{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T06:17:01Z","timestamp":1781072221257,"version":"3.54.1"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T00:00:00Z","timestamp":1742169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T00:00:00Z","timestamp":1742169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Egypt Natl Canc Inst"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Machine learning (ML) is a significant area of artificial intelligence, which can improve the accuracy of predictive or diagnostic models for differentiating between prostate biopsy outcomes. This study aims to develop a novel decision-support ML model for classifying patients with biopsy-negative (cancer-free), clinically significant, and non-clinically significant prostate cancer across two prostate-specific antigen (PSA) cut-offs\u2009\u2264\u200910\u00a0ng\/ml and\u2009&gt;\u200910\u00a0ng\/ml.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>The data for the current study were retrieved from the records of two main hospitals in Riyadh, Saudi Arabia from July 2018 through July 2024. Six machine learning algorithms were employed, and the dataset was randomly divided into a training set and a validation set at a ratio of 8:2. The following metrics were used as performance indicators across the six algorithms: Accuracy, Precision, Recall, F1-score, and area under the curve. Recent data from the two hospitals was utilized for external validation.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The metrics for Random Forest, Extra Tree, and Decision Tree algorithms showed excellent capability in classifying the outcomes of prostate biopsy for the two PSA cut-offs. However, the metrics for the PSA cut-off\u2009&gt;\u200910\u00a0ng\/ml are higher than those for PSA\u2009\u2264\u200910\u00a0ng\/ml. For the three-class classification, the accuracy and area under the curve for the cut-off\u2009&gt;\u200910\u00a0ng\/ml were 0.96 and 0.99, respectively. While for the cut-off\u2009\u2264\u200910\u00a0ng\/ml they were 0.92 and 0.94 for Random Forest and 0.94 and 0.95 for the Extra Tree algorithm. The metrics of non-clinically significant and biopsy-negative cases outperformed those of clinically significant cases.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>ML models are proving to be effective tools in differentiating between prostate biopsy outcomes, enhancing diagnostic accuracy, and potentially transforming clinical practices in prostate cancer management.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s43046-025-00265-3","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T00:03:27Z","timestamp":1742169807000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study"],"prefix":"10.1186","volume":"37","author":[{"given":"Mostafa A.","family":"Arafa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karim H.","family":"Farhat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sherin F.","family":"Aly","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farrukh K.","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alaa","family":"Mokhtar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulaziz M.","family":"Althunayan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Waleed","family":"Al-Taweel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sultan S.","family":"Al-Khateeb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sami","family":"Azhari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danny M.","family":"Rabah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,17]]},"reference":[{"issue":"3","key":"265_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209\u201349. https:\/\/doi.org\/10.3322\/caac.21660.","journal-title":"CA Cancer J Clin"},{"key":"265_CR2","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.eururo.2019.08.005","volume":"77","author":"MB Culp","year":"2020","unstructured":"Culp MB, Soerjomataram I, Efstathiou JA, Bray F, Jemal A. Recent global patterns in prostate cancer incidence and mortality rates. Eur Urol. 2020;77:38\u201352. https:\/\/doi.org\/10.1016\/j.eururo.2019.08.005.","journal-title":"Eur Urol"},{"key":"265_CR3","unstructured":"Saudi Health Council. Cancer Incidence Report, Saudi Arabia 2018. 2022. https:\/\/shc.gov.sa\/Arabic\/NCC\/Activities\/AnnualReports\/2018.pdf. Accessed 20 May 2023."},{"issue":"7","key":"265_CR4","doi-asserted-by":"publisher","first-page":"733","DOI":"10.15537\/smj.2017.7.18643","volume":"38","author":"MA Arafa","year":"2017","unstructured":"Arafa MA, Farhat KH, Al-Atawi MA, Rabah DM. Prostate cancer screening in a low prevalence population. Saudi Med J. 2017;38(7):733\u20137. https:\/\/doi.org\/10.15537\/smj.2017.7.18643.","journal-title":"Saudi Med J"},{"issue":"6","key":"265_CR5","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1093\/jnci\/djaa154","volume":"113","author":"LM Hurwitz","year":"2021","unstructured":"Hurwitz LM, Agalliu I, Albanes D, Barry KH, Berndt SI, Cai Q, Chen C, Cheng I, Genkinger JM, Giles GG, Huang J, Joshu CE, Key TJ, Knutsen S, Koutros S, Langseth H, Li SX, MacInnis RJ, Markt SC, Penney KL, Perez-Cornago A, Rohan TE, Smith-Warner SA, Stampfer MJ, Stopsack KH, Tangen CM, Travis RC, Weinstein SJ, Lang PhDW, Jacobs EJ, Mucci LA, Platz EA, Cook MB. Prostate cancer cohort consortium (PC3) working group. recommended definitions of aggressive prostate cancer for etiologic epidemiologic research. J Natl Cancer Inst. 2021;113(6):727\u201334. https:\/\/doi.org\/10.1093\/jnci\/djaa154.","journal-title":"J Natl Cancer Inst."},{"issue":"1","key":"265_CR6","doi-asserted-by":"publisher","first-page":"54","DOI":"10.4103\/ua.ua_22_22","volume":"15","author":"MA Arafa","year":"2023","unstructured":"Arafa MA, Rabah DM, Khan F, Farhat KH, Ibrahim NK, Albekairi AA. False-positive magnetic resonance imaging prostate cancer correlatesand clinical implications. Urol Ann. 2023;15(1):54\u20139. https:\/\/doi.org\/10.4103\/ua.ua_22_22.","journal-title":"Urol Ann"},{"issue":"11","key":"265_CR7","doi-asserted-by":"publisher","first-page":"763381","DOI":"10.3389\/fonc.2021.763381","volume":"14","author":"L Wei","year":"2021","unstructured":"Wei L, Huang Y, Chen Z, Lei H, Qin X, Cui L, Zhuo Y. Artificial intelligence combined with big data to predict lymph node involvement in prostate cancer: a population-based study. Front Oncol. 2021;14(11):763381. https:\/\/doi.org\/10.3389\/fonc.2021.763381.","journal-title":"Front Oncol"},{"issue":"13","key":"265_CR8","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1093\/eurheartj\/ehy915","volume":"40","author":"GP Diller","year":"2019","unstructured":"Diller GP, Kempny A, Babu-Narayan SV, Henrichs M, Brida M, Uebing A, Lammers AE, Baumgartner H, Li W, Wort SJ, Dimopoulos K, Gatzoulis MA. Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients. Eur Heart J. 2019;40(13):1069\u201377. https:\/\/doi.org\/10.1093\/eurheartj\/ehy915.","journal-title":"Eur Heart J"},{"key":"265_CR9","doi-asserted-by":"publisher","unstructured":"Pfail, J.L., Lundon, D.J., Ratnani, P., Wagaskar, V., Wiklund, P., Tewari, A.K. (2021). A machine learning-driven approach to predict the outcome of prostate biopsy: identifying cancer, clinically significant disease, and unfavorable pathological features on prostate biopsy. In: Arabnia, H.R., Deligiannidis, L., Shouno, H., Tinetti, F.G., Tran, QN. (eds) Advances in computer vision and computational biology. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-71051-4_46.","DOI":"10.1007\/978-3-030-71051-4_46"},{"issue":"1","key":"265_CR10","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1097\/JU.0000000000003491","volume":"210","author":"JT Wei","year":"2023","unstructured":"Wei JT, Barocas D, Carlsson S, Coakley F, Eggener S, Etzioni R, Fine SW, Han M, Kim SK, Kirkby E, Konety BR, Miner M, Moses K, Nissenberg MG, Pinto PA, Salami SS, Souter L, Thompson IM, Lin DW. Early detection of prostate cancer: AUA\/SUO Guideline Part I: Prostate Cancer Screening. J Urol. 2023;210(1):46\u201353. https:\/\/doi.org\/10.1097\/JU.0000000000003491.","journal-title":"J Urol"},{"issue":"9","key":"265_CR11","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1097\/01.pas.0000173646.99337.b1","volume":"29","author":"JI Epstein","year":"2005","unstructured":"Epstein JI, Allsbrook WC Jr, Amin MB, Egevad LL, ISUP Grading Committee. The 2005 International Society of Urological Pathology (ISUP) consensus conference on gleason grading of prostatic carcinoma. Am J Surg Pathol. 2005;29(9):1228\u201342. https:\/\/doi.org\/10.1097\/01.pas.0000173646.99337.b1.","journal-title":"Am J Surg Pathol"},{"issue":"6","key":"265_CR12","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1177\/1756287213495915","volume":"5","author":"KC Cary","year":"2013","unstructured":"Cary KC, Cooperberg MR. Biomarkers in prostate cancer surveillance and screening: past, present, and future. Ther Adv Urol. 2013;5(6):318\u201329. https:\/\/doi.org\/10.1177\/1756287213495915.","journal-title":"Ther Adv Urol"},{"issue":"1","key":"265_CR13","doi-asserted-by":"publisher","first-page":"5157","DOI":"10.1038\/s41598-020-62015-w","volume":"10","author":"J Liu","year":"2020","unstructured":"Liu J, Dong B, Qu W, Wang J, Xu Y, Yu S, Zhang X. Using clinical parameters to predict prostate cancer and reduce the unnecessary biopsy among patients with PSA in the gray zone. Sci Rep. 2020;10(1):5157. https:\/\/doi.org\/10.1038\/s41598-020-62015-w.","journal-title":"Sci Rep"},{"issue":"6","key":"265_CR14","doi-asserted-by":"publisher","first-page":"1897","DOI":"10.1007\/s00345-020-03392-9","volume":"39","author":"M Perera","year":"2021","unstructured":"Perera M, Mirchandani R, Papa N, Breemer G, Effeindzourou A, Smith L, Swindle P, Smith E. PSA-based machine learning model improves prostate cancer risk stratification in a screening population. World J Urol. 2021;39(6):1897\u2013902. https:\/\/doi.org\/10.1007\/s00345-020-03392-9.","journal-title":"World J Urol"},{"issue":"4","key":"265_CR15","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1038\/s41391-021-00429-x","volume":"25","author":"PK Chiu","year":"2022","unstructured":"Chiu PK, Shen X, Wang G, Ho CL, Leung CH, Ng CF, Choi KS, Teoh JY. Enhancement of prostate cancer diagnosis by machine learning techniques: an algorithm development and validation study. Prostate Cancer Prostatic Dis. 2022;25(4):672\u20136. https:\/\/doi.org\/10.1038\/s41391-021-00429-x.","journal-title":"Prostate Cancer Prostatic Dis"},{"issue":"1","key":"265_CR16","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1002\/jmri.27793","volume":"55","author":"H Zheng","year":"2022","unstructured":"Zheng H, Miao Q, Liu Y, Raman SS, Scalzo F, Sung K. Integrative machine learning prediction of prostate biopsy results from negative multiparametric MRI. J Magn Reson Imaging. 2022;55(1):100\u201310. https:\/\/doi.org\/10.1002\/jmri.27793.","journal-title":"J Magn Reson Imaging"},{"issue":"2","key":"265_CR17","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.eururo.2017.02.026","volume":"72","author":"PC Moldovan","year":"2017","unstructured":"Moldovan PC, Van den Broeck T, Sylvester R, Marconi L, Bellmunt J, van den Bergh RCN, Bolla M, Briers E, Cumberbatch MG, Fossati N, Gross T, Henry AM, Joniau S, van der Kwast TH, Matveev VB, van der Poel HG, De Santis M, Schoots IG, Wiegel T, Yuan CY, Cornford P, Mottet N, Lam TB, Rouvi\u00e8re O. What is the negative predictive value of multiparametric magnetic resonance imaging in excluding prostate cancer at biopsy? A systematic review and meta-analysis from the european association of urology prostate cancer guidelines panel. Eur Urol. 2017;72(2):250\u201366. https:\/\/doi.org\/10.1016\/j.eururo.2017.02.026.","journal-title":"Eur Urol"},{"issue":"1","key":"265_CR18","doi-asserted-by":"publisher","first-page":"43","DOI":"10.21037\/qims-23-791","volume":"14","author":"Z Sun","year":"2024","unstructured":"Sun Z, Wang K, Wu C, Chen Y, Kong Z, She L, Song B, Luo N, Wu P, Wang X, Zhang X, Wang X. Using an artificial intelligence model to detect and localize visible clinically significant prostate cancer in prostate magnetic resonance imaging: a multicenter external validation study. Quant Imaging Med Surg. 2024;14(1):43\u201360. https:\/\/doi.org\/10.21037\/qims-23-791.","journal-title":"Quant Imaging Med Surg"},{"issue":"11","key":"265_CR19","doi-asserted-by":"publisher","first-page":"2703","DOI":"10.1007\/s11255-023-03722-x","volume":"55","author":"S Li","year":"2023","unstructured":"Li S, Wang KX, Li JL, He Y, Wang XY, Tang WR, Xie WH, Zhu W, Wu PS, Wang XP. AI-predicted mpMRI image features for the prediction of clinically significant prostate cancer. Int Urol Nephrol. 2023;55(11):2703\u201315. https:\/\/doi.org\/10.1007\/s11255-023-03722-x.","journal-title":"Int Urol Nephrol"},{"issue":"1","key":"265_CR20","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1186\/s12894-021-00849-w","volume":"21","author":"S Yu","year":"2021","unstructured":"Yu S, Tao J, Dong B, Fan Y, Du H, Deng H, Cui J, Hong G, Zhang X. Development and head-to-head comparison of machine-learning models to identify patients requiring prostate biopsy. BMC Urol. 2021;21(1):80. https:\/\/doi.org\/10.1186\/s12894-021-00849-w.","journal-title":"BMC Urol"},{"issue":"12","key":"265_CR21","doi-asserted-by":"publisher","first-page":"941349","DOI":"10.3389\/fonc.2022.941349","volume":"6","author":"S Chen","year":"2022","unstructured":"Chen S, Jian T, Chi C, Liang Y, Liang X, Yu Y, Jiang F, Lu J. Machine learning-based models enhance the prediction of prostate cancer. Front Oncol. 2022;6(12):941349. https:\/\/doi.org\/10.3389\/fonc.2022.941349.","journal-title":"Front Oncol"}],"container-title":["Journal of the Egyptian National Cancer Institute"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s43046-025-00265-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s43046-025-00265-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s43046-025-00265-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T00:03:31Z","timestamp":1742169811000},"score":1,"resource":{"primary":{"URL":"https:\/\/jenci.springeropen.com\/articles\/10.1186\/s43046-025-00265-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,17]]},"references-count":21,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["265"],"URL":"https:\/\/doi.org\/10.1186\/s43046-025-00265-3","relation":{},"ISSN":["2589-0409"],"issn-type":[{"value":"2589-0409","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,17]]},"assertion":[{"value":"3 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Approval was granted by the Ethics Committee the College of Medicine at King Saud University, Riyadh, Saudi Arabia (No: 19\/0299\/IRB). All study participants provided informed consent prior to participation in the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"8"}}