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

  • Pei Nie
  • Guangjie Yang
  • Zhenguang WangEmail author
  • Lei Yan
  • Wenjie Miao
  • Dapeng Hao
  • Jie Wu
  • Yujun Zhao
  • Aidi Gong
  • Jingjing Cui
  • Yan Jia
  • Haitao NiuEmail author
Imaging Informatics and Artificial Intelligence



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.

Key Points

• 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.


Angiomyolipoma 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


Confidence interval


Corticomedullary phase


Decision curve analysis


Excretory phase


Gray level co-occurrence matrix


Gray level run length matrix


Gray level size zone matrix


Homogeneous ccRCC


Inter-/intra- class correlation coefficient


Least absolute shrinkage and selection operator


Nomogram score


Nephrographic phase


Odds ratio


Picture archiving and communication system


Perivascular epithelioid cell


Radiomics score


Receiver operator characteristic


Region of interest


Support vector machine


Funding information

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.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• case-control study

• performed at one institution

Supplementary material

330_2019_6427_MOESM1_ESM.docx (25 kb)
ESM 1 (DOCX 25 kb)


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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Radiology DepartmentThe Affiliated Hospital of Qingdao UniversityQingdaoChina
  2. 2.PET-CT CenterThe Affiliated Hospital of Qingdao UniversityQingdaoChina
  3. 3.Pathology DepartmentThe Affiliated Hospital of Qingdao UniversityQingdaoChina
  4. 4.Huiying Medical Technology Co., LtdBeijingChina
  5. 5.Urology DepartmentThe Affiliated Hospital of Qingdao UniversityQingdaoChina

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