UCLouvain, Institut de Recherche Expérimentale et Clinique, Molecular Imaging Radiotherapy and Oncology (MIRO), Brussels, Belgium.
Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Leuven, Belgium.
Phys Med Biol. 2024 Oct 10;69(20). doi: 10.1088/1361-6560/ad80f6.
To demonstrate the feasibility of integrating fully-automated online adaptive proton therapy strategies (OAPT) within a commercially available treatment planning system and underscore what limits their clinical implementation. These strategies leverage existing deformable image registration (DIR) algorithms and state-of-the-art deep learning (DL) networks for organ segmentation and proton dose prediction.Four OAPT strategies featuring automatic segmentation and robust optimization were evaluated on a cohort of 17 patients, each undergoing a repeat CT scan. (1) DEF-INIT combines deformably registered contours with template-based optimization. (2) DL-INIT, (3) DL-DEF, and (4) DL-DL employ a nnU-Net DL network for organ segmentation and a controlling ROIs-guided DIR algorithm for internal clinical target volume (iCTV) segmentation. DL-INIT uses this segmentation alongside template-based optimization, DL-DEF integrates it with a dose-mimicking (DM) step using a reference deformed dose, and DL-DL merges it with DM on a reference DL-predicted dose. All strategies were evaluated on manual contours and contours used for optimization and compared with manually adapted plans. Key dose volume metrics like iCTV D98% are reported.iCTV D98% was comparable in manually adapted plans and for all strategies in nominal cases but dropped to 20 Gy in worst-case scenarios for a few patients per strategy, highlighting the need to correct segmentation errors in the target volume. Evaluations on optimization contours showed minimal relative error, with some outliers, particularly in template-based strategies (DEF-INIT and DL-INIT). DL-DEF achieves a good trade-off between speed and dosimetric quality, showing a passing rate (iCTV D98% > 94%) of 90% when evaluated against 2, 4 and 5 mm setup error and of 88% when evaluated against 7 mm setup error. While template-based methods are more rigid, DL-DEF and DL-DL have potential for further enhancements with proper DM algorithm tuning.Among investigated strategies, DL-DEF and DL-DL demonstrated promising within 10 min OAPT implementation results and significant potential for improvements.
为了展示在商业上可用的治疗计划系统中集成全自动在线自适应质子治疗策略（OAPT）的可行性，并强调限制其临床应用的因素。这些策略利用现有的变形图像配准（DIR）算法和最先进的深度学习（DL）网络进行器官分割和质子剂量预测。评估了四种具有自动分割和稳健优化功能的 OAPT 策略，这些策略基于一组 17 名患者，每位患者都进行了重复 CT 扫描。（1）DEF-INIT 将可变形注册的轮廓与基于模板的优化相结合。（2）DL-INIT、（3）DL-DEF 和（4）DL-DL 使用 nnU-Net DL 网络进行器官分割和控制 ROI 引导的 DIR 算法进行内部临床靶区（iCTV）分割。DL-INIT 使用此分割以及基于模板的优化，DL-DEF 将其与使用参考变形剂量的剂量模拟（DM）步骤集成，DL-DL 将其与参考 DL 预测剂量的 DM 合并。所有策略都在手动轮廓和用于优化的轮廓上进行了评估，并与手动调整的计划进行了比较。报告了关键剂量体积指标，如 iCTV D98%。在名义情况下，手动调整的计划和所有策略的 iCTV D98% 都相当，但在每个策略的几个患者中，最坏情况下降至 20 Gy，这突出了需要纠正目标体积中的分割错误。在优化轮廓上的评估显示出最小的相对误差，有一些异常值，特别是在基于模板的策略（DEF-INIT 和 DL-INIT）中。DL-DEF 在速度和剂量质量之间取得了良好的平衡，在针对 2、4 和 5 mm 摆位误差进行评估时，通过率（iCTV D98%>94%）为 90%，在针对 7 mm 摆位误差进行评估时，通过率为 88%。虽然基于模板的方法更严格，但 DL-DEF 和 DL-DL 具有通过适当的 DM 算法调整进一步改进的潜力。在所研究的策略中，DL-DEF 和 DL-DL 展示了在 10 分钟内 OAPT 实施的有希望的结果，并且具有显著的改进潜力。
