{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:29:01Z","timestamp":1786120141782,"version":"build-2736575974"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"OmicXHealth"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The cfDNA methylation\u2013based 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/milmed\/usag226","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T12:28:10Z","timestamp":1777638490000},"page":"673-682","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence\/Machine Learning\u2013Driven cfDNA Methylation Blood Test for Early Cancer Detection in Military Personnel With Environmental Toxin Exposure"],"prefix":"10.1093","volume":"191","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4242-9926","authenticated-orcid":false,"given":"Mythili","family":"Subharam","sequence":"first","affiliation":[{"name":"OmicXHealth Inc Research and Development, , San Ramon, CA 94582,","place":["United 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