Development and Validation of a Classification System to Identify High-Grade Dysplasia and Esophageal Adenocarcinoma in Barrett's Esophagus Using Narrow-Band Imaging

Whole circumferential endoscopic submucosal dissection of superficial adenocarcinoma in long-segment Barrett's esophagus: A case report

BACKGROUND

Esophageal adenocarcinoma (EAC) derived from long-segment Barrett's esophagus (LSBE) is extremely rare in Asia. LSBE-related EAC is often difficult to diagnose in the horizontal extent. If the tumor has spread throughout the LSBE, whole circumferential endoscopic submucosal dissection (ESD) should be performed, which is difficult to complete safely. Additionally, whole circumferential ESD can bring refractory postoperative stenosis. We hereby report a case of EAC involving the whole circumference of the LSBE, achieving complete endoscopic removal without complications.

CASE SUMMARY

An 85-year-old man with the chief complaint of dysphagia underwent esophagogastroduodenoscopy. We suspected a flat-type cancerous lesion that extended the whole circumference of the LSBE (C 3.5, M 4.0) using narrow-band imaging magnification endoscopy (NBI-M). We achieved circumferential en bloc resection of the lesion safely with special ESD techniques. Histology of the ESD specimens demonstrated that the superficial EAC extended the whole circumference of the LSBE, and papillary or well-differentiated tubular adenocarcinoma was confined in the lamina propria mucosa showing a vertical negative margin. To prevent post-ESD stenosis, we performed endoscopic local injection of steroids, followed by oral administration of steroids. There was no evidence of esophageal refractory stenosis or tumor recurrence 30 mo after ESD. In summary, we experienced a rare case of LSBE-related EAC. The horizontal tumor extent was accurately diagnosed by NBI-M. Additionally, we achieve whole circumferential ESD safely without postoperative refractory stenosis.

CONCLUSION

NBI-M, ESD, and steroid therapy enabled the curative resection of superficial full circumferential LSBE-related EAC without refractory postoperative stenosis.

Diagnostic accuracy of narrow-band imaging endoscopy with targeted biopsies compared with standard endoscopy with random biopsies in patients with Barrett's esophagus: A systematic review and meta-analysis

BACKGROUND AND AIM

Endoscopic surveillance for dysplasia in Barrett's esophagus (BE) with random biopsies is the primary diagnostic tool for monitoring clinical progression into esophageal adenocarcinoma. As an alternative, narrow-band imaging (NBI) endoscopy offers targeted biopsies that can improve dysplasia detection. This study aimed to evaluate NBI-guided targeted biopsies' diagnostic accuracy for detecting dysplasia in patients undergoing endoscopic BE surveillance compared with the widely used Seattle protocol.

METHODS

Cochrane DTA Register, MEDLINE/PubMed, EMBASE, OpenGrey, and bibliographies of identified papers were searched until 2018. Two independent investigators resolved discrepancies by consensus, study selection, data extraction, and quality assessment. Data on sensitivity, specificity, and predictive values were pooled and analyzed using a random-effects model.

RESULTS

Of 9528 identified articles, six studies comprising 493 participants were eligible for quantitative synthesis. NBI-targeted biopsy showed high diagnostic accuracy in detection of dysplasia in BE with a sensitivity of 76% (95% confidence interval [CI]: 0.61-0.91), specificity of 99% (95% CI: 0.99-1.00), positive predictive value of 97% (95% CI: 0.96-0.99), and negative predictive value of 84% (95% CI: 0.69-0.99) for detection of all grades of dysplasia. The receiver-operating characteristic curve for NBI model performance was 0.8550 for detecting all dysplasia.

CONCLUSION

Narrow-band imaging-guided biopsy demonstrated high diagnostic accuracy and might constitute a valid substitute for random biopsies during endoscopic surveillance for dysplasia in BE.

Advances in optical gastrointestinal endoscopy: a technical review

Optical endoscopy is the primary diagnostic and therapeutic tool for management of gastrointestinal (GI) malignancies. Most GI neoplasms arise from precancerous lesions; thus, technical innovations to improve detection and diagnosis of precancerous lesions and early cancers play a pivotal role in improving outcomes. Over the last few decades, the field of GI endoscopy has witnessed enormous and focused efforts to develop and translate accurate, user-friendly, and minimally invasive optical imaging modalities. From a technical point of view, a wide range of novel optical techniques is now available to probe different aspects of light-tissue interaction at macroscopic and microscopic scales, complementing white light endoscopy. Most of these new modalities have been successfully validated and translated to routine clinical practice. Herein, we provide a technical review of the current status of existing and promising new optical endoscopic imaging technologies for GI cancer screening and surveillance. We summarize the underlying principles of light-tissue interaction, the imaging performance at different scales, and highlight what is known about clinical applicability and effectiveness. Furthermore, we discuss recent discovery and translation of novel molecular probes that have shown promise to augment endoscopists' ability to diagnose GI lesions with high specificity. We also review and discuss the role and potential clinical integration of artificial intelligence-based algorithms to provide decision support in real time. Finally, we provide perspectives on future technology development and its potential to transform endoscopic GI cancer detection and diagnosis.

Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of artificial intelligence: a pilot study

BACKGROUND

The accurate differentiation between T1a and T1b Barrett's-related 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 on white-light images.

METHODS

Endoscopic images from three tertiary care centers 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) were evaluated using 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 score, and accuracy of the AI system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.74, and 0.71, respectively. There was no statistically significant difference between the performance of the AI system and that of experts, who showed 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 equally to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and real-life settings. Nevertheless, the correct prediction of submucosal invasion in Barrett's cancer remains challenging for both experts and AI.

Current Status of Image-Enhanced Endoscopy for Early Identification of Esophageal Neoplasms

Advanced esophageal cancer is known to have a poor prognosis. The early detection of esophageal neoplasms, including esophageal dysplasia and early esophageal cancer, is highly important for the accurate treatment of the disease. However, esophageal dysplasia and early esophageal cancer are usually subtle and can be easily missed. In addition to the early detection, proper pretreatment evaluation of the depth of invasion of esophageal cancer is very important for curative treatment. The progression of non-invasive diagnosis via image-enhanced endoscopy techniques has been shown to aid the early detection and estimate the depth of invasion of early esophageal cancer and, as a result, may provide additional opportunities for curative treatment. Here, we review the advancement of image-enhanced endoscopy-related technologies and their role in the early identification of esophageal neoplasms.

Artificial Intelligence in Surveillance of Barrett's Esophagus

A study by Waterhouse and colleagues in a previous issue of Cancer Research describes the development and prospective validation of an artificial intelligence approach in conjunction with spectral imaging to enhance endoscopic detection of Barrett's esophagus-related neoplasia. The authors developed a novel spectral endoscope with external optics suitable for routine Barrett's esophagus surveillance with diffuse tissue reflectance to define multispectral data correlated with histopathology. A convolutional neural network was trained on the absis of the spectral signatures acquired as part of a small, prospective clinical trial to distinguish Barrett's esophagus from Barrett's esophagus neoplasia. The results from the study suggest the utility of artificial intelligence for diagnosis of Barrett's esophagus.See related article by Waterhouse et al., Cancer Res 2021;81:3415-25.

Endoscopic management of Barrett's esophagus: Western perspective of current status and future prospects

Barrett's esophagus (BE) is a precursor to esophageal adenocarcinoma and current practice is to establish endoscopic surveillance once diagnosed, in order to identify early dysplasia and neoplasia that has the potential to undergo endoscopic eradication therapy (EET). Before embarking upon EET the clinical team has a duty to consider all viable options and come to a plan based on recent evidence. The therapeutic approach varies greatly but largely adheres to the mantra of 'Detect-Resect-Ablate', in which high-quality endoscopy identifies BE associated pathology, associated lesions (if present) undergo safe endoscopic resection and remaining intestinal metaplasia in the esophagus is ablated to prevent recurrence of dysplasia. In this review, current practice, pitfalls, complications, and the future perspectives on practice in this field are discussed. The Western perspective is focused on here, with an outline of the differences in clinical practice with Asian nations and attempts to bridge these differences.

Systematic Review on Optical Diagnosis of Early Gastrointestinal Neoplasia

BACKGROUND

Meticulous endoscopic characterization of gastrointestinal neoplasias (GN) is crucial to the clinical outcome. Hereby the indication and type of resection (endoscopically, en-bloc or piece-meal, or surgical resection) are determined. By means of established image-enhanced (IEE) and magnification endoscopy (ME) GN can be characterized in terms of malignancy and invasion depth. In this context, the statistical evidence and accuracy of these diagnostic procedures should be elucidated. Here, we present a systematic review of the literature.

RESULTS

21 Studies could be found which met the inclusion criteria. In clinical prospective trials and meta-analyses, the diagnostic accuracy of >90% for characterization of malignant neoplasms could be documented, if ME with IEE was used in squamous cell esophageal cancer, stomach, or colonic GN.

CONCLUSIONS

Currently, by means of optical diagnosis, today's gastrointestinal endoscopy is capable of determining the histological subtype, exact lateral spread, and depth of invasion of a lesion. The prerequisites for this are an exact knowledge of the anatomical structures, the endoscopic classifications based on them, and a systematic learning process, which can be supported by training courses. More prospective clinical studies are required, especially in the field of Barrett's esophagus and duodenal neoplasia.

Convolutional Neural Networks for the evaluation of cancer in Barrett's esophagus: Explainable AI to lighten up the black-box

Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of early-cancerous tissues in Barrett's esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts' previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts' delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model's sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts' insights, demonstrating how human knowledge may influence the correct computational learning.

Spectral Endoscopy Enhances Contrast for Neoplasia in Surveillance of Barrett's Esophagus

Early detection of esophageal neoplasia enables curative endoscopic therapy, but the current diagnostic standard of care has low sensitivity because early neoplasia is often inconspicuous with conventional white-light endoscopy. Here, we hypothesized that spectral endoscopy could enhance contrast for neoplasia in surveillance of patients with Barrett's esophagus. A custom spectral endoscope was deployed in a pilot clinical study of 20 patients to capture 715 in vivo tissue spectra matched with gold standard diagnosis from histopathology. Spectral endoscopy was sensitive to changes in neovascularization during the progression of disease; both non-dysplastic and neoplastic Barrett's esophagus showed higher blood volume relative to healthy squamous tissue (P = 0.001 and 0.02, respectively), and vessel radius appeared larger in neoplasia relative to non-dysplastic Barrett's esophagus (P = 0.06). We further developed a deep learning algorithm capable of classifying spectra of neoplasia versus non-dysplastic Barrett's esophagus with high accuracy (84.8% accuracy, 83.7% sensitivity, 85.5% specificity, 78.3% positive predictive value, and 89.4% negative predictive value). Exploiting the newly acquired library of labeled spectra to model custom color filter sets identified a potential 12-fold enhancement in contrast between neoplasia and non-dysplastic Barrett's esophagus using application-specific color filters compared with standard-of-care white-light imaging (perceptible color difference = 32.4 and 2.7, respectively). This work demonstrates the potential of endoscopic spectral imaging to extract vascular properties in Barrett's esophagus, to classify disease stages using deep learning, and to enable high-contrast endoscopy. SIGNIFICANCE: The results of this pilot first-in-human clinical trial demonstrate the potential of spectral endoscopy to reveal disease-associated vascular changes and to provide high-contrast delineation of neoplasia in the esophagus. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/12/3415/F1.large.jpg.

Mesoscopic fluorescence lifetime imaging: Fundamental principles, clinical applications and future directions

Fluorescence lifetime imaging (FLIm) is an optical spectroscopic imaging technique capable of real-time assessments of tissue properties in clinical settings. Label-free FLIm is sensitive to changes in tissue structure and biochemistry resulting from pathological conditions, thus providing optical contrast to identify and monitor the progression of disease. Technical and methodological advances over the last two decades have enabled the development of FLIm instrumentation for real-time, in situ, mesoscopic imaging compatible with standard clinical workflows. Herein, we review the fundamental working principles of mesoscopic FLIm, discuss the technical characteristics of current clinical FLIm instrumentation, highlight the most commonly used analytical methods to interpret fluorescence lifetime data and discuss the recent applications of FLIm in surgical oncology and cardiovascular diagnostics. Finally, we conclude with an outlook on the future directions of clinical FLIm.

Taking the Next Steps in Endoscopic Visual Assessment of Barrett's Esophagus: A Pilot Study

Purpose

Patients with Barrett's esophagus (BE) undergo surveillance endoscopies to assess for pre-cancerous changes. We developed a simple endoscopic classification method for predicting non-dysplastic BE (NDBE), low-grade dysplasia (LGD)/indefinite for dysplasia (ID) and high-grade dysplasia (HGD)/early esophageal adenocarcinoma (EAC).

Patients and Methods

Twenty-two patients with BE underwent endoscopy using the PENTAX Medical MagniView gastroscope and OPTIVISTA processor. Sixty-six video-still images were analyzed to characterize the microsurface, microvasculature and the presence of a demarcation line. Class A was characterized by regular microvascular and microsurface patterns and absence of a demarcation line, class B by changes in the microvascular and/or microsurface patterns compared to the background mucosa with presence of a demarcation line, and class C by irregular microvascular and/or irregular microsurface patterns with presence of a demarcation line.

Results

Of the class A images, 97.9% were NDBE. For class B, 69.2% were LGD/ID and 30.8% NDBE. One hundred percent of the class C samples were HGD/EAC. The sensitivity of our classification system was 93.8%, specificity 92%, positive predictive value 78.9%, negative predictive value 97.9% and an accuracy 92.4%.

Conclusion

In this study, we developed a simple classification system for the prediction of NDBE, LGD/ID and HGD/EAC. Its real-time clinical applicability will be validated prospectively.

Current Trends in Endoscopic Diagnosis and Treatment of Early Esophageal Cancer

Simple Summary

Early esophageal cancer is diagnosed in the context of reflux disease, surveillance of Barrett’s metaplasia, or during upper gastrointestinal endoscopy for other indications. High definition and virtual or dye chromoendoscopy are mandatory for the screening and evaluation of neoplasia. Endoscopic treatment options include endoscopic mucosal resection (EMR) or endoscopic submucosal dissection (ESD). Resection is considered curative if histopathology confirms low or absent risk of lymph node metastasis. Barrett’s high-grade dysplasia or early adenocarcinoma is treated by EMR or ESD, followed by ablation of Barrett’s epithelium to avoid metachronous cancer. ESD is the treatment of choice for squamous cell neoplasia. Excellent outcomes have been reported if the ESD of squamous cell cancer with slight submucosal infiltration and thus substantial risk for lymph node metastasis was combined with adjuvant chemo-radiotherapy. In contrast, infiltration of squamous cell cancer exceeding the lamina propria mucosae is not curative. However, despite a substantial risk of lymph node metastasis, excellent outcomes have recently been reported if endoscopic resection of tumors with up to 200 µm submucosal infiltration was combined with adjuvant chemo-radiotherapy.

Abstract

Diagnosis of esophageal adenocarcinoma mostly occurs in the context of reflux disease or surveillance of Barrett’s metaplasia. Optimal detection rates are obtained with high definition and virtual or dye chromoendoscopy. Smaller lesions can be treated with endoscopic mucosal resection. Endoscopic submucosal dissection (ESD) is an option for larger lesions. Endoscopic resection is considered curative (i.e., without significant risk of lymph node metastasis) if histopathology confirms en bloc and R0 resection of a well-differentiated (G1/2) tumor without infiltration of lymphatic or blood vessels and the maximal submucosal infiltration depth is 500µm. Ablation of remaining Barrett’s metaplasia is important, to reduce the risk of metachronous cancer. Esophageal squamous cell cancer is associated with different risk factors, and most of the detected lesions are diagnosed during upper gastrointestinal endoscopy for other indications. Virtual high definition and dye chromoendoscopy with Lugol’s solution are used for screening and evaluation. ESD is the preferred resection technique. The criteria for curative resection are similar to Barrett’s cancer, but the maximum infiltration depth must not exceed lamina propria mucosae. Although a submucosal infiltration depth of up to 200 µm carries a substantial risk of lymph node metastasis, ESD combined with adjuvant chemo-radiotherapy gives excellent results. The complication rates of endoscopic resection are low, and the functional outcomes are favorable compared to surgery.

Barrett esophagus: What to expect from Artificial Intelligence?

The evaluation and assessment of Barrett's esophagus is challenging for both expert and nonexpert endoscopists. However, the early diagnosis of cancer in Barrett's esophagus is crucial for its prognosis, and could save costs. Pre-clinical and clinical studies on the application of Artificial Intelligence (AI) in Barrett's esophagus have shown promising results. In this review, we focus on the current challenges and future perspectives of implementing AI systems in the management of patients with Barrett's esophagus.

Application of Artificial Intelligence in Gastrointestinal Endoscopy

Artificial intelligence (AI), also known as computer-aided diagnosis, is a technology that enables machines to process information and functions at or above human level and has great potential in gastrointestinal endoscopy applications. At present, the research on medical image recognition usually adopts the deep-learning algorithm based on the convolutional neural network. AI has been used in gastrointestinal endoscopy including esophagogastroduodenoscopy, capsule endoscopy, colonoscopy, etc. AI can help endoscopic physicians improve the diagnosis rate of various lesions, reduce the rate of missed diagnosis, improve the quality of endoscopy, assess the severity of the disease, and improve the efficiency of endoscopy. The diversity, susceptibility, and imaging specificity of gastrointestinal endoscopic images are all difficulties and challenges on the road to intelligence. We need more large-scale, high-quality, multicenter prospective studies to explore the clinical applicability of AI, and ethical issues need to be taken into account.

Endoscopy and Barrett's Esophagus: Current Perspectives in the US and Japan

Barrett's esophagus (BE) is a precancerous disease that can lead to esophageal adenocarcinoma (EAC). Recently, the incidence of EAC arising from BE has been increasing, and EAC has now become a threat in many countries. However, there are many gaps among the various countries in terms of definitions and concepts and these gaps prevent discussing BE on the same footing. In order to eradicate BE, it is a global necessity to fill in these remaining gaps. We focused on the gaps and reviewed recent evidence and trends as well as the background of gaps between the US and Japan as two of the leading countries in the field of medical research. We also review the rapid advances in endoscopic techniques in relation to both diagnosis and therapy that are considered to be useful to eliminate the gaps between countries.

Barrett's esophagus: current standards in advanced imaging

Esophageal adenocarcinoma (EAC) continues to be one of the fastest rising incident cancers in the Western population with the majority of patients presenting with late stage disease and associated with a dismal 5-year survival rate. Barrett's esophagus (BE) is the only identifiable precursor lesion to EAC. Strategies to screen for and survey BE are critical to detect earlier cancers and reduce morbidity and mortality related to EAC. A high-quality endoscopic examination with careful inspection of the Barrett's segment and adherence to the Seattle protocol for tissue sampling are critical. Advanced imaging modalities offer the potential to improve dysplasia detection, predict histopathology in real time and guide endoscopic eradication therapy (EET). Several technologies have been studied and although most are not yet recommended for routine clinical practice, high definition white light endoscopy (HD-WLE) as well as chromoendoscopy (including virtual chromoendoscopy) improved dysplasia detection in numerous studies supporting their use. Future studies should evaluate the role of artificial intelligence in optimizing detection of dysplasia in BE patients.

Diagnostic utility of a novel magnifying endoscopic classification system for superficial Barrett's esophagus-related neoplasms: a nationwide multicenter study

BACKGROUND

Currently, no classification system using magnification endoscopy for the diagnosis of superficial Barrett's esophagus (BE)-related neoplasia has been widely accepted. This nationwide multicenter study aimed to validate the diagnostic accuracy and reproducibility of the magnification endoscopy classification system, including the diagnostic flowchart developed by the Japan Esophageal Society-Barrett's esophagus working group (JES-BE) for superficial Barrett's esophagus-related neoplasms.

METHODS

The JES-BE acquired high-definition magnification narrow-band imaging (HM-NBI) images of non-dysplastic and dysplastic BE from 10 domestic institutions. A total of 186 high-quality HM-NBI images were selected. Thirty images were used for the training phase and 156 for the validation (test) phase. We invited five non-experts and five expert reviewers. In the training phase, the reviewers discussed how to correctly predict the histology based on the JES-BE criteria. In the validation phase, they evaluated whether the criteria accurately predicted the histology results according to the diagnostic flowchart. The validation phase was performed immediately after the training phase and at 6 weeks thereafter.

RESULTS

The sensitivity and specificity for all reviewers were 87% and 97%, respectively. Overall accuracy, positive predictive value, and negative predictive value were 91%, 98%, and 83%, respectively. The overall strength of inter-observer and intra-observer agreements for dysplastic histology prediction was κ = 0.77 and κ = 0.83, respectively. No significant difference in diagnostic accuracy and reproducibility between experts and non-experts was found.

CONCLUSION

The JES-BE classification system, including the diagnostic flowchart for predicting dysplastic BE, is acceptable and reliable, regardless of the clinician's experience level.

Best Practices in Surveillance for Barrett's Esophagus

Barrett's esophagus is the precursor lesion for esophageal adenocarcinoma. The goals of endoscopic surveillance are to detect dysplasia and early esophageal adenocarcinoma in order to improve patient outcomes. Despite the ongoing debate regarding the efficacy of surveillance, all current gastrointestinal societies recommend surveillance at this time. Optimal surveillance technique includes adequate inspection time, evaluation using high-definition white light and chromoendoscopy, appropriate documentation of the metaplastic segment using the Prague C & M criteria as well as the Paris classification should lesions be found, utilization of the Seattle biopsy protocol, and endoscopic resection of visible lesions.

Advanced Imaging and Sampling in Barrett's Esophagus: Artificial Intelligence to the Rescue?

Because the current Barrett's esophagus (BE) surveillance protocol suffers from sampling error of random biopsies and a high miss-rate of early neoplastic lesions, many new endoscopic imaging and sampling techniques have been developed. None of these techniques, however, have significantly increased the diagnostic yield of BE neoplasia. In fact, these techniques have led to an increase in the amount of visible information, yet endoscopists and pathologists inevitably suffer from variations in intra- and interobserver agreement. Artificial intelligence systems have the potential to overcome these endoscopist-dependent limitations.

Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma

BACKGROUND AND AIM

Conventional endoscopy for the early detection of esophageal and esophagogastric junctional adenocarcinoma (E/J cancer) is limited because early lesions are asymptomatic, and the associated changes in the mucosa are subtle. There are no reports on artificial intelligence (AI) diagnosis for E/J cancer from Asian countries. Therefore, we aimed to develop a computerized image analysis system using deep learning for the detection of E/J cancers.

METHODS

A total of 1172 images from 166 pathologically proven superficial E/J cancer cases and 2271 images of normal mucosa in esophagogastric junctional from 219 cases were used as the training image data. A total of 232 images from 36 cancer cases and 43 non-cancerous cases were used as the validation test data. The same validation test data were diagnosed by 15 board-certified specialists (experts).

RESULTS

The sensitivity, specificity, and accuracy of the AI system were 94%, 42%, and 66%, respectively, and that of the experts were 88%, 43%, and 63%, respectively. The sensitivity of the AI system was favorable, while its specificity for non-cancerous lesions was similar to that of the experts. Interobserver agreement among the experts for detecting superficial E/J was fair (Fleiss' kappa = 0.26, z = 20.4, P < 0.001).

CONCLUSIONS

Our AI system achieved high sensitivity and acceptable specificity for the detection of E/J cancers and may be a good supporting tool for the screening of E/J cancers.

Measuring Quality in Barrett's Esophagus: Time to Embrace Quality Indicators

Endoscopic eradication therapy is a safe and effective therapy that has revolutionized the management of patients with Barrett's esophagus (BE)-related neoplasia. Despite this, there remains significant heterogeneity in clinical practice with consequent variation in patient outcomes. The aim of this article was to align consensus statements based on the best available evidence and expert opinion from the United States and United Kingdom to develop robust and measurable quality indicators that help to ensure patients with BE-related neoplasia receive the highest possible quality of care uniformly.

NBI utility in oncologic surgery: An organ by organ review

The main aims of the oncologic surgeon should be an early tumor diagnosis, complete surgical resection, and a careful post-treatment follow-up to ensure a prompt diagnosis of recurrence. Radiologic and endoscopic methods have been traditionally used for these purposes, but their accuracy might sometimes be suboptimal. Technological improvements could help the clinician during the diagnostic and therapeutic management of tumors. Narrow band imaging (NBI) belongs to optical image techniques, and uses light characteristics to enhance tissue vascularization. Because neoangiogenesis is a fundamental step during carcinogenesis, NBI could be useful in the diagnostic and therapeutic workup of tumors. Since its introduction in 2001, NBI use has rapidly spread in different oncologic specialties with clear advantages. There is an active interest in this topic as demonstrated by the thriving literature. It is unavoidable for clinicians to gain in-depth knowledge about the application of NBI to their specific field, losing the overall view on the topic. However, by looking at other fields of application, clinicians could find ideas to improve NBI use in their own specialty. The aim of this review is to summarize the existing literature on NBI use in oncology, with the aim of providing the state of the art: we present an overview on NBI fields of application, results, and possible future improvements in the different specialties.

Barrett's Esophageal Adenocarcinoma Involving a White Globe Appearance within the Long-Segment Barrett's Esophagus

The diagnosis of Barrett's esophageal adenocarcinoma (BEA) in patients with Barrett's esophagus (BE) using endoscopy can be difficult and there are few specific endoscopic findings for BEA. However, white globe appearance (WGA) has been reported to be a specific endoscopic finding for early gastric cancer. We encountered a 51-year-old male patient with BEA exhibiting WGA. Esophagogastroduodenoscopy identified a red, depressed lesion of 10 mm within the long-segment BE (LSBE), while magnifying endoscopy with narrow-band imaging identified WGA. Endoscopic submucosal dissection (ESD) was performed based on our suspicion of BEA. Based on the ESD findings, we diagnosed adenocarcinoma accompanying LSBE histopathologically. WGA was identified, and intraglandular necrotic debris was discovered histologically at the same site. Therefore, WGA may be helpful in the diagnosis of BEA.

Advanced endoscopic imaging for detecting and guiding therapy of early neoplasias of the esophagus

Esophageal cancers, largely adenocarcinoma in Western countries and squamous cell cancer in Asia, present a significant burden of disease and remain one of the most lethal of cancers. Key to improving survival is the development and adoption of new imaging modalities to identify early neoplastic lesions, which may be small, multifocal, subsurface, and difficult to detect by standard endoscopy. Such advanced imaging is particularly relevant with the emergence of ablative techniques that often require multiple endoscopic sessions and may be complicated by bleeding, pain, strictures, and recurrences. Assessing the specific location, depth of involvement, and features correlated with neoplastic progression or incomplete treatment may optimize treatments. While not comprehensive of all endoscopic imaging modalities, we review here some of the recent advances in endoscopic luminal imaging, particularly with surface contrast enhancement using virtual chromoendoscopy, highly magnified subsurface imaging with confocal endomicroscopy, optical coherence tomography, elastic scattering spectroscopy, angle-resolved low-coherence interferometry, and light scattering spectroscopy. While there is no single ideal imaging modality, various multimodal instruments are also being investigated. The future of combining computer-aided assessments, molecular markers, and improved imaging technologies to help localize and ablate early neoplastic lesions shed hope for improved disease outcome.

A Gratifying Step forward for the Application of Artificial Intelligence in the Field of Endoscopy: A Narrative Review

Endoscopy is the optimal choice of diagnosis of gastrointestinal (GI) diseases. Following the advancements made in medical technology, different kinds of novel endoscopy-methods have emerged. Although the significant progress in the penetration of endoscopic tools that have markedly improved the diagnostic rate of GI diseases, there are still some limitations, including instability of human diagnostic performance caused by intensive labor burden and high missed diagnosis rate of subtle lesions. Recently, artificial intelligence (AI) has been applied gradually to assist endoscopists in addressing these issues.

Is Malignant Potential of Barrett's Esophagus Predictable by Endoscopy Findings?

Given that endoscopic findings can be used to predict the potential of neoplastic progression in Barrett's esophagus (BE) cases, the detection rate of dysplastic Barrett's lesions may become higher even in laborious endoscopic surveillance because a special attention is consequently paid. However, endoscopic findings for effective detection of the risk of neoplastic progression to esophageal adenocarcinoma (EAC) have not been confirmed, though some typical appearances are suggestive. In the present review, endoscopic findings that can be used predict malignant potential to EAC in BE cases are discussed. Conventional results obtained with white light endoscopy, such as length of BE, presence of esophagitis, ulceration, hiatal hernia, and nodularity, are used as indicators of a higher risk of neoplastic progression. However, there are controversies in some of those findings. Absence of palisade vessels may be also a new candidate predictor, as that reveals degree of intense inflammation and of cyclooxygenase-2 protein expression with accelerated cellular proliferation. Furthermore, an open type of mucosal pattern and enriched stromal blood vessels, which can be observed by image-enhanced endoscopy, including narrow band imaging, have been confirmed as factors useful for prediction of neoplastic progression of BE because they indicate more frequent cyclooxygenase-2 protein expression along with accelerated cellular proliferation. Should the malignant potential of BE be shown predictable by these endoscopic findings, that would simplify methods used for an effective surveillance, because patients requiring careful monitoring would be more easily identified. Development in the near future of a comprehensive scoring system for BE based on clinical factors, biomarkers and endoscopic predictors is required.

Role of artificial intelligence in the diagnosis of oesophageal neoplasia: 2020 an endoscopic odyssey

The past decade has seen significant advances in endoscopic imaging and optical enhancements to aid early diagnosis. There is still a treatment gap due to the underdiagnosis of lesions of the oesophagus. Computer aided diagnosis may play an important role in the coming years in providing an adjunct to endoscopists in the early detection and diagnosis of early oesophageal cancers, therefore curative endoscopic therapy can be offered. Research in this area of artificial intelligence is expanding and the future looks promising. In this review article we will review current advances in artificial intelligence in the oesophagus and future directions for development.

Assisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networks

Barrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities. However, they still suffer from drawbacks because of the lack of available data for machine learning purposes, thus implying reduced recognition rates. This work introduces Generative Adversarial Networks to generate high-quality endoscopic images, thereby identifying Barrett's esophagus and adenocarcinoma more precisely. Further, Convolution Neural Networks are used for feature extraction and classification purposes. The proposed approach is validated over two datasets of endoscopic images, with the experiments conducted over the full and patch-split images. The application of Deep Convolutional Generative Adversarial Networks for the data augmentation step and LeNet-5 and AlexNet for the classification step allowed us to validate the proposed methodology over an extensive set of datasets (based on original and augmented sets), reaching results of 90% of accuracy for the patch-based approach and 85% for the image-based approach. Both results are based on augmented datasets and are statistically different from the ones obtained in the original datasets of the same kind. Moreover, the impact of data augmentation was evaluated in the context of image description and classification, and the results obtained using synthetic images outperformed the ones over the original datasets, as well as other recent approaches from the literature. Such results suggest promising insights related to the importance of proper data for the accurate classification concerning computer-assisted Barrett's esophagus and adenocarcinoma detection.

Curriculum for optical diagnosis training in Europe: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement

This manuscript represents an official Position Statement of the European Society of Gastrointestinal Endoscopy (ESGE) aiming to guide general gastroenterologists to develop and maintain skills in optical diagnosis during endoscopy. In general, this requires additional training beyond the core curriculum currently provided in each country. In this context, ESGE have developed a European core curriculum for optical diagnosis practice across Europe for high quality optical diagnosis training. 1:  ESGE suggests that every endoscopist should have achieved general competence in upper and/or lower gastrointestinal (UGI/LGI) endoscopy before commencing training in optical diagnosis of the UGI/LGI tract, meaning personal experience of at least 300 UGI and/or 300 LGI endoscopies and meeting the ESGE quality measures for UGI/LGI endoscopy. ESGE suggests that every endoscopist should be able and competent to perform UGI/LGI endoscopy with high definition white light combined with virtual and/or dye-based chromoendoscopy before commencing training in optical diagnosis. 2:  ESGE suggests competency in optical diagnosis can be learned by attending a validated optical diagnosis training course based on a validated classification, and self-learning with a minimum number of lesions. If no validated training course is available, optical diagnosis can only be learned by attending a non-validated onsite training course and self-learning with a minimum number of lesions. 3:  ESGE suggests endoscopists are competent in optical diagnosis after meeting the pre-adoption and learning criteria, and meeting competence thresholds by assessing a minimum number of lesions prospectively during real-time endoscopy. ESGE suggests ongoing in vivo practice by endoscopists to maintain competence in optical diagnosis. If a competent endoscopist does not perform in vivo optical diagnosis on a regular basis, ESGE suggests repeating the learning and competence phases to maintain competence.Key areas of interest were optical diagnosis training in Barrett's esophagus, esophageal squamous cell carcinoma, early gastric cancer, diminutive colorectal lesions, early colorectal cancer, and neoplasia in inflammatory bowel disease. Condition-specific recommendations are provided in the main document.

Clinical and pathological predictors of failure of endoscopic therapy for Barrett's related high-grade dysplasia and early esophageal adenocarcinoma

BACKGROUND AND AIMS

Multimodal endoscopic treatment for Barrett's esophagus (BE) related high-grade dysplasia (HGD) and early esophageal adenocarcinoma (EAC) is safe and effective. However, there is a paucity of data to predict the response to endoscopic treatment. This study aimed to identify predictors of failure to achieve complete eradication of neoplasia (CE-N) and complete eradication of intestinal metaplasia (CE-IM).

METHODS

We performed a retrospective analysis of prospectively collected data of all HGD/EAC cases treated endoscopically at a tertiary referral center. Only patients with confirmed HGD/EAC from initial endoscopic mucosal resection (EMR) were included. Potential predictive variables including clinical characteristics, endoscopic features, and index histologic parameters of the EMR specimens were evaluated using multivariate Cox regression.

RESULTS

A total of 457 patients were diagnosed with HGD/EAC by initial EMR from January 2008 to January 2019. Of these, 366 patients who underwent subsequent endoscopic treatment with or without RFA were included. Cumulative incidence rates at 3 years for CE-N and CE-IM were 91.4% (95% CI 87.8-94.2%) and 66.8% (95% CI 61.2-72.3%), respectively during a median follow-up period of 35 months. BE segment of 3-10 cm (HR 0.45; 95% CI 0.36-0.57) and > 10 cm (HR 0.25; 95% CI 0.15-0.40) were independent clinical predictors associated with failure to achieve CE-N. With respect to CE-IM, increasing age (HR 0.88; 95% CI 0.78-1.00) was another predictor along with BE segment of 3-10 cm (HR 0.37; 95% CI 0.28-0.49) and > 10 cm (HR 0.15; 95% CI 0.07-0.30). Lymphovascular invasion increased the risk of CE-N and CE-IM failure in EAC cases.

CONCLUSION

Failure to achieve CE-N and CE-IM is associated with long-segment BE and other clinical variables. Patients with these predictors should be considered for a more intensive endoscopic treatment approach at expert centers.

The Efficacy of Linked Color Imaging in the Endoscopic Diagnosis of Barrett's Esophagus and Esophageal Adenocarcinoma

BACKGROUND

The present study aimed to evaluate the efficacy of linked color imaging (LCI) in diagnosing Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC).

METHODS

A total of 112 and 12 consecutive patients with BE and EAC were analyzed. The visibility scores of BE and EAC ranging from 4 (excellent visibility) to 0 (not detectable) were evaluated by three trainees and three experts using white light imaging (WLI), LCI mode, and blue laser imaging bright (BLI-b) mode. In addition, L∗a∗b∗ color values and color differences (ΔE∗) were evaluated using the CIELAB color space system.

RESULTS

The visibility score of the BE in LCI mode (2.94 ± 1.32) was significantly higher than those in WLI (2.46 ± 1.48) and BLI-b mode (2.35 ± 1.46) (p < 0.01). The color difference (ΔE∗) from the adjacent gastric mucosa in LCI mode (17.11 ± 8.53) was significantly higher than those in other modes (12.52 ± 9.37 in WLI and 11.96 ± 6.59 in BLI-b mode, p < 0.01). The visibility scores of EAC in LCI mode (2.56 ± 1.47) and BLI-b mode (2.51 ± 1.28) were significantly higher than that in WLI (1.64 ± 1.46) (p < 0.01). The color difference (ΔE∗) from the adjacent normal Barrett's mucosa in LCI mode (19.96 ± 7.97) was significantly higher than that in WLI (12.95 ± 11.86) (p = 0.03).

CONCLUSION

The present findings suggest that LCI increases the visibility of BE and EAC and contributes to the improvement of the detection of these lesions.

Features of Esophageal Adenocarcinoma in Magnifying Narrow-Band Imaging

BACKGROUND

Several endoscopic classifications for esophageal adenocarcinoma have been proposed; however, gastric adenocarcinoma is much more common than esophageal or esophagogastric junctional (EGJ) adenocarcinoma in East Asian countries. We, therefore, investigated whether an endoscopic diagnostic algorithm for gastric adenocarcinoma could be used for esophageal or EGJ adenocarcinoma.

METHODS

One hundred eighteen consecutive patients who underwent endoscopic resection or surgery for intramucosal esophageal or EGJ adenocarcinoma, at the Osaka International Cancer Institute between January 2006 and December 2017, were included in this retrospective study. Their lesions were classified as Siewert type 1 or 2, and the presence of endoscopic magnifying narrow-band imaging findings for diagnosing gastric adenocarcinoma was evaluated.

RESULTS

We evaluated 125 adenocarcinomas in 118 patients (29 type 1 and 96 type 2). Demarcation lines (DLs) were seen in 7 (24%) type 1 and 53 (55%) type 2 lesions. Irregular mucosal patterns were present in 2 (7%) type 1 and 22 (23%) type 2 lesions. Irregular vascular patterns were present in 26 (90%) type 1 and 50 (52%) type 2 lesions. According to the magnifying endoscopy diagnostic algorithm for gastric adenocarcinoma, only 7 (24%) type 1 and 52 (54%) type 2 lesions were correctly diagnosed as cancers (p = 0.005).

CONCLUSION

The magnifying endoscopy diagnostic algorithm for gastric cancer may not be useful for esophageal or EGJ adenocarcinomas because of the low visibility of DLs, especially in Siewert type 1 adenocarcinoma.

Quality indicators in diagnostic upper gastrointestinal endoscopy

Upper gastrointestinal (UGI) endoscopy contributes a major clinical service with consistently growing demand around the world. Its utility corresponds to varying epidemiological issues throughout the globe, with cancer screening and surveillance being of the utmost priority. Despite high accuracy in neoplasia detection, UGI endoscopy remains a highly operator-dependent procedure, characterized by a substantial rate of missed pathology. Despite an overall lack of high-quality performance measures, there is an increased level of awareness about the need for quality control of this procedure, which is reflected in several guidelines and position statements published in recent years. It is widely recognized that quality assessment should go beyond mere technical aspects of the examination, and include both pre- and post-procedural factors. By this means, quality control encompasses the entire patient experience with the health care provider, from appropriate indication and physical assessment, through high-quality endoscopy service, to appropriate follow up and patient satisfaction. This article aims to review the available and emerging quality metrics for UGI endoscopy, taken mostly from Western endoscopy societies, with references to Asian recommendations where appropriate. The paper is limited solely to diagnostic UGI endoscopy and does not include performance measures for therapeutic procedures.

Endoscopic submucosal dissection/endoscopic mucosal resection guidelines for esophageal cancer

The Japan Gastroenterological Endoscopy Society has developed endoscopic submucosal dissection/endoscopic mucosal resection guidelines. These guidelines present recommendations in response to 18 clinical questions concerning the preoperative diagnosis, indications, resection methods, curability assessment, and surveillance of patients undergoing endoscopic resection for esophageal cancers based on a systematic review of the scientific literature.

Volumetric laser endomicroscopy in Barrett's esophagus: ready for primetime

Barrett's esophagus (BE) is the condition where intestinal metaplastic changes are found in the normal stratified squamous epithelium of the esophagus predisposing an individual to dysplasia and esophageal adenocarcinoma (EAC). It tends to affect males and is often the result of chronic gastroesophageal reflux disease (GERD). The current standard of therapy for diagnosing Barrett's is white light endoscopy (WLE) with biopsies obtained using the Seattle protocol. Multiple newer advanced modalities have been developed to improve diagnostic abilities, including volumetric laser endomicroscopy (VLE). This technique utilizes second generation optical coherence tomography (OCT) to provide an enhanced circumferential image to a depth of 3 mm with the potential for improved diagnostic yield for dysplasia, particularly submucosal lesions or lesions not seen by WLE. It has also been evaluated in guiding mapping of endotherapy as well as post therapy surveillance for recurrence. Although the results have been promising when used with current diagnostic standards, overall data are limited to support the routine use of VLE.

Seattle protocol vs narrow band imaging guided biopsy in screening of Barrett's esophagus in gastroesophageal reflux disease patients

Barrett's esophagus has 0.5% to 7% risk of progression to esophageal adenocarcinoma. The method of obtaining biopsies to diagnose Barrett's is challenging. Seattle protocol has been considered as the gold standard, however its difficulty limits its applicability in practice. Narrow band imaging guided biopsy has been proposed as an alternative.To investigate the accuracy, sensitivity, specificity and applicability of Narrow band guided biopsy as a screening tool for Barret's esophagus in gastroesophageal reflux patients.Endoscopy was done in 2 different sessions 2 weeks apart for 100 patients in Alexandria, Egypt. Patients had at least one of the following: Chronic Gastroesophageal reflux disease, frequent Gastroesophageal reflux disease, or two or more risk factors for Barrett's esophagus. All patients with known Barrett's esophagus were excluded.Seventeen patients had Barrett's esophagus either by one of the two techniques or by both, 4 patients by both methods, 7 patients by narrow band imaging alone and 6 patients by Seattle protocol alone (P < .001, κ = 0.461). Sensitivity, specificity, negative predictive value and positive predictive value for Seattle protocol were 58.8%, 100%, 92.2%, 100% vs 76.5%, 100%, 95.4%, 100% respectively for narrow band imaging. A mean of 7.73 samples/patient was taken in Seattle protocol vs 3.42 samples in narrow band imaging (P < .001). A mean of 8.63 minutes was consumed in Seattle protocol vs 2.65 minutes in narrow band imaging (P < .001).Narrow band imaging guided biopsy might have higher accuracy, sensitivity and negative predictive value as well as fewer number of biopsies and shorter time of the procedure compared to Seattle protocol which might increases its applicability as screening protocol for Barrett's esophagus. However, further larger multicentric studies are needed.

Development and validation of the international Blue Light Imaging for Barrett's Neoplasia Classification

BACKGROUND AND AIMS

Detecting subtle Barrett's neoplasia during surveillance endoscopy can be challenging. Blue-light imaging (BLI) is a novel advanced endoscopic technology with high-intensity contrast imaging that may improve the identification of Barrett's neoplasia. The aim of this study was to develop and validate the first classification to enable characterization of neoplastic and non-neoplastic Barrett's esophagus using BLI.

METHODS

In phase 1, descriptors pertaining to neoplastic and non-neoplastic Barrett's esophagus were identified to form the classification, named the Blue Light Imaging for Barrett's Neoplasia Classification (BLINC). Phase 2 involved validation of these component criteria by 10 expert endoscopists assessing 50 BLI images. In phase 3, a web-based training module was developed to enable 15 general (nonexpert) endoscopists to use BLINC. They then validated the classification with an image assessment exercise in phase 4, and their pre- and post-training results were compared.

RESULTS

In phase 1 the descriptors were grouped into color, pit, and vessel pattern categories to form the classification. In phase 2 the sensitivity of neoplasia identification was 96.0% with a very good level of agreement among the experts (κ = .83). In phase 3, 15 general endoscopists completed the training module. In phase 4 their pretraining sensitivity (85.3%) improved significantly to 95.7% post-training with a good level of agreement (κ = .67).

CONCLUSIONS

We developed and validated a new classification system (BLINC) for the optical diagnosis of Barrett's neoplasia using BLI. Despite the limitations of this image-based study with a high prevalence of neoplasia, we believe it has the potential to improve the optical diagnosis of Barrett's neoplasia given the high degree of sensitivity (96%) noted. It is also a promising tool for training in Barrett's esophagus optical diagnosis using BLI.

Pre-Cancerous Stomach Lesion Detections with Multispectral-Augmented Endoscopic Prototype

In this paper, we are interested in the in vivo detection of pre-cancerous stomach lesions. Pre-cancerous lesions are unfortunately rarely explored in research papers as most of them are focused on cancer detection or conducted ex-vivo. For this purpose, a novel prototype is introduced. It consists of a standard endoscope with multispectral cameras, an optical setup, a fiberscope, and an external light source. Reflectance spectra are acquired in vivo on 16 patients with a healthy stomach, chronic gastritis, or intestinal metaplasia. A specific pipeline has been designed for the classification of spectra between healthy mucosa and different pathologies. The pipeline includes a wavelength clustering algorithm, spectral features computation, and the training of a classifier in a “leave one patient out” manner. Good classification results, around 80%, have been obtained, and two attractive wavelength ranges were found in the red and near-infrared ranges: [745, 755 nm] and [780, 840 nm]. The new prototype and the associated results give good arguments in favor of future common use in operating rooms, during upper gastrointestinal exploration of the stomach for the detection of stomach diseases.

A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology

Background and aim  The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research. In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders. The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians. This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy.

Modeling clinical assessor intervariability using deep hypersphere encoder–decoder networks

In medical imaging, a proper gold-standard ground truth as, e.g., annotated segmentations by assessors or experts is lacking or only scarcely available and suffers from large intervariability in those segmentations. Most state-of-the-art segmentation models do not take inter-observer variability into account and are fully deterministic in nature. In this work, we propose hypersphere encoder–decoder networks in combination with dynamic leaky ReLUs, as a new method to explicitly incorporate inter-observer variability into a segmentation model. With this model, we can then generate multiple proposals based on the inter-observer agreement. As a result, the output segmentations of the proposed model can be tuned to typical margins inherent to the ambiguity in the data. For experimental validation, we provide a proof of concept on a toy data set as well as show improved segmentation results on two medical data sets. The proposed method has several advantages over current state-of-the-art segmentation models such as interpretability in the uncertainty of segmentation borders. Experiments with a medical localization problem show that it offers improved biopsy localizations, which are on average 12% closer to the optimal biopsy location.

Challenges to diagnostic standardization of Barrett's esophagus in Asia

Barrett's esophagus (BE), a premalignant condition of the lower esophagus, is increasingly prevalent in Asia. However, endoscopic and histopathological criteria vary widely between studies across Asia, making it challenging to assess comparability between geographical regions. Furthermore, guidelines from various societies worldwide provide differing viewpoints and definitions, leading to diagnostic challenges that affect prognostication of the condition. In this review, the authors discuss the controversies surrounding the diagnosis of BE, particularly in Asia. Differences between guidelines worldwide are summarized with further discussion regarding various classifications of BE used, different definitions of gastroesophageal junction used across geographical regions and the clinical implications of intestinal metaplasia in the setting of BE. Although many guidelines recommend the Seattle protocol as the preferred approach regarding dysplasia surveillance in BE, some limitations exist, leading to poor adherence. Newer technologies, such as acetic acid-enhanced magnification endoscopy, narrow band imaging, Raman spectroscopy, molecular approaches and the use of artificial intelligence appear promising in addressing these problems, but further studies are required before implementation into routine clinical practice. The Asian Barrett's Consortium also outlines its ongoing plans to tackle the challenge of standardizing the diagnosis of BE in Asia.