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Nomograms for predicting long-term overall survival and disease-specific survival of patients with clear cell renal cell carcinoma

Authors Zhang GH, Wu Y, Zhang JS, Fang ZQ, Liu ZX, Xu ZH, Fan YD

Received 21 April 2018

Accepted for publication 9 August 2018

Published 6 September 2018 Volume 2018:11 Pages 5535—5544

DOI https://doi.org/10.2147/OTT.S171881

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Colin Mak

Peer reviewer comments 3

Editor who approved publication: Dr Jianmin Xu


Guanghao Zhang,1,* Yun Wu,2,* Jiashu Zhang,3 Zhiqing Fang,4 Zhaoxu Liu,4 Zhonghua Xu,4 Yidong Fan4

1Department of Financial Mathematics and Financial Engineering, School of Ecomonics, Shandong University, Jinan, Shandong, People’s Republic of China; 2Department of Medical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, People’s Republic of China; 3Department of Breast Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, People’s Republic of China; 4Department of Urology, Qilu Hospital of Shandong University, Jinan, Shandong, People’s Republic of China

*These authors contributed equally to this work

Objectives: The aim of this study was to establish comprehensive and practical nomograms, based on significant clinicopathological parameters, for predicting the overall survival (OS) and the disease-specific survival (DSS) of patients with clear cell renal cell carcinoma (ccRCC).
Patients and methods: The data of 35,151 ccRCC patients, diagnosed between 2004 and 2014, were obtained from the database of the Surveillance, Epidemiology, and End Results (SEER) program. The Kaplan–Meier method and Cox proportional hazards regression model were used to evaluate the prognostic effects of multiple clinicopathological variables on survival. Based on Cox models, a nomogram was constructed to predict the probabilities of OS and DSS for an individual patient. The predictive performance of nomograms was evaluated using the concordance index (C-index) and calibration curves.
Results: According to univariate and multivariate analyses, age at diagnosis, sex, race, marital status, surgical approach, tumor node metastasis (TNM) stage, and Fuhrman grade significantly correlated with the survival outcomes. These characteristics were used to establish nomograms. The nomograms showed good accuracy in predicting 3-, 5-, and 10-year OS and DSS, with a C-index of 0.79 (95% CI, 0.79–0.80) for OS and 0.87 (95% CI, 0.86–0.88) for DSS. All calibration curves revealed excellent consistency between predicted and actual survival.
Conclusion: Nomograms were developed to predict death from ccRCC treated with nephrectomy. These new prognostic tools could aid in improving the predictive accuracy of survival outcomes, thus leading to reasonable individualized treatment.

Keywords: clear cell renal cell carcinoma, prognosis, nomogram, survival analysis

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