{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:22:25Z","timestamp":1777890145726,"version":"3.51.4"},"reference-count":102,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006335","name":"Covenant University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006335","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Prostate cancer is the sixth most common cause of cancer-related death in males globally and is often identified as a secondary malignancy. Early detection and appropriate management increase the patient\u2019s life expectancy. In predicting prostate cancer outcomes, artificial intelligence models are currently employed increasingly, although they are not widely applicable due to an unclear understanding of their performances. This systematic review and meta-analysis examined the pooled predictive performances of these models using area under the curve (AUC) metrics across different prediction endpoints: overall survival (OS), progression-free survival (PFS), toxicity or quality of life (TQL), recurrence distance metastasis (RDM), and treatment response (TR).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>PubMed and Scopus were systematically searched for studies that reported prediction models for prostate cancer outcomes. In accordance with PRISMA criteria, we carried out a systematic review of prediction models for prostate cancer, with a registered protocol in PROSPERO (CRD42025611480). Using the Prediction Model Quality Score (PMQS), qualified studies were assessed for methodological quality. We conducted a random-effects meta-analysis, pooling AUCs for each prediction endpoint; heterogeneity was assessed using I\u00b2, \u03c4\u00b2, and Q statistics. Funnel plots, Egger\u2019s regression test, and the trim-and-fill method were used to assess publication bias.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 144 studies were selected based on the eligibility criteria for systematic review, but only 85 were included in the meta-analysis. The overall pooled AUC in all prediction endpoints were 0.808, 0.792, 0.845, 0.835, and 0.805 for OS, PFS, RDM, TR, and TQL, respectively, indicating good predictive performance. Funnel plots showed no significant asymmetry.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The models evaluated in this review demonstrated over 75% accuracy in predicting prostate cancer with varied performance across prediction endpoints. Standardized model reporting and robust external validation are needed to improve reproducibility and clinical translation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic Review Registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>https:\/\/www.crd.york.ac.uk\/PROSPERO\/view\/CRD42025611480<\/jats:ext-link>\n                      , identifier CRD42025611480.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1705780","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:27:40Z","timestamp":1777613260000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction models in prostate cancer: a systematic review and meta-analysis"],"prefix":"10.3389","volume":"16","author":[{"given":"Emmanuel Nsedu","family":"Israel","sequence":"first","affiliation":[{"name":"Department of Biochemistry, College of Science and Technology, Covenant University","place":["Ota, Ogun, Nigeria"]},{"name":"Covenant Applied Informatics and Communication - Africa Centre of Excellence (CApIC-ACE), Covenant University","place":["Ota, Ogun, Nigeria"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaze N.","family":"Niels","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, College of Science and Technology, Covenant University","place":["Ota, Ogun, Nigeria"]},{"name":"Covenant Applied Informatics and Communication - Africa Centre of Excellence (CApIC-ACE), Covenant University","place":["Ota, Ogun, Nigeria"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omobolanle Oluwatosin","family":"Makanjuola","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, College of Science and Technology, Covenant University","place":["Ota, Ogun, Nigeria"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oluwakemi A.","family":"Rotimi","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, College of Science and Technology, Covenant University","place":["Ota, Ogun, Nigeria"]},{"name":"Covenant Applied Informatics and Communication - Africa Centre of Excellence (CApIC-ACE), Covenant University","place":["Ota, Ogun, Nigeria"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Israel S.","family":"Afolabi","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, College of Science and Technology, Covenant University","place":["Ota, Ogun, Nigeria"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"101542","DOI":"10.1016\/j.tranon.2022.101542","article-title":"An overview of prostate cancer (PCa) diagnosis: Potential role of miRNAs","volume":"26","author":"Bilal","year":"2022","journal-title":"Transl Oncol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3390\/uro2020014","article-title":"Recent advances in prostate cancer (PCa) diagnostics","volume":"2","author":"Abdelrazek","year":"2022","journal-title":"Uro"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp1606181","article-title":"Predicting the future\u2014big data, machine learning, and clinical medicine","volume":"176","author":"Rashighi","year":"2017","journal-title":"Physiol Behav"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1136\/esmoopen-2017-000198","article-title":"A new era of oncology through artificial intelligence","volume":"2","author":"Curioni-Fontecedro","year":"2017","journal-title":"ESMO Open"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.b606","article-title":"Prognosis and prognostic research: Application and impact of prognostic models in clinical practice","volume":"338","author":"Moons","year":"2009","journal-title":"BMJ"},{"key":"B6","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1007\/s10729-020-09522-4","article-title":"Personalized treatment for coronary artery disease patients: a machine learning approach","volume":"23","author":"Bertsimas","year":"2020","journal-title":"Health Care Manag Sci"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00976-8","article-title":"Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study","volume":"7","author":"Lee","year":"2024","journal-title":"NPJ Digit Med"},{"key":"B8","doi-asserted-by":"publisher","first-page":"100","DOI":"10.12688\/f1000research.156122.1","article-title":"Optimizing machine learning performance for medical imaging analyses in low-resource environments: The prospects of CNN-based feature extractors","volume":"14","author":"Itunuoluwa","year":"2025","journal-title":"F1000Research"},{"key":"B9","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-981-15-7078-0_3","article-title":"Convolutional neural networks: An overview and its applications in pattern recognition","volume":"195","author":"Patil","year":"2021","journal-title":"Smart Innov Syst Technol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1590\/s0100-39842014000200013","article-title":"Functional magnetic resonance imaging in oncology: State of the art | Resson\u00e2ncia magn\u00e9tica funcional na oncologia: Estado da arte","volume":"47","author":"Guimaraes","year":"2014","journal-title":"Radiol Bras"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1634\/theoncologist.2018-0257","article-title":"Applying artificial intelligence to address the knowledge gaps in cancer care","volume":"24","author":"Simon","year":"2019","journal-title":"Oncologist"},{"key":"B12","article-title":"CC BY-NC-ND 4.0 REPOSITORY RECORD na\u00efve bayes vs. decision trees vs. neural networks in the classification of training web pages","volume":"4","author":"Hinde","year":"2009","journal-title":"IJCSI Int J Comput Sci Issues"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.11613\/bm.2014.003","article-title":"Understanding logistic regression analysis","volume":"24","author":"Sperandei","year":"2014","journal-title":"Biochem Med"},{"key":"B14","doi-asserted-by":"publisher","first-page":"102549","DOI":"10.1016\/j.artmed.2023.102549","article-title":"Systematic review and meta-analysis of prediction models used in cervical cancer","volume":"139","author":"Jha","year":"2023","journal-title":"Artif Intell Med"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-017-0566-6","article-title":"Comparison of machine learning techniques to predict all-cause mortality using fitness data: The Henry Ford exercise testing (FIT) project","volume":"17","author":"Sakr","year":"2017","journal-title":"BMC Med Inform Decis Mak"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1136\/bmj.e5793","article-title":"Prognosis research strategy (PROGRESS) 4: Stratified medicine research","volume":"346","author":"Hingorani","year":"2013","journal-title":"BMJ"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/786013","article-title":"Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features","volume":"2015","author":"Adetiba","year":"2015","journal-title":"Sci World J"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1109\/bibm55620.2022.9995633","article-title":"A multi-omics classifier for prediction of androgen deprivation treatment response in prostate cancer patients","author":"Isewon","year":"2022","journal-title":"IEEE"},{"key":"B19","article-title":"Procedures for performing systematic reviews","author":"Kitchenham","year":"2004"},{"key":"B20","article-title":"Reconstructing the giant: On the importance of rigour in documenting the literature search process","author":"Vom Brocke","year":"2009"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.7326\/m18-1376","article-title":"PROBAST: A tool to assess the risk of bias and applicability of prediction model studies","volume":"170","author":"Wolff","year":"2019","journal-title":"Ann Intern Med"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i03","article-title":"Conducting meta-analyses in R with the metafor package","volume":"36","author":"Viechtbauer","year":"2010","journal-title":"J Stat Softw"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1016\/0197-2456(86)90046-2","article-title":"Meta-analysis in clinical trials","volume":"188","author":"Dersimonian","year":"1986"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.3736\/jcim20090918","article-title":"Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement","volume":"6","author":"Moher","year":"2009","journal-title":"PloS Med"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1007\/s00432-023-04816-w","article-title":"Application of machine learning algorithm in prediction of lymph node metastasis in patients with intermediate and high-risk prostate cancer","volume":"149","author":"Wang","year":"2023","journal-title":"J Cancer Res Clin Oncol"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-023-06278-1","article-title":"Development and external validation of a multivariable [68Ga]Ga-PSMA-11 PET-based prediction model for lymph node involvement in men with intermediate or high-risk prostate cancer","volume":"50","author":"Muehlematter","year":"2023","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.4103\/aja.aja_39_17","article-title":"External validation and newly development of a nomogram to predict overall survival of abiraterone-treated, castration-resistant patients with metastatic prostate cancer","volume":"20","author":"Yang","year":"2018","journal-title":"Asian J Androl"},{"key":"B28","doi-asserted-by":"publisher","first-page":"101161","DOI":"10.1016\/j.imu.2023.101161","article-title":"High-performance prediction models for prostate cancer radiomics","volume":"37","author":"Isaksson","year":"2023","journal-title":"Inf Med Unlocked"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00659-w","article-title":"Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer","volume":"5","author":"Lee","year":"2022","journal-title":"NPJ Digit Med"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1038\/s41416-023-02441-5","article-title":"Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence","volume":"129","author":"Hou","year":"2023","journal-title":"Br J Cancer"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1016\/j.annonc.2024.08.1721","article-title":"Development and validation of a machine learning-based risk model for metastatic disease in nmCRPC patients","volume":"34","author":"Wang","year":"2024","journal-title":"Ann Oncol"},{"key":"B32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-023-45804-x","article-title":"Development of machine learning prognostic models for overall survival of prostate cancer patients with lymph node-positive","volume":"13","author":"Peng","year":"2023","journal-title":"Sci Rep"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1016\/j.urolonc.2020.05.011","article-title":"Prospective development of a prostate cancer risk calculator in a racially diverse population: The Kaiser Permanente Prostate Cancer Risk Calculator","volume":"38","author":"Presti","year":"2020","journal-title":"Urol Oncol Semin Orig Investig"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.7150\/ijms.45730","article-title":"The prediction value of PI-RADS v2 score in high-grade prostate cancer: A multicenter retrospective study","volume":"17","author":"Chen","year":"2020","journal-title":"Int J Med Sci"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1016\/j.euo.2018.09.008","article-title":"A unified prostate cancer risk prediction model combining the Stockholm3 test and magnetic resonance imaging","volume":"2","author":"Palsdottir","year":"2019","journal-title":"Eur Urol Oncol"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2017.5667","article-title":"A magnetic resonance imaging-based prediction model for prostate biopsy risk stratification","volume":"4","author":"Mehralivand","year":"2018","journal-title":"JAMA Oncol"},{"key":"B37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0168917","article-title":"Development and external validation of the Korean prostate cancer risk calculator for high-grade prostate cancer: Comparison with two western risk calculators in an Asian cohort","volume":"12","author":"Park","year":"2017","journal-title":"PloS One"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.2147\/cmar.s250907","article-title":"Association between contrast-enhanced ultrasound indicators and prostate cancer biochemical recurrence after treatment","volume":"12","author":"Mei","year":"2020","journal-title":"Cancer Manag Res"},{"key":"B39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7717\/peerj.12912","article-title":"Development and validation of a multi-parameter nomogram for predicting prostate cancer: A retrospective analysis from Handan Central Hospital in China","volume":"10","author":"Nan","year":"2022","journal-title":"PeerJ"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12165321","article-title":"Predictors of extraprostatic extension in patients with prostate cancer","volume":"12","author":"Kim","year":"2023","journal-title":"J Clin Med"},{"key":"B41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00432-023-05574-5","article-title":"Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer","volume":"150","author":"Zhang","year":"2024","journal-title":"J Cancer Res Clin Oncol"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.3390\/app12168156","article-title":"Machine-learning-based digital twin system for predicting the progression of prostate cancer","volume":"12","author":"Kim","year":"2022","journal-title":"Appl Sci"},{"key":"B43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/diagnostics12102545","article-title":"A nomogram for predicting prostate cancer with lymph node involvement in robot-assisted radical prostatectomy era: A retrospective multicenter cohort study in Japan (The MSUG94 Group)","volume":"12","author":"Kawase","year":"2022","journal-title":"Diagnostics"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.708730","article-title":"MRI-based nomogram of prostate maximum sectional area and its zone area for prediction of prostate cancer","volume":"11","author":"Jiang","year":"2021","journal-title":"Front Oncol"},{"key":"B45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12894-021-00840-5","article-title":"Development and internal validation of a prediction model of prostate cancer on initial transperineal template-guided prostate biopsy","volume":"21","author":"Chen","year":"2021","journal-title":"BMC Urol"},{"key":"B46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/cancers13143381","article-title":"Definition of outcome-based prostate-specific antigen (Psa) thresholds for advanced prostate cancer risk prediction","volume":"13","author":"Ferraro","year":"2021","journal-title":"Cancers (Basel)"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.21037\/qims-21-365","article-title":"Value of transrectal contrast-enhanced ultrasound with clinical indicators in the prediction of bone metastasis in prostate cancer","volume":"12","author":"Hong","year":"2022","journal-title":"Quant Imaging Med Surg"},{"key":"B48","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3441813","article-title":"Development and validation of a nomogram including lymphocyte-to-monocyte ratio for initial prostate biopsy: a double-center retrospective study","volume":"23","author":"Zhou","year":"2021","journal-title":"Asian J Androl"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.941349","article-title":"Machine learning-based models enhance the prediction of prostate cancer","volume":"12","author":"Chen","year":"2022","journal-title":"Front Oncol"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.785684","article-title":"Machine learning-based prediction of pathological upgrade from combined transperineal systematic and MRI-targeted prostate biopsy to final pathology: a multicenter retrospective study","volume":"12","author":"Zhuang","year":"2022","journal-title":"Front Oncol"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.21037\/tau-20-1154","article-title":"Development and validation of a nomogram for predicting prostate cancer in men with prostate-specific antigen grey zone based on retrospective analysis of clinical and multi-parameter magnetic resonance imaging\/transrectal ultrasound fusion-derived data","volume":"9","author":"Ding","year":"2020","journal-title":"Transl Androl Urol"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.2147\/cmar.s330591","article-title":"Application of machine learning techniques to predict bone metastasis in patients with prostate cancer","volume":"13","author":"Liu","year":"2021","journal-title":"Cancer Manag Res"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.1097\/md.0000000000025602","article-title":"Based on biomedical index data","volume":"100","author":"Guo","year":"2021","journal-title":"Med (Baltimore)"},{"key":"B54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0236553","article-title":"Dr. Answer ai for prostate cancer: Clinical outcome prediction model and service","volume":"15","author":"Rho","year":"2020","journal-title":"PloS One"},{"key":"B55","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1097\/ju.0000000000000272","article-title":"Radiomics features measured with multiparametric magnetic resonance imaging predict prostate cancer aggressiveness","volume":"202","author":"Hectors","year":"2019","journal-title":"J Urol"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.21037\/qims.2019.12.06","article-title":"Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI","volume":"10","author":"Li","year":"2020","journal-title":"Quant Imaging Med Surg"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/917502","article-title":"Modified logistic regression models using gene coexpression and clinical features to predict prostate cancer progression","volume":"2013","author":"Zhao","year":"2013","journal-title":"Comput Math Methods Med"},{"key":"B58","doi-asserted-by":"publisher","DOI":"10.1016\/j.urolonc.2021.08.007","article-title":"A machine learning approach to predict progression on active surveillance for prostate cancer","volume":"40","author":"Nayan","year":"2022","journal-title":"Urol Oncol Semin Orig Investig"},{"key":"B59","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/s00345-020-03256-2","article-title":"Comparing a new risk prediction model with prostate cancer risk calculator apps in a Taiwanese population","volume":"39","author":"Chen","year":"2021","journal-title":"World J Urol"},{"key":"B60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12957-023-03049-y","article-title":"The predictive value of perioperative circulating markers on surgical complications in patients undergoing robotic-assisted radical prostatectomy","volume":"21","author":"Lu","year":"2023","journal-title":"World J Surg Oncol"},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1157384","article-title":"Development, comparison, and validation of four intelligent, practical machine learning models for patients with prostate-specific antigen in the gray zone","volume":"13","author":"Liu","year":"2023","journal-title":"Front Oncol"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.3390\/app10113854","article-title":"A machine learning approach to predict an early biochemical recurrence after a radical prostatectomy","volume":"10","author":"Park","year":"2020","journal-title":"Appl Sci"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.2147\/cmar.s253997","article-title":"Prospective study of the clinical impact of epithelial and mesenchymal circulating tumor cells in localized prostate cancer","volume":"12","author":"Liu","year":"2020","journal-title":"Cancer Manag Res"},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.704645","article-title":"Urban\u2013rural differences in clinical characteristics of prostate cancer at initial diagnosis: A single-center observational study in Anhui Province, China","volume":"11","author":"Song","year":"2021","journal-title":"Front Oncol"},{"key":"B65","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1287995","article-title":"Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study","volume":"14","author":"Zhou","year":"2024","journal-title":"Front Oncol"},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocac141","article-title":"A smart, practical, deep learning-based clinical decision support tool for patients in the prostate-specific antigen gray zone: Model development and validation","volume":"29","author":"Song","year":"2022","journal-title":"J Am Med Inf Assoc"},{"key":"B67","doi-asserted-by":"publisher","first-page":"897","DOI":"10.4103\/1008-682x.129940","article-title":"Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score","volume":"16","author":"Hu","year":"2014","journal-title":"Asian J Androl"},{"key":"B68","doi-asserted-by":"publisher","DOI":"10.3390\/app10062000","article-title":"Castration-resistant prostate cancer outcome prediction using phased long short-term memory with irregularly sampled serial data","volume":"10","author":"Park","year":"2020","journal-title":"Appl Sci"},{"key":"B69","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.mri.2023.12.009","article-title":"A machine learning radiomics model based on bpMRI to predict bone metastasis in newly diagnosed prostate cancer patients","volume":"107","author":"Xinyang","year":"2024","journal-title":"Magn Reson Imaging"},{"key":"B70","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41391-019-0153-2","article-title":"Role of the DNA damage response in prostate cancer formation, progression and treatment","volume":"23","author":"Zhang","year":"2020","journal-title":"Prostate Cancer Prostatic Dis"},{"key":"B71","doi-asserted-by":"publisher","DOI":"10.3390\/app13020891","article-title":"Partial correlation analysis and neural-network-based prediction model for biochemical recurrence of prostate cancer after radical prostatectomy","volume":"13","author":"Kim","year":"2023","journal-title":"Appl Sci"},{"key":"B72","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15133438","article-title":"An apparent diffusion coefficient-based machine learning model can improve prostate cancer detection in the grey area of the prostate imaging reporting and data system category 3: A single-centre experience","volume":"15","author":"Gaudiano","year":"2023","journal-title":"Cancers (Basel)"},{"key":"B73","doi-asserted-by":"publisher","DOI":"10.1016\/j.radi.2024.03.015","article-title":"Predictive value of magnetic resonance imaging diffusion parameters using artificial intelligence in low-and intermediate-risk prostate cancer patients treated with stereotactic ablative radiotherapy: A pilot study","volume":"30","author":"Kedves","year":"2024","journal-title":"Radiography"},{"key":"B74","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.948662","article-title":"Machine learning prediction of prostate cancer from transrectal ultrasound video clips","volume":"12","author":"Wang","year":"2022","journal-title":"Front Oncol"},{"key":"B75","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1157949","article-title":"Comparison of machine learning models based on multi-parametric magnetic resonance imaging and ultrasound videos for the prediction of prostate cancer","volume":"13","author":"Qi","year":"2023","journal-title":"Front Oncol"},{"key":"B76","doi-asserted-by":"publisher","DOI":"10.3390\/app11136225","article-title":"A study of predictive models for early outcomes of post-prostatectomy incontinence: machine learning approach vs. Logistic regression analysis approach","volume":"11","author":"Park","year":"2021","journal-title":"Appl Sci"},{"key":"B77","doi-asserted-by":"publisher","DOI":"10.1177\/0962280219854487","article-title":"Evaluation of multiple prediction models: A novel view on model selection and performance assessment","volume":"29","author":"Westphal","year":"2020","journal-title":"Stat Methods Med Res"},{"key":"B78","doi-asserted-by":"publisher","DOI":"10.1038\/nbt1206-1565","article-title":"What is a support vector machine","volume":"24","author":"Noble","year":"2006","journal-title":"Nat Biotechnol"},{"key":"B79","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(14)71116-7","article-title":"Nomograms in oncology: More than meets the eye","volume":"16","author":"Balachandran","year":"2015","journal-title":"Lancet Oncol"},{"key":"B80","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/S0933-3657(01)00077-X","article-title":"Machine learning for medical diagnosis: history, state of the art and perspective","volume":"23","author":"Kononenko","year":"2001","journal-title":"Artif Intell Med"},{"key":"B81","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1201\/b15618-8","article-title":"Introduction to computational intelligence techniques and areas of their applications in medicine","author":"Niknejad","year":"2013","journal-title":"Med Appl Artif Intell"},{"key":"B82","article-title":"Receiver operating characteristic curve analysis in diagnostic accuracy studies : A guide to interpreting the area under the curve value","volume":"23","author":"\u00c7orbac\u0131o\u011flu","year":"2023"},{"key":"B83","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2024.127520","article-title":"Comparing multi-class classifier performance by multi-class ROC analysis : A nonparametric approach","volume":"583","author":"Xu","year":"2024"},{"key":"B84","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0118432","article-title":"The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets","volume":"10","author":"Saito","year":"2015"},{"key":"B85","doi-asserted-by":"publisher","DOI":"10.1177\/1460458221989402","article-title":"Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression","volume":"27","author":"Gumaei","year":"2021","journal-title":"Heal Inf J"},{"key":"B86","doi-asserted-by":"publisher","DOI":"10.1016\/j.apr.2025.102456","article-title":"An integrated feature selection and machine learning framework for PM10 concentration prediction","volume":"16","author":"Kalantari","year":"2025","journal-title":"Atmos pollut Res"},{"key":"B87","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-024-01783-9","article-title":"Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences","volume":"15","author":"Mylona","year":"2024","journal-title":"Insights Imaging"},{"key":"B88","doi-asserted-by":"publisher","DOI":"10.3390\/app13010576","article-title":"Radiomics approach to the detection of prostate cancer using multiparametric MRI: A validation study using prostate-cancer-tissue-mimicking phantoms","volume":"13","author":"Alshomrani","year":"2023","journal-title":"Appl Sci"},{"key":"B89","doi-asserted-by":"publisher","DOI":"10.1177\/15330338231166766","article-title":"Predicting model of biochemical recurrence of prostate carcinoma (PCa-BCR) using MR perfusion-weighted imaging-based radiomics","volume":"22","author":"An","year":"2023","journal-title":"Technol Cancer Res Treat"},{"key":"B90","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/cancers13225672","article-title":"Development of a radiomic-based model predicting lymph node involvement in prostate cancer patients","volume":"13","author":"Bourbonne","year":"2021","journal-title":"Cancers (Basel)"},{"key":"B91","doi-asserted-by":"publisher","DOI":"10.3390\/cancers12040814","article-title":"External validation of an MRI-derived radiomics model to predict biochemical recurrence after surgery for high-risk prostate cancer","volume":"12","author":"Bourbonne","year":"2020","journal-title":"Cancers (Basel)"},{"key":"B92","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2017.10.009","article-title":"Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes","volume":"46","author":"Altazi","year":"2018","journal-title":"Phys Med"},{"key":"B93","doi-asserted-by":"publisher","DOI":"10.1097\/igc.0000000000000987","article-title":"Nomogram predicting overall survival in operable cervical cancer patients","volume":"27","author":"Zheng","year":"2017","journal-title":"Int J Gynecol Cancer"},{"key":"B94","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s00345-024-04947-w","article-title":"A PSMA PET\/CT-based risk model for prediction of concordance between targeted biopsy and combined biopsy in detecting prostate cancer","volume":"42","author":"An","year":"2024","journal-title":"World J Urol"},{"key":"B95","doi-asserted-by":"publisher","DOI":"10.1177\/11795549211049750","article-title":"Nomogram incorporating contrast-enhanced ultrasonography predicting time to the development of castration-resistant prostate cancer","volume":"15","author":"Zhao","year":"2021","journal-title":"Clin Med Insights Oncol"},{"key":"B96","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12894-023-01384-6","article-title":"Prognostic significance of surgery and radiotherapy in elderly patients with localized prostate cancer: establishing and time-based external validation a nomogram from SEER-based study","volume":"24","author":"Zhanghuang","year":"2024","journal-title":"BMC Urol"},{"key":"B97","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-023-44911-z","article-title":"Development and validation of a nomogram to predict cancer-specific survival in nonsurgically treated elderly patients with prostate cancer","volume":"13","author":"Zhang","year":"2023","journal-title":"Sci Rep"},{"key":"B98","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12894-023-01316-4","article-title":"Machine learning-based prediction model and visual interpretation for prostate cancer","volume":"23","author":"Chen","year":"2023","journal-title":"BMC Urol"},{"key":"B99","doi-asserted-by":"publisher","first-page":"W1","DOI":"10.7326\/M14-0698","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): Explanation and elaboration","volume":"162","author":"Moons","year":"2015","journal-title":"Ann Intern Med"},{"key":"B100","doi-asserted-by":"publisher","DOI":"10.1016\/j.aca.2025.344838","article-title":"Analytica Chimica Acta The importance of choosing a proper validation strategy in predictive models. Part 2: Recipes for (avoiding) overfitting-a tutorial","volume":"1384","author":"Lopez","year":"2026","journal-title":"Anal Chim Acta"},{"key":"B101","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13014-020-01543-1","article-title":"Single-center versus multi-center data sets for molecular prognostic modeling: a simulation study","volume":"15","author":"Samaga","year":"2020"},{"key":"B102","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.606400","article-title":"A review of cancer genetics and genomics studies in Africa","volume":"10","author":"Rotimi","year":"2021","journal-title":"Front Oncol"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1705780\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:27:42Z","timestamp":1777613262000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1705780\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,1]]},"references-count":102,"alternative-id":["10.3389\/fonc.2026.1705780"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2026.1705780","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,1]]},"article-number":"1705780"}}