{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T02:21:21Z","timestamp":1790994081606,"version":"4.1.0"},"reference-count":52,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: Prostate cancer is one of the most common malignancies in men and a leading cause of cancer-related mortality. Early detection is essential to ensure curative treatment and favorable outcomes, but traditional diagnostic approaches\u2014such as serum prostate-specific antigen (PSA) testing, digital rectal examination (DRE), and histopathological confirmation following biopsy\u2014are limited by suboptimal accuracy and variability. Multiparametric magnetic resonance imaging (mpMRI) has improved diagnostic performance but remains highly dependent on reader expertise. Artificial intelligence (AI) offers promising opportunities to enhance diagnostic accuracy, reproducibility, and efficiency in prostate cancer detection. Objective: To evaluate the diagnostic accuracy and reporting timeliness of AI-based technologies compared with conventional diagnostic methods in the early detection of prostate cancer. Methods: Following PRISMA 2020 guidelines, PubMed, Scopus, Web of Science, and Cochrane Library were searched for studies published between January 2015 and April 2025. Eligible designs included randomized controlled trials, cohort, case\u2013control, and pilot studies applying AI-based technologies to early prostate cancer diagnosis. Data on AUC-ROC, sensitivity, specificity, predictive values, diagnostic odds ratio (DOR), and time-to-reporting were narratively synthesized due to heterogeneity. Risk of bias was assessed using the QUADAS-AI tool. Results: Twenty-three studies involving 23,270 patients were included. AI-based technologies achieved a median AUC-ROC of 0.88 (range 0.70\u20130.93), with median sensitivity and specificity of 0.86 and 0.83, respectively. Compared with radiologists, AI or AI-assisted readings improved or matched diagnostic accuracy, reduced inter-reader variability, and decreased reporting time by up to 56%. Conclusions: AI-based technologies show strong diagnostic performance in early prostate cancer detection. However, methodological heterogeneity and limited standardization restrict generalizability. Large-scale prospective trials are required to validate clinical integration.<\/jats:p>","DOI":"10.3390\/cancers17213503","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T03:33:07Z","timestamp":1761881587000},"page":"3503","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Improving Early Prostate Cancer Detection Through Artificial Intelligence: Evidence from a Systematic Review"],"prefix":"10.3390","volume":"17","author":[{"given":"Vincenzo","family":"Ciccone","sequence":"first","affiliation":[{"name":"Radiology Unit, Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d\u2019Aragona, 84100 Salerno, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1600-4682","authenticated-orcid":false,"given":"Marina","family":"Garofano","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry, University of Salerno, Via S. Allende, 84081 Baronissi, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosaria","family":"Del Sorbo","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry, University of Salerno, Via S. Allende, 84081 Baronissi, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriele","family":"Mongelli","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry, University of Salerno, Via S. Allende, 84081 Baronissi, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariella","family":"Izzo","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry, University of Salerno, Via S. Allende, 84081 Baronissi, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7591-152X","authenticated-orcid":false,"given":"Francesco","family":"Negri","sequence":"additional","affiliation":[{"name":"Radiology Unit, Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d\u2019Aragona, 84100 Salerno, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberta","family":"Buonocore","sequence":"additional","affiliation":[{"name":"Radiology Unit, Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d\u2019Aragona, 84100 Salerno, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Salerno","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry, University of Salerno, Via S. Allende, 84081 Baronissi, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1370-4900","authenticated-orcid":false,"given":"Rosario","family":"Gnazzo","sequence":"additional","affiliation":[{"name":"Radiology Unit, Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d\u2019Aragona, 84100 Salerno, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaetano","family":"Ungaro","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry, University of Salerno, Via S. Allende, 84081 Baronissi, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5247-9901","authenticated-orcid":false,"given":"Alessia","family":"Bramanti","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry, University of Salerno, Via S. Allende, 84081 Baronissi, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,30]]},"reference":[{"key":"ref_1","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.eururo.2024.11.013","article-title":"Recent Patterns and Trends in Global Prostate Cancer Incidence and Mortality: An Update","volume":"87","author":"Schafer","year":"2025","journal-title":"Eur. Urol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.eururo.2024.03.027","article-title":"EAU-EANM-ESTRO-ESUR-ISUP-SIOG Guidelines on Prostate Cancer-2024 Update. Part I: Screening, Diagnosis, and Local Treatment with Curative Intent","volume":"86","author":"Cornford","year":"2024","journal-title":"Eur. Urol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102163","DOI":"10.1016\/j.tranon.2024.102163","article-title":"Prostate-specific antigen, digital rectal examination, and prostate cancer detection: A study based on more than 7000 transrectal ultrasound-guided prostate biopsies in Ghana","volume":"51","author":"Mensah","year":"2025","journal-title":"Transl. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"251","DOI":"10.4111\/icu.20210429","article-title":"Diagnostic accuracy of prostate-specific antigen below 4 ng\/mL as a cutoff for diagnosing prostate cancer in a hospital setting: A systematic review and meta-analysis","volume":"63","author":"Jin","year":"2022","journal-title":"Investig. Clin. Urol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/s13193-012-0142-6","article-title":"A Review on the Clinical Utility of PSA in Cancer Prostate","volume":"3","author":"Adhyam","year":"2012","journal-title":"Indian J. Surg. Oncol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1016\/S0140-6736(16)32401-1","article-title":"Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): A paired validating confirmatory study","volume":"389","author":"Ahmed","year":"2017","journal-title":"Lancet"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1590\/0100-3984.2013.1863","article-title":"Multiparametric magnetic resonance imaging of the prostate: Current concepts","volume":"47","author":"Bittencourt","year":"2014","journal-title":"Radiol. Bras."},{"key":"ref_9","first-page":"31","article-title":"A Systematic Review of Artificial Intelligence in Prostate Cancer","volume":"13","author":"Kuchakulla","year":"2021","journal-title":"Res. Rep. Urol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"J\u00f3\u017awiak, R., Sobecki, P., and Lorenc, T. (2023). Intraobserver and Interobserver Agreement between Six Radiologists Describing mpMRI Features of Prostate Cancer Using a PI-RADS 2.1 Structured Reporting Scheme. Life, 13.","DOI":"10.3390\/life13020580"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Twilt, J.J., van Leeuwen, K.G., Huisman, H.J., F\u00fctterer, J.J., and de Rooij, M. (2021). Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review. Diagnostics, 11.","DOI":"10.3390\/diagnostics11060959"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/S1470-2045(24)00220-1","article-title":"Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): An international, paired, non-inferiority, confirmatory study","volume":"25","author":"Saha","year":"2024","journal-title":"Lancet Oncol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chow, J.C.L. (2025). Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review. Algorithms, 18.","DOI":"10.3390\/a18030156"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1016\/S0140-6736(24)00651-2","article-title":"The Lancet Commission on prostate cancer: Planning for the surge in cases","volume":"403","author":"James","year":"2024","journal-title":"Lancet"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2632010x241226887","DOI":"10.1177\/2632010X241226887","article-title":"Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin","volume":"17","author":"Rahman","year":"2024","journal-title":"Clin. Pathol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Santamato, V., Tricase, C., Faccilongo, N., Iacoviello, M., and Marengo, A. (2024). Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach. Appl. Sci., 14.","DOI":"10.3390\/app142210144"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Alowais, S.A., Alghamdi, S.S., Alsuhebany, N., Alqahtani, T., Alshaya, A.I., Almohareb, S.N., Aldairem, A., Alrashed, M., Bin Saleh, K., and Badreldin, H.A. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Med. Educ., 23.","DOI":"10.1186\/s12909-023-04698-z"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Schardt, C., Adams, M.B., Owens, T., Keitz, S., and Fontelo, P. (2007). Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Med. Inform. Decis. Mak., 7.","DOI":"10.1186\/1472-6947-7-16"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1007\/s00330-019-06417-z","article-title":"Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network","volume":"30","author":"Aldoj","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1186\/s13244-023-01386-w","article-title":"Does deep learning software improve the consistency and performance of radiologists with various levels of experience in assessing bi-parametric prostate MRI?","volume":"14","author":"Arslan","year":"2023","journal-title":"Insights Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1002\/jmri.27595","article-title":"Performance of Deep Learning and Genitourinary Radiologists in Detection of Prostate Cancer Using 3-T Multiparametric Magnetic Resonance Imaging","volume":"54","author":"Cao","year":"2021","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3134","DOI":"10.1007\/s00330-024-11287-1","article-title":"Evaluation of a deep learning prostate cancer detection system on biparametric MRI against radiological reading","volume":"35","author":"Debs","year":"2025","journal-title":"Eur. Radiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6867","DOI":"10.1007\/s00330-020-07030-1","article-title":"Using decision curve analysis to benchmark performance of a magnetic resonance imaging-based deep learning model for prostate cancer risk assessment","volume":"30","author":"Deniffel","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2010","DOI":"10.3390\/tomography8040168","article-title":"Quantib Prostate Compared to an Expert Radiologist for the Diagnosis of Prostate Cancer on mpMRI: A Single-Center Preliminary Study","volume":"8","author":"Faiella","year":"2022","journal-title":"Tomography"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Giannini, V., Mazzetti, S., Cappello, G., Doronzio, V.M., Vassallo, L., Russo, F., Giacobbe, A., Muto, G., and Regge, D. (2021). Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers. Diagnostics, 11.","DOI":"10.3390\/diagnostics11060973"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4915","DOI":"10.1007\/s00330-024-11323-0","article-title":"AI-powered prostate cancer detection: A multi-centre, multi-scanner validation study","volume":"35","author":"Giganti","year":"2025","journal-title":"Eur. Radiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1007\/s00330-021-08320-y","article-title":"Deep learning-assisted prostate cancer detection on bi-parametric MRI: Minimum training data size requirements and effect of prior knowledge","volume":"32","author":"Hosseinzadeh","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1007\/s00330-022-08978-y","article-title":"A concurrent, deep learning-based computer-aided detection system for prostate multiparametric MRI: A performance study involving experienced and less-experienced radiologists","volume":"33","author":"Labus","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e230750","DOI":"10.1148\/radiol.230750","article-title":"Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI","volume":"311","author":"Lin","year":"2024","journal-title":"Radiology"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1067\/j.cpradiol.2024.04.003","article-title":"Part II: Effect of different evaluation methods to the application of a computer-aided prostate MRI detection\/diagnosis (CADe\/CADx) device on reader performance","volume":"53","author":"Maki","year":"2024","journal-title":"Curr. Probl. Diagn. Radiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"903","DOI":"10.2214\/AJR.19.22573","article-title":"Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI","volume":"215","author":"Mehralivand","year":"2020","journal-title":"Am. J. Roentgenol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mehta, P., Antonelli, M., Singh, S., Grondecka, N., Johnston, E.W., Ahmed, H.U., Emberton, M., Punwani, S., and Ourselin, S. (2021). AutoProstate: Towards Automated Reporting of Prostate MRI for Prostate Cancer Assessment Using Deep Learning. Cancers, 13.","DOI":"10.3390\/cancers13236138"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1148\/radiol.2019190938","article-title":"Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment","volume":"293","author":"Schelb","year":"2019","journal-title":"Radiology"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1007\/s00330-020-07086-z","article-title":"Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment","volume":"31","author":"Schelb","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1186\/s13244-023-01421-w","article-title":"A multicenter study of artificial intelligence-aided software for detecting visible clinically significant prostate cancer on mpMRI","volume":"14","author":"Sun","year":"2023","journal-title":"Insights Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2234","DOI":"10.1002\/jmri.29660","article-title":"Assessing the Performance of Artificial Intelligence Assistance for Prostate MRI: A Two-Center Study Involving Radiologists With Different Experience Levels","volume":"61","author":"Sun","year":"2025","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1007\/s00261-023-03989-9","article-title":"Artificial intelligence as diagnostic aiding tool in cases of Prostate Imaging Reporting and Data System category 3: The results of retrospective multi-center cohort study","volume":"48","author":"Wang","year":"2023","journal-title":"Abdom. Radiol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"109894","DOI":"10.1016\/j.ejrad.2021.109894","article-title":"Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience","volume":"142","author":"Youn","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1097\/RLI.0000000000000878","article-title":"Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection: Experience, Performance, and Identification of the Need for Intermittent Recalibration","volume":"57","author":"Zhang","year":"2022","journal-title":"Investig. Radiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2030","DOI":"10.1007\/s00261-018-1824-5","article-title":"Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI","volume":"44","author":"Zhong","year":"2019","journal-title":"Abdom. Radiol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1007\/s11547-025-02017-8","article-title":"The added value of artificial intelligence using Quantib Prostate for the detection of prostate cancer at multiparametric magnetic resonance imaging","volume":"130","author":"Russo","year":"2025","journal-title":"Radiol. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1016\/S0140-6736(18)32279-7","article-title":"Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990\u20132017: A systematic analysis for the Global Burden of Disease Study 2017","volume":"392","author":"James","year":"2018","journal-title":"Lancet"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1097\/JU.0000000000000757","article-title":"Predictive Factors of Missed Clinically Significant Prostate Cancers in Men with Negative Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis","volume":"204","author":"Pagniez","year":"2020","journal-title":"J. Urol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1002\/jmri.27692","article-title":"Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions","volume":"54","author":"Hectors","year":"2021","journal-title":"J. Magn. Reason. Imaging"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e222276","DOI":"10.1148\/radiol.222276","article-title":"Interactive Explainable Deep Learning Model Informs Prostate Cancer Diagnosis at MRI","volume":"307","author":"Hamm","year":"2023","journal-title":"Radiology"},{"key":"ref_47","first-page":"pgae191","article-title":"The impact of generative artificial intelligence on socioeconomic inequalities and policy making","volume":"3","author":"Capraro","year":"2024","journal-title":"Proc. Natl. Acad. Sci. Nexus"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e2515672","DOI":"10.1001\/jamanetworkopen.2025.15672","article-title":"AI-Assisted vs Unassisted Identification of Prostate Cancer in Magnetic Resonance Images","volume":"8","author":"Twilt","year":"2025","journal-title":"JAMA Netw. Open"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ramli Hamid, M.T., Ab Mumin, N., Abdul Hamid, S., Mohd Ariffin, N., Mat Nor, K., Saib, E., and Mohamed, N.A. (2025). Comparative analysis of diagnostic performance in mammography: A reader study on the impact of AI assistance. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0322925"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"110749","DOI":"10.1016\/j.ejrad.2023.110749","article-title":"Bridging the experience gap in prostate multiparametric magnetic resonance imaging using artificial intelligence: A prospective multi-reader comparison study on inter-reader agreement in PI-RADS v2.1, image quality and reporting time between novice and expert readers","volume":"161","author":"Forookhi","year":"2023","journal-title":"Eur. J. Radiol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1007\/s11547-022-01555-9","article-title":"Biparametric prostate MRI: Impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers","volume":"127","author":"Cipollari","year":"2022","journal-title":"La Radiol. Medica"},{"key":"ref_52","unstructured":"Muneer, A., Waqas, M., Saad, M.B., Showkatian, E., Bandyopadhyay, R., Xu, H., Li, W., Chang, J.Y., Liao, Z., and Haymaker, C. (2025). From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research. arXiv."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/21\/3503\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T05:33:35Z","timestamp":1761975215000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/21\/3503"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,30]]},"references-count":52,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["cancers17213503"],"URL":"https:\/\/doi.org\/10.3390\/cancers17213503","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,30]]}}}