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Objective  To investigate the epidemiological data, prognostic factors, and treatment outcomes of bone angiosarcoma (BA).    Methods  This retrospective study was based on the Surveillance, Epidemiology, and End Results (SEER) database. The medical records of BA patients were selected from the SEER database from 1975 to 2016. Variables including patients' baseline demographics (age, sex, marital status, and year of diagnosis), tumor characteristics (tumor size, grade, and SEER Historic Stage A), and treatment (surgery and radiotherapy) were selected for further analysis. The research endpoints were overall survival (OS) and cancer‐specific survival (CSS). The optimal cutoff values of continuous variables including age, year of diagnosis, and tumor size were identified using the X‐tail program. Univariate Cox regression was used to identify potential prognostic factors and multivariate Cox regression was used to identify independent prognostic factors. All prognostic factors were included to predict the survival time compared to the median OS and CSS times  via  the novel nomograms. To validate the internal validation of nomograms, we analyzed the concordance indices (C‐index).    Results  This study enrolled a total of 271 patients with malignant vascular bone tumors among residents of the United States between 1975 and 2016. After applying the exclusion criteria (one case without active follow‐up), this study included 152 patients with BA. The median survival time of BA was significantly shorter than that of malignant vascular bone tumors for OS (9 months  vs  27 months,  P  < 0.001). Age, year of diagnosis, tumor size, grade, stage, and surgery were identified as potential prognostic factors for OS or CSS in univariate Cox regression. However, only age ( P  < 0.001,  P  < 0.001), stage ( P  = 0.002,  P  < 0.001), and surgery ( P  = 0.001,  P  = 0.002) were independent prognostic factors for CSS and OS, respectively, in the multivariate analysis. Younger patients less than 54 years have significantly better prognosis for CSS/OS than patients between 54 and 67 years (Hazard ratios [ HRs ]: 1.651 [1.763–3.575], 2.557 [1.395–4.687]) and more than 67 years ( HRs : 4.404 [2.237–8.670], 5.113 [2.923–8.942]). For CSS/OS, the survival time of patients with localized stage was significantly longer than that of patients with regional stage ( HRs : 1.530 [0.725–3.228], 1.548 [0.834–2.873]) and that of patients with distant stage ( HRs : 1.706 [0.899–3.237], 2.101 [1.254–3.520]). Patients with surgery had more survival time than patients without surgery for CSS/OS ( HRs : 2.861 [1.542–5.310], 2.103 [1.308–3.379]). All factors were further included to generate nomograms for CSS and OS. The C‐indexes for the internal validation of OS and CSS prediction were 0.787 (95% confidence interval [ CI ]: 0.738–0.836) and 0.768 (95%  CI : 0.717–0.819), respectively.    Conclusions  Age, stage, and surgery were closely associated with prognosis in patients with BA, and this clinical model was a favorable tool to evaluate survival possibilities.

A Clinical Model of Bone Angiosarcoma Patients: A Population‐based Analysis of Epidemiology, Prognosis, and Treatment