{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T09:06:56Z","timestamp":1783933616020,"version":"3.55.0"},"reference-count":33,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"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":["J of Clinical Ultrasound"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Accurate grading of prostate cancer is critical for treatment strategies and risk stratification. This study aims to develop a machine learning (ML) model integrating Dynamic Contrast\u2010Enhanced Magnetic Resonance Imaging (DCE\u2010MRI) pharmacokinetic parameters with Prostate\u2010Specific Antigen (PSA) values to predict ISUP grade metastatic risk groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included 102 patients with histologically confirmed prostate cancer. DCE\u2010MRI pharmacokinetic parameters (Ktrans, Kep, Ve, CER, MaxSlope, IAUGC) were standardized. The dataset was balanced using the Synthetic Minority Oversampling Technique and split into training, validation, and test sets. ML models, including Random Forest, were evaluated using Area Under the Curve (AUC) values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The Random Forest classifier achieved the highest performance, with an AUC of 0.92. Precision\u2010recall analysis identified an optimal threshold of 0.3, balancing sensitivity and specificity for high\u2010risk group detection. SHAP analysis highlighted PSA, MaxSlope, and Kep as key predictors contributing to model accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Integrating DCE\u2010MRI parameters with PSA values using ML algorithms enhances the prediction of ISUP grade metastatic risk groups. This method provides a robust tool for metastasis screening and personalized treatment in prostate cancer.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcu.70129","type":"journal-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T01:24:03Z","timestamp":1762565043000},"page":"808-816","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrative Machine Learning Model Leveraging\n                    <scp>DCE<\/scp>\n                    \u2010\n                    <scp>MRI<\/scp>\n                    and\n                    <scp>PSA<\/scp>\n                    Values for Advanced Risk Stratification in Prostate Cancer"],"prefix":"10.1002","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3951-7344","authenticated-orcid":false,"given":"Kemal","family":"Panc","sequence":"first","affiliation":[{"name":"Department of Radiology Ministry of Health Karako\u00e7an State Hospital  Elaz\u0131\u011f Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1609-6775","authenticated-orcid":false,"given":"Sumeyye","family":"Sekmen","sequence":"additional","affiliation":[{"name":"Department of Radiology Ministry of Health Patnos State Hospital  A\u011fr\u0131 Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0858-7304","authenticated-orcid":false,"given":"Hasan","family":"Gundogdu","sequence":"additional","affiliation":[{"name":"Department of Radiology; Faculty of Medicine Samsun University  Samsun Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2696-9616","authenticated-orcid":false,"given":"Mustafa","family":"Basaran","sequence":"additional","affiliation":[{"name":"Department of Radiology; Faculty of Medicine Samsun University  Samsun Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9492-093X","authenticated-orcid":false,"given":"Hande Melike","family":"Bulbul","sequence":"additional","affiliation":[{"name":"Department of Radiology; Faculty of Medicine Recep Tayyip Erdogan University  Rize Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5321-8439","authenticated-orcid":false,"given":"Enes","family":"Gurun","sequence":"additional","affiliation":[{"name":"Department of Radiology; Faculty of Medicine Samsun University  Samsun Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2019.05637"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/bjc.2011.595"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/life13091944"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/pros.23067"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/PAS.0000000000000530"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/abca02"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1593\/tlo.13922"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010010\u20102037\u20107"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.7161"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.14140847"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(21)00082-0"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30314-7"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.21037\/qims-21-365"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.18383\/j.tom.2018.00027"},{"key":"e_1_2_10_17_1","first-page":"1691","article-title":"Machine Learning for Optimal Individual Survival Prediction in Resectable Upper Gastrointestinal Cancer","volume":"149","author":"Jung J.\u2010O.","year":"2022","journal-title":"Research Square"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010021\u201086785\u2010z"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/iju.14948"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.4103\/wjnm.WJNM_77_19"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2019.08.025"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1200\/OP.21.00198"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010020\u201007363\u2010x"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.21873\/invivo.11786"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.21873\/invivo.13211"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clgc.2020.04.006"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010022\u201008945\u20107"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.5489\/cuaj.4146"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.7150\/thno.49186"},{"key":"e_1_2_10_31_1","doi-asserted-by":"publisher","DOI":"10.1111\/bju.13465"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12967\u2010023\u201004318\u2010w"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-020-04863-2"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.3892\/ol.2018.8384"}],"container-title":["Journal of Clinical Ultrasound"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jcu.70129","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/jcu.70129","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jcu.70129","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T08:25:06Z","timestamp":1783931106000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jcu.70129"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":33,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1002\/jcu.70129"],"URL":"https:\/\/doi.org\/10.1002\/jcu.70129","archive":["Portico"],"relation":{},"ISSN":["0091-2751","1097-0096"],"issn-type":[{"value":"0091-2751","type":"print"},{"value":"1097-0096","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]},"assertion":[{"value":"2025-02-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-22","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}