCAE ≥1级毒性。8名接受BGRT的NSCLC患者贡献了99个器官-分次观测，覆盖24个器官轨迹（脊髓、心脏、食管）。尽管样本量小，但密集的时间表型分析使我们得以全面探究个体剂量反应动态。研究发现，存在可行的AI驱动早期预警窗口：毒性发生前数次治疗即出现风险评级上升。密集的BED表示揭示了毒性前出现的生物学相关空间剂量纹理特征，而这些特征在传统体积平均剂量分析中被抹平。COMPASS为人工智能赋能的自适应放疗建立了概念验证，治疗可由持续更新的数字孪生驱动，实时追踪每位患者的生物响应演化。
原文摘要 · Abstract (English)
Radiotherapy continues to become more precise and data dense, with current treatment regimens generating high frequency imaging and dosimetry streams ideally suited for AI driven temporal modeling to characterize how normal tissues evolve with time. Each fraction in biologically guided radiotherapy(BGRT) treated non small cell lung cancer (NSCLC) patients records new metabolic, anatomical, and dose information. However, clinical decision making is largely informed by static, population based NTCP models which overlook the dynamic, unique biological trajectories encoded in sequential data. We developed COMPASS (Comprehensive Personalized Assessment System) for safe radiotherapy, functioning as a temporal digital twin architecture utilizing per fraction PET, CT, dosiomics, radiomics, and cumulative biologically equivalent dose (BED) kinetics to model normal tissue biology as a dynamic time series process. A GRU autoencoder was employed to learn organ specific latent trajectories, which were classified via logistic regression to predict eventual CTCAE grade 1 or higher toxicity. Eight NSCLC patients undergoing BGRT contributed to the 99 organ fraction observations covering 24 organ trajectories (spinal cord, heart, and esophagus). Despite the small cohort, intensive temporal phenotyping allowed for comprehensive analysis of individual dose response dynamics. Our findings revealed a viable AI driven early warning window, as increasing risk ratings occurred from several fractions before clinical toxicity. The dense BED driven representation revealed biologically relevant spatial dose texture characteristics that occur before toxicity and are averaged out with traditional volume based dosimetry. COMPASS establishes a proof of concept for AI enabled adaptive radiotherapy, where treatment is guided by a continually updated digital twin that tracks each patients evolving biological response.
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