{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T02:39:07Z","timestamp":1792809547522,"version":"4.5.0"},"reference-count":51,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:00:00Z","timestamp":1792627200000},"content-version":"vor","delay-in-days":21,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100002794","name":"Cancerfonden","doi-asserted-by":"publisher","award":["21 1389 Pj"],"award-info":[{"award-number":["21 1389 Pj"]}],"id":[{"id":"10.13039\/501100002794","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002794","name":"Cancerfonden","doi-asserted-by":"publisher","award":["22 0599 FE (Blom)"],"award-info":[{"award-number":["22 0599 FE (Blom)"]}],"id":[{"id":"10.13039\/501100002794","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002794","name":"Cancerfonden","doi-asserted-by":"publisher","award":["25 4029 FE (Dubois)"],"award-info":[{"award-number":["25 4029 FE (Dubois)"]}],"id":[{"id":"10.13039\/501100002794","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004359","name":"Vetenskapsr\u00e5det","doi-asserted-by":"publisher","award":["2021\u201003139 (Blom)"],"award-info":[{"award-number":["2021\u201003139 (Blom)"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Colorectal cancer remains a major global health burden, and although population\u2010based screening is beneficial, current programs have notable limitations. Risk\u2010stratified screening has been proposed, yet existing prediction models vary in performance and in the inclusion of predictors. Data\u2010adaptive modeling (machine learning) may strengthen risk prediction but depends on high\u2010quality input data. Expert consensus methods offer a structured way to identify clinically meaningful variables for future tools.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To establish expert consensus on the selection of relevant variables and refinement of self\u2010reported questionnaire items for next\u2010generation colorectal cancer risk\u2010prediction tools.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A modified Delphi study was conducted with seventeen internationally recognized experts. The process consisted of anonymized, email\u2010based rounds aimed at refining a questionnaire for colorectal cancer risk prediction. Across three iterative rounds, experts rated item relevance, proposed revisions, and identified missing domains while reviewing group\u2010level summaries of ratings and comments. Items not meeting predefined thresholds were removed, and validated lifestyle\u2010related items were incorporated. Consensus was defined as 15 of 17 experts rating an item within a similar three\u2010point range.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      All seventeen experts completed all rounds. The final\n                      <jats:italic>I\u2010CRC\u2010RISK Questionnaire<\/jats:italic>\n                      comprises 23 items across seven categories:\n                      <jats:italic>Demography<\/jats:italic>\n                      ,\n                      <jats:italic>Health Status, Family Health History, Dietary Patterns, Physical Activity, Smoking<\/jats:italic>\n                      , and\n                      <jats:italic>Alcohol Consumption<\/jats:italic>\n                      . Expert consensus was reached for 19 items.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      The\n                      <jats:italic>I<\/jats:italic>\n                      \u2010\n                      <jats:italic>CRC<\/jats:italic>\n                      \u2010\n                      <jats:italic>RISK Questionnaire<\/jats:italic>\n                      provides an expert\u2010informed set of self\u2010reported variables to support future risk prediction efforts. It offers a structured basis for further validation and psychometric testing before prospective data collection may be undertaken. This foundational work could enhance the predictive performance of both traditional and machine\u2010learning approaches by improving the consistency and relevance of input data.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.72348","type":"journal-article","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T10:03:34Z","timestamp":1792663414000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Core Self\u2010Reported Variables for Next\u2010Generation Colorectal Cancer Risk Prediction: An International Expert Consensus Study"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8766-3864","authenticated-orcid":false,"given":"Hanna","family":"Dubois","sequence":"first","affiliation":[{"name":"Department of Clinical Science and Education S\u00f6dersjukhuset Karolinska Institutet  Stockholm Sweden"},{"name":"Theme Cancer Karolinska 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