Endoscopic submucosal dissection comparing with surgical resection in patients with early gastric cancer - A single center experience in Taiwan

High-Speed and Accurate Diagnosis of Gastrointestinal Disease: Learning on Endoscopy Images Using Lightweight Transformer with Local Feature Attention

In response to the pressing need for robust disease diagnosis from gastrointestinal tract (GIT) endoscopic images, we proposed FLATer, a fast, lightweight, and highly accurate transformer-based model. FLATer consists of a residual block, a vision transformer module, and a spatial attention block, which concurrently focuses on local features and global attention. It can leverage the capabilities of both convolutional neural networks (CNNs) and vision transformers (ViT). We decomposed the classification of endoscopic images into two subtasks: a binary classification to discern between normal and pathological images and a further multi-class classification to categorize images into specific diseases, namely ulcerative colitis, polyps, and esophagitis. FLATer has exhibited exceptional prowess in these tasks, achieving 96.4% accuracy in binary classification and 99.7% accuracy in ternary classification, surpassing most existing models. Notably, FLATer could maintain impressive performance when trained from scratch, underscoring its robustness. In addition to the high precision, FLATer boasted remarkable efficiency, reaching a notable throughput of 16.4k images per second, which positions FLATer as a compelling candidate for rapid disease identification in clinical practice.

The Role of Specialized Instruments for Advanced Endoscopic Resections in Gastrointestinal Disease

INTRODUCTION

Advanced endoscopic therapy techniques have been developed and have created alternative treatment options to surgical therapy for several gastrointestinal diseases. This work will focus on new endoscopic tools for special indications of advanced endoscopic resections (ER), especially endoscopic submucosal dissection (ESD), which were developed in our institution. This paper aims to analyze these specialized instruments and identify their status.

METHODS

Initially, the technical process of ESD was analyzed, and the following limitations of the different endoscopic steps and the necessary manipulations were determined: the problem of traction-countertraction, the grasping force needed to pull on tissue, the instrument tip maneuverability, the limited angulation/triangulation, and the mobility of the scope and instruments. Five instruments developed by our team were used: the Endo-dissector, additional working channel system, external independent next-to-the-scope grasper, 3D overtube working station, and over-the-scope grasper. The instruments were used and applied according to their special functions in dry lab, experimental in vivo, and clinical conditions by the members of our team.

RESULTS

The Endo-dissector has a two-fold function: (1) grasping submucosal tissue with enough precision and strength to pull it off the surrounding mucosa and muscle, avoiding damage during energy application and (2) effectively dividing tissue using monopolar energy. The AWC system quickly fulfills the lack of a second working channel as needed to complete the endoscopic task on demand. The EINTS grasper can deliver a serious grasping force, which may be necessary for a traction-countertraction situation during endoscopic resection for lifting a larger specimen. The 3D overtube multifunctional platform provides surgical-like work with bimanual-operated instruments at the tip of the scope, which allows for a coordinated approach during lesion treatment. The OTSG is a grasping tool with very special features for cleaning cavities with debris.

CONCLUSIONS

The research and development of instruments with special features can solve unmet needs in advanced endoscopic procedures. The latter may help to increase indications for the endoscopic resections of gut lesions in the future.

American Society for Gastrointestinal Endoscopy guideline on endoscopic submucosal dissection for the management of early esophageal and gastric cancers: methodology and review of evidence

This document from the American Society for Gastrointestinal Endoscopy (ASGE) provides a full description of the methodology used in the review of the evidence used to inform the final guidance outlined in the accompanying Summary and Recommendations document regarding the role of endoscopic submucosal dissection (ESD) in the management of early esophageal and gastric cancers. This guideline used the Grading of Recommendations, Assessment, Development and Evaluation framework and specifically addresses the role of ESD versus EMR and/or surgery, where applicable, for the management of early esophageal squamous cell carcinoma (ESCC), esophageal adenocarcinoma (EAC), and gastric adenocarcinoma (GAC) and their corresponding precursor lesions. For ESCC, the ASGE suggests ESD over EMR for patients with early-stage, well-differentiated, nonulcerated cancer >15 mm, whereas in patients with similar lesions ≤15 mm, the ASGE suggests either ESD or EMR. The ASGE suggests against surgery for such patients with ESCC, whenever possible. For EAC, the ASGE suggests ESD over EMR for patients with early-stage, well-differentiated, nonulcerated cancer >20 mm, whereas in patients with similar lesions measuring ≤20 mm, the ASGE suggests either ESD or EMR. For GAC, the ASGE suggests ESD over EMR for patients with early-stage, well or moderately differentiated, nonulcerated intestinal type cancer measuring 20 to 30 mm, whereas for patients with similar lesions <20 mm, the ASGE suggests either ESD or EMR. The ASGE suggests against surgery for patients with such lesions measuring ≤30 mm, whereas for lesions that are poorly differentiated, regardless of size, the ASGE suggests surgical evaluation over endosic approaches.

American Society for Gastrointestinal Endoscopy guideline on endoscopic submucosal dissection for the management of early esophageal and gastric cancers: summary and recommendations

This clinical practice guideline from the American Society for Gastrointestinal Endoscopy (ASGE) provides an evidence-based summary and recommendations regarding the role of endoscopic submucosal dissection (ESD) in the management of early esophageal and gastric cancers. It is accompanied by the document subtitled "Methodology and Review of Evidence," which provides a detailed account of the methodology used for the evidence review. This guideline was developed using the Grading of Recommendations, Assessment, Development and Evaluation framework and specifically addresses the role of ESD versus EMR and/or surgery, where applicable, for the management of early esophageal squamous cell carcinoma (ESCC), esophageal adenocarcinoma (EAC), and gastric adenocarcinoma (GAC) and their corresponding precursor lesions. For ESCC, the ASGE suggests ESD over EMR for patients with early-stage, well-differentiated, nonulcerated cancer >15 mm, whereas in patients with similar lesions ≤15 mm, the ASGE suggests either ESD or EMR. The ASGE suggests against surgery for such patients with ESCC, whenever possible. For EAC, the ASGE suggests ESD over EMR for patients with early-stage, well-differentiated, nonulcerated cancer >20 mm, whereas in patients with similar lesions measuring ≤20 mm, the ASGE suggests either ESD or EMR. For GAC, the ASGE suggests ESD over EMR for patients with early-stage, well- or moderately differentiated, nonulcerated intestinal type cancer measuring 20 to 30 mm, whereas for patients with similar lesions <20 mm, the ASGE suggests either ESD or EMR. The ASGE suggests against surgery for patients with such lesions measuring ≤30 mm, whereas for lesions that are poorly differentiated, regardless of size, we suggest surgical evaluation over endoscopic approaches.

Semantic Segmentation of Digestive Abnormalities from WCE Images by Using AttResU-Net Architecture

Colorectal cancer is one of the most common malignancies and the leading cause of cancer death worldwide. Wireless capsule endoscopy is currently the most frequent method for detecting precancerous digestive diseases. Thus, precise and early polyps segmentation has significant clinical value in reducing the probability of cancer development. However, the manual examination is a time-consuming and tedious task for doctors. Therefore, scientists have proposed many computational techniques to automatically segment the anomalies from endoscopic images. In this paper, we present an end-to-end 2D attention residual U-Net architecture (AttResU-Net), which concurrently integrates the attention mechanism and residual units into U-Net for further polyp and bleeding segmentation performance enhancement. To reduce outside areas in an input image while emphasizing salient features, AttResU-Net inserts a sequence of attention units among related downsampling and upsampling steps. On the other hand, the residual block propagates information across layers, allowing for the construction of a deeper neural network capable of solving the vanishing gradient issue in each encoder. This improves the channel interdependencies while lowering the computational cost. Multiple publicly available datasets were employed in this work, to evaluate and verify the proposed method. Our highest-performing model was AttResU-Net, on the MICCAI 2017 WCE dataset, which achieved an accuracy of 99.16%, a Dice coefficient of 94.91%, and a Jaccard index of 90.32%. The experiment findings show that the proposed AttResU-Net overcomes its baselines and provides performance comparable to existing polyp segmentation approaches.

A Robust Deep Model for Classification of Peptic Ulcer and Other Digestive Tract Disorders Using Endoscopic Images

Accurate patient disease classification and detection through deep-learning (DL) models are increasingly contributing to the area of biomedical imaging. The most frequent gastrointestinal (GI) tract ailments are peptic ulcers and stomach cancer. Conventional endoscopy is a painful and hectic procedure for the patient while Wireless Capsule Endoscopy (WCE) is a useful technology for diagnosing GI problems and doing painless gut imaging. However, there is still a challenge to investigate thousands of images captured during the WCE procedure accurately and efficiently because existing deep models are not scored with significant accuracy on WCE image analysis. So, to prevent emergency conditions among patients, we need an efficient and accurate DL model for real-time analysis. In this study, we propose a reliable and efficient approach for classifying GI tract abnormalities using WCE images by applying a deep Convolutional Neural Network (CNN). For this purpose, we propose a custom CNN architecture named GI Disease-Detection Network (GIDD-Net) that is designed from scratch with relatively few parameters to detect GI tract disorders more accurately and efficiently at a low computational cost. Moreover, our model successfully distinguishes GI disorders by visualizing class activation patterns in the stomach bowls as a heat map. The Kvasir-Capsule image dataset has a significant class imbalance problem, we exploited a synthetic oversampling technique BORDERLINE SMOTE (BL-SMOTE) to evenly distribute the image among the classes to prevent the problem of class imbalance. The proposed model is evaluated against various metrics and achieved the following values for evaluation metrics: 98.9%, 99.8%, 98.9%, 98.9%, 98.8%, and 0.0474 for accuracy, AUC, F1-score, precision, recall, and loss, respectively. From the simulation results, it is noted that the proposed model outperforms other state-of-the-art models in all the evaluation metrics.

Endoscopic Resection Versus Surgery in the Treatment of Early Gastric Cancer: A Systematic Review and Meta-Analysis

Background and Aim

Endoscopic resection (ER) is the preferred approach to treat early gastric cancer (EGC) in patients without suspected lymph node involvement and that meet the criteria for ER. Surgery is a more aggressive treatment, but it may be associated with less recurrence and the need for reintervention. Previous meta-analyses comparing ER with surgery for EGC did not incorporate the most recent studies, making accurate conclusions not possible.

Methods

This systematic review and meta-analysis aimed to examine complete resection, length of hospital stay (LOHS), adverse events (AEs), serious AEs, recurrence, 5-year overall survival (OS), and 5-year cancer-specific survival (CSS) in patients with EGC.

Results

A total of 29 cohorts studies involving 20559 patients were included. The ER (n = 7709) group was associated with a lower incidence of AEs (RD = -0.07, 95%CI = -0.1, -0.04, p < 0.0001) and shorter LOHS (95% CI -5.89, -5.32; p < 0,00001) compared to surgery (n = 12850). However, ER was associated with lower complete resection rates (RD = -0.1, 95%CI = -0.15, -0.06; p < 0.00001) and higher rates of recurrence (RD = 0.07, 95%CI = 0.06; p < 0.00001). There were no significant differences between surgery and ER in 5-year OS (RD = -0.01, 95%CI = -0.04, 0.02; p = 0.38), 5-year CSS (RD = 0.01, 95%CI = 0.00, 0.02; p < 0.17), and incidence of serious AEs (RD = -0.03, 95%CI = -0.08, 0.01; p = 0.13).

Conclusions

ER and surgery are safe and effective treatments for EGC. ER provides lower rates of AEs and shorter LOHS compared to surgery. Although ER is associated with lower complete resection rates and a higher risk of recurrence, the OS and CSS were similar between both approaches.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42021255328.

The management of postoperative upper alimentary tract fistulas: A single-center experience of endoscopic tissue glue repair and recommendations of a systematic review

BACKGROUND/PURPOSE

The appropriate management of postoperative upper alimentary tract fistula (UATF) remains uncertain. The efficacy of esophagogastroduodenoscopy (EGD) tissue glue repair in the treatment of patients with postoperative UATF was explored. We also conducted a systematic review of the literature regarding the inpatient management of UATF.

METHODS

Totally 24 patients received EGD tissue glue repair for postoperative UATF at our institute from April 2014 to April 2020. Independent characteristics of size of fistula, location of the UATF, complications, and recurrences were analyzed. PubMed and Cochrane Library databases were reviewed. A pooled analysis was performed, and subgroup analysis was conducted separately for different anatomic locations and techniques.

RESULTS

With a mean follow-up of 40 months, the fistula failed to close with EGD tissue glue repair in 2 of 24 patients (8.3%). Eight patients required repeated EGD tissue glue repair, which was more frequent in oral or thoracic UATF (p = 0.053), but all achieved a successful seal in the EGD tissue after glue repair alone (n = 22). The fistula size was correlated with the demand for repeated EGD tissue glue repair (p = 0.017). Besides, a total of 30 studies regarding 2356 cases of postoperative UATF between 2010 and 2021 were retrieved and analyzed. Several non-operative methods were generally accepted as the initial approach, with a non-inferior success rate compared to operative techniques.

CONCLUSIONS

The results suggest that no single approach toward UATF is superior in terms of success rate and healing time. The potential advantages of EGD tissue glue repair after drainage were more suitable for patients with postoperative UATF and multiple comorbidities.

Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model

Wireless capsule endoscopy is a noninvasive wireless imaging technology that becomes increasingly popular in recent years. One of the major drawbacks of this technology is that it generates a large number of photos that must be analyzed by medical personnel, which takes time. Various research groups have proposed different image processing and machine learning techniques to classify gastrointestinal tract diseases in recent years. Traditional image processing algorithms and a data augmentation technique are combined with an adjusted pretrained deep convolutional neural network to classify diseases in the gastrointestinal tract from wireless endoscopy images in this research. We take advantage of pretrained models VGG16, ResNet-18, and GoogLeNet, a convolutional neural network (CNN) model with adjusted fully connected and output layers. The proposed models are validated with a dataset consisting of 6702 images of 8 classes. The VGG16 model achieved the highest results with 96.33% accuracy, 96.37% recall, 96.5% precision, and 96.5% F1-measure. Compared to other state-of-the-art models, the VGG16 model has the highest Matthews Correlation Coefficient value of 0.95 and Cohen's kappa score of 0.96.

Is cardia cancer a special type of gastric cancer? A differential analysis of early cardia cancer and non-cardia cancer

Background: The prognosis of early cardia cancer and non-cardia cancer is still controversial. It is difficult to collect a large number of cases with complete information in clinical practice. Our study was aimed to identify the differences in clinicopathological characteristics and outcomes of early cardia gastric cancer and non-cardia gastric cancer. Methods: All cases analyzed were from Surveillance, Epidemiology, and End Results database. The data of the patients with early gastric cancer from 2004 to 2010 was retrospectively analyzed. Patients were distributed to cardia cancer group and non-cardia cancer group. Univariate and multivariate analyses were performed to examine differences between groups. The competitive risk model was made to compare the association with cardia cancer and non-cardia cancer about the causes of death. Propensity score matching (PSM) was performed to reduce the bias. Results: We found that cardia cancer was more common in male patients and the White than that in non-cardia cancer at early stage, signet ring cell carcinoma was more common in non-cardia cancer, and the differentiation of non-cardia cancer was worse. Univariate analysis showed that age, marital status, race, tumor location, histology, grade, stage, and operation or not can determine the prognosis. And the prognosis of patients with cardia cancer was worse than that of non-cardia cancer, according to lymph node metastasis and the depth of tumor invasion. Multivariate analysis showed cardia cancer was an independent prognostic factor for poor prognosis. After PSM, cardia cancer still exhibited poor prognosis. Conclusions: At early stage, cardia cancer had a poor prognosis compared with non-cardia cancer. The prevention and treatment of early cardia cancer need to be seriously treated.