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Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective



To develop a radiomics model with all-relevant imaging features from multiphasic computed tomography (CT) for differentiating clear cell renal cell carcinoma (ccRCC) from non-ccRCC and to investigate the possible radiogenomics link between the imaging features and a key ccRCC driver gene—the von Hippel-Lindau (VHL) gene mutation.


In this retrospective two-center study, two radiomics models were built using random forest from a training cohort (170 patients), where one model was built with all-relevant features and the other with minimum redundancy maximum relevance (mRMR) features. A model combining all-relevant features and clinical factors (sex, age) was also built. The radiogenomics association between selected features and VHL mutation was investigated by Wilcoxon rank-sum test. All models were tested on an independent validation cohort (85 patients) with ROC curves analysis.


The model with eight all-relevant features from corticomedullary phase CT achieved an AUC of 0.949 and an accuracy of 92.9% in the validation cohort, which significantly outperformed the model with eight mRMR features (seven from nephrographic phase and one from corticomedullary phase) with an AUC of 0.851 and an accuracy of 81.2%. Combining age and sex did not benefit the performance. Five out of eight all-relevant features were significantly associated with VHL mutation, while all eight mRMR features were significantly associated with VHL mutation (false discovery rate-adjusted p < 0.05).


All-relevant features in corticomedullary phase CT can be used to differentiate ccRCC from non-ccRCC. Most subtype-discriminative imaging features were found to be significantly associated with VHL mutation, which may underlie the molecular basis of the radiomics features.

Key Points

• All-relevant features in corticomedullary phase CT can be used to differentiate ccRCC from non-ccRCC with high accuracy.

• Most RCC-subtype-discriminative CT features were associated with the key RCC-driven gene—the VHL gene mutation.

• Radiomics model can be more accurate and interpretable when the imaging features could reflect underlying molecular basis of RCC.

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Fig. 1
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Fig. 4



Area under the ROC curve


Clear cell renal cell carcinoma


Chromophobe renal cell carcinoma


Computed tomography


False discovery rate


Gray-level co-occurrence matrix


Gray-level run length matrix


Gray level size zone matrix


Hypoxia-inducible factor


Intraclass correlation coefficient


Magnetic resonance imaging


Minimum redundancy maximum relevance ensemble


Neighborhood gray-tone difference matrix


Papillary renal cell carcinoma


Renal cell carcinoma


Receiver operating characteristic curve


von Hippel-Lindau


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This study has received funding from the National Natural Science Foundation of China (no. 61571432) and Shenzhen Basic Research Program (JCYJ20170413162354654).

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Correspondence to Guiqin Liu or Guang-yu Wu.

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The scientific guarantor of this publication is Hairong Zheng.

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 (Zhi-Cheng Li) has significant statistical expertise.

Informed consent

Written informed consent was obtained from all patients.

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Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• multicenter study

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Li, ZC., Zhai, G., Zhang, J. et al. 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 29, 3996–4007 (2019).

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  • Renal cell carcinomas
  • Diagnostic imaging
  • Radiomics
  • von Hippel-Lindau disease