New Preoperative Nomogram Using the Centrality Index to Predict High Nuclear Grade Clear Cell Renal Carcinoma
Authors Feng Z, Lou S, Zhang L, Zhang L, Lan W, Wang M, Shen Q, Hu Z, Chen F
Received 2 September 2019
Accepted for publication 2 December 2019
Published 3 January 2020 Volume 2019:11 Pages 10921—10928
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 4
Editor who approved publication: Dr Eileen O'Reilly
Zhan Feng,1 Shuangshuang Lou,1 Lixia Zhang,1 Liang Zhang,2 Wenting Lan,3 Minhong Wang,4 Qijun Shen,5 Zhengyu Hu,6 Feng Chen1
1Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, People’s Republic of China; 2Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310003, People’s Republic of China; 3Department of Radiology, Ningbo First Hospital, Ningbo 315000, People’s Republic of China; 4Department of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu 241000, People’s Republic of China; 5Department of Radiology, Hangzhou First People’s Hospital, Hangzhou 310003, People’s Republic of China; 6Department of Radiology, Second People’s Hospital of Yuhang District, Hangzhou 310003, People’s Republic of China
Correspondence: Feng Chen
Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou 310003, People’s Republic of China
Objective: Nuclear grading is an independent prognosis factor of clear-cell renal cell carcinoma (ccRCC). A non-invasive preoperative predictive WHO/International Society of Urologic Pathology (WHO/ISUP) grading of ccRCC model is needed for clinical use. The anatomical complexity scoring system can span a variety of image modalities. The Centrality index (CI) is a quantitatively anatomical score commonly used for renal tumors. The purpose of this study was to develop a simple model to predict WHO/ISUP grading based on CI.
Materials and methods: The data in this study were from 248 ccRCC patients from five hospitals. We developed three predictive models using training data from 167 patients: a CI-only model, a valuable clinical parameter model and a fusion model of CI with valuable clinical parameters. We compared and evaluated the three models by discrimination, clinical usefulness and calibration, then tested them in a set of validation data from 81 patients.
Results: The fusion model consisting of CI and tumor size (valuable clinical parameter) had an area under the curve (AUC) of 0.82. In the validation set, the AUC was 0.85. The decision curve showed that the model had a good net benefit between the threshold probabilities of 5–80%. And the calibration curve showed good calibration in the training set and validation set.
Conclusion: This study confirms that CI is associated with the WHO/ISUP grade of ccRCC, and the possibility that a bivariate model incorporating tumor size may help urologist’s evaluation patients’ prognostic.
Keywords: kidney, carcinoma, renal cell, nomograms, validation studies, decision support techniques, anatomy, nephrectomy
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