Prognostic factors in Chinese patients with metastatic castration-resistant prostate cancer treated with docetaxel-based chemotherapy
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Updated: May 16, 2026

Generation of Prostate Cancer Cell Models of Resistance to the Anti-mitotic Agent Docetaxel
Published on: September 8, 2017
Yuan-Yuan Qu1, Bo Dai, Yun-Yi Kong
1Department of Urology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
Abstract:
This study aims to evaluate the potential value of patient characteristics in predicting overall survival (OS) in patients with metastatic castration-resistant prostate cancer (mCRPC) treated with docetaxel-based thermotherapy. A total of 115 patients with mCRPC undergoing a docetaxel q3w regimen were enrolled in this study. A survival analysis was performed using the Kaplan-Meier method. Cox proportional hazards models were used to evaluate the prognostic value of all covariates for OS. OS was also analysed after stratifying patients according to the results of multivariate analysis. The median OS for the entire cohort was 17.0 months. The multivariate analysis showed that the prostate-specific antigen doubling time (PSADT), baseline haemoglobin (Hb) concentration, alkaline phosphatase (ALP) concentration, cycles of chemotherapy and time to castration resistance were independent prognostic factors of OS. According to the presence of PSADT <46.3 days and baseline ALP ≥ 110 IU l(-1), all patients were divided into three risk groups: low-risk group (no risk factors), intermediate-risk group (one risk factor) and high-risk group (two risk factors). Median OSs for patients in low-, intermediate- and high-risk groups were 28.0 months (95% CI: 23.8-32.2), 21.0 months (95% CI: 18.9-23.1) and 11.0 months (95% CI: 7.6-14.4), respectively (P<0.001). In conclusion, PSADT, baseline Hb concentration, ALP concentration, cycles of chemotherapy and time to castration resistance were independent prognostic factors of OS in Chinese patients with mCRPC treated with docetaxel. PSADT combined with the baseline ALP concentration could be a useful risk stratification parameter for evaluating survival outcomes.
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