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Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using MRI-based radiomics

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Currently, there is a lack of noninvasive methods to stratify ccRCC prognosis prior to any invasive therapies. The purpose of this study was to preoperatively predict the tumor stage, size, grade, and necrosis (SSIGN) score of ccRCC using MRI-based radiomics.

Methods

A multicenter cohort of 364 histopathologically confirmed ccRCC patients (272 low [< 4] and 92 high [≥ 4] SSIGN score) with preoperative T2-weighted and T1-contrast-enhanced MRI were retrospectively identified and divided into training (254 patients) and testing sets (110 patients). The performance of a manually optimized radiomics model was assessed by measuring accuracy, sensitivity, specificity, area under receiver operating characteristic curve (AUROC), and area under precision-recall curve (AUPRC) on an independent test set, which was not included in model training. Lastly, its performance was compared to that of a machine learning pipeline, Tree-Based Pipeline Optimization Tool (TPOT).

Results

The manually optimized radiomics model using Random Forest classification and Analysis of Variance feature selection methods achieved an AUROC of 0.89, AUPRC of 0.81, accuracy of 0.89 (95% CI 0.816–0.937), specificity of 0.95 (95% CI 0.875–0.984), and sensitivity of 0.72 (95% CI 0.537–0.852) on the test set. The TPOT using Extra Trees Classifier achieved an AUROC of 0.94, AUPRC of 0.83, accuracy of 0.89 (95% CI 0.816–0.937), specificity of 0.95 (95% CI 0.875–0.984), and sensitivity of 0.72 (95% CI 0.537–0.852) on the test set.

Conclusion

Preoperative MR radiomics can accurately predict SSIGN score of ccRCC, suggesting its promise as a prognostic tool that can be used in conjunction with diagnostic markers.

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Data availability

The material is not available for public use to protect patient information.

Code availability

The code is available for public use. The link is provided under Code Availability in Methods.

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Funding

National Cancer Institute (NCI) of the National Institutes of Health (Award Number R03CA249554) and Research Scholar Grant by RSNA Research & Education Foundation.

Author information

Author notes

  1. Ji Whae Choi and Rong Hu contributed equally.

Authors and Affiliations

  1. Warren Alpert Medical School, Brown University, Providence, RI, 02903, USA

    Ji Whae Choi, Subhanik Purkayastha, Sun Ho Ahn & Harrison X. Bai

  2. Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA

    Ji Whae Choi, Subhanik Purkayastha, Jing Wu, Sun Ho Ahn & Harrison X. Bai

  3. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Rong Hu

  4. Hunan Province Engineering Technology Research Center of Computer Vision and Intelligent Medical Treatment, Changsha, 410083, China

    Rong Hu

  5. Joint Laboratory of Mobile Health, Ministry of Education and China Mobile, Hunan, 410083, China

    Rong Hu & Chengzhang Zhu

  6. Department of Radiology, Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China

    Yijun Zhao & Jing Wu

  7. Department of Radiology, Mayo Clinic Hospital, Scottsdale, AZ, 85054, USA

    Aidan J. McGirr & Alvin C. Silva

  8. Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, 19104, PA, USA

    S. William Stavropoulos & Michael C. Soulen

  9. Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, 19104, PA, USA

    Matthew B. Palmer & Paul J. L. Zhang

  10. College of Literature and Journalism, Central South University, Changsha, 410083, China

    Chengzhang Zhu

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  1. Ji Whae Choi
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Correspondence to Ji Whae Choi.

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Choi, J.W., Hu, R., Zhao, Y. et al. Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using MRI-based radiomics. Abdom Radiol 46, 2656–2664 (2021). https://doi.org/10.1007/s00261-020-02876-x

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  • DOI: https://doi.org/10.1007/s00261-020-02876-x

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