{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T22:00:12Z","timestamp":1790200812865,"version":"4.1.0"},"reference-count":37,"publisher":"Wiley","issue":"22","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["acsjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer"],"published-print":{"date-parts":[[2025,11,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Multicancer early detection (MCED) tests are more than a new class of blood\u2010based tests; they are complex medical innovations representing an integrated diagnostic platform combining molecular and computational technologies. They embody a paradigm shift in how to conceptualize, detect, and manage cancer\u2014carrying the potential to improve outcomes and reduce disparities, yet\u00a0also the risk of exacerbating them. Real\u2010world evidence remains limited, and existing evidence point to substantial heterogeneity even in standard\u2010of\u2010care screening practices\u2014reflecting patterns of overuse and underuse, fluctuations, and practice variation\u2014despite notable advances in cancer treatment and technology over time. Integrating complex medical innovations into equally complex health systems poses significant challenges, underscoring the urgent need for model\u2010based policy guidance to support their incorporation as a complement to population\u2010based screening within standard\u2010of\u2010care pathways. In this editorial, existing policy\u2010oriented dynamic simulation models on MCED tests are summarized, and insights on how modeling frameworks should evolve in parallel with the growing complexity of medical technologies are offered. Traditional approaches often rest on the implicit assumption that evidence reviews lead linearly to interpretation, policy, and adoption\u2014without accounting for feedback between these stages. Evidence\u2010based guideline formation as a feedback process is revisited as is how modelers develop a suite of flexible models tailored to distinct policy questions. Models that coexist and evolve iteratively as new evidence emerges, thereby capturing the adaptive and evolving nature of the problem itself. Such an approach must transcend disciplinary silos, enabling the integration of diverse data sources and supporting innovative portfolio approaches with methodological flexibility.<\/jats:p>","DOI":"10.1002\/cncr.70160","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T08:04:31Z","timestamp":1762761871000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["At the brink of a paradigm shift in early cancer detection: Insights and directions for the modeling community"],"prefix":"10.1002","volume":"131","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2319-0818","authenticated-orcid":false,"given":"\u00d6zge","family":"Karanfil","sequence":"first","affiliation":[{"name":"College of Administrative Sciences and Economics Ko\u00e7 University  Istanbul Turkey"},{"name":"School of Medicine Ko\u00e7 University  Istanbul Turkey"},{"name":"MIT Sloan School of Management  Cambridge Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8892-9601","authenticated-orcid":false,"given":"Zeynep","family":"Ak\u015fin","sequence":"additional","affiliation":[{"name":"College of Administrative Sciences and Economics Ko\u00e7 University  Istanbul Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4294-7142","authenticated-orcid":false,"given":"Raheelah","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Division of Health Services Research and Management School of Health Sciences City St George's University of London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1531-5983","authenticated-orcid":false,"given":"Rifat","family":"Atun","sequence":"additional","affiliation":[{"name":"Department of Health Policy and Management Harvard T.H. Chan School of Public Health Harvard University  Boston Massachusetts USA"},{"name":"Department of Global Health Social Medicine Harvard Medical School Harvard University  Boston Massachusetts USA"},{"name":"Department of Global Health and Population Harvard T.H.Chan School of Public Health Harvard University  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5788-5805","authenticated-orcid":false,"given":"Maarten","family":"Ijzerman","sequence":"additional","affiliation":[{"name":"Erasmus School of Health Policy &amp; Management Erasmus University Rotterdam  Rotterdam the Netherlands"},{"name":"Cancer Health Services Research Centre for Health Policy Melbourne School of Population and Global Health Faculty of Medicine, Dentistry and Health Sciences University of Melbourne  Melbourne Victoria 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