Leggett, Cadman L.

Introduction: Young-onset esophageal adenocarcinoma (EAC) often presents at advanced stages and is associated with poor outcomes. We aimed to examine the epidemiology of young-onset Barrett’s esophagus (BE) using the TriNetX database. Methods: Young-onset BE patients (age 18 to younger than 50 y) with esophagogastroduodenoscopy (EGD) within 1 year before index BE diagnosis between 2014 and 2023 were included. Negative binomial regression was used to evaluate incident-time trends, and logistic regression to assess for independent predictors of young-onset BE. Results: BE diagnosis was present in 76,691 (3.3%) of 2,337,786 patients that underwent EGD, of which 14,120 (18%) had prevalent young-onset BE. BE dysplasia/EAC was present in 6% of young-onset patients at initial diagnosis. Conventional BE risk factors also predicted young-onset BE: GERD (OR=2.4), White race (OR=2.4), male sex (OR=2.2), smoking (OR=1.2), and obesity (OR=1.1). Each additional risk factor increased the probability of BE prevalence by 74% in young patients versus 54% in older patients. The prevalence of young-onset BE was 4.5% with GERD and ≥3 additional risk factors, and 2.3% when ≥3 risk factors were present. Discussion: Young-onset BE accounts for 18% of all BE cases, demonstrates a striking 6% rate of dysplasia/neoplasia at initial diagnosis, and has a prevalence of 4.5% in those with GERD and ≥3 risk factors. These findings suggest that young-onset BE is an important patient population that merits further study. Additional studies are necessary to study the cost-effectiveness of BE screening in younger patients with multiple risk factors.

INTRODUCTION: Ongoing surveillance after successful endoscopic eradication therapy for Barrett's esophagus (BE)-related neoplasia is essential to detect and manage recurrence. Despite established surveillance interval recommendations after complete eradication of intestinal metaplasia (CE-IM), patient compliance with repeat endoscopy is frequently suboptimal. Understanding patient drivers to adherence is essential to inform interventions that address the high rates of overdue surveillance and loss to follow-up. METHODS: Patients with BE-related neoplasia who achieved CE-IM were eligible for inclusion and classified as delayed or on-time for endoscopic surveillance. Patients were prospectively enrolled and participated in structured virtual interviews designed to explore barriers and facilitators to on-time care. A rapid qualitative analysis approach was applied, continuing until thematic saturation was reached. RESULTS: Thirty-nine patients were interviewed: 22 (56.4%) were delayed, and 17 (43.6%) were on-time for surveillance. Patients in the delayed group had longer mean BE segment lengths and a higher prevalence of tobacco use. Geographic distance from the treatment center was the most frequently cited barrier to follow-up (n = 17; 43.5%). Other common barriers were access limitations, financial concerns, misunderstanding of disease course, and test-related anxiety. DISCUSSION: Patient-related drivers of delay in postendoscopic eradication therapy surveillance are largely logistical, socioeconomical, and psychological in nature. Solutions to reduce the significant burden on the patient and achieve timely surveillance may include transitioning patients back to their local endoscopists after CE-IM or incorporating nonendoscopic surveillance methods through patient centered approaches.

INTRODUCTION: The American College of Gastroenterology assembled a multidisciplinary task force to evaluate the current state and future direction of artificial intelligence (AI) in gastroenterology, hepatology, and endoscopy leading to the development of consensus-based recommendations for responsible AI integration in clinical practice. METHODS: A total of 32 subject-matter experts and 12 industry partners, representing diverse practice settings and expertise, conducted subgroup literature reviews across 5 key areas (endoscopy, practice management clinical applications, training and education, inflammatory bowel disease and liver disease, ethics and equity). Draft statements were developed and rated on a 5-point Likert scale using a modified Delphi process. A consensus was set at ≥70% combined agreement. Nonconsensus items were revised and revoted electronically. RESULTS: A total of 43 statements, 40 (93%) reached consensus in round 1 and the remaining 3 achieved consensus after round 2. Evidence supports computer-aided detection improving adenoma detection rate and miss rate in controlled studies, with mixed real-world impact and insufficient long-term outcomes (e.g., interval colon cancer rate). Recommendations emphasize thorough validation and reduction of bias by heterogeneous Data sets. Outside endoscopy, ambient AI scribes, natural language processing (NLP)-enabled coding, workflow optimization, and previous authorization support show potential. Training recommendations endorse a structured AI curriculum while preserving independent procedural competence to avoid deskilling. In inflammatory bowel disease and hepatology, AI could help improve diagnostic accuracy, help predict risk of disease progression, and help guide therapy. Equity, governance, and reimbursement statements call for chain-of-custody data protections, specialty-society oversight, and payment models that reward quality and cost reduction. DISCUSSION: This consensus outlines how AI can augment rather than replace clinical expertise while promoting safety, transparency, interoperability, and equity. Priorities include pragmatic and prospective trials, multi-institutional data-sharing consortia, bias mitigation, and workforce training to enable trustworthy and clinically impactful AI adoption in gastroenterology, liver, and endoscopy care.

Abstract Colorectal cancer incidence and mortality have declined over time, due in part to high-quality screening and surveillance colonoscopy. Nevertheless, postcolonoscopy colorectal cancer (PCCRC) occurs in up to 7% of cases and is inversely related to examination quality. Artificial intelligence-assisted colonoscopy aims to improve performance metrics and, ultimately, patient outcomes. Multiple randomized trials show that computer-aided polyp detection (CADe) increases adenoma detection, predominantly for diminutive lesions (≤5 mm). Computer-aided polyp characterization (CADx) enables real-time optical diagnosis, potentially shifting management of diminutive polyps by supporting resect-and-discard and diagnose-and-leave in situ strategies. Computer-aided quality assessment (CAQ) systems monitor key metrics—including cecal intubation rate, withdrawal time, speed, and mucosal exposure. Whether CADe alone leads to a reduction in PCCRC or cancer-related mortality remains to be determined; in the near term, a combined approach using CADe, CADx, and CAQ is most likely to deliver the greatest improvements in patient outcomes.

Goals: A systematic review and meta-analysis of published clinical validity studies was conducted to evaluate the predictive performance of the TSP-9 test. Background: Identifying patients with Barrett’s esophagus (BE) who will progress to high-grade dysplasia (HGD) or esophageal adenocarcinoma (EAC) is challenging. The tissue systems pathology (TSP-9) test can predict risk of progression to HGD/EAC in BE patients. Study: Databases were searched for studies that assessed the clinical validity of TSP-9, and data describing progressors, non-progressors, TSP-9 results, and hazard ratios (HR) with 95% confidence intervals (CIs) were extracted. Odds ratios (OR), sensitivity, specificity, and prevalence-adjusted positive and negative predictive values (PPV adj /NPV adj ) were calculated and used for meta-analysis. Results: Six studies met eligibility criteria, comprising 699 patients. ORs and HRs for TSP-9 had mean common effect size estimates of 6.52 (95% CI: 4.40-9.66, P <0.0001, I 2 =33%) and 6.66 (95% CI: 4.59-9.66, P <0.0001, I 2 =0%), respectively, for predicting progression to HGD/EAC. Mean common effect size estimates were 61% (95% CI: 54%-68%) for sensitivity, 81% (95% CI: 78%-84%) for specificity, 28% (95% CI: 17%-42%) for PPV adj (high risk), 14% (95% CI: 9%-21%) for PPV adj (high/int risk), and 97% (95% CI: 96%-98%) for NPV adj with minimal inter-study heterogeneity ( I 2 =79%, 21%, 0%, 0%, and 0%, respectively). Conclusions: Effect estimates of TSP-9 performance demonstrate that the test provides risk stratification for BE patients. The TSP-9 test can provide clinically impactful results to enable escalation of care for high-risk patients or to identify low-risk patients who can be safely managed with routine surveillance.