不确定性引导的数字孪生框架用于头颈部肿瘤在线自适应质子治疗：可行性研究

中文摘要 AI 辅助

目的：头颈部（HN）质子治疗跨越六至七周的解剖变化期，而离线重计划大约需要一周时间。我们提出了一种不确定性引导的数字孪生（UGDT）框架，在治疗前预测治疗日解剖结构，并评估其是否能生成具有临床质量的在线自适应质子治疗（APT）计划。方法：利用来自88例既往治疗的头颈部患者的302个纵向形变库，通过基于预训练CT基础模型构建的两步多图谱可变形图像配准（DIR）将其迁移到每位新患者的治疗计划CT（TPCT）上，为每位患者生成约284个带轮廓的预测CT（pdCT）。传播的临床靶体积（CTV）轮廓的离散度定义了患者特异性稳健边界。在十例患者中，触发重计划的质量保证CT（QACT）代表治疗日解剖结构，医生批准的重计划作为基线。与QACT最相似的pdCT（pdCT-H）和来自最低四分位数的pdCT（pdCT-L）被计划到基线计划质量的约5%以内，在QACT上前向计算，并重新优化以生成在线APT计划。主要结果：pdCT计划得分在基线的-0.7%（pdCT-H）和-1.0%（pdCT-L）以内。在QACT上的前向计算将高剂量CTV D98%降至88.3%和85.5%。在线重新优化后，D98%恢复至98.3±0.3%和98.2±0.3%，而基线为98.5±0.4%。脊髓和脑干剂量保持在耐受范围内，计划质量得分在基线的-1.1%（p=0.19）和-1.7%（p=0.01）以内。意义：UGDT利用治疗前的解剖预测生成了与医生批准的离线重计划质量相当的在线APT计划，从而实现了从反应性离线重计划向预期性在线适应的转变。

英文摘要

Objective: Head and neck (HN) proton therapy spans six to seven weeks of anatomical change, while offline replanning takes about a week. We present an uncertainty-guided digital twin (UGDT) framework that forecasts treatment-day anatomy before treatment and evaluate whether it generates online adaptive proton therapy (APT) plans of clinical quality. Approach: A library of 302 longitudinal deformations from 88 previously treated HN patients was transported onto each new patient's treatment planning CT (TPCT) using two-step multi-atlas deformable image registration (DIR) built on a pretrained CT foundation model, generating about 284 predicted CTs (pdCTs) with contours per patient. Dispersion of propagated clinical target volume (CTV) contours defined a patient-specific robust margin. In ten patients, the quality assurance CT (QACT) triggering a replan represented treatment-day anatomy, and the physician-approved replan was the baseline. The pdCT most similar to the QACT (pdCT-H) and one from the lowest quartile (pdCT-L) were planned to within about 5% of baseline plan quality, forward-calculated on the QACT, and reoptimized to generate online APT plans. Main results: pdCT plans scored within -0.7% (pdCT-H) and -1.0% (pdCT-L) of baseline. Forward calculation on QACT reduced high-dose CTV D98% to 88.3% and 85.5%. After online reoptimization, D98% recovered to 98.3 +/- 0.3% and 98.2 +/- 0.3%, versus 98.5 +/- 0.4% at baseline. Spinal cord and brainstem doses remained below tolerance, and plan quality scores were within -1.1% (p = 0.19) and -1.7% (p = 0.01) of baseline. Significance: UGDT generated online APT plans comparable in quality to physician-approved offline replans using anatomy forecast before treatment, enabling a transition from reactive offline replanning toward anticipatory online adaptation.

发表机构

• Emory University（埃默里大学）
• Taipei Medical University Hospital（台北医学大学附属医院）
• University of Wisconsin（威斯康星大学）
• University of Chicago（芝加哥大学）

机构由 AI 辅助整理，请以论文原文为准。