{
  "abstract": "Background\n\nColorectal cancer remains a major global health burden, and although population-based screening is beneficial, current programs have notable limitations. Risk-stratified screening has been proposed, yet existing prediction models vary in performance and in the inclusion of predictors. Data-adaptive modeling (machine learning) may strengthen risk prediction but depends on high-quality input data. Expert consensus methods offer a structured way to identify clinically meaningful variables for future tools.Objective\n\nTo establish expert consensus on the selection of relevant variables and refinement of self-reported questionnaire items for next-generation colorectal cancer risk-prediction tools.Methods\n\nA modified Delphi study was conducted with seventeen internationally recognized experts. The process consisted of anonymized, email-based 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-level summaries of ratings and comments. Items not meeting predefined thresholds were removed, and validated lifestyle-related items were incorporated. Consensus was defined as 15 of 17 experts rating an item within a similar three-point range.Results\n\nAll seventeen experts completed all rounds. The final I-CRC-RISK Questionnaire comprises 23 items across seven categories: Demography, Health Status, Family Health History, Dietary Patterns, Physical Activity, Smoking, and Alcohol Consumption. Expert consensus was reached for 19 items.Conclusion\n\nThe I-CRC-RISK Questionnaire provides an expert-informed set of self-reported 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-learning approaches by improving the consistency and relevance of input data.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.",
    "Theme Cancer, Karolinska University Hospital, Stockholm, Sweden.",
    "Department of Perioperative and Intensive Care, Södersjukhuset, Stockholm, Sweden.",
    "Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.",
    "Division of Biostatistics, School of Public Health, University of California at Berkeley, California, USA.",
    "Department of Surgery, Södersjukhuset, Stockholm, Sweden."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Hanna Dubois",
      "orcid": "0000-0002-8766-3864"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Eva Torbjörnsson",
      "orcid": "0000-0003-0720-4981"
    },
    {
      "affs": [
        3
      ],
      "name": "Jessica Norrbom",
      "orcid": "0000-0002-6004-1117"
    },
    {
      "affs": [
        4
      ],
      "name": "Alan Hubbard",
      "orcid": "0000-0002-3769-0127"
    },
    {
      "affs": [
        0
      ],
      "name": "Mirna Enskär"
    },
    {
      "affs": [
        4
      ],
      "name": "Rachael V Phillips",
      "orcid": "0000-0002-8474-591X"
    },
    {
      "affs": [
        4
      ],
      "name": "Mark van der Laan"
    },
    {
      "affs": [
        0,
        5
      ],
      "name": "Johannes Blom",
      "orcid": "0000-0002-4938-6640"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3ybtgw8axa8pb.bib",
  "cited_by": 0,
  "doi": "10.1002/cam4.72348",
  "fulltext_url": "https://europepmc.org/articles/PMC13688420",
  "id": "3ybtgw8axa8pb",
  "issns": [
    "2045-7634"
  ],
  "landing_url": "https://europepmc.org/article/MED/42910538",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RC254-282"
    ],
    [
      "openalex",
      "2730|Colorectal Cancer Screening and Detection"
    ]
  ],
  "openalex": "W7223186743",
  "pdf_url": "https://europepmc.org/articles/PMC13688420?pdf=render",
  "pmcid": "PMC13688420",
  "pmid": "42910538",
  "published": "2026-10-01",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/3ybtgw8axa8pb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:14Z",
      "native_id": "MED:42910538",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42910538"
    },
    {
      "first_seen": "2026-10-30T13:44:33Z",
      "native_id": "96ba4380cbea4350857924b7d945d355",
      "source": "doaj",
      "url": "https://doi.org/10.1002/cam4.72348"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2730|Colorectal Cancer Screening and Detection",
      "subfield": "oncology"
    }
  ],
  "title": "Core Self-Reported Variables for Next-Generation Colorectal Cancer Risk Prediction: An International Expert Consensus Study.",
  "title_text": "Core Self-Reported Variables for Next-Generation Colorectal Cancer Risk Prediction: An International Expert Consensus Study.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:33Z",
  "url": "https://research.draconitic.com/paper/3ybtgw8axa8pb",
  "venue": "Cancer medicine",
  "versions": []
}