A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma

• Published in: European radiology 2020-02, Vol.30 (2), p.1274-1284
• Main Authors: Nie, Pei, Yang, Guangjie, Wang, Zhenguang, Yan, Lei, Miao, Wenjie, Hao, Dapeng, Wu, Jie, Zhao, Yujun, Gong, Aidi, Cui, Jingjing, Jia, Yan, Niu, Haitao
• Format: Article
• Language: English
• Subjects: Angiomyolipoma; Calibration; Clear cell-type renal cell carcinoma; Computed tomography; Confidence intervals; Construction; Decision analysis; Demographics; Demography; Diagnostic Radiology; Diagnostic systems; Differential diagnosis; Differentiation; Feature extraction; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Kidney cancer; Medicine; Medicine & Public Health; Neuroradiology; Nomograms; Predictions; Radiology; Radiomics; Surgery; Training; Ultrasound
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-019-06427-x

Objectives To develop and validate a radiomics nomogram for preoperative differentiating renal angiomyolipoma without visible fat (AML.wovf) from homogeneous clear cell renal cell carcinoma (hm-ccRCC). Methods Ninety-nine patients with AML.wovf ( n  = 36) and hm-ccRCC ( n  = 63) were divided into a training set ( n  = 80) and a validation set ( n  = 19). Radiomics features were extracted from corticomedullary phase and nephrographic phase CT images. A radiomics signature was constructed and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed. Nomogram performance was assessed with respect to calibration, discrimination, and clinical usefulness. Results Fourteen features were used to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.879; 95%; confidence interval [CI], 0.793–0.966) and the validation set (AUC, 0.846; 95% CI, 0.643–1.000). The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.896; 95% CI, 0.810–0.983) and the validation set (AUC, 0.949; 95% CI, 0.856–1.000) and showed better discrimination capability ( p