中国临床医学影像杂志2026，Vol.37Issue(6)：419-423,5.DOI:10.12117/jccmi.2026.06.009
基于多种机器学习模型预测膀胱癌病理分级的价值研究
The value of multiple machine learning modelsfor predicting pathological grading of bladder cancer
摘要
Abstract
Objective:To establish a predictive model for bladder cancer pathological grading using multi-phase CT ra-diomics and integrated clinical indicators.Methods:A retrospective analysis was conducted on CT and clinical data from 208 patients diagnosed with bladder cancer.The data were divided into a training group(n=166)and a validation group(n=42)in an 8∶2 ratio.The Radcloud research platform was used to perform layer-by-layer segmentation of plain,arterial,and venous phase images to obtain regions of interest and extract radiomics features.Redundant features were excluded using variance se-lection,univariate selection,and LASSO algorithms.Three machine learning algorithms were used to construct nine models based on plain scans,enhanced scans,and combined scans.The model with the best predictive performance was selected and combined with clinical independent risk factors to establish a fusion model.The predictive performance of each model was e-valuated using receiver operating characteristic(ROC)curves.Results:Models established based on different phases and classi-fiers demonstrated good predictive performance.Among them,the radiomics model constructed using the BernoulliNB classifier based on combined scans exhibited the highest comprehensive diagnostic performance,with an AUC of 0.836 in the training group and 0.820 in the validation group.The fusion model,established by combining it with independent risk factors,showed that the predictive model using the BernoulliNB classifier had the best performance,with an AUC of 0.781 in the training group and 0.861 in the validation group.Conclusion:The fusion model established based on multi-phase CT radiomics features and clinical independent risk factors demonstrated good predictive performance in the pathological grading of bladder cancer.关键词
膀胱肿瘤/体层摄影术,X线计算机Key words
Urinary Bladder Neoplasms/Tomography,X-Ray Computed分类
医药卫生引用本文复制引用
潘镜齐,黄日升,陈冠峰,潘灿玉,孙晓琪,陈世煌..基于多种机器学习模型预测膀胱癌病理分级的价值研究[J].中国临床医学影像杂志,2026,37(6):419-423,5.基金项目
福建省科技创新联合资金项目资助(编号:2024Y9442). （编号:2024Y9442）
