4.4 Article

Spine metastasis in patients with prostate cancer: Survival prognosis assessment

期刊

PROSTATE
卷 81, 期 2, 页码 91-101

出版社

WILEY
DOI: 10.1002/pros.24084

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clastic; blastic lesions; overall survival; prostate cancer; spine metastasis

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Prognostic factors for survival in prostate cancer patients with spine metastasis include Eastern Cooperative Oncology Group/World Health Organization personnel status, SpM radiotherapy, and presence of osteolytic lesions. Updating prognostic scoring algorithms is recommended to tailor treatments to individual patients.
Background Patients presenting spine metastasis (SpM) from prostate cancer (PC) form a heterogeneous population, through this study, we aimed to clarify and update their prognostic assessment. Methods The patient data used in this study was obtained from a French national multicenter database of patients treated for PC with SpM between 2014 and 2017. A total of 72 patients and 365 SpM cases were diagnosed. Results The median overall survival time for all patients following the event of SpM was 28.8 months. First, we identified three significant survival prognostic factors of PC patients with SpM: good Eastern Cooperative Oncology Group/World Health Organization personnel status (Status 0 hazard ratio [HR]: 0.031, 95% confidence interval [CI]: 0.008-0.127;p < .0001) or (Status 1 HR: 0.163, 95% CI: 0.068-0.393;p < .0001) and SpM radiotherapy (HR: 2.923, 95% CI: 1.059-8.069;p < .0001). Secondly, the presence of osteolytic lesions of the spine (vs. osteoblastic) was found to represent an independent prognosis factor for longer survival [HR: 0.424, 95% CI: 0.216-0.830;p = .01]. Other factors including the number of SpM, surgery, extraspinal metastasis, synchrone metastasis, metastasis-free survival, and SpM recurrence were not identified as being prognostically relevant to the survival of patients with PC. Conclusion Survival and our ability to estimate it in patients presenting PC with SpM have improved significantly. Therefore, we advocate the relevance of updating SpM prognostic scoring algorithms by incorporating data regarding the timeline of PC as well as the presence of osteolytic SpM to conceive treatments that are adapted to each patient.

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