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Esophageal cancer (EC) is a pressing global health concern, ranking as the eighth most common cancer and the sixth leading cause for cancer-related deaths worldwide. Esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC) are the two major histological types of esophageal cancer associated with distinct risk factors and geographical distributions. Unfortunately, the outcomes for both types of EC remain discouraging, with a five-year survival rate of less than 20% when diagnosed at advanced stages. Advanced endoscopic techniques have the potential to vastly enhance patient outcomes and impede the progression of pre-malignant lesions to cancer. However, low screening rates with endoscopy due to its invasive nature and high cost hinder its effectiveness. Despite extensive research on risk predictors, a significant number of cases still go undiagnosed, highlighting the need for improved screening techniques that can be implemented at the population level. To increase uptake, a shift towards minimally invasive, well-tolerated and cost-effective non-endoscopic technologies is crucial. The implementation of such devices in primary care settings, specifically targeting high-risk populations, can be a promising strategy. With early detection and enrollment in surveillance programs, there is hope for substantial improvement in morbidity and mortality rates through modern minimally invasive endoscopic and surgical techniques., (© 2024. Indian Society of Gastroenterology.)

Introduction: Endoscopic eradication therapy (EET) is standard of care for T1a esophageal adenocarcinoma (EAC). However, data on outcomes in high-risk T1a EAC are limited. We assessed and compared outcomes after EET of low-risk and high-risk T1a EAC, including intraluminal EAC recurrence, extraesophageal metastases, and overall survival., Methods: Patients who underwent EET for T1a EAC at 3 referral Barrett's esophagus endotherapy units between 1996 and 2022 were included. Patients with submucosal invasion, positive deep margins, or metastases at initial diagnosis were excluded. High-risk T1a EAC was defined as T1a EAC with poor differentiation and/or lymphovascular invasion, with low-risk disease being defined without these features. All pathology was systematically assessed by expert gastrointestinal pathologists. Baseline and follow-up endoscopy and pathology data were abstracted. Time-to-event analyses were performed to compare outcomes between groups., Results: One hundred eighty-eight patients with T1a EAC were included (high risk, n = 45; low risk, n = 143) with a median age of 70 years, and 84% were men. Groups were comparable for age, sex, Barrett's esophagus length, lesion size, and EET technique. Rates of delayed extraesophageal metastases (11.1% vs 1.4%) were significantly higher in the high-risk group ( P = 0.02). There was no significant difference in the rates of intraluminal EAC recurrence ( P = 0.79) and overall survival ( P = 0.73) between the 2 groups., Discussion: Patients with high-risk T1a EAC undergoing successful EET had a substantially higher rate of extraesophageal metastases compared with those with low-risk T1a EAC on long-term follow-up. These data should be factored into discussions with patients while selecting treatment approaches. Additional prospective data in this area are critical., (Copyright © 2023 by The American College of Gastroenterology.)

Introduction: Screening for Barrett's esophagus (BE) is suggested in those with risk factors, but remains underutilized. BE/esophageal adenocarcinoma (EAC) risk prediction tools integrating multiple risk factors have been described. However, accuracy remains modest (area under the receiver-operating curve [AUROC] ≤0.7), and clinical implementation has been challenging. We aimed to develop machine learning (ML) BE/EAC risk prediction models from an electronic health record (EHR) database., Methods: The Clinical Data Analytics Platform, a deidentified EHR database of 6 million Mayo Clinic patients, was used to predict BE and EAC risk. BE and EAC cases and controls were identified using International Classification of Diseases codes and augmented curation (natural language processing) techniques applied to clinical, endoscopy, laboratory, and pathology notes. Cases were propensity score matched to 5 independent randomly selected control groups. An ensemble transformer-based ML model architecture was used to develop predictive models., Results: We identified 8,476 BE cases, 1,539 EAC cases, and 252,276 controls. The BE ML transformer model had an overall sensitivity, specificity, and AUROC of 76%, 76%, and 0.84, respectively. The EAC ML transformer model had an overall sensitivity, specificity, and AUROC of 84%, 70%, and 0.84, respectively. Predictors of BE and EAC included conventional risk factors and additional novel factors, such as coronary artery disease, serum triglycerides, and electrolytes., Discussion: ML models developed on an EHR database can predict incident BE and EAC risk with improved accuracy compared with conventional risk factor-based risk scores. Such a model may enable effective implementation of a minimally invasive screening technology., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)