Empirical Therapy Versus Tailored Therapy of Helicobacter pylori in Korea: Results of the K-CREATE Study.

BACKGROUND: The optimal duration of regimens for tailored therapy based on genotypic resistance for clarithromycin has yet to be established. AIM: This study was a nationwide, multicenter, randomized trial comparing empirical therapy with tailored therapy based on genotypic resistance for first-line eradication of Helicobacter pylori. We also compared the eradication rates of 7- and 14-day regimens for each group. PATIENTS AND METHODS: Patients with H. pylori infection were first randomized to receive empirical or tailored therapy. Patients in each group were further randomized into 7- or 14-day regimens. Empirical therapy consisted of a triple therapy (TT) regimen (twice-daily doses of pantoprazole 40 mg, amoxicillin 1 g, and clarithromycin 500 mg) for 7 or 14 days. Tailored therapy consisted of TT of 7 or 14 days in patients without genotypic resistance. Patients with genotypic resistance were treated with bismuth quadruple therapy (BQT) regimens (twice-daily doses of pantoprazole 40 mg, three daily doses of metronidazole 500 mg, and four times daily doses of bismuth 300 mg and tetracycline 500 mg) for 7 or 14 days. A 13C-urea breath test assessed eradication rates. The primary outcome was eradication rates of each group. RESULTS: A total of 593 patients were included in the study. The eradication rates were 65.7% (201/306) in the empirical therapy group and 81.9% (235/287) in the tailored therapy group for intention-to-treat analysis (p < 0.001). In the per-protocol analysis, the eradication rates of the empirical therapy and tailored groups were 70.3% (201/286) and 85.5% (235/274) (p < 0.001), respectively. There was no difference in compliance between the two groups. The rate of adverse events was higher in the tailored group compared to the empirical group (p < 0.001). DISCUSSION: Our study confirmed that tailored therapy based on genotypic resistance was more effective than empirical therapy for H. pylori eradication in Korea. However, no significant difference was found between 7- and 14-day regimens for each group. Future studies are needed to determine the optimal duration of therapy for empirical and tailored therapy regimens.

Antibiotic Resistance of Helicobacter pylori: Mechanisms and Clinical Implications.

Helicobacter pylori is a pathogenic bacterium associated with various gastrointestinal diseases, including chronic gastritis, peptic ulcers, mucosa-associated lymphoid tissue lymphoma, and gastric cancer. The increasing rates of H. pylori antibiotic resistance and the emergence of multidrug-resistant strains pose significant challenges to its treatment. This comprehensive review explores the mechanisms underlying the resistance of H. pylori to commonly used antibiotics and the clinical implications of antibiotic resistance. Additionally, potential strategies for overcoming antibiotic resistance are discussed. These approaches aim to improve the treatment outcomes of H. pylori infections while minimizing the development of antibiotic resistance. The continuous evolution of treatment perspectives and ongoing research in this field are crucial for effectively combating this challenging infection.

Deep learning-based clinical decision support system for gastric neoplasms in real-time endoscopy: development and validation study.

BACKGROUND : Deep learning models have previously been established to predict the histopathology and invasion depth of gastric lesions using endoscopic images. This study aimed to establish and validate a deep learning-based clinical decision support system (CDSS) for the automated detection and classification (diagnosis and invasion depth prediction) of gastric neoplasms in real-time endoscopy. METHODS : The same 5017 endoscopic images that were employed to establish previous models were used for the training data. The primary outcomes were: (i) the lesion detection rate for the detection model, and (ii) the lesion classification accuracy for the classification model. For performance validation of the lesion detection model, 2524 real-time procedures were tested in a randomized pilot study. Consecutive patients were allocated either to CDSS-assisted or conventional screening endoscopy. The lesion detection rate was compared between the groups. For performance validation of the lesion classification model, a prospective multicenter external test was conducted using 3976 novel images from five institutions. RESULTS : The lesion detection rate was 95.6 % (internal test). On performance validation, CDSS-assisted endoscopy showed a higher lesion detection rate than conventional screening endoscopy, although statistically not significant (2.0 % vs. 1.3 %; P = 0.21) (randomized study). The lesion classification rate was 89.7 % in the four-class classification (advanced gastric cancer, early gastric cancer, dysplasia, and non-neoplastic) and 89.2 % in the invasion depth prediction (mucosa confined or submucosa invaded; internal test). On performance validation, the CDSS reached 81.5 % accuracy in the four-class classification and 86.4 % accuracy in the binary classification (prospective multicenter external test). CONCLUSIONS : The CDSS demonstrated its potential for real-life clinical application and high performance in terms of lesion detection and classification of detected lesions in the stomach.

Efficacy comparison of 7- and 14-day P-CAB based bismuth-containing quadruple regimen with PPI based bismuth-containing quadruple regimen for Helicobacter pylori infection: rationale and design of an open-label, multicenter, randomized controlled trial.

BACKGROUND: Owing to its strong acid inhibition, potassium-competitive acid blocker (P-CAB) based regimens for Helicobacter pylori (H. pylori) eradication are expected to offer clinical advantages over proton pump inhibitor (PPI) based regimens. This study aims to compare the efficacy and adverse effects of a 7-day and a 14-day P-CAB-based bismuth-containing quadruple regimen (PC-BMT) with those of a 14-day PPI-based bismuth-containing quadruple regimen (P-BMT) in patients with high clarithromycin resistance. METHODS: This randomized multicenter controlled clinical trial will be performed at five teaching hospitals in Korea. Patients with H. pylori infection who are naive to treatment will be randomized into one of three regimens: 7-day or 14-day PC-BMT (tegoprazan 50 mg BID, bismuth subcitrate 300 mg QID, metronidazole 500 mg TID, and tetracycline 500 mg QID) or 14-day P-BMT. The eradication rate, treatment-related adverse events, and drug compliance will be evaluated and compared among the three groups. Antibiotic resistance testing by culture will be conducted during the trial, and these data will be used to interpret the results. A total of 366 patients will be randomized to receive 7-day PC-BMT (n = 122), 14-day PC-BMT (n = 122), or 14-day P-BMT (n = 122). The H. pylori eradication rates in the PC-BMT and P-BMT groups will be compared using intention-to-treat and per-protocol analyses. DISCUSSION: This study will demonstrate that the 7-day or 14-day PC-BMT is well tolerated and achieve similar eradication rates to those of 14-day P-BMT. Additionally, the 7-day PC-BMT will show fewer treatment-related adverse effects and higher drug compliance, owing to its reduced treatment duration. TRIAL REGISTRATION: Korean Clinical Research Information Service registry, KCT0007444. Registered on 28 June 2022, https://cris.nih.go.kr/cris/index/index.do .

Clinical Decision Support System for All Stages of Gastric Carcinogenesis in Real-Time Endoscopy: Model Establishment and Validation Study.

BACKGROUND: Our research group previously established a deep-learning-based clinical decision support system (CDSS) for real-time endoscopy-based detection and classification of gastric neoplasms. However, preneoplastic conditions, such as atrophy and intestinal metaplasia (IM) were not taken into account, and there is no established model that classifies all stages of gastric carcinogenesis. OBJECTIVE: This study aims to build and validate a CDSS for real-time endoscopy for all stages of gastric carcinogenesis, including atrophy and IM. METHODS: A total of 11,868 endoscopic images were used for training and internal testing. The primary outcomes were lesion classification accuracy (6 classes: advanced gastric cancer, early gastric cancer, dysplasia, atrophy, IM, and normal) and atrophy and IM lesion segmentation rates for the segmentation model. The following tests were carried out to validate the performance of lesion classification accuracy: (1) external testing using 1282 images from another institution and (2) evaluation of the classification accuracy of atrophy and IM in real-world procedures in a prospective manner. To estimate the clinical utility, 2 experienced endoscopists were invited to perform a blind test with the same data set. A CDSS was constructed by combining the established 6-class lesion classification model and the preneoplastic lesion segmentation model with the previously established lesion detection model. RESULTS: The overall lesion classification accuracy (95% CI) was 90.3% (89%-91.6%) in the internal test. For the performance validation, the CDSS achieved 85.3% (83.4%-97.2%) overall accuracy. The per-class external test accuracies for atrophy and IM were 95.3% (92.6%-98%) and 89.3% (85.4%-93.2%), respectively. CDSS-assisted endoscopy showed an accuracy of 92.1% (88.8%-95.4%) for atrophy and 95.5% (92%-99%) for IM in the real-world application of 522 consecutive screening endoscopies. There was no significant difference in the overall accuracy between the invited endoscopists and established CDSS in the prospective real-clinic evaluation (P=.23). The CDSS demonstrated a segmentation rate of 93.4% (95% CI 92.4%-94.4%) for atrophy or IM lesion segmentation in the internal testing. CONCLUSIONS: The CDSS achieved high performance in terms of computer-aided diagnosis of all stages of gastric carcinogenesis and demonstrated real-world application potential.

Clinical Practice Guideline for Gastritis in Korea.

Gastritis is a disease characterized by inflammation of the gastric mucosa. It is very common and has various classification systems such as the updated Sydney system. As there is a lot of evidence that Helicobacter pylori infection is associated with the development of gastric cancer and that gastric cancer can be prevented by eradication, H. pylori gastritis has been emphasized recently. The incidence rate of gastric cancer in Korea is the highest in the world, and due to the spread of screening endoscopy, atrophic gastritis and intestinal metaplasia are commonly diagnosed in the general population. However, there have been no clinical guidelines developed in Korea for these lesions. Therefore, this clinical guideline has been developed by the Korean College of Helicobacter and Upper Gastrointestinal Research for important topics that are frequently encountered in clinical situations related to gastritis. Evidence-based guidelines were developed through systematic review and de novo processes, and eight recommendations were made for eight key questions. This guideline needs to be periodically revised according to the needs of clinical practice or as important evidence about this issue is published in the future.

Randomized trial of oral sulfate solution versus polyethylene glycol-ascorbic acid for bowel cleansing in elderly people.

BACKGROUND AND AIM: The efficacy and safety of the recently introduced low-volume purgatives in elderly people are not well known. Therefore, in this trial, we aimed to evaluate and compare the efficacy of two low-volume agents, oral sulfate solution (OSS) and 2-L polyethylene glycol with ascorbic acid (PEG-Asc), in elderly people. METHODS: A prospective, randomized, single-blinded, multicenter, non-inferiority trial was performed at three university-affiliated hospitals in South Korea. Outpatients aged 65-80 years, who underwent elective colonoscopy, were enrolled. The primary outcome was the rate of adequate bowel preparation assessed using the Boston Bowel Preparation Scale. RESULTS: A total of 199 subjects were randomized into the OSS (n = 99) or the 2-L PEG-Asc (n = 100) group. Of them, 189 subjects were included in the analysis of the primary outcome (OSS group 95 vs PEG-Asc group 94). The proportion of adequate bowel preparation was 89.5% (85/95) in the OSS group and 93.6% (88/94) in the 2-L PEG-Asc group. OSS was not inferior to 2-L PEG-Asc according to the prespecified non-inferiority margin of -15% (95% confidence interval for the difference, -12.1 to 3.8). Vomiting (11.6% vs 2.1%) and thirst (24.2% vs 11.7%) were more common in the OSS group than in the 2-L PEG-Asc group. CONCLUSIONS: OSS is an effective low-volume purgative that is non-inferior to 2-L PEG-Asc in elderly people. Both the low-volume agents were identified to be well tolerated and safe in the healthy elderly population.

Correction: Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis.

[This corrects the article DOI: 10.2196/33267.].

Computer-aided diagnosis of esophageal cancer and neoplasms in endoscopic images: a systematic review and meta-analysis of diagnostic test accuracy.

BACKGROUND AND AIMS: Diagnosis of esophageal cancer or precursor lesions by endoscopic imaging depends on endoscopist expertise and is inevitably subject to interobserver variability. Studies on computer-aided diagnosis (CAD) using deep learning or machine learning are on the increase. However, studies with small sample sizes are limited by inadequate statistical strength. Here, we used a meta-analysis to evaluate the diagnostic test accuracy (DTA) of CAD algorithms of esophageal cancers or neoplasms using endoscopic images. METHODS: Core databases were searched for studies based on endoscopic imaging using CAD algorithms for the diagnosis of esophageal cancer or neoplasms and presenting data on diagnostic performance, and a systematic review and DTA meta-analysis were performed. RESULTS: Overall, 21 and 19 studies were included in the systematic review and DTA meta-analysis, respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD algorithms for the diagnosis of esophageal cancer or neoplasms in the image-based analysis were 0.97 (95% confidence interval [CI], 0.95-0.99), 0.94 (95% CI, 0.89-0.96), 0.88 (95% CI, 0.76-0.94), and 108 (95% CI, 43-273), respectively. Meta-regression showed no heterogeneity, and no publication bias was detected. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD algorithms for the diagnosis of esophageal cancer invasion depth were 0.96 (95% CI, 0.86-0.99), 0.90 (95% CI, 0.88-0.92), 0.88 (95% CI, 0.83-0.91), and 138 (95% CI, 12-1569), respectively. CONCLUSIONS: CAD algorithms showed high accuracy for the automatic endoscopic diagnosis of esophageal cancer and neoplasms. The limitation of a lack in performance in external validation and clinical applications should be overcome.

Efficacy of eradication therapy in Helicobacter pylori-negative gastric mucosa-associated lymphoid tissue lymphoma: A meta-analysis.

BACKGROUND AND AIMS: The role of eradication therapy in Helicobacter pylori-negative gastric mucosa-associated lymphoid tissue (MALT) lymphoma remains controversial. The aim of this study was to investigate the efficacy of H. pylori eradication therapy as a first-line treatment for H. pylori-negative gastric MALT lymphoma. METHODS: A literature search of studies published until October 2019 was performed using electronic databases. Studies that reported treatment response to eradication therapy as an initial treatment for patients with H. pylori-negative gastric MALT lymphoma were eligible for inclusion. The primary outcome was the complete remission rate after eradication therapy. RESULTS: Twenty-five studies were included in the analyses. The overall pooled complete remission rate was 29.3% (95% confidence interval [CI], 22.2%-37.4%, I2  = 41.5%). There was no publication bias, and the sensitivity analyses showed consistent results. The pooled complete remission rates were lower in the subgroups of studies that had a higher incidence of translocation t(11;18)(q21;q21) (19.9%, 95% CI, 11.6%-32.0%), studies that used serological tests to exclude H. pylori infection (27.5%, 95% CI, 20.1%-36.4%), and studies where non-response to eradication therapy was determined at <12 months after treatment (27.0%, 95% CI, 15.5%-42.7%). Meta-regression analysis revealed that the pooled estimate was not significantly different in terms of the characteristics of individual studies. CONCLUSIONS: Although the complete remission rate after eradication therapy is not high, it can be used as an initial treatment option in a subset of patients with H. pylori-negative gastric MALT lymphoma. Further studies to identify subgroups of patients who may benefit from eradication therapy are needed.

Consistency of Helicobacter pylori eradication rates of first-line concomitant and sequential therapies in Korea: A nationwide multicenter retrospective study for the last 10 years.

BACKGROUND: Eradication rate of standard triple therapy for H. pylori has declined to unacceptable level, and alternative regimens such as concomitant and sequential therapy have been introduced. We aimed to assess the consistency of eradication rates of concomitant and sequential therapies as for the first-line H. pylori eradication in Korea. METHODS: A nationwide multicenter retrospective study was conducted including 18 medical centers from January 2008 to December 2017. We included 3,800 adults who had test to confirm H. pylori eradication within 1 year after concomitant or sequential therapy. RESULTS: Concomitant and sequential therapy were prescribed for 2508 and 1292 patients, respectively. The overall eradication rate of concomitant therapy was significantly higher than that of sequential therapy (91.8% vs. 86.1%, p < .001). In time trend analysis, the eradication rates of concomitant therapy were 90.2%, 88.2%, 92.1%, 94.3%, 91.1%, and 93.4% for each year from 2012 to 2017 with an increasing trend (p = .0146), while those of ST showed no significant trend (p = .0873). Among 263 patients with second-line therapy, bismuth quadruple therapy showed significantly higher eradication rate than quinolone-based triple therapy (73.9% vs. 51.5% in ITT analysis, p = .001; 82.7% vs. 63.0% in PP analysis, p = .002). CONCLUSION: Concomitant therapy is the best regimen for the first-line H. pylori eradication showing consistently higher eradication rate with an increasing trend for the last 10 years in Korea. Bismuth quadruple therapy should be considered for second-line therapy after eradication failure using non-bismuth quadruple therapy.

Bedside risk-scoring model for predicting 6-week mortality in cirrhotic patients undergoing endoscopic band ligation for acute variceal bleeding.

BACKGROUND AND AIM: Acute variceal bleeding (AVB) is a fatal adverse event of cirrhosis, and endoscopic band ligation (EBL) is the standard treatment for AVB. We developed a novel bedside risk-scoring model to predict the 6-week mortality in cirrhotic patients undergoing EBL for AVB. METHODS: Cox regression analysis was used to assess the relationship of clinical, biological, and endoscopic variables with the 6-week mortality risk after EBL in a derivation cohort (n = 1373). The primary outcome was the predictive accuracy of the new model for the 6-week mortality in the validation cohort. Moreover, we tested the adequacy of the mortality risk-based stratification and the discriminative performance of our new model in comparison with the Child-Turcotte-Pugh (CTP) and the model for end-stage liver disease scores in the validation cohort (n = 200). RESULTS: On multivariate Cox regression analysis, five objective variables (use of beta-blockers, hepatocellular carcinoma, CTP class C, hypovolemic shock at initial presentation, and history of hepatic encephalopathy) were scored to generate a 12-point risk-prediction model. The model stratified the 6-week mortality risk in patients as low (3.5%), intermediate (21.1%), and high (53.4%) (P < 0.001). Time-dependent area under the receiver operating characteristic curve for 6-week mortality showed that this model was a better prognostic indicator than the CTP class alone in the derivation (P < 0.001) and validation (P < 0.001) cohorts. CONCLUSIONS: A simplified scoring model with high potential for generalization refines the prediction of 6-week mortality in high-risk cirrhotic patients, thereby aiding the targeting and individualization of treatment strategies for decreasing the mortality rate.

Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study.

BACKGROUND: In a previous study, we examined the use of deep learning models to classify the invasion depth (mucosa-confined versus submucosa-invaded) of gastric neoplasms using endoscopic images. The external test accuracy reached 77.3%. However, model establishment is labor intense, requiring high performance. Automated deep learning (AutoDL) models, which enable fast searching of optimal neural architectures and hyperparameters without complex coding, have been developed. OBJECTIVE: The objective of this study was to establish AutoDL models to classify the invasion depth of gastric neoplasms. Additionally, endoscopist-artificial intelligence interactions were explored. METHODS: The same 2899 endoscopic images that were employed to establish the previous model were used. A prospective multicenter validation using 206 and 1597 novel images was conducted. The primary outcome was external test accuracy. Neuro-T, Create ML Image Classifier, and AutoML Vision were used in establishing the models. Three doctors with different levels of endoscopy expertise were asked to classify the invasion depth of gastric neoplasms for each image without AutoDL support, with faulty AutoDL support, and with best performance AutoDL support in sequence. RESULTS: The Neuro-T-based model reached 89.3% (95% CI 85.1%-93.5%) external test accuracy. For the model establishment time, Create ML Image Classifier showed the fastest time of 13 minutes while reaching 82.0% (95% CI 76.8%-87.2%) external test accuracy. While the expert endoscopist's decisions were not influenced by AutoDL, the faulty AutoDL misled the endoscopy trainee and the general physician. However, this was corrected by the support of the best performance AutoDL model. The trainee gained the most benefit from the AutoDL support. CONCLUSIONS: AutoDL is deemed useful for the on-site establishment of customized deep learning models. An inexperienced endoscopist with at least a certain level of expertise can benefit from AutoDL support.

Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis.

BACKGROUND: Interpretation of capsule endoscopy images or movies is operator-dependent and time-consuming. As a result, computer-aided diagnosis (CAD) has been applied to enhance the efficacy and accuracy of the review process. Two previous meta-analyses reported the diagnostic performance of CAD models for gastrointestinal ulcers or hemorrhage in capsule endoscopy. However, insufficient systematic reviews have been conducted, which cannot determine the real diagnostic validity of CAD models. OBJECTIVE: To evaluate the diagnostic test accuracy of CAD models for gastrointestinal ulcers or hemorrhage using wireless capsule endoscopic images. METHODS: We conducted core databases searching for studies based on CAD models for the diagnosis of ulcers or hemorrhage using capsule endoscopy and presenting data on diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS: Overall, 39 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of ulcers (or erosions) were .97 (95% confidence interval, .95-.98), .93 (.89-.95), .92 (.89-.94), and 138 (79-243), respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of hemorrhage (or angioectasia) were .99 (.98-.99), .96 (.94-0.97), .97 (.95-.99), and 888 (343-2303), respectively. Subgroup analyses showed robust results. Meta-regression showed that published year, number of training images, and target disease (ulcers vs erosions, hemorrhage vs angioectasia) was found to be the source of heterogeneity. No publication bias was detected. CONCLUSIONS: CAD models showed high performance for the optical diagnosis of gastrointestinal ulcer and hemorrhage in wireless capsule endoscopy.

Computer-Aided Diagnosis of Diminutive Colorectal Polyps in Endoscopic Images: Systematic Review and Meta-analysis of Diagnostic Test Accuracy.

BACKGROUND: Most colorectal polyps are diminutive and benign, especially those in the rectosigmoid colon, and the resection of these polyps is not cost-effective. Advancements in image-enhanced endoscopy have improved the optical prediction of colorectal polyp histology. However, subjective interpretability and inter- and intraobserver variability prohibits widespread implementation. The number of studies on computer-aided diagnosis (CAD) is increasing; however, their small sample sizes limit statistical significance. OBJECTIVE: This review aims to evaluate the diagnostic test accuracy of CAD models in predicting the histology of diminutive colorectal polyps by using endoscopic images. METHODS: Core databases were searched for studies that were based on endoscopic imaging, used CAD models for the histologic diagnosis of diminutive colorectal polyps, and presented data on diagnostic performance. A systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS: Overall, 13 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of diminutive colorectal polyps (adenomatous or neoplastic vs nonadenomatous or nonneoplastic) were 0.96 (95% CI 0.93-0.97), 0.93 (95% CI 0.91-0.95), 0.87 (95% CI 0.76-0.93), and 87 (95% CI 38-201), respectively. The meta-regression analysis showed no heterogeneity, and no publication bias was detected. Subgroup analyses showed robust results. The negative predictive value of CAD models for the diagnosis of adenomatous polyps in the rectosigmoid colon was 0.96 (95% CI 0.95-0.97), and this value exceeded the threshold of the diagnosis and leave strategy. CONCLUSIONS: CAD models show potential for the optical histological diagnosis of diminutive colorectal polyps via the use of endoscopic images. TRIAL REGISTRATION: PROSPERO CRD42021232189; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=232189.

Establishing Machine Learning Models to Predict Curative Resection in Early Gastric Cancer with Undifferentiated Histology: Development and Usability Study.

BACKGROUND: Undifferentiated type of early gastric cancer (U-EGC) is included among the expanded indications of endoscopic submucosal dissection (ESD); however, the rate of curative resection remains unsatisfactory. Endoscopists predict the probability of curative resection by considering the size and shape of the lesion and whether ulcers are present or not. The location of the lesion, indicating the likely technical difficulty, is also considered. OBJECTIVE: The aim of this study was to establish machine learning (ML) models to better predict the possibility of curative resection in U-EGC prior to ESD. METHODS: A nationwide cohort of 2703 U-EGCs treated by ESD or surgery were adopted for the training and internal validation cohorts. Separately, an independent data set of the Korean ESD registry (n=275) and an Asan medical center data set (n=127) treated by ESD were chosen for external validation. Eighteen ML classifiers were selected to establish prediction models of curative resection with the following variables: age; sex; location, size, and shape of the lesion; and whether ulcers were present or not. RESULTS: Among the 18 models, the extreme gradient boosting classifier showed the best performance (internal validation accuracy 93.4%, 95% CI 90.4%-96.4%; precision 92.6%, 95% CI 89.5%-95.7%; recall 99.0%, 95% CI 97.8%-99.9%; and F1 score 95.7%, 95% CI 93.3%-98.1%). Attempts at external validation showed substantial accuracy (first external validation 81.5%, 95% CI 76.9%-86.1% and second external validation 89.8%, 95% CI 84.5%-95.1%). Lesion size was the most important feature in each explainable artificial intelligence analysis. CONCLUSIONS: We established an ML model capable of accurately predicting the curative resection of U-EGC before ESD by considering the morphological and ecological characteristics of the lesions.

Artificial Intelligence for the Determination of a Management Strategy for Diminutive Colorectal Polyps: Hype, Hope, or Help.

Most colorectal polyps are diminutive, and malignant potential for these polyps is uncommon, especially for those in the rectosigmoid. However, many diminutive polyps are still being resected to determine whether these are adenomas or serrated/hyperplastic polyps. Resecting all the diminutive polyps is not cost-effective. Therefore, gastroenterologists have proposed optical diagnosis using image-enhanced endoscopy for polyp characterization. These technologies have achieved favorable outcomes, but are not widely available. Artificial intelligence has been used in clinical medicine to classify lesions. Here, artificial intelligence technology for the characterization of colorectal polyps is discussed in a decision-making context regarding diminutive colorectal polyps.

Endoscopic Submucosal Dissection of Early Gastric Cancer with Mixed-Type Histology: A Systematic Review.

BACKGROUND: Endoscopic submucosal dissection (ESD) criteria are histologically categorized by early gastric cancer (EGC) with differentiated- and undifferentiated-type histology. However, EGC is histologically heterogenous and there have been no separate criteria for EGC with mixed-type histology [EGC-MH; differentiated-type predominant EGC mixed with an undifferentiated component (EGC-MD) or undifferentiated-type predominant EGC mixed with a differentiated component (EGC-MU)]. Moreover, therapeutic outcomes of ESD for EGC-MH have not been clearly described. AIM: This study aimed to evaluate the feasibility of ESD for EGC-MH. METHODS: We searched core databases for specific inclusion factors: patients with EGC-MH, intervention of ESD, and at least one of the following outcomes: rate of en bloc, complete, curative resection, recurrence, procedure-related adverse event, lymphovascular invasion (LVI), or lymph node metastasis (LNM) that enabled evaluation of feasibility of ESD. RESULTS: A total of eight (systematic review) and four studies (meta-analysis) were included. There was no robustness in age, location, or morphology of EGC-MH. Moderately differentiated adenocarcinoma was frequent in pre-ESD biopsy. EGC-MH showed larger size, deeper invasion, and higher rates of LVI/LNM than pure-type EGC. Total en bloc, complete resection, and curative resection rates were 94.6% (95% confidence interval 86.6-97.9%), 77.8% (57.9-89.9%), and 55.1% (50.4-59.6%), respectively. There was no LNM or extra-gastric recurrence after ESD if the EGC-MD met the curative resection criteria. However, the EGC-MD itself was a risk factor for non-curative resection. (Margin positivity was the most common reason.) CONCLUSIONS: Although ESD seems to be technically feasible, inaccurate prediction of lateral or vertical margin leads to lower curative resection rate. Application of more strict indication is needed for EGC-MH.

Ambient air pollution in gastrointestinal endoscopy unit.

BACKGROUND: The gastrointestinal endoscopy unit is frequently exposed to gastrointestinal gas expelled from patients and electrocoagulated tissue through carbonation. This can be potentially harmful to the health of not only the healthcare personnel but also patients who undergo endoscopy. This study aimed to measure the air quality in the endoscopy unit. METHODS: We measured indoor air quality indices (CO2, total volatile organic compounds (VOCs), PM2.5, NO2, CO, and ozone) using portable passive air quality monitoring sensors in the procedural area, recovery area, and cleansing-of-equipment area, at 1-min intervals for 1 week, and the type and number of endoscopic procedures were recorded. RESULTS: CO2, PM2.5, NO2, and ozone levels were the highest in the cleansing area, followed by the procedural and recovery areas, and VOC level was highest in the procedural area. The proportion of poor-quality level of CO2 and VOCs was highest in the procedural area and that of NO2 was highest in the cleansing area. The proportion of tolerable to poor-quality (exceeding acceptable level) level of CO2 and total VOCs in the procedural area was 26% and 19.2% in all measurement times, respectively. The proportion of tolerable to poor-quality level of NO2 in the cleansing area of the endoscopy unit was 32.1% in all measurement times. Multivariate analyses revealed that tolerable to poor-quality (exceeding acceptable level) level of VOCs was associated with the number of endoscopic procedures (odds ratio, 1.79; 95% confidence interval, 1.42-2.27) and PM2.5 level (1.27, 1.12-1.44). Moreover, tolerable to poor-quality level of CO2 was associated with the number of colonoscopy (5.35, 1.19-24.02), especially with electrocoagulation procedures (24.31, 1.31-452.44) in the procedural area. CONCLUSIONS: Healthcare personnel and patients who undergo endoscopy are frequently exposed to ambient air pollution. Health-related protective strategies for ambient air pollution in the endoscopy unit are warranted. CLINICALTRIALS. GOV REGISTRATION NUMBER: NCT03724565.

Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy.

BACKGROUND: Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of H pylori infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification. OBJECTIVE: This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of H pylori infection using endoscopic images. METHODS: Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of H pylori infection and with application of AI for the prediction of H pylori infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS: Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of H pylori infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with H pylori infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images. CONCLUSIONS: An AI algorithm is a reliable tool for endoscopic diagnosis of H pylori infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome. TRIAL REGISTRATION: PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957.