Impediments to implementation of real-time pathology prediction in Barrett’s esophagus and colorectal polyps

Background: Barrett’s esophagus (BE) requires surveillance to identify potential neoplasia at early stage. Standard surveillance regimen includes random four-quadrant biopsies by Seattle protocol. Main limitations of random biopsies are high risk of sampling error, difficulties in histology interpretation, common inadequate classification of pathohistological changes, increased risk of bleeding and time necessary to acquire the final diagnosis. Probe-based confocal laser endomicroscopy (pCLE) has emerged as a potential tool with an aim to overcome these obvious limitations. Summary: pCLE represents real-time microscopic imaging method that offers evaluation of epithelial and subepithelial structures with 1000-fold magnification. In theory, pCLE has potential to eliminate the need for biopsy in BE patient. The main advantages would be real-time diagnosis and decision making, greater diagnostic accuracy and to evaluate larger area compared to random biopsies. Clinical pCLE studies in esophagus show high diagnostic accuracy and its high negative predictive value offers high reliability and confidence to exclude dysplastic and neoplastic lesions. However, it still cannot replace histopathology due to lower positive predictive value and sensitivity. Key messages: Despite promising results, its role in routine use in patients with Barrett’s esophagus remains questionable primarily due to lack of well-organized double-blind randomized trials.

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ASGE Technology Committee systematic review and meta-analysis assessing the ASGE Preservation and Incorporation of Valuable Endoscopic Innovations thresholds for adopting real-time imaging–assisted endoscopic targeted biopsy during endoscopic surveillance of Barrett’s esophagus

Gastrointestinal Endoscopy ◽

10.1016/j.gie.2016.01.007 ◽

2016 ◽

Vol 83 (4) ◽

pp. 684-698.e7 ◽

Cited By ~ 81

Author(s):

Nirav Thosani ◽

Barham K. Abu Dayyeh ◽

Prateek Sharma ◽

Harry R. Aslanian ◽

Brintha K. Enestvedt ◽

...

Keyword(s):

Systematic Review ◽

Real Time ◽

Barrett’S Esophagus ◽

Barrett's Esophagus ◽

Meta Analysis ◽

Endoscopic Surveillance ◽

Targeted Biopsy ◽

Real Time Imaging

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385 MIR-24-3P REGULATES CDX2 DURING THE PROCESS OF INTESTINALIZATION OF THE CARDIAC-TYPE EPITHELIUM IN A HUMAN MODEL OF BARRETT’S ESOPHAGUS

Diseases of the Esophagus ◽

10.1093/dote/doab052.385 ◽

2021 ◽

Vol 34 (Supplement_1) ◽

Author(s):

Manuel Pera ◽

Marta Garrido ◽

Gabriel Gil ◽

Matteo Fassan ◽

Marta Climent ◽

...

Keyword(s):

Real Time ◽

Barrett’S Esophagus ◽

Esophageal Adenocarcinoma ◽

Cell Lines ◽

Real Time Pcr ◽

Barrett's Esophagus ◽

Intermediate Stage ◽

Human Model ◽

Adenocarcinoma Cell ◽

Cdx2 Expression

Abstract Cardiac-type epithelium has been proposed as an intermediate stage between normal squamous epithelium and intestinal metaplasia in the development of Barrett’s esophagus. Deregulation of certain miRNAs and their effects on CDX2 expression might contribute to the intestinalization process of cardiac-type epithelium. The aim of this study was to identify miRNAs differentially expressed between CDX2 positive and negative glands of Barrett’s esophagus and to examine the function of specific miRNAs on the regulation of CDX2. Methods miRNA expression profiling using OpenArrayTM analysis in microdissected cardiac-type glands with and without fully CDX2 expression was performed in biopsies from patients who developed cardiac-type epithelium in the remnant esophagus after esophagectomy. Data were validated using real-time PCR in esophageal adenocarcinoma cell lines and in situ and real-time PCR miRNA/CDX2/MUC2 co-expression analysis in cardiac-type mucosa samples. The effect of miR-24-3p precursor transfection on CDX2 expression was assessed in the esophageal adenocarcinoma cell lines FLO-1 and KYAE-1. Results CDX2 positive glands were characterized by an unique miRNA profile with a significant downregulation of miR-24-3p, miR-520e-3p, miR-548a-1, miR-597-5p, miR-133a-3p, miR-30a-5p, miR-638, miR-625-3p, miR-1255b-1, miR-1260a and upregulation of miR-590 (Figure 1A). miRNA-24-3p was identified as potential regulator of CDX2 gene expression in three bioinformatics algorithms, and this was confirmed in esophageal adenocarcinoma cell lines (Figure 1C). Furthermore, miR-24-3p expression negatively correlates with CDX2 in cardiac-type mucosa samples with different stages of mucosal intestinalization (Figure 1B). Conclusion These results imply that miRNA-24-3p directly targets CDX2, and downregulation of miRNA-24-3p is associated with the acquisition of an intestinal phenotype in cardiac-type epithelium.

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CAESNet: Convolutional AutoEncoder based Semi-supervised Network for improving multiclass classification of endomicroscopic images

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocz089 ◽

2019 ◽

Vol 26 (11) ◽

pp. 1286-1296 ◽

Cited By ~ 2

Author(s):

Li Tong ◽

Hang Wu ◽

May D Wang

Keyword(s):

Decision Making ◽

Real Time ◽

Barrett’S Esophagus ◽

Barrett's Esophagus ◽

Multiclass Classification ◽

Semisupervised Learning ◽

Training Data ◽

Confocal Laser Endomicroscopy ◽

Convolutional Autoencoder

Abstract Objective This article presents a novel method of semisupervised learning using convolutional autoencoders for optical endomicroscopic images. Optical endomicroscopy (OE) is a newly emerged biomedical imaging modality that can support real-time clinical decisions for the grade of dysplasia. To enable real-time decision making, computer-aided diagnosis (CAD) is essential for its high speed and objectivity. However, traditional supervised CAD requires a large amount of training data. Compared with the limited number of labeled images, we can collect a larger number of unlabeled images. To utilize these unlabeled images, we have developed a Convolutional AutoEncoder based Semi-supervised Network (CAESNet) for improving the classification performance. Materials and Methods We applied our method to an OE dataset collected from patients undergoing endoscope-based confocal laser endomicroscopy procedures for Barrett’s esophagus at Emory Hospital, which consists of 429 labeled images and 2826 unlabeled images. Our CAESNet consists of an encoder with 5 convolutional layers, a decoder with 5 transposed convolutional layers, and a classification network with 2 fully connected layers and a softmax layer. In the unsupervised stage, we first update the encoder and decoder with both labeled and unlabeled images to learn an efficient feature representation. In the supervised stage, we further update the encoder and the classification network with only labeled images for multiclass classification of the OE images. Results Our proposed semisupervised method CAESNet achieves the best average performance for multiclass classification of OE images, which surpasses the performance of supervised methods including standard convolutional networks and convolutional autoencoder network. Conclusions Our semisupervised CAESNet can efficiently utilize the unlabeled OE images, which improves the diagnosis and decision making for patients with Barrett’s esophagus.

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