AI may help predict risk of Barrett’s esophagus and esophageal cancer

Precancerous changes in the cells of the esophagus, a condition called Barrett’s esophagus, is a risk factor for esophageal cancer. Barrett’s esophagus is caused by gastroesophageal reflux disease (GERD), which occurs when stomach acid repeatedly flows back into the esophagus, irritating the lining of the esophagus.

Specialists recommend screening for people who have multiple risk factors for Barrett’s esophagus, yet despite the availability of minimally invasive tools, screening rates for Barrett’s esophagus are low. Prasad Iyer, M.D., a Mayo Clinic gastroenterologist and researcher in Phoenix, Arizona, is working to change that.

Dr. Iyer and a team of researchers developed and tested a tool that uses artificial intelligence (AI) to predict the risk of Barrett’s esophagus and esophageal cancer based on data from a large database of de-identified electronic health records. The results of their study were published in Clinical and Translational Gastroenterology in 2023.

Dr. Iyer and his team used an AI model developed based on de-identified electronic health records of 6 million Mayo Clinic patients to create a risk prediction tool that can determine Barrett’s esophagus and esophageal cancer risk at least a year before diagnosis.

The risk prediction tool can be integrated with an electronic health record and, when appropriate, prompt a health care professional to consider screening a patient for Barrett’s esophagus.

Based on clinical, endoscopy, laboratory and pathology notes in the electronic health records, the researchers identified 8,476 people with Barrett’s esophagus, 1,539 people with esophageal cancer and 252,276 people in the control group. They then used these groups to develop predictive models for the risk prediction tool.

The results of the study demonstrated that the tool’s predictive models had a high level of accuracy:

• The tool predicted Barrett’s esophagus with 76% sensitivity (proportion of samples correctly identified as negative), 76% specificity (proportion of samples correctly identified as positive), and an area under the receiver-operating curve (AUROC) of 0.84. AUROC is a metric used to measure the quality of predictions produced by an AI model. It ranges from 0 to 1, with 1 being the highest quality.
• The tool predicted esophageal cancer with 84% sensitivity, 70% specificity and an AUROC of 0.84.
• The tool also identified known risk factors for Barrett’s esophagus and esophageal cancer, as well as new risk factors to consider, including coronary artery disease, triglyceride levels and electrolyte levels.

“This tool could be integrated into the electronic health record and combined with a minimally invasive (nonendoscopic) screening tool and used by health care professionals in primary care,” he says.