Nomogram application to predict overall and cancer-specific survival in osteosarcoma
Received 22 June 2018
Accepted for publication 7 September 2018
Published 8 November 2018 Volume 2018:10 Pages 5439—5450
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Antonella D'Anneo
Weipeng Zheng,1,* Yuanping Huang,1,* Haoyi Chen,2 Ning Wang,1 Wende Xiao,3 YingJie Liang,3 Xin Jiang,1 Wenzhou Su,4 Shifeng Wen4
1Department of Orthopedics, Guangzhou First People’s Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510180, People’s Republic of China; 2Department of Orthopedics, Guangzhou Chest Hospital, Guangzhou, Guangdong 510180, People’s Republic of China; 3Department of Orthopedics, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510180, People’s Republic of China; 4Department of Orthopedics, Guangzhou First People’s Hospital, Guangzhou Medical University, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510180, People’s Republic of China
*These authors contributed equally to this work
Purpose: A prognostic nomogram was applied to predict survival in osteosarcoma patients.
Patients and methods: Data collected from 2,195 osteosarcoma patients in the Surveillance, Epidemiology, and End Results (SEER) database between 1983 and 2014 were analyzed. Independent prognostic factors were identified via univariate and multivariate Cox analyses. These were incorporated into a nomogram to predict 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) rates. Internal and external data were used for validation. Concordance indices (C-indices) were used to estimate nomogram accuracy.
Results: Patients were randomly assigned into a training cohort (n=1,098) or validation cohort (n=1,097). Age at diagnosis, tumor site, histology, tumor size, tumor stage, use of surgery, and tumor grade were identified as independent prognostic factors via univariate and multivariate Cox analyses (all P<0.05) and then included in the prognostic nomogram. C-indices for OS and CSS prediction in the training cohort were 0.763 (95% CI 0.761–0.764) and 0.764 (95% CI 0.762–0.765), respectively. C-indices for OS and CSS prediction in the external validation cohort were 0.739 (95% CI 0.737–0.740) and 0.740 (95% CI, 0.738–0.741), respectively. Calibration plots revealed excellent consistency between actual survival and nomogram prediction.
Conclusion: Nomograms were constructed to predict OS and CSS for osteosarcoma patients in the SEER database. They provide accurate and individualized survival prediction.
Keywords: cancer-specific survival, nomogram, osteosarcoma, overall survival, prognosis, SEER database
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