A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma
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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).
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.
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.
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.
• 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.
KeywordsAngiomyolipoma Clear cell renal cell carcinoma Tomography, X-ray computed Radiomics
AML without visible fat
Analysis of variance
Area under the curve
Body mass index
Clear cell renal cell carcinoma
Decision curve analysis
Gray level co-occurrence matrix
Gray level run length matrix
Gray level size zone matrix
Inter-/intra- class correlation coefficient
Least absolute shrinkage and selection operator
Picture archiving and communication system
Perivascular epithelioid cell
Receiver operator characteristic
Region of interest
Support vector machine
This study has received funding by the National Natural Science Foundation of China (81701688 and 81601527); the Natural Science Foundation of Shandong Province (ZR2017BH096 and ZR2017MH036); the Key Research and Development Project of Shandong Province (2018GSF118078); and the Postdoctoral Science Foundation of China (2018M642617). None of these funding sources had any role in study design, the collection, analysis and interpretation of data, the writing of the report, or the decision to submit the paper for publication.
Compliance with ethical standards
The scientific guarantor of this publication is Zhenguang Wang.
Conflict of interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
One of the authors (Guangjie Yang) has significant statistical expertise.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• case-control study
• performed at one institution
- 8.Li ZC, Zhai G, Zhang J et al (2018) Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective. Eur Radiol. https://doi.org/10.1007/s00330-018-5872-6