• Jun Zhang, Jin Yang, Hai-Qiang Wang, Zhenyu Pan, Xiaoni Yan, Chuanyu Hu, Yuanjie Li, and Jun Lyu.
• Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University.
• Medicine (Baltimore). 2019 Jun 1; 98 (23): e15988e15988.

AbstractThis study aimed to establish a comprehensive prognostic system for osteosarcoma based on a large population database with high quality.The Surveillance, Epidemiology, and End Results (SEER) Program database was used to identify patients with osteosarcoma from 1973 to 2015. Multivariate analysis was performed to screen statistically significant variables. A nomogram was constructed by R software to predict the 3-, 5- and 10-year survival rates. Predictive abilities were compared by C-indexes, calibration plots, integrated discrimination improvement (IDI), net reclassification improvement (NRI), as well as decision curve analysis (DCA).In total, 4505 osteosarcoma patients were identified. They were divided into training (70%, n = 3153) and validating (30%, n = 1352) groups. Multivariate analyses identified independent predictors. Subsequently, the nomogram system of a new model was established, which comprised 7 variables as age, sex, site, decade of diagnosis (DOD), extent of disease (EOD), tumor size and patients undergoing tri-modality therapy (surgery, radiotherapy and chemotherapy). It provided better C-indexes than the model without therapies (0.727, 0.712 vs 0.705, 0.668) in the 2 cohort, respectively. As well, the new model had good performances in the calibration plots. Moreover, both IDI and NRI improved for 3-, 5- and 10-year follow-up of C-indexes. Finally, DCA demonstrated that the nomogram of new model was clinically meaningful.We developed a reliable nomogram for prognostic determinants and treatment outcome analysis of osteosarcoma, thus helping better choose medical examinations and optimize therapeutic regimen under the cooperation among oncologists and surgeons.

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