Leveraging Natural Language Processing to Accurately Identify Histologically Confirmed Esophageal and Gastric...

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.

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.

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).

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).