预后导向的肺癌调强放疗计划优化新方法  

Prognosis-guided optimization of intensity-modulated radiation therapy plans for lung cancer

作　　者：李华莉 宋婷[1] 刘嘉雯 李永宝 姜兆静[4] 窦文[4] 周凌宏[1,3] LI Huali;SONG Ting;LIU Jiawen;LI Yongbao;JIANG Zhaojing;DOU Wen;ZHOU Linghong(Department of Biomedical Engineering,Southern Medical University,Guangzhou 510515,China;State Key Laboratory of Oncology in South China,Sun Yat-sen University Cancer Center,Guangzhou 510060,China;Department of Radiotherapy,Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences),Southern Medical University,Guangzhou 510080,China;Zhujiang Hospital,Southern Medical University,Guangzhou 510280,China)

机构地区：[1]南方医科大学生物医学工程学院,广东广州510515 [2]中山大学肿瘤防治中心,华南肿瘤学国家重点实验室,广东广州510060 [3]南方医科大学附属广东省人民医院(广东省医学科学院)放疗科,广东广州510080 [4]南方医科大学珠江医院,广东广州510280

出　　处：《南方医科大学学报》2025年第3期643-649,共7页Journal of Southern Medical University

基　　金：国家自然科学基金(82472117,82272131);广东省基础与应用基础研究基金(2024A1515011831,2024A1515010820)。

摘　　要：目的以肺癌行调强放疗为研究对象,提出一种将预后预测模型纳入放疗计划逆向优化框架中,直接引导放疗计划向预后风险最小方向逼近的调强放疗计划新优化方法。方法构建以最小预后风险为主目标函数、临床剂量限值为约束条件的混合通量分布优化模型,预后风险事件包括肿瘤局部控制失败、放射性心脏毒性发生和放射性肺炎二级以上事件发生。以3种预后预测概率加权形式构建总风险概率模型,并以最小化总风险概率为主目标项、靶区剂量均匀性为次目标项、常见指南约定的计划感兴趣区域特定剂量统计指征项为约束条件。为验证优化方法的有效性,实验选取15例非小细胞肺癌调强放射治疗计划,分别采用所提出计划逆向优化方法(本研究计划)和现行的基于剂量-体积限值的计划优化方法(临床参考计划)生成计划,比较不同计划在临床关注的剂量学指征项与预后预测项上的计划质量差异。结果本研究计划较临床参考计划整体可达相近的物理剂量统计水平,同时理论模型计算的预期预后结果有所改善。本研究计划靶区的D95%基本与临床计划保持一致(100.33%vs 102.57%,P=0.056),心脏和肺的平均剂量从9.83、9.50 Gy降低至7.02、8.40 Gy,差异具有统计学意义(t=4.537、4.104,P<0.05)。与临床计划相比,所提方法优化得到计划通过理论模型预测的局部控制失败概率相近(60.05%vs 59.66%),同时放射性心脏毒性概率降低1.41%。结论本研究所提出的纳入预后预测模型引导的肺癌调强放疗计划自动优化新方法,可以兼容现有放疗计划设计需满足循证医学体系下的物理剂量统计分布要求,同时进一步优化患者的预期预后,为实现肿瘤患者更高预后、更优生存质量的个性化放疗奠定技术基础。Objective To propose a new method for optimizing radiotherapy planning for lung cancer by incorporating prognostic models that take into account individual patient information and assess the feasibility of treatment planning optimization directly guided by minimizing the predicted prognostic risk.Methods A mixed fluence map optimization objective was constructed,incorporating the outcome-based objective and the physical dose constraints.The outcome-based objective function was constructed as an equally weighted summation of prognostic prediction models for local control failure,radiation-induced cardiac toxicity,and radiation pneumonitis considering clinical risk factors.These models were derived using Cox regression analysis or Logistic regression.The primary goal was to minimize the outcome-based objective with the physical dose constraints recommended by the clinical guidelines.The efficacy of the proposed method for optimizing treatment plans was tested in 15 cases of non-small cell lung cancer in comparison with the conventional dose-based optimization method(clinical plan),and the dosimetric indicators and predicted prognostic outcomes were compared between different plans.Results In terms of the dosemetric indicators,D95%of the planning target volume obtained using the proposed method was basically consistent with that of the clinical plan(100.33%vs 102.57%,P=0.056),and the average dose of the heart and lungs was significantly decreased from 9.83 Gy and 9.50 Gy to 7.02 Gy(t=4.537,P<0.05)and 8.40 Gy(t=4.104,P<0.05),respectively.The predicted probability of local control failure was similar between the proposed plan and the clinical plan(60.05%vs 59.66%),while the probability of radiation-induced cardiac toxicity was reduced by 1.41%in the proposed plan.Conclusion The proposed optimization method based on a mixed objective function of outcome prediction and physical dose provides effective protection against normal tissue exposure to improve the outcomes of lung cancer patients following radiotherapy.

关 键 词：预后引导计划优化 调强放疗计划 计划逆向优化 

分 类 号：R734.2[医药卫生—肿瘤] R730.55[医药卫生—临床医学]