Liu Xiaonan, Chen Deqi, Liu Yuxiang, Men Kuo, Dai Jianrong, Quan Hong, Chen Xinyuan
School of Physics and Technology, Wuhan University, Wuhan 430072, China; National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Phys Med. 2025 Jan;129:104873. doi: 10.1016/j.ejmp.2024.104873. Epub 2024 Dec 21.
Automated treatment plan generation is essential for magnetic resonance imaging (MRI)-guided adaptive radiotherapy (MRIgART) to ensure standardized treatment-plan quality. We proposed a novel cross-technique transfer learning (CTTL)-based strategy for online MRIgART autoplanning.
We retrospectively analyzed the data from 210 rectal cancer patients. A source dose prediction model was initially trained using a large volume of volumetric-modulated arc therapy data. Subsequently, a single patient's pretreatment data was employed to construct a CTTL-based dose prediction model (CTTL_M) for each new patient undergoing MRIgART. The CTTL_M predicted dose distributions for subsequent treatment fractions. We optimized an auto plan using the parameters based on dose prediction. Performance of our CTTL_M was assessed using dose-volume histogram and mean absolute error (MAE). Our auto plans were compared with clinical plans regarding plan quality, efficiency, and complexity.
CTTL_M significantly improved the dose prediction accuracy, particularly in planning target volumes (median MAE: 1.27 % vs. 7.06 %). The auto plans reduced high-dose exposure to the bladder (D: 2,601.93 vs. 2,635.43 cGy, P < 0.001) and colon (D: 2,593.22 vs. 2,624.89 cGy, P < 0.001). The mean colon dose decreased from 1,865.08 to 1,808.16 cGy (P = 0.035). The auto plans maintained similar planning time, monitor units, and plan complexity as clinical plans.
We proposed an online ART autoplanning method for generating high-quality plans with improved organ sparing. Its high degree of automation can standardize planning quality across varying expertise levels, mitigating subjective assessment and errors.
自动治疗计划生成对于磁共振成像（MRI）引导的自适应放射治疗（MRIgART）至关重要，以确保标准化的治疗计划质量。我们提出了一种基于新型跨技术迁移学习（CTTL）的在线MRIgART自动计划策略。
我们回顾性分析了210例直肠癌患者的数据。最初使用大量容积调强弧形治疗数据训练一个源剂量预测模型。随后，对于每位接受MRIgART的新患者，使用其治疗前数据构建基于CTTL的剂量预测模型（CTTL_M）。CTTL_M预测后续治疗分次的剂量分布。我们基于剂量预测使用参数优化自动计划。使用剂量体积直方图和平均绝对误差（MAE）评估我们的CTTL_M的性能。将我们的自动计划与临床计划在计划质量、效率和复杂性方面进行比较。
CTTL_M显著提高了剂量预测准确性，尤其是在计划靶体积方面（中位数MAE：1.27%对7.06%）。自动计划减少了膀胱的高剂量暴露（D：2601.93对2635.43 cGy，P < 0.001）和结肠的高剂量暴露（D：2593.22对2624.89 cGy，P < 0.001）。结肠平均剂量从1865.08降至1808.16 cGy（P = 0.035）。自动计划与临床计划在计划时间、监测单位和计划复杂性方面保持相似。
我们提出了一种在线自适应放射治疗自动计划方法，用于生成具有更好器官保护的高质量计划。其高度自动化可以使不同专业水平的计划质量标准化，减少主观评估和误差。
