Evidence map›Paper›PMID 42885470›Full record

ArticleCancer medicine2026

Implementation of Risk Prediction Models Using Electronic Health Data for Early Detection of Pancreatic Cancer: A Prospective Pilot Feasibility Study.

Bechien U Wu, Tiffany Q Luong, Eva Lustigova, Christie Jeon, Eric J Puttock, Rebecca H Moon, Mercedes A Munis, Wansu Chen

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Article in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Bechien U WuDepartment of Research and Evaluation, Southern California Permanente Medical Group, Pasadena, California, USA.ORCID https://orcid.org/0000-0002-3304-2704
Tiffany Q LuongDepartment of Research and Evaluation, Southern California Permanente Medical Group, Pasadena, California, USA.ORCID https://orcid.org/0000-0002-5825-9248
Eva LustigovaTulane Center for Clinical Research, Tulane University School of Medicine, New Orleans, Louisiana, USA.ORCID https://orcid.org/0000-0002-6719-6090
Christie JeonDepartment of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, California, USA.
Eric J PuttockDepartment of Research and Evaluation, Southern California Permanente Medical Group, Pasadena, California, USA.ORCID https://orcid.org/0000-0003-3963-314X
Rebecca H MoonDepartment of Gastroenterology, Kaiser Permanente Los Angeles Medical Center, Los Angeles, California, USA.ORCID https://orcid.org/0000-0001-7463-1743
Mercedes A MunisDepartment of Research and Evaluation, Southern California Permanente Medical Group, Pasadena, California, USA.
Wansu ChenDepartment of Research and Evaluation, Southern California Permanente Medical Group, Pasadena, California, USA.ORCID https://orcid.org/0000-0002-3486-7468

Funding

National Cancer Institute of the National Institutes of Health R01CA230442
6 · The paper itself

Abstract

BACKGROUND AND

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.

methodsThis 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.

resultsOf 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.

conclusionsReal-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.

Indexed as

Early Detection of CancerElectronic Health RecordsPancreatic NeoplasmsAgedAged, 80 and overFeasibility StudiesFemaleHumansMaleMiddle AgedPilot ProjectsPredictive Learning ModelsProspective StudiesRisk AssessmentRisk Factors

Identifiers

PMID42885470

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.