Negative surveillance endoscopy occurs frequently in patients with short-segment non-dysplastic Barrett's esophagus

Intestinal metaplasia in follow-up endoscopies among Asian patients with short-segment Barrett's esophagus: Regression, sampling error, and associated factors

BACKGROUND

The percentage of and factors associated with the regression of Barrett's esophagus (BE) or its characteristic intestinal metaplasia (IM) remain unclear, and conflicting results have been reported because of diverse regression and sampling error definitions. Thus, we investigated the rates of IM regression, sampling error, and associated factors.

METHODS

Forty-two patients with proven short-segment BE with IM who underwent two follow-up endoscopies with biopsies of Barrett's mucosa were retrospectively analyzed. Additional Alcian blue and MUC2 staining were done on the biopsy specimens without IM in hematoxylin-eosin staining. Only patients with negative hematoxylin-eosin, Alcian blue, and MUC2 staining for IM in both follow-up endoscopies were considered to have true regression. When all three stains were negative for IM in the first, but positive in the second follow-up endoscopy, we considered IM persisting and declared sampling error.

RESULTS

Among the 18 patients without IM at the first follow-up endoscopy, only five (11.9%) were judged to have true regression. Prolonged proton-pump inhibitor use was significantly associated with regression. Limited experience of the endoscopist, and insufficient biopsy number were significantly related to sampling error. Receiver operating characteristic (ROC) curve analysis showed the best cut-off value of the biopsy number/maximal-length (cm) ratio to predict sampling error was 2.25.

CONCLUSION

In our patients with short-segment BE, 11.9% experienced regression of IM. Prolonged proton-pump inhibitors treatment was associated with regression. An insufficient biopsy number was related to a missed IM, which may be eliminated by maintaining biopsy number/maximal-length (cm) ratio ≥2.25.

Magnitude and Time-Trend Analysis of Postendoscopy Esophageal Adenocarcinoma: A Systematic Review and Meta-analysis

BACKGROUND & AIMS

Identification of postendoscopy esophageal adenocarcinoma (PEEC) among Barrett's esophagus (BE) patients presents an opportunity to improve survival of esophageal adenocarcinoma (EAC). We aimed to estimate the proportion of PEEC within the first year after BE diagnosis.

METHODS

Multiple databases (Medline, Embase, Scopus, and Cochrane databases) were searched until September 2020 for original studies with at least 1-year follow-up evaluation that reported EAC and/or high-grade dysplasia (HGD) in the first year after index endoscopy in nondysplastic BE, low-grade dysplasia, or indefinite dysplasia. The proportions of PEEC defined using EAC alone and EAC+HGD were calculated by dividing EAC or EAC+HGD in the first year over the total number of EAC or EAC+HGD, respectively.

RESULTS

We included 52 studies with 145,726 patients and a median follow-up period of 4.8 years. The proportion of PEEC (EAC) was 21% (95% CI, 13-31) and PEEC (EAC+HGD) was 26% (95% CI, 19-34). Among studies with nondysplastic BE only, the PEEC (EAC) proportion was 17% (95% CI, 11-23) and PEEC (EAC+HGD) was 14% (95% CI, 8-19). Among studies with 5 or more years of follow-up evaluation, the PEEC (EAC) proportion was 10% and PEEC (EAC+HGD) was 19%. Meta-regression analysis showed a strong inverse relationship between PEEC and incident EAC (P < .001). The PEEC (EAC) proportion increased from 5% in studies published before 2000 to 30% after 2015. Substantial heterogeneity was observed for most analyses.

CONCLUSIONS

PEEC accounts for a high proportion of HGD/EACs and is proportional to reduction in incident EAC. Using best endoscopic techniques now and performing future research on improving neoplasia detection through implementation of quality measures and educational tools is needed to reduce PEEC.

Size of Lugol-unstained lesions as a predictor for risk of progression in premalignant lesions of the esophagus

BACKGROUND AND AIMS

At present, the surveillance strategy for premalignant esophageal lesions in China is based solely on the pathologic diagnosis in Lugol's chromoendoscopy (LCE). In this study, we sought to determine the degree to which various unstained features under LCE may lead to improved ability to predict the risk of progression in esophageal lesions.

METHODS

We re-examined and followed up on 1058 subjects who had Lugol-unstained lesions (LULs) together with a pathologic diagnosis that was lower than severe dysplasia at baseline screening based on a population-based randomized controlled trial over a median time of 5.8 years. We established a logistic regression model and calculated the adjusted cumulative incidence of severe dysplasia or malignancy.

RESULTS

LUL size was predictive of progression to malignant lesions in individuals with a nondysplastic diagnosis (adjusted odd ratio_{6-10 mm vs ≤5 mm}, 6.7; 95% confidence interval, 1.7-25.7; adjusted odds ratio_{>10 mm vs ≤5 mm}, 27.9; 95% confidence interval, 7.3-105.7), and the corresponding adjusted cumulative incidence of malignant lesions was 3.6 and 13.2 per 100 persons. This is higher than that of small (≤5 mm) lesions, which showed mild dysplasia (2.7 per 100 persons), a condition for which surveillance every 3 years is recommended. Under the current approach, 65.3% of interval cancers missed at surveillance would be detected if individuals with medium (6-10 mm) and large (>10 mm) nondysplastic LULs were additionally monitored.

CONCLUSIONS

We propose a modified surveillance strategy that combines findings under LCE examination and the pathologic analysis, where follow-up endoscopy is recommended for individuals with relatively large nondysplastic lesions.

Adversarial convolutional network for esophageal tissue segmentation on OCT images

Automatic segmentation is important for esophageal OCT image processing, which is able to provide tissue characteristics such as shape and thickness for disease diagnosis. Existing automatical segmentation methods based on deep convolutional networks may not generate accurate segmentation results due to limited training set and various layer shapes. This study proposed a novel adversarial convolutional network (ACN) to segment esophageal OCT images using a convolutional network trained by adversarial learning. The proposed framework includes a generator and a discriminator, both with U-Net alike fully convolutional architecture. The discriminator is a hybrid network that discriminates whether the generated results are real and implements pixel classification at the same time. Leveraging on the adversarial training, the discriminator becomes more powerful. In addition, the adversarial loss is able to encode high order relationships of pixels, thus eliminating the requirements of post-processing. Experiments on segmenting esophageal OCT images from guinea pigs confirmed that the ACN outperforms several deep learning frameworks in pixel classification accuracy and improves the segmentation result. The potential clinical application of ACN for detecting eosinophilic esophagitis (EoE), an esophageal disease, is also presented in the experiment.

Detection of palisade vessels as a landmark for Barrett's esophagus in a Western population

BACKGROUND

In Japan, palisade vessels (PV) are used to distinguish the esophagogastric junction (EGJ). Elsewhere, the EGJ is defined by the upper end of the gastric folds (GF) and PV are considered difficult to detect. This study evaluated the detection rate of PV in Western patients with Barrett's esophagus (BE) using white light imaging (WLI) and narrow band imaging (NBI), and quantified any discordance between Western and Japanese criteria for the EGJ.

METHODS

In 25 BE patients, the presence and location of PV and GF were determined and biopsies were obtained. High-quality images of the EGJ were collected under different conditions (insufflations-desufflation, WLI-NBI, forward-retroflex approach), resulting in eight different images per patient. The presence of PV on each still image was assessed by a panel of six Western and Japanese endoscopists with expertise in BE.

RESULTS

PV were observed in ≥ 1 images by a majority of the panel (≥ 4 raters) in 100 % of patients during insufflation versus 60 % during desufflation (p < 0.001). WLI and NBI detected PV in 100 and 92 %, respectively (p = 0.50). Interobserver agreement of the panel was 'moderate' (κ = 0.51). During endoscopy PV were located a median of 1 cm distal of the GF in 15 patients (63 %), with intestinal metaplasia (IM) in this discordant zone, in 27 % of patients.

CONCLUSIONS

PV are visible in most Western BE patients and are best inspected during insufflation. The location of the GF and PV differed in a substantial group of patients, partially with IM in this discordant zone.

Automated segmentation and characterization of esophageal wall in vivo by tethered capsule optical coherence tomography endomicroscopy

Optical coherence tomography (OCT) is an optical diagnostic modality that can acquire cross-sectional images of the microscopic structure of the esophagus, including Barrett's esophagus (BE) and associated dysplasia. We developed a swallowable tethered capsule OCT endomicroscopy (TCE) device that acquires high-resolution images of entire gastrointestinal (GI) tract luminal organs. This device has a potential to become a screening method that identifies patients with an abnormal esophagus that should be further referred for upper endoscopy. Currently, the characterization of the OCT-TCE esophageal wall data set is performed manually, which is time-consuming and inefficient. Additionally, since the capsule optics optimally focus light approximately 500 µm outside the capsule wall and the best quality images are obtained when the tissue is in full contact with the capsule, it is crucial to provide feedback for the operator about tissue contact during the imaging procedure. In this study, we developed a fully automated algorithm for the segmentation of in vivo OCT-TCE data sets and characterization of the esophageal wall. The algorithm provides a two-dimensional representation of both the contact map from the data collected in human clinical studies as well as a tissue map depicting areas of BE with or without dysplasia. Results suggest that these techniques can potentially improve the current TCE data acquisition procedure and provide an efficient characterization of the diseased esophageal wall.