NIH-funded research R01 grant

AI-powered screening to detect Barrett’s esophagus and early esophageal cancer

Official title: Minimally Invasive Molecular Approaches for the Detection of Barrett’s Esophagus and Esophageal Adenocarcinoma

Funded at Mayo Clinic Arizona · NIH-11384589 · Data updated 2026-11-12

A Mayo Clinic team is testing an AI-assisted tool that uses health data to identify people at risk for Barrett’s esophagus and early esophageal cancer so they can get timely endoscopic evaluation.

More Adenocarcinoma research and trials

Quick facts

Grant typeR01 grant
Study typeNIH-funded research
Funding institutionMayo Clinic Arizona
NIH-funded
Lab location1 site (Scottsdale, United States)
Project startJuly 2, 2019
Project endJuly 31, 2030
Funding year2026
Award (this year)$634,585
NIH project number5R01CA241164-08
Project IDNIH-11384589 on NIH RePORTER

Who could benefit from this research

Could help

Patients with symptoms or risk factors for Barrett’s esophagus or esophageal adenocarcinoma, and individuals who may benefit from risk-guided screening.

Less likely to help

Low-risk individuals with no BE/EAC risk factors may not benefit from intensive screening.

What this research studies

This project aims to improve early detection of Barrett’s esophagus and esophageal adenocarcinoma by using an artificial intelligence system that analyzes electronic health records to estimate risk. The approach builds on prior progress showing AI can create a more accurate risk score than current guidelines, guiding who should be offered endoscopic screening or surveillance. It focuses on integrating clinical data, endoscopic findings, and dysplasia risk to streamline referral and monitoring pathways. If successful, the system could reduce missed cases and unnecessary procedures, by personalizing screening decisions. It involves Mayo Clinic Arizona investigators and collaborators, with a plan spanning several years to validate and refine the tool in real-world settings.

Why it matters

Potential benefit: If successful, patients at higher risk could be identified earlier and receive timely endoscopic evaluation, potentially improving survival.

How similar studies have gone: Prior AI-based risk tools for BE/EAC have shown improved accuracy (e.g., higher AUC) but require further real-world validation.

Where this research is happening

  • Mayo Clinic Arizona
    Scottsdale, United States
    Active

Researchers

  • Principal investigator: Iyer, Prasad G. — Mayo Clinic Arizona
  • Study coordinator: Iyer, Prasad G.

Related research

Other NIH-funded projects on the same topics.

About this research

  1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
  2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
  3. For full project details, budget, and progress reports, visit the official NIH RePORTER page.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.