Quantitative analysis of submucosal excision depth in endoscopic resection for early Barrett’s cancer

Background Following endoscopic resection of early-stage Barrett’s esophageal adenocarcinoma (BEA), further oncologic management then fundamentally relies upon the accurate assessment of histopathologic risk criteria, which requires there to be sufficient amounts of submucosal tissue in the resection specimens. Methods In 1685 digitized tissue sections from endoscopic mucosal resection (EMR) or endoscopic submucosal dissection (ESD) performed for 76 early BEA cases from three experienced centers, the submucosal thickness was determined, using software developed in-house. Neoplastic lesions were manually annotated. Results No submucosa was seen in about a third of the entire resection area (mean 33.8 % [SD 17.2 %]), as well as underneath cancers (33.3 % [28.3 %]), with similar results for both resection methods and with respect to submucosal thickness. ESD results showed a greater variability between centers than EMR. In T1b cancers, a higher rate of submucosal defects tended to correlate with R1 resections. Conclusion The absence of submucosa underneath about one third of the tissue of endoscopically resected BEAs should be improved. Results were more center-dependent for ESD than for EMR. Submucosal defects can potentially serve as a parameter for standardized reports.

Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

Update on endoscopic treatment of Barrett’s oesophagus and Barrett’s oesophagus–related neoplasia

Endoscopic therapy of early Barrett’s oesophagus–related neoplasia is the treatment of choice for low-grade-dysplasia, high-grade dysplasia and mucosal Barrett’s cancer. Low-grade-dysplasia without any visible lesion should be ablated, preferably with radiofrequency ablation. In cases with the presence of a visible lesion, high-grade dysplasia and early Barrett’s adenocarcinoma, endoscopic resection techniques like multiband ligation endoscopic resection or endoscopic submucosal dissection should be applied. After complete resection of all visible neoplastic lesions, ablation of the remaining Barrett’s oesophagus should be performed to prevent recurrence. Ablation techniques available are radiofrequency ablation, argon plasma coagulation and cryoablation.