ArticlePancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]2023
Machine learning versus regression for prediction of sporadic pancreatic cancer.
Article in Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.], 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.The American journal of gastroenterology · 2024Pooled it
- Implementation of Risk Prediction Models Using Electronic Health Data for Early Detection of Pancreatic Cancer: A Prospective Pilot Feasibility Study.Cancer medicine · 2026Article
- Heuriskance: a novel paradigm for systematic earlier detection of sporadic pancreatic cancer.Journal of the National Cancer Institute · 2026Article
- Development and validation of a machine learning model for predicting liver metastasis in pancreatic cancer: a retrospective cohort study.Gland surgery · 2026Article
- Ovarian cancer recurrence prediction: comparing confirmatory to real-world predictors with machine learning.ESMO real world data and digital oncology · 2026Article
- Review
- Review
- Survival Machine Learning Methods Improve Prediction of Histologic Transformation in Follicular and Marginal Zone Lymphomas.Cancers · 2025Article
- Machine learning survival models for Non-alcoholic fatty liver disease based on a health checkup cohort.BMC gastroenterology · 2025Article
- Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review.BMC medical research methodology · 2025Article
- Tailored Strategies are Needed to Address Racial and Ethnic Disparities in Pancreatic Cancer Risk Prediction.Gastro hep advances · 2025Review
- Explainable artificial intelligence in pancreatic cancer prediction: from transparency to clinical decision-making.Frontiers in oncology · 2025Review
- A machine-learning prediction model to identify risk of firearm injury using electronic health records data.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study.BMC medical informatics and decision making · 2024Article
- Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model.Biomedical engineering online · 2024Article
- survex: an R package for explaining machine learning survival models.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
Abstract
BACKGROUND/
objectivesThere is currently no widely accepted approach to identify patients at increased risk for sporadic pancreatic cancer (PC). We aimed to compare the performance of two machine-learning models with a regression-based model in predicting pancreatic ductal adenocarcinoma (PDAC), the most common form of PC.
methodsThis retrospective cohort study consisted of patients 50-84 years of age enrolled in either Kaiser Permanente Southern California (KPSC, model training, internal validation) or the Veterans Affairs (VA, external testing) between 2008 and 2017. The performance of random survival forests (RSF) and eXtreme gradient boosting (XGB) models were compared to that of COX proportional hazards regression (COX). Heterogeneity of the three models were assessed.
resultsThe KPSC and the VA cohorts consisted of 1.8 and 2.7 million patients with 1792 and 4582 incident PDAC cases within 18 months, respectively. Predictors selected into all three models included age, abdominal pain, weight change, and glycated hemoglobin (A1c). Additionally, RSF selected change in alanine transaminase (ALT), whereas the XGB and COX selected the rate of change in ALT. The COX model appeared to have lower AUC (KPSC: 0.737, 95% CI 0.710-0.764; VA: 0.706, 0.699-0.714), compared to those of RSF (KPSC: 0.767, 0.744-0.791; VA: 0.731, 0.724-0.739) and XGB (KPSC: 0.779, 0.755-0.802; VA: 0.742, 0.735-0.750). Among patients with top 5% predicted risk from all three models (N = 29,663), 117 developed PDAC, of which RSF, XGB and COX captured 84 (9 unique), 87 (4 unique), 87 (19 unique) cases, respectively.
conclusionsThe three models complement each other, but each has unique contributions.
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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.