LIQUID NITROGEN SPRAY CRYOTHERAPY FOR ERADICATION OF DYSPLASTIC BARRETT'S ESOPHAGUS: RESULTS FROM A MULTICENTER PROSPECTIVE REGISTRY

BACKGROUND AND AIMS

Liquid nitrogen spray cryotherapy (SCT) is an alternative to radiofrequency ablation (RFA) for eradication of dysplastic Barrett's esophagus (BE). We aimed to assess the safety, efficacy, and durability of SCT in a multicenter U.S. registry.

METHODS

This is a multicenter prospective registry of adults with BE treated with truFreeze Spray Cryotherapy (4 community and 11 academic sites, 2013-2022). Complete eradication of intestinal metaplasia (CEIM) and dysplasia (CED) were assessed in BE with dysplasia or intramucosal adenocarcinoma (IMC). Kaplan-Meier analysis of CEIM and CED was performed. Hazard ratios for CEIM stratified by baseline risk factors were calculated.

RESULTS

Among 138 subjects, with LGD (24%), HGD (49%) and IMC (27%), 34% received prior RFA therapy. Subjects received a median of 2 SCT sessions. Adverse events were uncommon, with 5.5% reporting strictures and 0.7% a perforation. Rates of CEIM and CED, respectively, were 66% and 84% after two years, and 67% and 92% after three years. In RFA-naive patients, CEIM was 77% and CED was 96% at 3 years. Increasing BE length (adjusted hazard ratio [95% CI]:0.90 [0.83-0.96] per cm) and prior treatment with RFA (0.39 [0.22-0.69]) were associated with a lower rate of CEIM. Recurrence occurred in 8.8% (n=6) at a mean follow-up of 2.5 years after CEIM.

CONCLUSION

In this largest reported prospective cohort, liquid nitrogen SCT was safe and effective for treatment of dysplastic and neoplastic BE. Response was lower in those with prior failed RFA; in that cohort approximately 50% attained CEIM at 3 years.

Artificial intelligence for pancreatic cancer detection: Recent development and future direction

Artificial intelligence (AI) has been increasingly utilized in medical applications, especially in the field of gastroenterology. AI can assist gastroenterologists in imaging-based testing and prediction of clinical diagnosis, for examples, detecting polyps during colonoscopy, identifying small bowel lesions using capsule endoscopy images, and predicting liver diseases based on clinical parameters. With its high mortality rate, pancreatic cancer can highly benefit from AI since the early detection of small lesion is difficult with conventional imaging techniques and current biomarkers. Endoscopic ultrasound (EUS) is a main diagnostic tool with high sensitivity for pancreatic adenocarcinoma and pancreatic cystic lesion. The standard tumor markers have not been effective for diagnosis. There have been recent research studies in AI application in EUS and novel biomarkers to early detect and differentiate malignant pancreatic lesions. The findings are impressive compared to the available traditional methods. Herein, we aim to explore the utility of AI in EUS and novel serum and cyst fluid biomarkers for pancreatic cancer detection.