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    Korean J Urol. 2007 Apr;48(4):408-415. Korean.
    Published online Apr 30, 2007.
    https://doi.org/10.4111/kju.2007.48.4.408
    Copyright © 2007 The Korean Urological Association
    Original Article

    Predictive Variables of the Progression to Androgen Independent Prostate Cancer after Combined Androgen Blockade

    Seung Chol Park, Han Yong Choi,1 Choung Soo Kim,2 Sung Joon Hong,3 Wun Jae Kim,4 Sang Eun Lee,5 Jae Mann Song,6 Jin Han Yoon,7 and Joung Sik Rim
      • Department of Urology, Wonkwang University, Iksan, Korea.
      • 1Department of Urology, Sungkyunkwan University, Seoul, Korea.
      • 2Department of Urology, University of Ulsan, Ulsan, Korea.
      • 3Department of Urology, Yonsei University, Seoul, Korea.
      • 4Department of Urology, Chungbuk National University, Cheongju, Korea.
      • 5Department of Urology, Seoul National University, Seongnam, Korea.
      • 6Department of Urology, Yonsei University Wonju College of Medicine, Wonju, Korea.
      • 7Department of Urology, Dong-A University College of Medicine, Busan, Korea.
    • Corresponding author (Email: jsrim@wonkwang.ac.kr )
    Received February 14, 2007; Accepted March 26, 2007.

    Abstract

    Purpose

    Despite of the effectiveness of androgen deprivation therapy for prostate cancer, it progress to androgen independent prostate cancer (AIPC) after various periods of time. The objective of this study was to analyze the clinical and pathological variables that predict progression to AIPC after combined androgen blockade (CAB).

    Materials and Methods

    We retrospectively reviewed the medical records of 343 patients who were treated with CAB for prostate cancer. Binary logistic regression test was used to analyze the independent predictors for the progression to AIPC. The time to AIPC, according to variables, was assessed by the Kaplan-Meier method and the variables were compared using the Log-Rank test.

    Results

    The mean follow-up was 42.1 months (range: 12-120). Seventy seven patients (33.3%) experienced progression to AIPC at a median of 20.2 months (range: 6-72). On univariate analysis, the percentage of positive prostate biopsies, the Gleason score, the T stage, the extent of bone metastasis, lymph node metastasis, the pretreatment PSA level, the nadir PSA and the PSA level at 3 and 6 months all had a significant relationship with the progression to AIPC. The receiver operating characteristic curve analysis for the nadir PSA showed that the optimal cut-off point to predict progression to AIPC was 0.5ng/ml with an area under curve of 0.769. A multivariate analysis demonstrated that the Gleason score (>7), the nadir PSA (>0.5ng/ml), and the PSA level at 6 months (>4.0ng/ml) were significantly correlated with the progression to AIPC.

    Conclusions

    This study suggested that Gleason score, the nadir PSA and the PSA level at 6 months were independent variables to predict progression to AIPC after CAB. The PSA level at 6 months may be the most accurate variable to predict progression to AIPC.

    Keywords
    Prostate cancer; Hormone; Progression

    Figures

    Fig. 1
    Study Schema. CAB: combined androgen blockade, AIPC: androgen independent prostate cancer.

    Fig. 2
    Receiver operating characteristics curve analysis of the nadir prostate-specific antigen (PSA) to predict progression to androgen independent prostate cancer. AUC: area under curve.

    Fig. 3
    Time to progression to androgen independent prostate cancer according to the T stage, the extent of bone metastasis, the pretreatment prostate-specific antigen (PSA), the nadir PSA, the PSA level at 3 months and the PSA level at 6 months.

    Tables

    Table 1
    Baseline descriptive characteristics of the 231 patients

    Table 2
    Univariate analysis of the quantitative variables according to the progression to androgen independent prostate cancer

    Table 3
    Univariate analysis of the distribution of qualitative variables according to the progression to androgen independent prostate cancer

    Table 4
    Binary logistic regression analysis of the predictive variables of androgen independent prostate cancer

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    MeSH Terms
    Area Under Curve
    Biopsy
    Follow-Up Studies
    Humans
    Logistic Models
    Lymph Nodes
    Medical Records
    Multivariate Analysis
    Neoplasm Grading
    Neoplasm Metastasis
    Prostate*
    Prostatic Neoplasms*
    ROC Curve
    Retrospective Studies
    Metrics
    Share
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