Feasibility of a Radiomics Nomogram of Multiparametric Magnetic Resonance Imaging to Differentiate Fat-Poor Renal Angiomyolipoma from Nodular Renal Clear Cell Carcinoma
10.3969/j.issn.1005-5185.2024.09.016
- VernacularTitle:多参数MRI放射组学诺模图鉴别结节型肾透明细胞癌与乏脂血管平滑肌脂肪瘤的可行性
- Author:
Shun CHAI
1
;
Yawen YANG
;
Chuanxian MA
;
Zhanlong MA
Author Information
1. 南京医科大学第一附属医院放射科,江苏南京 210029
- Keywords:
Angiomyolipoma;
Carcinoma,renal cell;
Magnetic resonance imaging;
Radiomics;
Nomogram;
Diagnosis,differential;
Pathology,surgical
- From:
Chinese Journal of Medical Imaging
2024;32(9):950-955
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To investigate the value of MRI multi-sequence-based radiomic nomogram in identifying clear cell renal cell carcinoma from fat-poor renal angiomyolipoma in small renal masses(≤4 cm).Materials and Methods A retrospective analysis was performed for 78 renal masses in 75 patients with pathologically confirmed cases in the First Affiliated Hospital of Nanjing Medical University from July 2017 to December 2022,including 56 cases of renal clear cell carcinoma and 22 cases of fat-deficient angiomyolipoma,and all participants were divided into a training set(n=55)and a validation set(n=23)in a ratio of 7∶3.Radiomics features were extracted from T2WI and diffusion-weighted imaging sequences,and the t-test and minimum absolute shrinkage and selection algorithm were used for feature selection,the radiomics model was constructed,and the radiomics score was calculated.The clinical characteristics and subjective characteristics of MRI were evaluated to establish a clinical model,and the radiomics nomogram was constructed based on the radiomics score and clinical features,and the calibration,discrimination and clinical practicability of the nomogram were evaluated.Results A total of2 632 radiomics features were extracted from each patient,and 4 features were used to construct a radiomics model.The radiomics model had good discrimination ability in the training set[area under the curve(AUC)=0.979,95％CI 0.937-1.000)]and the validation set(AUC=0.833,95％CI 0.626-1.000).The radiomics nomogram had good calibration and discrimination ability in the training set(AUC=0.988,95％CI 0.963-1.000)and validation set(AUC=0.867,95％CI 0.698-1.000),which was better than the clinical model(AUC=0.725,95％CI 0.478-0.972)and radiomics model(AUC=0.833,95％CI 0.626-1.000)in the test set.Decision curve analysis showed that the clinical utility of nomogram was better than that of clinical factor model and radiomics features.Conclusion MRI-based radiomics nomogram combined with radiomics scores and clinical factors can be used to non-invasively distinguish clear cell renal cell carcinoma from alipid-deficient angiomyolipoma before surgery.
