国际医学放射学杂志2025，Vol.48Issue(5)：541-547,7.DOI:10.19300/j.2025.L22062
基于MRI的机器学习模型预测中低位直肠癌新辅助放化疗疗效的价值
Value of MRI-based machine learning models in predicting the efficacy of neoadjuvant chemaradiotherapy for mid-low rectal cancer
摘要
Abstract
Objective To explore a noninvasive method for evaluating the efficacy of neoadjuvant chemoradiotherapy(nCRT)in patients with mid-low rectal cancer and to provide new insights for selecting optimal treatment strategies.Methods A total of 212 patients who underwent preoperative nCRT and radical resection with pathologically confirmed were retrospectively enrolled.Based on postoperative pathology,patients were divided into a pathological complete response(pCR)group(30 cases)and a non-pCR(npCR)group(182 cases).All patients were randomly assigned to a training set(170 cases;pCR 25 cases,npCR 145 cases)and a validation set(42 cases;pCR 5 cases,npCR 37 cases)at an 8∶2 ratio.Clinical features in the training set were screened using the Boruta algorithm,and selected features were combined with imaging indicators[tumor stage,tumor length,circumferential resection margin(CRM),extramural vascular invasion(EMVI),and MRI tumor regression grade(mrTRG)]to build three supervised machine learning classification models—logistic regression(LR),naïve Bayes(NB),and neural network(NN)—for predicting nCRT efficacy.Model performance was evaluated on the validation set.Interobserver agreement for imaging indicators was assessed using intraclass correlation coefficients(ICC)and Kappa statistics.Predictive performance was assessed with receiver operating characteristic(ROC)curves,area under the curve(AUC),accuracy,sensitivity,and specificity.Results Interobserver agreement between two radiologists for imaging indicators was excellent(ICC＞0.9,all κ＞0.8).Among the three models,the NN model showed the best predictive performance(training set:AUC 0.931,accuracy 0.900,sensitivity 0.890,specificity 0.875;validation set:AUC 0.687,accuracy 0.855,sensitivity 0.866,specificity 0.723).Conclusion Machine learning models constructed through feature selection can effectively predict the efficacy of nCRT in mid-low rectal cancer.Combining multiple indicators improves the preoperative predictive accuracy of mrTRG.关键词
直肠癌/新辅助放化疗/磁共振成像/肿瘤退缩分级/机器学习Key words
Rectal cancer/Neoadjuvant chemoradiotherapy/Magnetic resonance imaging/Tumor regression grade/Machine learning分类
医药卫生引用本文复制引用
赵凤姝,张芮,秦佳明,刘天琦,董文瑾,陈梦馨,马翔云,王文红..基于MRI的机器学习模型预测中低位直肠癌新辅助放化疗疗效的价值[J].国际医学放射学杂志,2025,48(5):541-547,7.基金项目
天津市卫生健康行业国家智能社会治理实验特色基地2023年度揭榜挂帅科研项目(TJHIA-2023-015) （TJHIA-2023-015）
天津市科委科学技术普及项目(23KPHDRC00260) （23KPHDRC00260）
