MRI-based radiomics machine learning model for differentiating small renal cell carcinoma from fat-poor renal angiomyolipoma
10.12025/j.issn.1008-6358.2023.20222164
- VernacularTitle: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
- Author:
Rui-Ting WANG
1
,
2
;
Lian-Ting ZHONG
;
Xian-Pan PAN
;
Lei CHEN
;
Meng-Su ZENG
;
Yu-Qin DING
;
Jian-Jun ZHOU
Author Information
1. 上海市影像医学研究所,上海 200032
2. 复旦大学附属中山医院放射科,上海 200032
- Keywords:
magnetic resonance imaging;
radiomics;
small renal cell carcinoma;
fat-poor renal angiomyolipoma
- From:
Chinese Journal of Clinical Medicine
2023;30(6):940-945
- CountryChina
- Language:Chinese
-
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.
