Preoperative predictive factors and further risk stratification of biochemical recurrence in clinically localized high-risk prostate cancer

BACKGROUND - To identify preoperative predictive factors for biochemical recurrence (BCR) and to further stratify its risk in high-risk localized prostate cancer patients receiving radical prostatectomy (RP).

METHODS - Subjects included 195 high-risk prostate cancer patients undergoing RP from 2000 to 2012.

RP consisted of retropubic radical prostatectomy and robot-assisted radical prostatectomy, involving 84 cases and 111 cases, respectively. BCR was defined as a prostate serum antigen (PSA) level ≥0. 2 ng/mL. BCR-free survival (BCRFS) was calculated using the Kaplan-Meier method. Preoperative predictors of BCR were assessed with Cox's proportional hazard regression analysis.

RESULTS - Eighty-nine patients (45. 6 %) experienced recurrence. BCRFS rates 3 and 5 years after RP were 58 and 50 %, respectively. Prostate volume, transition zone volume, and Gleason score were not significantly associated with BCR. Patients with higher preoperative PSA, PSA density (PSAD), PSA density of the transition zone, percentage of positive cores (PPC), and PPC from the dominant side showed significantly lower BCRFS. The PPC from the dominant side and PSAD were significant independent prognostic factors for BCR. Using these variables, the hazard ratio of BCR could be calculated and patients stratified into three risk groups. The 5-year BCRFS rates for Groups 1, 2, and 3 were 64. 9 %, 48. 1 %, and 21. 3 %, respectively.

CONCLUSIONS - Patients with high-risk localized prostate cancer as currently defined do not have uniformly poor prognosis after RP. PPC from the dominant side and PSAD are significant predictors of BCR. These factors can identify high-risk patients with very poor prognosis.

International journal of clinical oncology. 2015 Nov 19 [Epub ahead of print]

Riu Hamada, Jun Nakashima, Makoto Ohori, Yoshio Ohno, Osamu Komori, Kunihiro Yoshioka, Masaaki Tachibana

Department of Urology, Tokyo Medical University Hospital, Tokyo Medical University, 6-7-2 Nishi-shinjyuku, Shinjuku-ku, Tokyo, 160-8582, Japan. Department of Urology, Tokyo Medical University Hospital, Tokyo Medical University, 6-7-2 Nishi-shinjyuku, Shinjuku-ku, Tokyo, 160-8582, Japan. , Department of Urology, Tokyo Medical University Hospital, Tokyo Medical University, 6-7-2 Nishi-shinjyuku, Shinjuku-ku, Tokyo, 160-8582, Japan. , Department of Urology, Tokyo Medical University Hospital, Tokyo Medical University, 6-7-2 Nishi-shinjyuku, Shinjuku-ku, Tokyo, 160-8582, Japan. , Department of Mathematical Analysis and Statistical Inference, The Institute of Statistical Mathematics, Tokyo, Japan. , Department of Urology, Tokyo Medical University Hospital, Tokyo Medical University, 6-7-2 Nishi-shinjyuku, Shinjuku-ku, Tokyo, 160-8582, Japan. , Department of Urology, Tokyo Medical University Hospital, Tokyo Medical University, 6-7-2 Nishi-shinjyuku, Shinjuku-ku, Tokyo, 160-8582, Japan.

PubMed

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