Novel nomograms individually predict the survival of patients with soft tissue sarcomas after surgery
Authors Zhang SL, Wang ZM, Wang WR, Wang X, Zhou YH
Received 4 December 2018
Accepted for publication 10 March 2019
Published 15 April 2019 Volume 2019:11 Pages 3215—3225
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Cristina Weinberg
Peer reviewer comments 3
Editor who approved publication: Dr Alexandra R. Fernandes
Shi-Long Zhang,1,* Zhi-Ming Wang,2,3,* Wen-Rong Wang,4 Xin Wang,5 Yu-Hong Zhou2
1Institute of Fudan-Minhang Academic Health System, Minhang Branch, Zhongshan Hospital, Fudan University, Shanghai, 201199, People’s Republic of China; 2Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China; 3Department of Medical Oncology, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, 361000, People’s Republic of China; 4Faculty of Physical Education, Shandong Normal University, Jinan, 250014, People’s Republic of China; 5Department of Acupuncture and Moxibustion, Central Hospital of Shanghai, Xuhui District, Shanghai, 200031, People’s Republic of China
*These authors contributed equally to this work
Background: The aim of the study was to build and validate practical nomograms to better predict the overall survival (OS) and cancer-specific survival (CSS) of the patients with soft tissue sarcomas (STS) who underwent surgery.
Methods: Patient data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We identified 8804 patients who underwent surgery with STS between 2007 and 2015, and randomly divided them into the training (n=6164) and validation (n=2640) cohorts. The Cox regression analysis and cumulative incidence function were performed to identify the independent prognostic factors associated with OS and CSS, respectively. The performance of the nomograms was evaluated using Harrell’s concordance index (C-index) and the calibration curves. Decision curve analysis (DCA) was introduced to compare the clinical practicality between the nomograms and the AJCC staging system.
Results: Eight independent prognostic factors for OS and seven for CSS were determined and then used to build the nomograms for 3- and 5-year OS and CSS, respectively. The C-indexes of the nomograms for predicting OS were 0.788 in the internal validation and 0.823 in external validation, significantly higher than C-index of the AJCC staging system (P<0.001). The similar results were obtained in the validation cohort. Internal and external calibration curves for the predicting 3- and 5-year OS and CSS showed excellent agreement between the prediction and the actual survival outcomes. In addition, DCA demonstrated that our nomograms were superior over the AJCC staging system with obtaining more clinical net benefits.
Conclusions: We established and validated the nomograms that could accurately predict the 3- and 5-year OS and CSS for STS patients who underwent surgery. The nomograms showed more robust and applicable performance than the AJCC staging system for predicting OS and CSS.
Keywords: SEER, soft tissue sarcomas, prognosis, nomogram, decision curve analysis
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