Prognosis-guided optimization of intensity-modulated radiation therapy plans for lung cancer.
10.12122/j.issn.1673-4254.2025.03.22
- Author:
Huali LI
1
;
Ting SONG
1
;
Jiawen LIU
1
;
Yongbao LI
2
;
Zhaojing JIANG
3
;
Wen DOU
3
;
Linghong ZHOU
1
Author Information
1. Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
2. State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
3. Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China.
- Publication Type:Journal Article
- Keywords:
intensity-modulated radiation therapy;
inverse planning optimization;
prognosis-guided treatment planning optimization
- MeSH:
Humans;
Lung Neoplasms/radiotherapy*;
Radiotherapy Planning, Computer-Assisted/methods*;
Prognosis;
Radiotherapy, Intensity-Modulated/methods*;
Carcinoma, Non-Small-Cell Lung/radiotherapy*;
Radiotherapy Dosage;
Female;
Male;
Middle Aged
- From:
Journal of Southern Medical University
2025;45(3):643-649
- CountryChina
- Language:Chinese
-
Abstract:
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.
