Implementation of Risk Prediction Models Using Electronic Health Data for Early Detection of Pancreatic Cancer: A Prospective Pilot Feasibility Study.
4 affiliations
- 1Department of Research and Evaluation, Southern California Permanente Medical Group, Pasadena, California, USA.
- 2Department of Gastroenterology, Kaiser Permanente Los Angeles Medical Center, Los Angeles, California, USA.
- 3Tulane Center for Clinical Research, Tulane University School of Medicine, New Orleans, Louisiana, USA.
- 4Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, California, USA.
Abstract
Background and aims
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.Methods
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.Results
Of 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
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.
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BibTeX
@article{wu2026implementation,
title = {{Implementation of Risk Prediction Models Using Electronic Health Data for Early Detection of Pancreatic Cancer: A Prospective Pilot Feasibility Study.}},
author = {Bechien U Wu and Tiffany Q Luong and Eva Lustigova and Christie Jeon and Eric J Puttock and Rebecca H Moon and Mercedes A Munis and Wansu Chen},
journal = {Cancer medicine},
year = {2026},
month = oct,
doi = {10.1002/cam4.72368},
url = {https://europepmc.org/article/MED/42885470},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Implementation of Risk Prediction Models Using Electronic Health Data for Early Detection of Pancreatic Cancer: A Prospective Pilot Feasibility Study.
AU - Bechien U Wu
AU - Tiffany Q Luong
AU - Eva Lustigova
AU - Christie Jeon
AU - Eric J Puttock
AU - Rebecca H Moon
AU - Mercedes A Munis
AU - Wansu Chen
PY - 2026
DA - 2026/10/01
JO - Cancer medicine
DO - 10.1002/cam4.72368
UR - https://europepmc.org/article/MED/42885470
AB - Background and aims 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.Methods 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.Results Of 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 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.
SN - 2045-7634
ER -