{
  "abstract": "Background and aims\n\nWe 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.Methods\n\nThis 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.Results\n\nOf 1251 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.Conclusions\n\nReal-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": [
    "Department of Research and Evaluation, Southern California Permanente Medical Group, Pasadena, California, USA.",
    "Department of Gastroenterology, Kaiser Permanente Los Angeles Medical Center, Los Angeles, California, USA.",
    "Tulane Center for Clinical Research, Tulane University School of Medicine, New Orleans, Louisiana, USA.",
    "Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, California, USA."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Bechien U Wu",
      "orcid": "0000-0002-3304-2704"
    },
    {
      "affs": [
        0
      ],
      "name": "Tiffany Q Luong",
      "orcid": "0000-0002-5825-9248"
    },
    {
      "affs": [
        2
      ],
      "name": "Eva Lustigova",
      "orcid": "0000-0002-6719-6090"
    },
    {
      "affs": [
        3
      ],
      "name": "Christie Jeon"
    },
    {
      "affs": [
        0
      ],
      "name": "Eric J Puttock",
      "orcid": "0000-0003-3963-314X"
    },
    {
      "affs": [
        1
      ],
      "name": "Rebecca H Moon",
      "orcid": "0000-0001-7463-1743"
    },
    {
      "affs": [
        0
      ],
      "name": "Mercedes A Munis"
    },
    {
      "affs": [
        0
      ],
      "name": "Wansu Chen",
      "orcid": "0000-0002-3486-7468"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ch9k3kpw6ayc6.bib",
  "cited_by": 0,
  "doi": "10.1002/cam4.72368",
  "fulltext_url": "https://europepmc.org/articles/PMC13673295",
  "id": "ch9k3kpw6ayc6",
  "issns": [
    "2045-7634"
  ],
  "landing_url": "https://europepmc.org/article/MED/42885470",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2730|Pancreatic and Hepatic Oncology Research"
    ]
  ],
  "openalex": "W7214067130",
  "pdf_url": "https://europepmc.org/articles/PMC13673295?pdf=render",
  "pmcid": "PMC13673295",
  "pmid": "42885470",
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ch9k3kpw6ayc6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:00Z",
      "native_id": "MED:42885470",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42885470"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2730|Pancreatic and Hepatic Oncology Research",
      "subfield": "oncology"
    }
  ],
  "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": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:00Z",
  "url": "https://research.draconitic.com/paper/ch9k3kpw6ayc6",
  "venue": "Cancer medicine",
  "versions": []
}