{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T21:10:18Z","timestamp":1793913018381,"version":"4.8.0"},"reference-count":97,"publisher":"Wiley","issue":"24","license":[{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","award":["EDDCPJT\\100018"],"award-info":[{"award-number":["EDDCPJT\\100018"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Predictive models could support clinicians in identifying patients who may benefit from cancer investigations. We aimed to examine published evidence on machine learning models (ML) developed to estimate cancer risk based on symptoms and other patient characteristics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using MEDLINE, Scopus, and EMBASE, we performed a systematic review of studies published in 2014\u20132024, which included data on signs\/symptoms for cancer risk prediction. We used the QUADAS\u2010AI tools to assess study quality. We performed a quantitative synthesis of diagnostic performance, including accuracy, sensitivity, specificity, area under the curve (AUC). Adherence to TRIPOD guidelines was assessed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among the 5646 initially identified articles, 34 met inclusion criteria. Included studies most frequently examined lung (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20099 studies), mesothelioma (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20097), and gastrointestinal cancers (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20094) and used hospital electronic health records (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20098) or publicly available online datasets (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200913). In addition to signs\/symptoms (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200934), most models included sociodemographic characteristics (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200927) and lifestyle factors (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200920). In 70% of studies, internal validation was performed. ML models demonstrated variable performance, with AUC values ranging from 0.60 to 1 during validation. Random Forest, Support Vector Machine, Decision Tree, and Multilayer Perceptron showed the best predictive performance. Most of the studies (94.1%) had a high risk of bias for the index test.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ML models have been reported to demonstrate potential in managing complex data for cancer risk prediction. However, the current evidence is heterogeneous and frequently limited by bias and incomplete reporting. Further validation and thorough assessments of real\u2010world performance are necessary before these models can be considered reliable for clinical use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial Registration<\/jats:title>\n                    <jats:p>International Prospective Register of Systematic Reviews (PROSPERO) registration number: CRD42024548088<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.71463","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T13:10:26Z","timestamp":1765631426000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types"],"prefix":"10.1002","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9185-9747","authenticated-orcid":false,"given":"Flavia","family":"Pennisi","sequence":"first","affiliation":[{"name":"PhD National Programme in One Health Approaches to Infectious Diseases and Life Science Research, Department of Public Health, Experimental and Forensic Medicine University of Pavia  Pavia Italy"},{"name":"School of Medicine Universit\u00e0 Vita\u2010Salute San Raffaele  Milano Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefania","family":"Borlini","sequence":"additional","affiliation":[{"name":"School of Medicine Universit\u00e0 Vita\u2010Salute San Raffaele  Milano Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannah","family":"Harrison","sequence":"additional","affiliation":[{"name":"Department of Public Health and Primary Care, School of Clinical Medicine University of Cambridge  Cambridge UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rita","family":"Cuciniello","sequence":"additional","affiliation":[{"name":"School of Medicine Universit\u00e0 Vita\u2010Salute San Raffaele  Milano Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna Carole","family":"D'Amelio","sequence":"additional","affiliation":[{"name":"School of Medicine Universit\u00e0 Vita\u2010Salute San Raffaele  Milano Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Barclay","sequence":"additional","affiliation":[{"name":"Research Department of Behavioural Science and Health, Institute of Epidemiology and Health Care University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni Emanuele","family":"Ricciardi","sequence":"additional","affiliation":[{"name":"PhD National Programme in One Health Approaches to Infectious Diseases and Life Science Research, Department of Public Health, Experimental and Forensic Medicine University of Pavia  Pavia Italy"},{"name":"School of Medicine Universit\u00e0 Vita\u2010Salute San Raffaele  Milano Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Lyratzopoulos","sequence":"additional","affiliation":[{"name":"Research Department of Behavioural Science and Health, Institute of Epidemiology and Health Care University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"Renzi","sequence":"additional","affiliation":[{"name":"School of Medicine Universit\u00e0 Vita\u2010Salute San Raffaele  Milano Italy"},{"name":"Research Department of Behavioural Science and Health, Institute of Epidemiology and Health Care University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,12,13]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470\u20102045(19)30595\u20109"},{"key":"e_1_2_12_3_1","unstructured":"Public Health England. 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