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.
Nan Fang Yi Ke Da Xue Xue Bao. 2025 Mar 20;45(3):643-649. doi: 10.12122/j.issn.1673-4254.2025.03.22.
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.
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.
In terms of the dosemetric indicators, D of the planning target volume obtained using the proposed method was basically consistent with that of the clinical plan (100.33% 102.57%, =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 (=4.537, 0.05) and 8.40 Gy (=4.104, 0.05), respectively. The predicted probability of local control failure was similar between the proposed plan and the clinical plan (60.05% 59.66%), while the probability of radiation-induced cardiac toxicity was reduced by 1.41% in the proposed plan.
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.
提出一种新的方法，通过纳入考虑个体患者信息的预后模型来优化肺癌放疗计划，并评估在最小化预测的预后风险直接指导下进行治疗计划优化的可行性。
构建了一个混合通量图优化目标，纳入基于结果的目标和物理剂量约束。基于结果的目标函数构建为考虑临床风险因素的局部控制失败、放射性心脏毒性和放射性肺炎预后预测模型的等加权总和。这些模型通过Cox回归分析或Logistic回归得出。主要目标是在临床指南推荐的物理剂量约束下最小化基于结果的目标。与传统的基于剂量的优化方法（临床计划）相比，在所提出的方法在15例非小细胞肺癌中测试了优化治疗计划的效果，并比较了不同计划之间的剂量学指标和预测的预后结果。
在剂量学指标方面，使用所提出方法获得的计划靶体积的D与临床计划基本一致（100.33% 102.57%， =0.056），心脏和肺的平均剂量分别从9.83 Gy和9.50 Gy显著降低至7.02 Gy（ =4.537， 0.05）和8.40 Gy（ =4.104， 0.05）。所提出的计划与临床计划之间局部控制失败的预测概率相似（60.05% 59.66%），而所提出的计划中放射性心脏毒性的概率降低了1.41%。
所提出的基于结果预测和物理剂量混合目标函数的优化方法为正常组织暴露提供了有效的保护，以改善肺癌患者放疗后的预后。
