中国医疗设备2025，Vol.40Issue(5)：65-71,7.DOI:10.3969/j.issn.1674-1633.20240876
基于机器学习的肺癌放疗后放射性肺炎预测研究
Research on the Prediction of Radiation Pneumonitis After Radiotherapy for Lung Cancer Based on Machine Learning
摘要
Abstract
Objective To establish a prediction model of radiation pneumonitis based on the clinical dosimetric data of lung cancer patients before radiotherapy,and to explore the weights of characteristic factors affecting radiation pneumonitis.Methods The clinical dosimetric data of 126 lung cancer patients before radiotherapy were selected.Three machine learning methods,namely logistic regression analysis,support vector machine and random forest,were used as base classifiers.Radiation pneumonia ensemble learning(RPE)prediction model was established through the voting method.Results The accuracy of the RPE model in predicting radiation pneumonitis was 71.16%,the sensitivity was 68.31%,and the specificity was 73.98%.The receiver operating characteristic curve and the area under curve of RPE reached up to 0.798±0.082.In the weight analysis of the influencing factors of radiation pneumonitis,the weight of bilateral lung V20(the volume percentage of organs receiving at least 20 Gy dose,and so on for others)was the highest at 0.2423,followed by the weight of bilateral lung V40 at 0.1624.Conclusion The RPE model established based on the clinical dosimetric dataset of radiotherapy patients can effectively improve the prediction accuracy of radiation pneumonitis compared with three traditional base classifiers.Moreover,this model can provide a guiding basis in the design of radiotherapy plans and improve the quality of life of patients.关键词
机器学习/放射性肺炎/放射剂量学/集成学习/模型解释/权重分析Key words
machine learning/radiation pneumonitis/radiation dosimetry/ensemble learning/model interpretation/weight analysis分类
预防医学引用本文复制引用
杨睿,阙丹,杨丁懿,范勋,石学军,刘磊..基于机器学习的肺癌放疗后放射性肺炎预测研究[J].中国医疗设备,2025,40(5):65-71,7.基金项目
重庆医科大学附属第三医院院内孵化项目(KY22047). （KY22047）
