• Medicina · Sep 2022

    Review Meta Analysis

    Clinicopathological Significances of Positive Surgical Resection Margin after Radical Prostatectomy for Prostatic Cancers: A Meta-Analysis.

    • Minseok Kim, Daeseon Yoo, Jungsoo Pyo, and Wonjin Cho.
    • Department of Urology, Chosun University Hospital, Chosun University School of Medicine, Gwangju 61453, Korea.
    • Medicina (Kaunas). 2022 Sep 9; 58 (9).

    AbstractBackground and Objectives: This study aims to elucidate the positive rate and the clinicopathological significance of surgical margin after radical prostatectomy (RP) through a meta-analysis. Materials and Methods: This meta-analysis finally used 59 studies, including the information about the positive surgical margin (PSM) and those clinicopathological significances after RP. The subgroup analysis for the estimated rates of PSM was evaluated based on types of surgery, grade groups, and pathological tumor (pT) stages. We compared the clinicopathological correlations between positive and negative surgical margins (NSM). Results: The estimated PSM rate was 25.3% after RP (95% confidence interval [CI] 21.9-29.0%). The PSM rates were 26.0% (95% CI 21.5-31.1%) 28.0% (95% CI 20.2-37.5%) in robot-assisted RP and nerve-sparing RP, respectively. The PSM rate was significantly higher in high-grade groups than in low-grade groups. In addition, the higher pT stage subgroup had a high PSM rate compared to the lower pT stage subgroups. Patients with PSM showed significantly high PSA levels, frequent lymphovascular invasion, lymph node metastasis, and extraprostatic extension. Biochemical recurrences (BCRs) were 28.5% (95% CI 21.4-36.9%) and 11.8% (95% CI 8.1-16.9%) in PSM and NSM subgroups, respectively. Patients with PSM showed worse BCR-free survival than those with NSM (hazard ratio 2.368, 95% CI 2.043-2.744%). Conclusions: Our results showed that PSM was significantly correlated with worse clinicopathological characteristics and biochemical recurrence-free survival. Among the results in preoperative evaluations, grade group and tumor stage are useful for the prediction of PSM.

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