中国癌症防治杂志2025，Vol.17Issue(5)：568-575,8.DOI:10.3969/j.issn.1674-5671.2025.05.07
基于CT影像组学预测局部晚期直肠癌新辅助放化疗后的放射性直肠炎发生风险
Prediction risk of radiation proctitis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer using CT radiomics
摘要
Abstract
Objective To develop a machine learning model based on computed tomography(CT)radiomics for predicting the risk of radiation proctitis in patients with locally advanced rectal cancer(LARC)after neoadjuvant chemoradiotherapy(nCRT).Methods Patients with LARC underwent nCRT at Zhejiang Cancer Hospital from December 2021 to December 2024 were retrospectively enrolled as the training set(n=326),while patients underwent nCRT from August 2022 to August 2024 were prospectively enrolled as the validation set(n=104).The region of interest in mesorectal region was manually delineated on pre-treatment CT images.Handcrafted features and deep learning features were extracted using Pyradiomics and a pre-trained ResNet18 model,respectively.Feature selection was performed using univariable Logistic regression,Spearman correlation analysis,least absolute shrinkage and selection operator regression,and recursive feature elimination.Logistic regression,support vector machine,light gradient boosting machine,and eXtreme gradient boosting were employed to develop independent radiomics models(Rad-model),independent models based on deep learning features(DL-model),and the combined model integrating both feature sets(DLRad-model).The predictive performance of the models was evaluated using the area under the curve(AUC)of receiver operating characteristic.Results The incidence of grade 2 or higher radiation proctitis did not differ significantly between the training set and the validation set(36.81％vs 25.96％,P=0.056).Both the DL-model and the DLRad-model constructed based on eXtreme gradient boosting achieved AUC of 0.901(95％CI:0.874-0.927)and 0.918(95％CI:0.893-0.940),respectively,in the training set.In the validation set,these models achieved AUC of 0.747(95％CI:0.644-0.845)and 0.729(95％CI:0.620-0.829),respectively.Both of which were significantly higher than that of the Rad-model(all P＜0.05).However,the difference in AUC between the DL-model and the DLRad-model was not statistically significant(P＞0.05).Conclusions Deep learning features demonstrate advantages in predicting the risk of radiation proctitis after nCRT in patients with LARC,and providing an effective tool for clinical risk stratification.关键词
局部晚期直肠癌/新辅助放化疗/影像组学/深度学习/放射性直肠炎Key words
Locally advanced rectal cancer/Neoadjuvant chemoradiotherapy/Radiomics/Deep learning/Radiation proctitis分类
医药卫生引用本文复制引用
曹谦,周蒙,张臻,朱骥..基于CT影像组学预测局部晚期直肠癌新辅助放化疗后的放射性直肠炎发生风险[J].中国癌症防治杂志,2025,17(5):568-575,8.基金项目
国家自然科学基金面上项目(82574019) （82574019）
国家自然科学基金青年项目(82303672) （82303672）
国家卫生健康委员会科研基金(省部共建)重大项目(WKJ-ZJ-2305) （省部共建）
嘉兴市重点研发计划项目(2024BZ20004) （2024BZ20004）
