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A randomized trial compared peroral endoscopic myotomy with laparoscopic Heller’s myotomy plus Dor’s fundoplication in patients with symptomatic idiopathic achalasia. POEM was noninferior to LHM in controlling symptoms of achalasia at 2 years. Symptoms of gastroesophageal reflux were more common in the POEM group.

ObjectiveEndoscopic full-thickness resection (EFTR) is a novel treatment of colorectal lesions not amenable to conventional endoscopic resection. The aim of this prospective multicentre study was to assess the efficacy and safety of the full-thickness resection device.Design181 patients were recruited in 9 centres with the indication of difficult adenomas (non-lifting and/or at difficult locations), early cancers and subepithelial tumours (SET). Primary endpoint was complete en bloc and R0 resection.ResultsEFTR was technically successful in 89.5%, R0 resection rate was 76.9%. In 127 patients with difficult adenomas and benign histology, R0 resection rate was 77.7%. In 14 cases, lesions harboured unsuspected cancer, another 15 lesions were primarily known as cancers. Of these 29 cases, R0 resection was achieved in 72.4%; 8 further cases had deep submucosal infiltration >1000 µm. Therefore, curative resection could only be achieved in 13/29 (44.8%). In the subgroup with SET (n=23), R0 resection rate was 87.0%. In general, R0 resection rate was higher with lesions ≤2 cm vs >2 cm (81.2% vs 58.1%, p=0.0038). Adverse event rate was 9.9% with a 2.2% rate of emergency surgery. Three-month follow-up was available from 154 cases and recurrent/residual tumour was evident in 15.3%.ConclusionEFTR has a reasonable technical efficacy especially in lesions ≤2 cm with acceptable complication rates. Curative resection rate for early cancers was too low to recommend its primary use in this indication. Further comparative studies have to show the clinical value and long-term outcome of EFTR in benign colorectal lesions.Trial registration numberNCT02362126; Results.

ObjectiveArtificial intelligence (AI) may reduce underdiagnosed or overlooked upper GI (UGI) neoplastic and preneoplastic conditions, due to subtle appearance and low disease prevalence. Only disease-specific AI performances have been reported, generating uncertainty on its clinical value.DesignWe searched PubMed, Embase and Scopus until July 2020, for studies on the diagnostic performance of AI in detection and characterisation of UGI lesions. Primary outcomes were pooled diagnostic accuracy, sensitivity and specificity of AI. Secondary outcomes were pooled positive (PPV) and negative (NPV) predictive values. We calculated pooled proportion rates (%), designed summary receiving operating characteristic curves with respective area under the curves (AUCs) and performed metaregression and sensitivity analysis.ResultsOverall, 19 studies on detection of oesophageal squamous cell neoplasia (ESCN) or Barrett's esophagus-related neoplasia (BERN) or gastric adenocarcinoma (GCA) were included with 218, 445, 453 patients and 7976, 2340, 13 562 images, respectively. AI-sensitivity/specificity/PPV/NPV/positive likelihood ratio/negative likelihood ratio for UGI neoplasia detection were 90% (CI 85% to 94%)/89% (CI 85% to 92%)/87% (CI 83% to 91%)/91% (CI 87% to 94%)/8.2 (CI 5.7 to 11.7)/0.111 (CI 0.071 to 0.175), respectively, with an overall AUC of 0.95 (CI 0.93 to 0.97). No difference in AI performance across ESCN, BERN and GCA was found, AUC being 0.94 (CI 0.52 to 0.99), 0.96 (CI 0.95 to 0.98), 0.93 (CI 0.83 to 0.99), respectively. Overall, study quality was low, with high risk of selection bias. No significant publication bias was found.ConclusionWe found a high overall AI accuracy for the diagnosis of any neoplastic lesion of the UGI tract that was independent of the underlying condition. This may be expected to substantially reduce the miss rate of precancerous lesions and early cancer when implemented in clinical practice.

Preclinical, epidemiological, and small clinical studies suggest that green tea extract (GTE) and its major active component epigallocatechingallate (EGCG) exhibit antineoplastic effects in the colorectum. A randomized, double-blind trial of GTE standardized to 150 mg of EGCG b.i.d. vs placebo over 3 years was conducted to prevent colorectal adenomas (n = 1,001 with colon adenomas enrolled, 40 German centers). Randomization (1:1, n = 879) was performed after a 4-week run-in with GTE for safety assessment. The primary end point was the presence of adenoma/colorectal cancer at the follow-up colonoscopy 3 years after randomization. The safety profile of GTE was favorable with no major differences in adverse events between the 2 well-balanced groups. Adenoma rate in the modified intention-to-treat set (all randomized participants [intention-to-treat population] and a follow-up colonoscopy 26-44 months after randomization; n = 632) was 55.7% in the placebo and 51.1% in the GTE groups. This 4.6% difference was not statistically significant (adjusted relative risk 0.905; P = 0.1613). The respective figures for the per-protocol population were 54.3% (151/278) in the placebo group and 48.3% (129/267) in the GTE group, indicating a slightly lower adenoma rate in the GTE group, which was not significant (adjusted relative risk 0.883; P = 0.1169). GTE was well tolerated, but there was no statistically significant difference in the adenoma rate between the GTE and the placebo groups in the whole study population.

Objectives The gold standard for diagnosing an infection with SARS-CoV-2 is detection of viral RNA by nucleic acid amplification techniques. Test capacities, however, are limited. Therefore, numerous easy-to-use rapid antigen tests based on lateral flow technology have been developed. Manufacturer-reported performance data seem convincing, but real-world data are missing. Methods We retrospectively analysed all prospectively collected antigen tests results performed between 23.06.2020 and 26.11.2020, generated by non-laboratory personnel at the point-of-care from oro- or nasopharyngeal swab samples at the University Hospital Augsburg and compared them to concomitantly (within 24 h.) generated results from molecular tests. Results For a total of 3630 antigen tests, 3110 NAAT results were available. Overall, sensitivity, specificity, NPV and PPV of antigen testing were 59.4%, 99.0%, 98.7% and 64.8%, respectively. Sensitivity and PPV were lower in asymptomatic patients (47.6% and 44.4%, respectively) and only slightly higher in patients with clinical symptoms (66.7% and 85.0%, respectively). Some samples with very low Ct-values (minimum Ct 13) were not detected by antigen testing. 31 false positive results occurred. ROC curve analysis showed that reducing the COI cut-off from 1, as suggested by the manufacturer, to 0.9 is optimal, albeit with an AUC of only 0.66. Conclusion In real life, performance of lateral-flow-based antigen tests are well below the manufacturer's specifications, irrespective of patient’s symptoms. Their use for detection of individual patients infected with SARS-CoV2 should be discouraged. This does not preclude their usefulness in large-scale screening programs to reduce transmission events on a population-wide scale.

ObjectiveEndoscopic submucosal dissection (ESD) in a curative intent for submucosa-invasive early (T1) colorectal cancers (T1-CRCs) often leads to subsequent surgical resection in case of histologic parameters indicating higher risk of nodal involvement. In some cases, however, the expected benefit may be offset by the surgical risks, suggesting a more conservative approach.DesignRetrospective analysis of consecutive patients with T1-CRC who underwent ESD at 13 centres ending inclusion in 2019 (n=3373). Cases with high risk of nodal involvement (non-curative ESD: G3, submucosal invasion>1000 µm, lymphovascular involvement, budding or incomplete resection/R1) were analysed if follow-up data (endoscopy/imaging) were available, regardless of the postendoscopic management (follow-up vs surgery) selected by the multidisciplinary teams in these institutions. Comorbidities were classified according to Charlson Comorbidity Index (CCI). Outcomes were disease recurrence, death and disease-related death rates in the two groups. Rate of residual disease (RD) at both the previous resection site and regional lymph nodes was assessed in the surgical cases as well as from follow-up in the follow-up group.ResultsOf 604 patients treated by colorectal ESD for submucosally invasive cancer, 207 non-curative resections (34.3%) were included (138 male; mean age 67.6±10.9 years); in 65.2% of cases, no complete resection was achieved (R1). Of the 207 cases, 60.9% (n=126; median CCI: 3; IQR: 2–4) underwent surgical treatment with RD in 19.8% (25/126), while 39.1% (n=81, median CCI: 5; IQR: 4–6) were followed up by endoscopy in all cases. Patients in the follow-up group had a higher overall mortality (HR=3.95) due to non-CRC causes (n=9, mean survival after ESD 23.7±13.7 months). During this follow-up time, tumour recurrence and disease-specific survival rates were not different between the groups (median follow-up 30 months; range: 6–105).ConclusionFollowing ESD for a lesion at high risk of RD, follow-up only may be a reasonable choice in patients at high risk for surgery. Also, endoscopic resection quality should be improved.Trial registration number NCT03987828.

Objective Despite high stand‐alone performance, studies demonstrate that artificial intelligence (AI)‐supported endoscopic diagnostics often fall short in clinical applications due to human‐AI interaction factors. This video‐based trial on Barrett's esophagus aimed to investigate how examiner behavior, their levels of confidence, and system usability influence the diagnostic outcomes of AI‐assisted endoscopy. Methods The present analysis employed data from a multicenter randomized controlled tandem video trial involving 22 endoscopists with varying degrees of expertise. Participants were tasked with evaluating a set of 96 endoscopic videos of Barrett's esophagus in two distinct rounds, with and without AI assistance. Diagnostic confidence levels were recorded, and decision changes were categorized according to the AI prediction. Additional surveys assessed user experience and system usability ratings. Results AI assistance significantly increased examiner confidence levels (p < 0.001) and accuracy. Withdrawing AI assistance decreased confidence (p < 0.001), but not accuracy. Experts consistently reported higher confidence than non‐experts (p < 0.001), regardless of performance. Despite improved confidence, correct AI guidance was disregarded in 16% of all cases, and 9% of initially correct diagnoses were changed to incorrect ones. Overreliance on AI, algorithm aversion, and uncertainty in AI predictions were identified as key factors influencing outcomes. The System Usability Scale questionnaire scores indicated good to excellent usability, with non‐experts scoring 73.5 and experts 85.6. Conclusions Our findings highlight the pivotal function of examiner behavior in AI‐assisted endoscopy. To fully realize the benefits of AI, implementing explainable AI, improving user interfaces, and providing targeted training are essential. Addressing these factors could enhance diagnostic accuracy and confidence in clinical practice.

The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoE-EREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level.