The Effect of Adverse Surgical Margins on the Risk of Biochemical Recurrence after Robotic-Assisted Radical Prostatectomy

生化复发 不利影响 医学 前列腺切除术 前列腺癌 危险系数 断点群集区域 泌尿科 比例危险模型 手术切缘 低风险 内科学 外科 肿瘤科 癌症 置信区间 受体
作者
Enric Carbonell,Roger Matheu,Maria Muní,Joan Sureda,Mónica García-Sorroche,María J. Ribal,Antonio Alcaraz,Antoni Vilaseca
出处
期刊:Biomedicines [MDPI AG]
卷期号:10 (8): 1911-1911 被引量:7
标识
DOI:10.3390/biomedicines10081911
摘要

Positive surgical margins (PSM) after radical prostatectomy are associated with a greater risk of biochemical recurrence (BCR). However, not all PSM harbour the same prognosis for recurrence. We aim to determine the impact of different PSM characteristics and their coexistence on the risk of BCR. This retrospective study included 333 patients that underwent robotic-assisted radical prostatectomy for prostate cancer between 2015–2020 at a single institution. The effect of PSM and their adverse characteristics on the risk of BCR was assessed using Cox proportional hazard models. Kaplan–Meier was used to represent BCR-free survival stratified by margin status. With a median follow-up of 34.5 months, patients with PSM had a higher incidence of BCR, higher risk of relapse and lower BCR-free survival than negative margins (p < 0.001). We established as adverse characteristics: PSM length ≥ 3 mm, multifocality and Gleason at margin > 3. PSM ≥ 3 mm or multifocal PSM were associated with an increased risk for BCR compared to favourable margins (HR 3.50; 95% CI 2.05–5.95, p < 0.001 and HR 2.18; 95% CI 1.09–4.37, p = 0.028, respectively). The coexistence of these two adverse features in the PSM also conferred a higher risk for biochemical relapse and lower BCR-free survival. Adverse Gleason in the margin did not confer a higher risk for BCR than non-adverse margins in our models. We concluded that PSM are an independent predictor for BCR and that the presence of adverse characteristics, such as length and focality, and their coexistence in the PSM are associated with a greater risk of recurrence. Nevertheless, subclassifying PSM with adverse features did not enhance the model’s predictive performance in our cohort.
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