{
  "abstract": "Background & AimsWe previously developed multiple machine-learning and regression-based risk prediction models using retrospective electronic health records (EHR) to estimate near-term risk of pancreatic cancer (PC). This pilot study aimed to assess the feasibility of an early detection strategy combining initial EHR-based risk prediction with downstream imaging and blood-based testing.\n\nMethodsThis prospective study included adults aged 50-84 years with an estimated [≥]1% 18-month risk of PC, identified between January 2021 and March 2023 using four previously validated models. Patients with a history or active suspicion of PC were excluded. Participants completed cross-sectional imaging and CA19-9 labs at baseline and again at 18 months. Outcomes included enrollment rates, test completion rate, and burden of incidental findings.\n\nResultsOf 1,251 eligible patients, 102 (8.2%) enrolled (median age 69 years [IQR 62-75]; 58% women; 48% Hispanic, 34% White, 11% Black, and 7% Asian). At baseline, 89 (87%) completed imaging and 98 (96%) completed CA19-9. At 18 months, 79% completed both imaging and CA19-9. Ten (10%) participants underwent additional clinical evaluations triggered by imaging findings. Evaluations included an endoscopic ultrasound, renal imaging, and FibroScan. CA19-9 results were normal in most participants, though 18% had elevated levels at baseline.\n\nConclusionsReal-time EHR-based ML algorithms can be combined with blood- and imaging-based surveillance for early detection of PC with acceptable burden of incidental findings. However, improved patient engagement is a key aspect that will need to be addressed in order to successfully scale an AI-guided strategy for early detection.",
  "abstract_shown": true,
  "affiliations": [
    "Southern California Permanente Medical Group"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "B. U. Wu"
    },
    {
      "name": "T. Q. Luong"
    },
    {
      "name": "E. Lustigova"
    },
    {
      "name": "C. Jeon"
    },
    {
      "name": "E. J. Puttock"
    },
    {
      "name": "R. H. Moon"
    },
    {
      "name": "M. A. Munis"
    },
    {
      "name": "W. Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/11xz1fngzkes1.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.22.26363704",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.22.26363704v1.full",
  "id": "11xz1fngzkes1",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.22.26363704v1",
  "language": null,
  "licence": "CC-BY-ND",
  "licence_label": "CC BY-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "gastroenterology"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.22.26363704v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/11xz1fngzkes1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:19Z",
      "native_id": "10.64898/2026.09.22.26363704",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.22.26363704v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:gastroenterology",
      "subfield": "general"
    }
  ],
  "title": "Implementation of risk prediction models using electronic health data for early detection of pancreatic cancer: a prospective pilot feasibility study",
  "title_text": "Implementation of risk prediction models using electronic health data for early detection of pancreatic cancer: a prospective pilot feasibility study",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:19Z",
  "url": "https://research.draconitic.com/paper/11xz1fngzkes1",
  "venue": "medRxiv",
  "versions": []
}