Related Experiment Video For SEER
Updated: Jan 31, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Bone Metastases Pattern in Newly Diagnosed Metastatic Bladder Cancer: A Population-Based Study
Chao Zhang1, Lele Liu1, Fang Tao1
1Department of Bone and Soft Tissue Tumors, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Abstract:
Purpose: Based on a large-population analysis, we aimed to estimate the incidence and survival of bone metastases (BM) in initial bladder cancer (BC) patients and to identify the risk and prognostic factors of BC patients with BM. Patients and methods: Using the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database, bladder cancer patients diagnosed between 2010 and 2014 were retrieved. Multivariate logistic and Cox regression analyses were employed to identify risk factors and prognostic factors for BM in BC patients. A Kaplan-Meier analysis with log-rank test was used to estimate the overall survival for BC and the difference between the survival curves. Results: A total of 1,223 (1.39%) BC patients were diagnosed with de novo BM. Variables such as age between 41 to 60 years, black race, unmarried status, higher T stage, higher N stage, poor tumour differentiation grade, lung metastases, liver metastases, and brain metastases were positively associated with BM occurrence. The median survival for BC patients with BM was dramatically decreased to 4.0 months. Factors including advanced age, absence of surgery, and presence of lung, liver, or brain metastases all predicted worse survival. Conclusion: BM can dramatically decrease the survival of bladder cancer patients. The findings of the present study can provide population-based identification of risk and prognostic factors for BC patients with BM at initial diagnosis, which can be used for BM occurrence prediction and individualized treatment plan-making.
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