Feng Hongying, Shan Jie, Vargas Carlos E, Keole Sameer R, Rwigema Jean-Claude M, Yu Nathan Y, Ding Yuzhen, Zhang Lian, Hu Yanle, Schild Steven E, Wong William W, Vora Sujay A, Shen JiaJian, Liu Wei
Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona; College of Science, China Three Gorges University, Yichang, Hubei, China; Department of Radiation Oncology, Guangzhou Concord Cancer Center, Guangzhou, Guangdong, China.
Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona.
Int J Radiat Oncol Biol Phys. 2025 Mar 1;121(3):822-831. doi: 10.1016/j.ijrobp.2024.09.032. Epub 2024 Sep 20.
Online adaptive proton therapy (oAPT) is essential to address interfractional anatomical changes in patients receiving pencil beam scanning proton therapy. Artificial intelligence (AI)-based autosegmentation can increase the efficiency and accuracy. Linear energy transfer (LET)-based biological effect evaluation can potentially mitigate possible adverse events caused by high LET. New spot arrangement based on the verification computed tomography (vCT) can further improve the replan quality. We propose an oAPT workflow that incorporates all these functionalities and validate its clinical implementation feasibility with patients with prostate cancer.
AI-based autosegmentation tool AccuContour (Manteia) was seamlessly integrated into oAPT. Initial spot arrangement tool on the vCT for reoptimization was implemented using raytracing. An LET-based biological effect evaluation tool was developed to assess the overlap region of high dose and high LET in selected organs at risk. Eleven patients with prostate cancer were retrospectively selected to verify the efficacy and efficiency of the proposed oAPT workflow. The time cost of each component in the workflow was recorded for analysis.
The verification plan showed significant degradation of the clinical target volume coverage and rectum and bladder sparing due to the interfractional anatomical changes. Reoptimization on the vCT resulted in great improvement of the plan quality. No overlap regions of high dose and high LET distributions were observed in bladder or rectum in replans. Three-dimensional γ analyses in patient-specific quality assurance confirmed the accuracy of the replan doses before delivery (γ passing rate, 99.57% ± 0.46%) and after delivery (98.59% ± 1.29%). The robustness of the replans passed all clinical requirements. The average time for the complete execution of the workflow was 9.12 ± 0.85 minutes, excluding manual intervention time.
The AI-facilitated oAPT workflow demonstrated to be both efficient and effective by generating a replan that significantly improved the plan quality in prostate cancer treated with pencil beam scanning proton therapy.
在线自适应质子治疗（oAPT）对于解决接受笔形束扫描质子治疗患者的分次间解剖结构变化至关重要。基于人工智能（AI）的自动分割可提高效率和准确性。基于线性能量传递（LET）的生物效应评估有可能减轻高LET导致的潜在不良事件。基于验证计算机断层扫描（vCT）的新束斑排列可进一步提高重新计划的质量。我们提出了一种整合所有这些功能的oAPT工作流程，并验证其在前列腺癌患者中的临床实施可行性。
基于AI的自动分割工具AccuContour（Manteia）无缝集成到oAPT中。使用光线追踪在vCT上实现用于重新优化的初始束斑排列工具。开发了一种基于LET的生物效应评估工具，以评估选定危及器官中高剂量和高LET的重叠区域。回顾性选择11例前列腺癌患者，以验证所提出的oAPT工作流程的有效性和效率。记录工作流程中每个组件的时间成本进行分析。
验证计划显示，由于分次间解剖结构变化，临床靶区覆盖率以及直肠和膀胱的保护效果显著下降。在vCT上进行重新优化使计划质量有了很大提高。重新计划中未在膀胱或直肠中观察到高剂量和高LET分布的重叠区域。患者特异性质量保证中的三维γ分析证实了重新计划剂量在交付前（γ通过率，99.57%±0.46%）和交付后（98.59%±1.29%）的准确性。重新计划的稳健性通过了所有临床要求。排除人工干预时间，完整执行工作流程的平均时间为9.12±0.85分钟。
通过生成一个能显著提高笔形束扫描质子治疗前列腺癌计划质量的重新计划，人工智能辅助的oAPT工作流程证明是高效且有效的。
