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- Title
A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma.
- 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
- Abstract
<bold>Objectives: </bold>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).<bold>Methods: </bold>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.<bold>Results: </bold>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 < 0.05) compared with the clinical factor model (AUC, 0.788; 95% CI, 0.683-0.893) in the training set. Decision curve analysis demonstrated the nomogram outperformed the clinical factors model and radiomics signature in terms of clinical usefulness.<bold>Conclusions: </bold>The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating AML.wovf from hm-ccRCC, which might assist clinicians in tailoring precise therapy.<bold>Key Points: </bold>• Differential diagnosis between AML.wovf and hm-ccRCC is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of AML.wovf from hm-ccRCC with improved diagnostic efficacy. • The CT-based radiomics nomogram might spare unnecessary surgery for AML.wovf.
- Subjects
RENAL cell carcinoma; ANGIOMYOLIPOMA; NOMOGRAPHY (Mathematics); FAT; UNNECESSARY surgery
- Publication
European Radiology, 2020, Vol 30, Issue 2, p1274
- ISSN
0938-7994
- Publication type
journal article
- DOI
10.1007/s00330-019-06427-x