{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T07:31:06Z","timestamp":1768635066015,"version":"3.49.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>To evaluate the efficacy of a machine learning model for predicting prostate-specific antigen (PSA) persistence after radical prostatectomy (RP).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Data from 470 patients who underwent RP at the Affiliated Hospital of Qingdao University from January 2018 to June 2021 were retrospectively analyzed. Ten risk factors, including age, body mass index (BMI), preoperative PSA, biopsy Gleason score, total prostate specific antigen density (PSAD), clinical tumor stage, clinical lymph node status, seminal vesicle invasion, capsular invasion and positive surgical margin, were included in the analysis. The data were randomly divided into a training set and a test set at a ratio of 7:3, and seven different machine learning algorithms were compared. The confusion matrix, receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to evaluate the diagnostic performance of the model, and the random forest algorithm found to be the optimal prediction model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the entire cohort, 142 (30.21%) patients developed PSA persistence. Based on all included risk factors, the random forest model had the best effect among the seven models, with an AUC of 0.8607 in the training set and 0.8011 in the test set. The feature importance results showed that capsular invasion, positive surgical margin, preoperative PSA and biopsy Gleason score were the four most important risk factors for PSA persistence after RP.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The Random Forest algorithm performed excellently in this study and can be used to construct a predictive model for PSA persistence. By incorporating clinical data from the Asian region and exploring the risk factors for PSA persistence, this study contributes to the existing research and aids clinicians in assessing the risk of PSA persistence occurrence, enabling timely treatment planning and improving patient prognosis.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2024.1452265","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T05:10:30Z","timestamp":1733461830000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Predictive model for PSA persistence after radical prostatectomy using machine learning algorithms"],"prefix":"10.3389","volume":"14","author":[{"given":"Haotian","family":"Du","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guipeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongchao","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengxian","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuecheng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA: Cancer J Clin"},{"key":"B2","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3322\/caac.21763","article-title":"Cancer statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA: Cancer J Clin"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2018.07.005","article-title":"Cancer incidence and mortality patterns in Europe: Estimates for 40 countries and 25 major cancers in 2018","volume":"103","author":"Ferlay","year":"2018","journal-title":"Eur J Cancer (Oxford England: 1990)"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jncc.2022.02.002","article-title":"Cancer incidence and mortality in China, 2016","volume":"2","author":"Zheng","year":"2022","journal-title":"J Natl Cancer Center"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1011967","article-title":"Radical prostatectomy versus watchful waiting in early prostate cancer","volume":"364","author":"Bill-Axelson","year":"2011","journal-title":"New Engl J Med"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.urology.2007.02.054","article-title":"Intermediate-term potency, continence, and survival outcomes of radical prostatectomy for clinically high-risk or locally advanced prostate cancer","volume":"69","author":"Loeb","year":"2007","journal-title":"Urology"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2009.07.022","article-title":"Natural history of persistently elevated prostate specific antigen after radical prostatectomy: results from the SEARCH database","volume":"182","author":"Moreira","year":"2009","journal-title":"J urol"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2019.01.048","article-title":"Persistent prostate-specific antigen after radical prostatectomy and its impact on oncologic outcomes","volume":"76","author":"Preisser","year":"2019","journal-title":"Eur urol"},{"key":"B9","year":""},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.05.015","article-title":"Next-generation machine learning for biological networks","volume":"173","author":"Camacho","year":"2018","journal-title":"Cell"},{"key":"B11","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1186\/s13148-019-0736-8","article-title":"Random forest-based modelling to detect biomarkers for prostate cancer progression","volume":"11","author":"Toth","year":"2019","journal-title":"Clin epigenetics"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30314-9","article-title":"Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database","volume":"3","author":"Lee","year":"2021","journal-title":"Lancet Digital Health"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2023.09.011","article-title":"Decision trees and random forests","volume":"164","author":"Becker","year":"2023","journal-title":"Am J orthodontics dentofacial orthopedics: Off Publ Am Assoc Orthodontists its constituent societies Am Board Orthodontics"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1016\/j.euo.2021.01.001","article-title":"Management of persistently elevated prostate-specific antigen after radical prostatectomy: A systematic review of the literature","volume":"4","author":"Ploussard","year":"2021","journal-title":"Eur Urol Oncol"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2214122","article-title":"Fifteen-year outcomes after monitoring, surgery, or radiotherapy for prostate cancer","volume":"388","author":"Hamdy","year":"2023","journal-title":"New Engl J Med"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1311593","article-title":"Radical prostatectomy or watchful waiting in early prostate cancer","volume":"370","author":"Bill-Axelson","year":"2014","journal-title":"New Engl J Med"},{"key":"B17","doi-asserted-by":"publisher","first-page":"706309","DOI":"10.1155\/2012\/706309","article-title":"Complications and functional results of surgery for locally advanced prostate cancer","volume":"2012","author":"Joniau","year":"2012","journal-title":"Adv urol"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1007\/s10555-013-9470-4","article-title":"Validation of proposed prostate cancer biomarkers with gene expression data: a long road to travel","volume":"33","author":"Amaro","year":"2014","journal-title":"Cancer metastasis Rev"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM198710083171501","article-title":"Prostate-specific antigen as a serum marker for adenocarcinoma of the prostate","volume":"317","author":"Stamey","year":"1987","journal-title":"New Engl J Med"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-5347(17)32422-9","article-title":"The clinical usefulness of prostate specific antigen: update 1994","volume":"152","author":"Partin","year":"1994","journal-title":"J urol"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1111\/bju.v124.5","article-title":"PSA persistence after radical prostatectomy needs more than standard therapeutic options to improve outcomes","volume":"124","author":"Heidenreich","year":"2019","journal-title":"BJU Int"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1800256115","article-title":"Classification and interaction in random forests","volume":"115","author":"Denisko","year":"2018","journal-title":"Proc Natl Acad Sci United States America"},{"key":"B24","doi-asserted-by":"crossref","DOI":"10.1057\/9780230509993","article-title":"Classification and regression by randomForest","volume-title":"R News","author":"Liaw","year":"2002"},{"key":"B25","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1186\/s12911-019-1004-8","article-title":"Comparing different supervised machine learning algorithms for disease prediction","volume":"19","author":"Uddin","year":"2019","journal-title":"BMC Med Inf decision making"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbs034","article-title":"Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle","volume":"14","author":"Touw","year":"2013","journal-title":"Briefings Bioinf"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-med-051517-011947","article-title":"Treatment of advanced prostate cancer","volume":"70","author":"Teo","year":"2019","journal-title":"Annu Rev Med"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1016\/j.urolonc.2022.10.012","article-title":"MRI versus non-MRI diagnostic pathways before radical prostatectomy: Impact on nerve-sparing, positive surgical margins, and biochemical recurrence","volume":"41","author":"Patel","year":"2023","journal-title":"Urologic Oncol"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2024.1452265\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T05:10:32Z","timestamp":1733461832000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2024.1452265\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,6]]},"references-count":28,"alternative-id":["10.3389\/fonc.2024.1452265"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2024.1452265","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,6]]},"article-number":"1452265"}}