Purpose: Positive surgical margins (PSM) after robot-assisted radical prostatectomy (RARP) for prostate cancer (PCa) can increase the risk of biochemical recurrence and PCa-specific mortality. We aimed to evaluate the impact of multidisciplinary team meetings (MDTM) on reducing the incidence of PSM following RARP.
Methods: We retrospectively collected the clinical data of consecutive patients undergoing RARP at Hiroshima University between February 2017 and October 2023. The MDTM, comprising a radiologist, uropathologist, and urologist, reviewed the preoperative magnetic resonance imaging (MRI) and prostate biopsy results of each patient before RARP and considered the areas requiring attention during RARP. Surgeons were categorized as experienced or non-experienced based on the number of RARP procedures performed.
Results: In the pT2 population, the PSM rate was significantly lower in cases evaluated using the MDTM than in those not (11.1% vs. 24.0%; p = 0.0067). Cox regression analysis identified that a PSA level > 7 ng/mL (hazard ratio 2.2799) and nerve-sparing procedures (hazard ratio 2.2619) were independent predictors of increased PSM risk while conducting an MDTM (hazard ratio 0.4773) was an independent predictor of reduced PSM risk in the pT2 population. In the pathological T3 population, there was no significant difference in PSM rates between cases evaluated and not evaluated at an MDTM. In cases evaluated at an MDTM, similar PSM rates were observed regardless of surgeon experience (10.4% for non-experienced and 11.9% for experienced surgeons; p = 0.9999).
Conclusions: An MDTM can improve the PSM rate of pT2 PCa following RARP.
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http://dx.doi.org/10.1007/s00345-024-05261-1 | DOI Listing |
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Sci Rep
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DIAPath, Center for Microscopy and Molecular Imaging (CMMI), Université Libre de Bruxelles (ULB), 6041, Gosselies, Belgium.
Over the past decade, neuropathological diagnosis has undergone significant changes, integrating morphological features with molecular biomarkers. The molecular era has successfully refined neuropathological diagnostic accuracy; however, a substantial number of CNS tumor diagnoses remain challenging, particularly in children. DNA methylation classification has emerged as a powerful machine learning approach for clinical decision-making in CNS tumors.
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