中国临床医学2023，Vol.30Issue(6)：940-945,6.DOI:10.12025/j.issn.1008-6358.2023.20222164
基于MRI影像组学机器学习模型鉴别小肾癌与乏脂肪肾血管平滑肌脂肪瘤
MRI-based radiomics machine learning model for differentiating small renal cell carcinoma from fat-poor renal angiomyolipoma
摘要
Abstract
Objective To investigate the value of multi-phase MRI-based radiomics machine learning models in differentiating small renal cell carcinoma(sRCC)from fat-poor renal angiomyolipoma(fp-AML).Methods 79 cases of sRCCs and 35 cases of fp-AMLs(diameter≤4 cm)which were confirmed by pathology were retrospectively analyzed.The volume of interest(VOI)of the total tumor was manually delineated on the images of T2WI(T2),unenhanced phase(UP),corticomedullary phase(CMP)and nephrographic phase(NP)and then the radiomics of the VOIs were extracted respectively.The training set and the test set were set according to the ratio of 7∶3.The t-test,maximal relevance and minimal redundancy(mRMR)and the least absolute shrinkage and selection operator(LASSO)were used to select the radiomics features.The selected features were used to build classification models with logistic regression(LR)and support vector machine(SVM).The receiver operating characteristic(ROC)curve was used to evaluate the classification performances of the models.Results There were 4,12,3,11 and 15 optimal features obtained from T2、UP、CMP、NP and the combined four phases,respectively.The radiomics features based on NP or the combined four phases with LR model performed best,AUCs were respectively 0.956,0.986 in the training set and both were 0.881 in the test set.Conclusion The multi-phase MRI-based radiomics machine learning model has favorable diagnostic performance in differentiating sRCC from fp-AML.关键词
磁共振成像/影像组学/小肾癌/乏脂肪肾血管平滑肌脂肪瘤Key words
magnetic resonance imaging/radiomics/small renal cell carcinoma/fat-poor renal angiomyolipoma分类
医药卫生引用本文复制引用
王睿婷,钟莲婷,潘先攀,陈磊,曾蒙苏,丁玉芹,周建军..基于MRI影像组学机器学习模型鉴别小肾癌与乏脂肪肾血管平滑肌脂肪瘤[J].中国临床医学,2023,30(6):940-945,6.基金项目
福建省科技计划项目引导性项目(2019D025),福建省卫生健康科研人才培养项目医学创新课题(2019CXB33).Supported by Science and Technology Guided Project of Fujian Province(2019D025)and Scientific Research Cultivation and Medical Innovation Project of Fujian Province(2019CXB33). （2019D025）
