Implementation of risk prediction models using electronic health data for early detection of pancreatic cancer: a prospective pilot feasibility study.

Authors

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

Published in

medRxiv : the preprint server for health sciences. Sep 23, 2026. Epub Sep 23, 2026.

Abstract

We 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.
This 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.
Of 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.
Real-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.

PMID:
42818416
Bibliographic data and abstract were imported from PubMed on 01 Oct 2026.

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