{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:44:10Z","timestamp":1790271850007,"version":"4.1.0"},"reference-count":31,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:00:00Z","timestamp":1778889600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:00:00Z","timestamp":1778889600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Robotics Computer Surgery"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop a machine learning (ML) model predicting positive surgical margins (PSM) after robot\u2010assisted radical prostatectomy (RARP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a single\u2010centre retrospective analysis of 301 patients with RARP (211 for training and 90 for validation). Twenty\u2010four features were reduced to five using the Boruta algorithm. Seven ML models were developed and evaluated, and the optimal model was interpreted using SHAP.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>PSM incidence was 42.0%. The artificial neural network (ANN) performed best (AUC 0.808, accuracy 0.811, sensitivity 0.789, F1 0.779). SHAP identified clinical T stage, creatinine, high\u2010risk status, and positive biopsy percentage as risk factors, while neoadjuvant therapy was protective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The ANN model demonstrates potential in predicting PSM following RARP. Currently, it serves as an exploratory predictive tool for risk stratification, requiring further external validation prior to direct clinical application.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/rcs.70172","type":"journal-article","created":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T13:30:21Z","timestamp":1778938221000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Positive Surgical Margins in Robot\u2010Assisted Prostatectomy Using Machine Learning Models"],"prefix":"10.1002","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5771-8502","authenticated-orcid":false,"given":"Gen","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Urology, School of Clinical Medicine North Sichuan Medical College, Affiliated Hospital of North Sichuan Medical College  Nanchong Sichuan 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