{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T23:22:48Z","timestamp":1791588168064,"version":"4.3.4"},"reference-count":16,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T00:00:00Z","timestamp":1748390400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>Prostate cancer is the most prevalent male malignancy in Ghana, with a high-risk of metastatic progression. Early detection and adequate disease severity stratification are crucial for timely intervention, comprehensive management, and improved outcomes. This study evaluates and compares the predictive abilities of a multiparametric model and a PSA-alone model in forecasting metastasis in prostate cancer patients.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Objective<\/jats:title>\n<jats:p>To compare the performance of a multiparametric model and a PSA-alone model in predicting metastasis in prostate cancer patients in Ghana.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Methodology<\/jats:title>\n<jats:p>Logistic regression analyses were conducted on a dataset of 426 prostate cancer cases. The multiparametric model included variables such as age, BMI, marital status, ethnicity, socioeconomic status, clinical stage by DRE findings, PSA levels, and Gleason score. The PSA-alone model focused solely on PSA levels. Model performance metrics included Pseudo R-Squared, AUC, sensitivity, specificity, accuracy, PPV, NPV, FPR, FNR, and F1-Score. The Hosmer-Lemeshow test assessed the goodness-of-fit for the multiparametric model. All analyses were conducted at a 5% level of significance.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>The multiparametric model achieved a Pseudo R-Squared of 71.17%, AUC of 97.18%, sensitivity of 93.20%, specificity of 96.21%, accuracy of 92.25%, PPV of 85.62%, NPV of 96.24%, FPR of 8.24%, FNR of 6.80%, and F1-Score of 81.02%. The Hosmer-Lemeshow test yielded a non-significant p-value of 0.2405. The PSA-alone model had sensitivity of 32.24%, specificity of 91.76%, accuracy of 88.03%, PPV of 77.47%, NPV of 92.02%, FPR of 3.79%, FNR of 67.76%, F1-Score of 45.76%, and AUC of 73.79%. The multiparametric model\u2019s Prevalence Yield was 32.15% and Sensitivity Yield was 32.15%, compared to the PSA-alone model\u2019s 6.95% and 13.32%, respectively.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>Both models effectively predict metastasis in prostate cancer patients. The multiparametric model shows superior overall performance with higher Pseudo R-Squared, AUC, and a better balance in sensitivity, specificity, and accuracy. These results suggest the multiparametric model as a more robust tool for metastasis risk assessment in resource-poor settings. However, clinical context and patient characteristics should guide model choice for optimal outcomes.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0323180","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T18:09:07Z","timestamp":1748455747000},"page":"e0323180","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting prostate cancer metastasis in Ghana: Comparison of multiparametric and PSA models"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7265-7331","authenticated-orcid":true,"given":"Frank","family":"Obeng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joyce Naa Aklerh","family":"Okai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9304-1706","authenticated-orcid":true,"given":"Edward","family":"Sutherland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,5,28]]},"reference":[{"issue":"1","key":"pone.0323180.ref001","first-page":"2","article-title":"\u00c9pid\u00e9miologie du cancer de la prostate","volume":"32","author":"A Villers","year":"2008","journal-title":"Med Nucl"},{"key":"pone.0323180.ref002","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1186\/1471-2458-6-159","article-title":"Cancer mortality patterns in Ghana: a 10-year review of autopsies and hospital mortality","volume":"6","author":"EK Wiredu","year":"2006","journal-title":"BMC Public Health"},{"key":"pone.0323180.ref003","article-title":"GLOBOCAN 2020 report on global cancer burden: challenges and opportunities for surgical oncologists","author":"S. Deo","year":"2022","journal-title":"Annal Surgical Oncol"},{"key":"pone.0323180.ref004","author":"Advanced Prostate Cancer AUA_SUO Guideline -"},{"issue":"1","key":"pone.0323180.ref005","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.semradonc.2016.08.002","article-title":"Prostate cancer genetics: variation by race, ethnicity, and geography","volume":"27","author":"TR Rebbeck","year":"2017","journal-title":"Semin Radiat Oncol"},{"issue":"2","key":"pone.0323180.ref006","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1177\/1756287213513488","article-title":"The Prostate Health Index: a new test for the detection of prostate cancer","volume":"6","author":"S Loeb","year":"2014","journal-title":"Ther Adv Urol"},{"issue":"1","key":"pone.0323180.ref007","doi-asserted-by":"crossref","first-page":"208","DOI":"10.7150\/ijbs.27537","article-title":"Establishing a prediction model for prostate cancer bone metastasis","volume":"15","author":"S Chen","year":"2019","journal-title":"Int J Biol Sci"},{"issue":"12","key":"pone.0323180.ref008","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1093\/aje\/kwx235","article-title":"TMPRSS2:ERG gene fusions in prostate cancer of west African men and a meta-analysis of racial differences","volume":"186","author":"CK Zhou","year":"2017","journal-title":"Am J Epidemiol"},{"key":"pone.0323180.ref009","doi-asserted-by":"crossref","first-page":"2122","DOI":"10.1097\/01.ju.0000123981.03084.06","article-title":"Baseline staging of newly diagnosed prostate cancer: a summary of the literature","volume":"171","author":"S Abuzallouf","year":"2004","journal-title":"J Urol"},{"issue":"23","key":"pone.0323180.ref010","doi-asserted-by":"crossref","first-page":"33957","DOI":"10.1007\/s11356-021-17793-9","article-title":"Impacts of COVID-19 on sustainable development goals and effective approaches to maneuver them in the post-pandemic environment","volume":"29","author":"RM Elavarasan","year":"2022","journal-title":"Environ Sci Pollut Res Int"},{"key":"pone.0323180.ref011","article-title":"THE 17 GOALS.","volume-title":"Sustainable development","author":"Affairs UND of E and S","year":"2015"},{"key":"pone.0323180.ref012","article-title":"Logistic regression in the medical literature: Standards for use and reporting, with particular attention to one medical domain.","journal-title":"Sci Direct"},{"issue":"10","key":"pone.0323180.ref013","article-title":"Goodness of fit tests for the multiple logistic regression model.","volume":"9","journal-title":"Commun Stat - Theory and Methods"},{"issue":"4","key":"pone.0323180.ref014","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1016\/S0022-5347(01)66263-3","article-title":"Clinical efficacy of bone alkaline phosphatase and prostate specific antigen in the diagnosis of bone metastasis in prostate cancer","volume":"155","author":"JA Lorente","year":"1996","journal-title":"J Urol"},{"key":"pone.0323180.ref015","article-title":"Index for rating diagnostic tests \u2013 Youden.","volume-title":"Cancer","year":"1950"},{"key":"pone.0323180.ref016","article-title":"Estimation of the youden index and its associated cutoff point.","volume":"17","author":"Fluss","year":"2005","journal-title":"Biometric J"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0323180","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T18:09:25Z","timestamp":1748455765000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0323180"}},"subtitle":[],"editor":[{"given":"Yuki","family":"Arita","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,5,28]]},"references-count":16,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5,28]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0323180","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,28]]}}}