Artificial Intelligence/Machine Learning-Driven cfDNA Methylation Blood Test for Early Cancer Detection in Military Personnel With Environmental Toxin Exposure.

Authors

Mythili Subharam, Ryan Koehler, Tejas Sreedhar

Published in

Military medicine. Volume 191. Issue Supplement_1. Pages 673-682. Aug 01, 2026.

Abstract

Military personnel face heightened cancer risks from exposure to burn-pit emissions, per- and polyfluoroalkyl substances (PFAS), jet fuel, and radiation. Veterans show elevated malignancy rates, and active-duty personnel remain vulnerable because of ongoing operational exposures, underscoring the need for early detection. Traditional screening methods are invasive and have limited sensitivity for early-stage disease. This study evaluates the feasibility of artificial intelligence/machine learning (AI/ML)-based cell-free DNA (cfDNA) methylation analysis for early cancer detection using publicly available civilian datasets, to inform future application in high-risk military populations.
A Machine-Learning driven cfDNA methylation classifier framework was developed for early, minimally-invasive detection of cancers associated with toxic and occupational exposures. The platform uses bisulfite-sequenced data to identify differential methylation signatures that distinguish early stage hepatocellular carcinoma (HCC) from chronic liver disease and healthy controls. Proof-of-concept studies were conducted on public datasets for hepatocellular carcinoma (HCC) and esophageal cancer. Three cfDNA datasets (GSE93203, GSE63775, and PRJCA001372) were used for model training and k-fold cross-validation, and an independent WGBS liver-tissue dataset (PRJNA984754) served as a blind cross-assay validation set. Framework extensibility to other cancers was assessed through the EpiPanGI-Dx esophageal-cancer dataset (ESCC training, EAC validation).
Across 3 HCC models, internal cross-validation achieved 84%-94% accuracy (AUC 0.80-0.88). On the independent WGBS dataset validated against the 3 trained models, classifiers generalized with 83%-100% accuracy (AUC 0.80-1.00). Clinical validation using biobank cfDNA plasma samples from early-stage hepatocellular carcinoma and cirrhosis controls is ongoing. The esophageal cancer model reproduced published performance (AUC 0.94 for ESCC, 0.90 for EAC), demonstrating generalizability beyond HCC.
The cfDNA methylation-based AI/ML platform is promising for early multi-cancer detection in environmentally exposed military populations. Early results demonstrate feasibility and justify expansion to additional cancers, and larger clinical validation studies, potentially enhancing survivability through earlier intervention.

PMID:
42560195
Bibliographic data and abstract were imported from PubMed on 06 Aug 2026.

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