Positive Surgical Margins After Anterior Robot-assisted Radical Prostatectomy: Assessing the Learning Curve in a Multi-institutional Collaboration.

The learning curve for robot-assisted radical prostatectomy (RARP) remains controversial, with prior studies showing that, in contrast with evidence on open and laparoscopic radical prostatectomy, biochemical recurrence rates of experienced versus inexperienced surgeons did not differ.

To characterize the learning curve for positive surgical margins (PSMs) after RARP.

We analyzed the data of 13 090 patients with prostate cancer undergoing RARP by one of 74 surgeons from ten institutions in Europe and North America between 2003 and 2022.

Multivariable models were used to assess the association between surgeon experience at the time of each patient's operation and PSMs after surgery, with adjustment for preoperative prostate-specific antigen level, grade, stage, and year of surgery. Surgeon experience was coded as the number of robotic radical prostatectomies done by the surgeon before the index patient's operation.

Overall, 2838 (22%) men had PSMs on final pathology. After adjusting for case mix, we found a significant, nonlinear association between surgical experience and probability of PSMs after surgery, with a lower risk of PSMs for greater surgeon experience (p < 0.0001). The probabilities of PSMs for a patient treated by a surgeon with ten, 250, 500, and 2000 prior robotic procedures were 26%, 21%, 18%, and 14%, respectively (absolute risk difference between ten and 2000 procedures: 11%; 95% confidence interval: 9%, 14%). Similar results were found after stratifying patients according to extracapsular extension at final pathology. Results were also unaltered after excluding surgeons who had moved between institutions.

While we characterized the learning curve for PSMs after RARP, the relative contribution of surgical learning to the achievement of optimal outcomes remains controversial. Future investigations should focus on what experienced surgeons do to avoid positive margins and should explore the relationship between learning, margin rate, and biochemical recurrence. Understanding what margins affect recurrence and whether these margins are trainable or a result of other factors may shed light on where to focus future efforts in surgical education.

In patients receiving robotic radical prostatectomy for prostate cancer, we characterized the learning curve for positive margins. The risk of surgical margins decreased progressively with increasing experience, and plateaued around the 500th procedure. Understanding what margins affect recurrence and whether these margins are trainable or a result of other factors has implications for surgeons and patients, and it may shed light on where to focus future efforts in surgical education.

European urology oncology. 2023 Nov 29 [Epub ahead of print]

Carlo A Bravi, Paolo Dell'Oglio, Pietro Piazza, Simone Scarcella, Lorenzo Bianchi, Ugo Falagario, Filippo Turri, Iulia Andras, Fabrizio Di Maida, Ruben De Groote, Federico Piramide, Marcio Covas Moschovas, Nazareno Suardi, Carlo Terrone, Giuseppe Carrieri, Vipul Patel, Riccardo Autorino, Francesco Porpiglia, Andrew Vickers, Alberto Briganti, Francesco Montorsi, Alexandre Mottrie, Alessandro Larcher, Junior ERUS/Young Academic Urologist Working Group on Robot-assisted Surgery

Department of Urology, The Royal Marsden NHS Foundation Trust, London, UK; Department of Urology, Onze-Lieve-Vrouwziekenhuis Hospital, Aalst, Belgium; ORSI Academy, Ghent, Belgium. Electronic address: ., Department of Urology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy; Department of Urology, Antoni van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Amsterdam, The Netherlands; Interventional Molecular Imaging Laboratory, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands., Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy., Division of Urology, United Hospital of Ancona, School of Medicine Marche Polytechnic University, Ancona, Marche, Italy; Department of Urology, Azienda Ospedaliera Ospedali Riuniti Marche Nord, Pesaro, Italy., Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy., Department of Urology, ASST Santi Paolo e Carlo, University of Milan, Milan, Italy., Department of Urology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania., Department of Urology, Careggi University Hospital, Florence, Italy., Department of Urology, Onze-Lieve-Vrouwziekenhuis Hospital, Aalst, Belgium; ORSI Academy, Ghent, Belgium., School of Medicine, Division of Urology, Department of Oncology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy., AdventHealth Global Robotics Institute, Celebration, FL, USA., IRCCS Ospedale Policlinico San Martino, Genova, Italy; Department of Urology, Ospedali Civili of Brescia, Brescia, Italy., IRCCS Ospedale Policlinico San Martino, Genova, Italy., Department of Urology, Rush University, Chicago, IL, USA., Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy.