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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

  • Zhi-Cheng Li
  • Guangtao Zhai
  • Jinheng Zhang
  • Zhongqiu Wang
  • Guiqin Liu
  • Guang-yu Wu
  • Dong Liang
  • Hairong Zheng
Computer Applications
  • 10 Downloads

Abstract

Objectives

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.

Methods

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.

Results

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).

Conclusions

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.

Keywords

Renal cell carcinomas Diagnostic imaging Radiomics von Hippel-Lindau disease 

Abbreviations

AUC

Area under the ROC curve

ccRCC

Clear cell renal cell carcinoma

chRCC

Chromophobe renal cell carcinoma

CT

Computed tomography

FDR

False discovery rate

GLCM

Gray-level co-occurrence matrix

GLRLM

Gray-level run length matrix

GLSZM

Gray level size zone matrix

HIF

Hypoxia-inducible factor

ICC

Intraclass correlation coefficient

MRI

Magnetic resonance imaging

mRMRe

Minimum redundancy maximum relevance ensemble

NGTDM

Neighborhood gray-tone difference matrix

pRCC

Papillary renal cell carcinoma

RCC

Renal cell carcinoma

ROC

Receiver operating characteristic curve

VHL

von Hippel-Lindau

Notes

Funding

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

Compliance with ethical standards

Guarantor

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.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

Supplementary material

330_2018_5872_MOESM1_ESM.docx (38 kb)
ESM 1 (DOCX 37 kb)

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Zhi-Cheng Li
    • 1
  • Guangtao Zhai
    • 2
  • Jinheng Zhang
    • 1
  • Zhongqiu Wang
    • 3
  • Guiqin Liu
    • 4
  • Guang-yu Wu
    • 4
  • Dong Liang
    • 1
  • Hairong Zheng
    • 1
  1. 1.Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  2. 2.Institute of Image Communication and Network Engineering, School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Department of RadiologyThe Affiliated Hospital of Nanjing University of Chinese MedicineNanjingChina
  4. 4.Department of Radiology, Renji Hospital, School of MedicineShanghai Jiao Tong UniversityShanghaiChina

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