Controversies exist between the previous two prognostic nomograms for patients with bone metastatic prostate cancer (PCa), and a nomogram applied to western patients has yet to be established. Thus, we aimed to build a reliable and generic nomogram to individualize prognosis.The independent prognostic factors were identified in a retrospective study of 1556 patients with bone metastatic PCa registered in the Surveillance, Epidemiology and End Results (SEER) database. Besides, the prognostic nomogram was developed using R software according to the result of multivariable Cox regression analysis. Then, the discriminative ability of the nomogram was assessed by analyses of receiver operating characteristic curves (ROC curves). We also performed 1-, 2-, and 3-year calibrations of the nomogram by comparing the predicted survival to the observed survival. Furthermore, the model was externally validated using the data of 711 patients diagnosed at different times enrolled in the SEER database.Age ≥70 years, Gleason score ≥8, PSA value of 201 to 900 ng/ml, stage T4, stage N1, with liver metastases, and Asian/Pacific ethnicity were identified as independent prognostic factors. In the primary cohort, 1-, 2-, and 3-year area under the ROC curve (AUC) of the nomogram for predicting cancer-specific survival (CSS) were 0.71, 0.70, and 0.70, respectively. Besides 1-, 2-, and 3-year AUC were 0.70, 0.68, and 0.69, respectively, in the external validation cohort. Moreover, calibration curves presented perfect agreements between the nomogram-predicted and actual 1-, 2-, and 3-year CSS rate in both the primary and external validation cohorts. In other words, our nomogram has great predictive accuracy and reliability in predicting 1-, 2-, and 3-year CSS for patients with bone metastatic prostate cancer.This study established and validated a prognostic nomogram applied to not only Asian patients but western patients with bone metastatic PCa, which will be useful for patients' counseling and clinical trial designing.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6775397PMC
http://dx.doi.org/10.1097/MD.0000000000017197DOI Listing

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