ArticleGastro hep advances2026
Leveraging Natural Language Processing to Accurately Identify Histologically Confirmed Esophageal and Gastric Premalignant and Malignant Conditions Using a Large-Scale Health-Record Linked Genomic Biobank.
Article in Gastro hep advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Background and Aims: Our understanding of the epidemiology and natural history of gastroesophageal precancerous conditions is limited by a lack of robust analyses using large-scale individual-level data. We aimed to develop and validate a natural language processing (NLP) algorithm to identify and phenotype esophageal and gastric precancerous conditions and cancer and apply this to the Million Veteran Program (MVP), a uniquely powerful nationwide genomic biobank linked to individual-level electronic health record data. Methods: We identified 121,808 individuals in MVP who underwent upper endoscopy with biopsies. From these, 426 pathology notes from 426 individuals were used to develop and manually validate an NLP rule-based algorithm identifying intestinal metaplasia, dysplasia, and tumors of the stomach or esophagus. Anatomic subsite and "qualifier" terms were also evaluated (eg, dysplasia grade). Performance metrics were calculated. Results: The algorithm identified all prespecified conditions with excellent accuracy, ranging from 97.6% to 100% (Bonferroni-corrected 95% lower bound 94.5%-98.5%). For gastric intestinal metaplasia, the algorithm achieved 91.7% precision, or positive predictive value and 86.8% recall, or sensitivity (F1 score 89.2%), with 99.0% specificity and 98.3% negative predictive value; while for Barrett's esophagus, it achieved 98.9% precision and recall, or sensitivity (F1 score 98.9%), with 99.6% specificity and 99.6% negative predictive value. When applied to the full MVP cohort (N = 121,808), 13.2% had gastric intestinal metaplasia (mean age 65 years) and 14.5% had Barrett's esophagus (mean age 64 years). Conclusion: This study confirms the ability to use NLP on large-scale unstructured data linked to robust genetic and clinical data for future gastroesophageal precancer analyses and to inform targeted prevention and/or early detection interventions (eg, endoscopic surveillance).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.