{"title":"[以预后为导向的肺癌调强放疗方案优化]。","authors":"Huali Li, Ting Song, Jiawen Liu, Yongbao Li, Zhaojing Jiang, Wen Dou, Linghong Zhou","doi":"10.12122/j.issn.1673-4254.2025.03.22","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>In terms of the dosemetric indicators, D<sub>95%</sub> of the planning target volume obtained using the proposed method was basically consistent with that of the clinical plan (100.33% <i>vs</i> 102.57%, <i>P</i>=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 (<i>t</i>=4.537, <i>P<</i>0.05) and 8.40 Gy (<i>t</i>=4.104, <i>P<</i>0.05), respectively. The predicted probability of local control failure was similar between the proposed plan and the clinical plan (60.05% <i>vs</i> 59.66%), while the probability of radiation-induced cardiac toxicity was reduced by 1.41% in the proposed plan.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":18962,"journal":{"name":"南方医科大学学报杂志","volume":"45 3","pages":"643-649"},"PeriodicalIF":0.0000,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955899/pdf/","citationCount":"0","resultStr":"{\"title\":\"[Prognosis-guided optimization of intensity-modulated radiation therapy plans for lung cancer].\",\"authors\":\"Huali Li, Ting Song, Jiawen Liu, Yongbao Li, Zhaojing Jiang, Wen Dou, Linghong Zhou\",\"doi\":\"10.12122/j.issn.1673-4254.2025.03.22\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>In terms of the dosemetric indicators, D<sub>95%</sub> of the planning target volume obtained using the proposed method was basically consistent with that of the clinical plan (100.33% <i>vs</i> 102.57%, <i>P</i>=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 (<i>t</i>=4.537, <i>P<</i>0.05) and 8.40 Gy (<i>t</i>=4.104, <i>P<</i>0.05), respectively. The predicted probability of local control failure was similar between the proposed plan and the clinical plan (60.05% <i>vs</i> 59.66%), while the probability of radiation-induced cardiac toxicity was reduced by 1.41% in the proposed plan.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":18962,\"journal\":{\"name\":\"南方医科大学学报杂志\",\"volume\":\"45 3\",\"pages\":\"643-649\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-03-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955899/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"南方医科大学学报杂志\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12122/j.issn.1673-4254.2025.03.22\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"南方医科大学学报杂志","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12122/j.issn.1673-4254.2025.03.22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
摘要
目的:提出一种结合患者个体信息的预后模型优化肺癌放疗方案的新方法,并以预测预后风险最小化为直接指导,评估治疗方案优化的可行性。方法:构建以结果为导向目标和物理剂量约束相结合的混合通量图优化目标。基于结果的目标函数被构建为考虑临床危险因素的局部控制失败、辐射引起的心脏毒性和放射性肺炎的预后预测模型的等加权总和。这些模型采用Cox回归分析或Logistic回归得到。主要目标是在临床指南推荐的物理剂量限制下尽量减少以结果为基础的目标。以15例非小细胞肺癌患者为研究对象,与常规的以剂量为基础的优化治疗方案(临床方案)进行疗效比较,并比较不同方案的剂量学指标和预测预后。结果:在剂量学指标方面,采用本文方法获得的计划靶体积D95%与临床计划基本一致(100.33% vs 102.57%, P=0.056),心脏和肺的平均剂量分别从9.83 Gy和9.50 Gy显著降低到7.02 Gy (t=4.537, P0.05)和8.40 Gy (t=4.104, P0.05)。该方案与临床方案局部控制失败的预测概率相似(60.05% vs 59.66%),而辐射引起心脏毒性的概率降低了1.41%。结论:本文提出的基于预后预测和物理剂量混合目标函数的优化方法可有效保护肺癌患者免受正常组织暴露,改善放疗后预后。
[Prognosis-guided optimization of intensity-modulated radiation therapy plans for lung cancer].
Objectives: 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.
Conclusions: 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.