{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T15:55:30Z","timestamp":1784735730565,"version":"3.55.0"},"reference-count":32,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"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":["The Prostate"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Prostate\u2010specific antigen (PSA) alone is insufficient for the diagnosis of prostate cancer (PCa), particularly within the gray zone range of 4\u201310\u2009ng\/mL. Multiparametric magnetic resonance imaging (mpMRI), although widely used, has notable limitations in the diagnostic pathway. The aim of this study was to develop a biopsy prediction model by evaluating multiple machine learning (ML) algorithms incorporating PSA\u2010related variables, mpMRI findings, and hematologic parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>This study included patients who underwent either systematic biopsy or combined biopsy (systematic plus fusion) based on mpMRI findings and had pre\u2010biopsy PSA levels \u2264\u200910\u2009ng\/mL between 2017 and 2024 at our center. Laboratory findings, mpMRI results, and prostate biopsy outcomes were recorded. Based on the pathological evaluation of biopsy specimens, the patients were divided into two groups: those without malignancy (Group 1) and those with malignancy (Group 2). To develop a useful model for prediction, five ML techniques were applied: logistic regression, random forest (RF), extra trees, extreme gradient boosting (XGBoost) classifier, and light gradient\u2010boosting machine classifier.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>There were 1223 patients (84.5%) in Group 1 and 225 patients (15.5%) in Group 2. The model with the best accuracy was XGBoost, with a sensitivity of 94.74% and a specificity of 100% on the test data set. For the RF model, sensitivity and specificity on the test data set were determined to be 78.95% and 100%, respectively. The area under the curve (AUC) values for XGBoost and RF were 0.97 and 0.89, respectively. According to the permutation feature importance analysis, the three most influential variables were free\/total PSA, Prostate Imaging\u2010Reporting and Data System Score 4, and platelet\u2010to\u2010lymphocyte ratio.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>XGBoost and RF models demonstrated excellent performance, with high AUC values. To generalize the results, it is necessary to confirm the accuracy of ML models through external validation in different populations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pros.70168","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T19:36:08Z","timestamp":1774899368000},"page":"982-989","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Development of Machine Learning Models for Predicting Prostate Cancer in Biopsy Candidates Using Prostate\u2010Specific Antigen, Magnetic Resonance Imaging, and Hematologic Parameters"],"prefix":"10.1002","volume":"86","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2435-5482","authenticated-orcid":false,"given":"Deniz Noyan","family":"\u00d6zl\u00fc","sequence":"first","affiliation":[{"name":"Department of Urology Bak\u0131rk\u00f6y Dr. Sadi Konuk Training and Research Hospital University of Health Sciences Istanbul T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0823-7400","authenticated-orcid":false,"given":"Yusuf","family":"Ar\u0131kan","sequence":"additional","affiliation":[{"name":"Department of Urology Izmir Tepecik Training and Research Hospital University of Health Sciences Izmir T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4694-1319","authenticated-orcid":false,"given":"B\u00fc\u015fra","family":"Emir","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Faculty of Medicine \u0130zmir Katip Celebi University Izmir T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8441-1293","authenticated-orcid":false,"given":"Ali","family":"Ayten","sequence":"additional","affiliation":[{"name":"Department of Urology Gaziosmanpasa Training and Research Hospital University of Health Sciences \u0130stanbul T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8910-9859","authenticated-orcid":false,"given":"Ubeyd","family":"Sungur","sequence":"additional","affiliation":[{"name":"Department of Urology Bak\u0131rk\u00f6y Dr. Sadi Konuk Training and Research Hospital University of Health Sciences Istanbul T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1490-3756","authenticated-orcid":false,"given":"Mithat","family":"Ek\u015fi","sequence":"additional","affiliation":[{"name":"Department of Urology Bak\u0131rk\u00f6y Dr. Sadi Konuk Training and Research Hospital University of Health Sciences Istanbul T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1525-1243","authenticated-orcid":false,"given":"Hakan","family":"Polat","sequence":"additional","affiliation":[{"name":"Department of Urology Bak\u0131rk\u00f6y Dr. Sadi Konuk Training and Research Hospital University of Health Sciences Istanbul T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1420-4536","authenticated-orcid":false,"given":"Serdar","family":"Karada\u011f","sequence":"additional","affiliation":[{"name":"Department of Urology Bak\u0131rk\u00f6y Dr. Sadi Konuk Training and Research Hospital University of Health Sciences Istanbul T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4724-3053","authenticated-orcid":false,"given":"Alper","family":"Bitkin","sequence":"additional","affiliation":[{"name":"Department of Urology Bak\u0131rk\u00f6y Dr. Sadi Konuk Training and Research Hospital University of Health Sciences Istanbul T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2018.05.016"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00345-019-02719-5"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/pcan.2010.29"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41391-021-00429-x"},{"key":"e_1_2_12_6_1","first-page":"4445","article-title":"PCA3 Score vs PSA Free\/Total Accuracy in Prostate Cancer Diagnosis at Repeat Saturation Biopsy","volume":"31","author":"Pepe P.","year":"2011","journal-title":"Anticancer Research"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcna.2017.11.001"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.941349"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prnil.2019.01.001"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1155\/2024\/6114907"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2020.09.043"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11020354"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30226-9"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1159\/000540425"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1814259"},{"key":"e_1_2_12_16_1","first-page":"145","article-title":"Prediction of Prostate Cancer by Deep Learning With Multilayer Artificial Neural Network","volume":"13","author":"Takeuchi T.","year":"2019","journal-title":"Canadian Urological Association Journal"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12894-021-00849-w"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm14010183"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2020.09.042"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2015.08.052"},{"key":"e_1_2_12_21_1","first-page":"217","volume-title":"Reading and Understanding Multivariate Statistics","author":"Wright R. E.","year":"1995"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_12_24_1","first-page":"3146","volume-title":"Advances in Neural Information Processing Systems 30 (NeurIPS 2017)","author":"Ke G.","year":"2017"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00345-020-03392-9"},{"key":"e_1_2_12_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clgc.2020.04.006"},{"key":"e_1_2_12_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mjafi.2023.03.001"},{"key":"e_1_2_12_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.purol.2022.12.007"},{"key":"e_1_2_12_29_1","article-title":"68Ga\u2010PSMA PET\/CT Evaluation in Men Enrolled in Prostate Cancer Active Surveillance","volume":"95","author":"Pepe P.","year":"2023","journal-title":"Archivio Italiano di Urologia e Andrologia"},{"key":"e_1_2_12_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2024.03.027"},{"key":"e_1_2_12_31_1","doi-asserted-by":"publisher","DOI":"10.1097\/JU.0000000000003492"},{"key":"e_1_2_12_32_1","article-title":"Clinical Relevance of Digital Rectal Examination in Men With Low and Very Low Prostate\u2010Specific Antigen (PSA): Reassessing Its Diagnostic Value in the MRI Era and in Light of Modern Guidelines","volume":"17","author":"Franz T.","year":"2025","journal-title":"Cureus"},{"key":"e_1_2_12_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/pros.24188"}],"container-title":["The Prostate"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/pros.70168","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/pros.70168","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/pros.70168","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T06:18:46Z","timestamp":1778739526000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/pros.70168"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,30]]},"references-count":32,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["10.1002\/pros.70168"],"URL":"https:\/\/doi.org\/10.1002\/pros.70168","archive":["Portico"],"relation":{},"ISSN":["0270-4137","1097-0045"],"issn-type":[{"value":"0270-4137","type":"print"},{"value":"1097-0045","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,30]]},"assertion":[{"value":"2025-11-29","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-18","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}