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Choi JW, Hu R, Zhao Y, Purkayastha S, Wu J, McGirr AJ, Stavropoulos SW, Silva AC, Soulen MC, Palmer MB, Zhang PJL, Zhu C, Ahn SH, Bai HX. Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using MRI-based radiomics. Abdom Radiol (NY) 2021; 46:2656-2664. [PMID: 33386910 DOI: 10.1007/s00261-020-02876-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/15/2020] [Accepted: 11/18/2020] [Indexed: 12/26/2022]
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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Affiliation(s)
- Ji Whae Choi
- Warren Alpert Medical School, Brown University, Providence, RI, 02903, USA.
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA.
| | - Rong Hu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Province Engineering Technology Research Center of Computer Vision and Intelligent Medical Treatment, Changsha, 410083, China
- Joint Laboratory of Mobile Health, Ministry of Education and China Mobile, Hunan, 410083, China
| | - Yijun Zhao
- Department of Radiology, Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Subhanik Purkayastha
- Warren Alpert Medical School, Brown University, Providence, RI, 02903, USA
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA
| | - Jing Wu
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA
- Department of Radiology, Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Aidan J McGirr
- Department of Radiology, Mayo Clinic Hospital, Scottsdale, AZ, 85054, USA
| | - S William Stavropoulos
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Alvin C Silva
- Department of Radiology, Mayo Clinic Hospital, Scottsdale, AZ, 85054, USA
| | - Michael C Soulen
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Matthew B Palmer
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Paul J L Zhang
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Chengzhang Zhu
- Joint Laboratory of Mobile Health, Ministry of Education and China Mobile, Hunan, 410083, China
- College of Literature and Journalism, Central South University, Changsha, 410083, China
| | - Sun Ho Ahn
- Warren Alpert Medical School, Brown University, Providence, RI, 02903, USA
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA
| | - Harrison X Bai
- Warren Alpert Medical School, Brown University, Providence, RI, 02903, USA
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA
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