南京医科大学学报（自然科学版）2026，Vol.46Issue(9)：1348-1355,8.DOI:10.7655/NYDXBNSN260364
基于临床及剂量学特征构建模型预测鼻咽癌放疗后颞叶损伤的研究
Research on constructing a model based on clinical and dosimetric characteristics to predict temporal lobe injury after radiotherapy for nasopharyngeal carcinoma
摘要
Abstract
Objective:To establish a prediction model based on the clinical and dosimetric characteristics of patients with nasopharyngeal carcinoma(NPC),and assess its predictive efficacy for radiation-induced temporal lobe injury(TLI)following radiotherapy.Methods:We performed a retrospective analysis of the clinical data from 365 NPC patients(42 patients in the TLI group and 323 patients in the non-TLI group)who underwent radiotherapy-based comprehensive treatment in the radiotherapy department of our institution from June 2018 to November 2023.Clinical and dosimetric characteristics were collected,and univariate and multivariate logistic regression analyses were employed to identify independent risk factors associated with radiation-induced TLI.The random forest(RF)machine learning algorithm was used to construct a clinical feature model and a combined clinical and dosimetric feature model.Receiver operating characteristic(ROC)curves were plotted,and parameters such as the area under the ROC curve(AUC),sensitivity,and specificity were calculated.Calibration curves and decision curve analysis(DCA)were also conducted to evaluate the predictive performance of the models.Results:Univariate and multivariate logistic regression analyses revealed statistically significant differences in three features:T-stage(clinical characteristic),as well as D1cm3 and V70Gy(dosimetric characteristics)(all P＜0.05).The AUC values,sensitivity,specificity,and accuracy of the combined model and clinical model were 0.853 and 0.635,66.67％and 85.71％,86.38％and 39.94％,and 84.00％and 45.00％,respectively.The DeLong test indicated a statistically significant difference on predictive performance between combined and clinical models(P＜0.05).Both calibration curves and DCA results demonstrated that the combined model exhibited superior calibration and clinical net benefit.Conclusion:T-stage,D1cm3,and V70Gy are independent predictors of radiation-induced temporal lobe injury.The combined model based on these three factors demonstrates high predictive capability for temporal lobe injury following NPC radiotherapy.关键词
鼻咽癌/放射性颞叶损伤/机器学习/预测模型Key words
nasopharyngeal carcinoma/radiation-induced temporal lobe injury/machine learning/predictive model分类
医药卫生引用本文复制引用
万丽娟,李金凯,苏国义,胡昊,唐艳春,吴飞云..基于临床及剂量学特征构建模型预测鼻咽癌放疗后颞叶损伤的研究[J].南京医科大学学报（自然科学版）,2026,46(9):1348-1355,8.基金项目
国家重点研发计划(2022YFC2401604) （2022YFC2401604）
