已发表论文

诺模图用于预测骨肉瘤总体特征和癌症特异性存活率

 

Authors Zheng W, Huang Y, Chen H, Wang N, Xiao W, Liang Y, Jiang X, Su W, Wen S

Received 22 June 2018

Accepted for publication 7 September 2018

Published 8 November 2018 Volume 2018:10 Pages 5439—5450

DOI https://doi.org/10.2147/CMAR.S177945

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Justinn Cochran

Peer reviewer comments 2

Editor who approved publication: Dr Antonella D'Anneo

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 <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




Figure 1 Identification of optimal cutoff values of age of diagnosis (A–C) and tumor size (D–F) via X-tile analysis.