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Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma

  • Urologic Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background and Purpose

Nuclear grades of clear cell renal cell carcinoma (ccRCC) are usually confirmed by invasive methods. Radiomics is a quantitative tool that uses non-invasive medical imaging for tumor diagnosis and prognosis. In this study, a radiomics approach was proposed to analyze the association between preoperative computed tomography (CT) images and nuclear grades of ccRCC.

Methods

Our dataset included 320 ccRCC patients from two centers and was divided into a training set (n = 124), an internal test set (n = 123), and an external test set (n = 73). A radiomic feature set was extracted from unenhanced, corticomedullary phase, and nephrographic phase CT images. The maximizing independent classification information criteria function and recursive feature elimination with cross-validation were used to select effective features. Random forests were used to build a final model for predicting nuclear grades, and area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomic features and models.

Results

The radiomic features from the three CT phases could effectively distinguished the four nuclear grades. A combined model, merging radiomic features and clinical characteristics, obtained good predictive performances in the internal test set (AUC 0.77, 0.75, 0.79, and 0.85 for the four grades, respectively), and performance was further confirmed in the external test set, with AUCs of 0.75, 0.68, and 0.73 (no fourth-level data).

Conclusion

The combination of CT radiomic features and clinical characteristics could discriminate the nuclear grades in ccRCC, which may help in assisting treatment decision making.

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Acknowledgement

The authors would like to acknowledge the instrumental and technical support of the Multi-modal biomedical imaging experimental platform, Institute of Automation, Chinese Academy of Sciences.

Funding

This work was supported by the National Key R&D Program of China (2017YFC1308700, 2017YFA0205200, 2017YFC1309100, 2016YFC0103803, 2016YFC0103001), National Natural Science Foundation of China (91959130, 81971776, 81771924, 812716298, 81271629, 81227901), Beijing Natural Science Foundation (L182061), Wuxi Medical Innovation Team Program (CXTD002), Bureau of International Cooperation of Chinese Academy of Sciences (173211KYSB20160053), and the Youth Innovation Promotion Association CAS (2017175).

Author information

Author notes

  1. Hongyu Zhou, Haixia Mao, Di Dong, Changjie Pan have contributed equally to this work.

Authors and Affiliations

  1. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China

    Hongyu Zhou MS & Hairong Zheng PhD

  2. CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Hongyu Zhou MS, Di Dong PhD, Mengjie Fang MS, Dongsheng Gu MS & Jie Tian PhD

  3. Department of Radiology, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, Jiangsu, China

    Haixia Mao MD, Xueling Liu MD, Min Xu MD & Xiangming Fang MD

  4. University of Chinese Academy of Sciences, Beijing, China

    Hongyu Zhou MS, Di Dong PhD, Mengjie Fang MS, Dongsheng Gu MS, Hairong Zheng PhD & Jie Tian PhD

  5. Department of Pathology, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, Jiangsu, China

    Shudong Yang MD

  6. Center of Clinical Research, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, Jiangsu, China

    Jian Zou PhD

  7. Department of Radiology, Changzhou No. 2 People’s Hospital, Nanjing Medical University, Changzhou, Jiangsu, China

    Ruohan Yin MD & Changjie Pan MD

  8. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China

    Jie Tian PhD

  9. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, China

    Jie Tian PhD

Authors
  1. Hongyu Zhou MS
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  2. Haixia Mao MD
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Contributions

Research idea and study design: XF, HZ, JT. Data acquisition: HM, XL, MX, SY, JZ, RY. Data analysis/interpretation: HZ, DD, MF, DG, HZ, XF, JT. Supervision or mentorship: DD, HZ, XF, JT, CP, and XF.

Corresponding authors

Correspondence to Hairong Zheng PhD, Jie Tian PhD or Xiangming Fang MD.

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Disclosure

Hongyu Zhou, Haixia Mao, Di Dong, Mengjie Fang, Dongsheng Gu, Xueling Liu, Min Xu, Shudong Yang, Jian Zou, Ruohan Yin, Hairong Zheng, Jie Tian, Changjie Pan, and Xiangming Fang have declared no conflict of interest.

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Zhou, H., Mao, H., Dong, D. et al. Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma. Ann Surg Oncol 27, 4057–4065 (2020). https://doi.org/10.1245/s10434-020-08255-6

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  • DOI: https://doi.org/10.1245/s10434-020-08255-6

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