武警医学2025，Vol.36Issue(11)：937-943,7.
双参数MRI瘤周影像组学联合机器学习对前列腺癌术后切缘阳性的预测价值
Predictive value of dual-parameter MRI peritumoral radiomics combined with machine learning for positive surgical margins in prostate cancer after surgery
摘要
Abstract
Objective To explore the predictive value of dual-parameter MRI peritumoral radiomics combined with machine learning for positive surgical margins after prostate cancer radical resection.Methods A retrospective analysis of clinical data of 274 patients who underwent radical prostatectomy at the First Medical Center(the First Center)and the Third Medical Center(the Third Center)of PLA General Hospital.The patients from the First Center were randomly divided into training set and internal set at a ratio of 7∶3,and the data from the Third Center served as the external validation set.Radiomics features of the peritumoral regions of T2WI and ADC images were extracted using radiomics software,and the maximal relevance and minimal redundancy(mRMR)algorithm was applied to remove highly correlated features,followed by feature selection using the least absolute shrinkage and selection operator(LASSO)algorithm to construct the radiomics model.The performance of the omics models in three machine learning algorithms(Ex-tra Trees,XGBoost,and Random Forest)was compared,and the differences in AUC of the three machine learning algorithms were compared using DeLong test.Results In the omics model,Extra Trees achieved a training set AUC of 0.771,an internal test set AUC of 0.743,and an external validation set AUC of 0.726,outperfor-ming both XGBoost and RandomForest.In the ensemble model,Extra Trees achieved a training set AUC of 0.778,an internal test set AUC of 0.753,and an external validation set AUC of 0.777.The perform-ance of this model surpassed that of XGBoost and Random Forest.The DeLong test revealed no significant difference in AUC among the three machine learning algorithms in the omics model.No signifi-cant difference in AUC was observed among the three machine learning algorithms in the internal test set of the ensemble model.In the external validation set,the AUC difference between RandomForest and Extra Trees was not statistically significant(P=0.160).How-ever,the differences between RandomForest and XGBoost(P=0.006)and between Extra Trees and XGBoost(P=0.001)were both statistically significant.Conclusions Extra Trees can achieve satisfactory results in both the internal test set and the external validation set,and the overall performance of the model is the best.The combined model constructed using Extra Trees is characterized by good predictive efficacy for positive surgical margins after prostate cancer radical resection.关键词
双参数MRI/肿瘤周围/机器学习/影像组学/前列腺癌/切缘阳性Key words
dual-parameter MRI/peritumoral/machine learning/radiomics/prostate cancer/positive surgical margin分类
医药卫生引用本文复制引用
杜其聪,徐鸿昊,李小龙,张晓晶,吴斌,穆学涛..双参数MRI瘤周影像组学联合机器学习对前列腺癌术后切缘阳性的预测价值[J].武警医学,2025,36(11):937-943,7.基金项目
军队保健专项基金(24BJZ29) （24BJZ29）
