A rare case of an enterochromaffin-like neuroendocrine tumor associated with parietal cell dysfunction treated using endoscopic submucosal dissection.

Most gastric neuroendocrine tumors (NETs) develop from enterochromaffin-like (ECL) cells. ECL-cell NETs are classically categorized into three types according to their etiology. A 50-year-old woman presented with submucosal tumor-like lesions in the stomach, which were identified via esophagogastroduodenoscopy. Although esophagogastroduodenoscopy and pathological findings of biopsy specimens showed an absence of mucosal atrophy in the body of the stomach, sticky, adherent, dense mucus was observed. All lesions were diagnosed as ECL-cell NETs based on histological examination findings; however, ECL-cell NETs did not apply to any of the classic types I-III categorization based on laboratory, computed tomography, and 24-h intragastric pH monitoring test findings. Endoscopic submucosal dissection of the tumor was performed. Pathological findings of the excised specimen indicated that parietal cell hyperplasia with a protrusion, dilated fundic glands, and endocrine cell hyperplasia in the background mucosa, and parietal cells were not immunostained for the α-subunits of H+/K+-ATPase. Genetic analysis identified mutation in the ATP4A gene. The patient opted for additional gastric resection due to the risk of lymph node metastasis with deeper submucosal invasion and vascular infiltration. This report describes the first case of ECL-cell NETs caused by parietal cell dysfunction, which was treated via endoscopic submucosal dissection.

Timing of bleeding and thromboembolism associated with endoscopic submucosal dissection for gastric cancer in Japan.

BACKGROUND AND AIM: This study aimed to reveal the timing of bleeding and thromboembolism associated with endoscopic submucosal dissection (ESD) for early gastric cancer (EGC). METHODS: We retrospectively reviewed  10,320 patients who underwent ESD for EGC during November 2013-October 2016. We evaluated overall bleeding rates and their inter-group differences. Factors associated with early/late (cut-off 5 days) bleeding and thromboembolism frequency and its association with the intake of antithrombotic agents were investigated. RESULTS: Overall, the post-ESD bleeding rate was 4.7% (489/10 320); the median time to post-ESD bleeding was 4 days. The post-ESD bleeding rates were 3.2%, 8.7%, 15.5%, and 29.9% in those not taking antithrombotic agents, those taking antiplatelet agents, those taking anticoagulants (ACs), and those taking antiplatelet agents and ACs. Warfarin (odds ratio [OR], 9.16), direct oral ACs (OR, 4.16), chronic kidney disease with hemodialysis (OR, 2.93), thienopyridine (OR, 2.25), aspirin (OR, 1.66), tumor size >30 mm (OR, 1.86), multiple tumors' resection (OR, 1.54), and tumor in the lower third of the stomach (OR, 1.40) were independent risk factors for early bleeding. The independent risk factors for late bleeding were direct oral ACs (OR, 7.42), chronic kidney disease with hemodialysis (OR, 4.99), warfarin (OR, 3.90), thienopyridine (OR, 3.09), liver cirrhosis (OR, 2.43), cilostazol (OR, 1.93), aspirin (OR, 1.92), ischemic heart disease (OR, 1.77), and male sex (OR, 1.65). There were three (0.03%) thromboembolic events (cerebral infarction = 2, transient ischemic attack = 1). CONCLUSION: We revealed the timing of bleeding and risk factors for early/late bleeding and showed the thromboembolism frequency associated with ESD for EGC.

Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance.

Diagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations. We investigated the ability of AI to detect superficial esophageal squamous cell carcinoma (ESCC) from esophagogastroduodenoscopy (EGD) videos. We retrospectively collected 8428 EGD images of esophageal cancer to develop a convolutional neural network through deep learning. We evaluated the detection accuracy of the AI diagnosing system compared with that of 18 endoscopists. We used 144 EGD videos for the two validation sets. First, we used 64 EGD observation videos of ESCCs using both white light imaging (WLI) and narrow-band imaging (NBI). We then evaluated the system using 80 EGD videos from 40 patients (20 with superficial ESCC and 20 with non-ESCC). In the first set, the AI system correctly diagnosed 100% ESCCs. In the second set, it correctly detected 85% (17/20) ESCCs. Of these, 75% (15/20) and 55% (11/22) were detected by WLI and NBI, respectively, and the positive predictive value was 36.7%. The endoscopists correctly detected 45% (25-70%) ESCCs. With AI real-time assistance, the sensitivities of the endoscopists were significantly improved without AI assistance (p < 0.05). AI can detect superficial ESCCs from EGD videos with high sensitivity and the sensitivity of the endoscopist was improved with AI real-time support.

Vonoprazan-based triple therapy is effective for Helicobacter pylori eradication irrespective of clarithromycin susceptibility.

BACKGROUND: Helicobacter pylori causes peptic ulcers and accounts for over 90% of gastric cancers; however, eradication rates have been declining due to antimicrobial resistance. Vonoprazan (VPZ), a potassium-competitive acid blocker, produces rapid and profound gastric acid suppression and has shown promising effects in the improvement of H. pylori eradication rates. The efficacy and safety of VPZ-based triple therapy as a first-line regimen for H. pylori eradication and its relationship with clarithromycin (CAM) susceptibility were evaluated. METHODS: From May 2015 to September 2017, H. pylori-infected patients who underwent esophagogastroduodenoscopy with CAM susceptibility testing were prospectively enrolled. Patients received a 7-day triple therapy regimen (VAC) of VPZ (20 mg), amoxicillin (750 mg), and CAM (200 mg) twice daily. Eradication rates, demographics, CAM susceptibility, and safety profiles were assessed. RESULTS: VAC was administered to 146 patients (median age: 63, range: 22-85 years) (60% of whom were females) who underwent CAM susceptibility testing, and 131 patients underwent 13C-urea breath testing to evaluate eradication success. The prevalence of CAM resistance was 34.2%. The overall eradication rates of VAC in per protocol (PP) and "intention to treat" (ITT) analyses were 90.8% (n = 131) and 81.5% (n = 146), respectively. In PP analysis for CAM susceptibility, the eradication rates of VAC were comparable between CAM-sensitive (91.6%, n = 83) and CAM-resistant (89.4%, n = 47) strains. The corresponding rates from the ITT analysis were 80.0% (n = 95) and 84.0% (n = 50), respectively. No adverse events requiring discontinuation of VAC were observed. CONCLUSIONS: CAM-resistant H. pylori was prevalent in one-third of patients in the Tokyo metropolitan area. VPZ-based triple therapy was highly effective and well-tolerated irrespective of CAM susceptibility. Therefore, it could be a valuable first-line treatment regimen for H. pylori infection.

Artificial intelligence-based diagnostic system classifying gastric cancers and ulcers: comparison between the original and newly developed systems.

BACKGROUND: We previously reported for the first time the usefulness of artificial intelligence (AI) systems in detecting gastric cancers. However, the "original convolutional neural network (O-CNN)" employed in the previous study had a relatively low positive predictive value (PPV). Therefore, we aimed to develop an advanced AI-based diagnostic system and evaluate its applicability for the classification of gastric cancers and gastric ulcers. METHODS: We constructed an "advanced CNN" (A-CNN) by adding a new training dataset (4453 gastric ulcer images from 1172 lesions) to the O-CNN, which had been trained using 13 584 gastric cancer and 373 gastric ulcer images. The diagnostic performance of the A-CNN in terms of classifying gastric cancers and ulcers was retrospectively evaluated using an independent validation dataset (739 images from 100 early gastric cancers and 720 images from 120 gastric ulcers) and compared with that of the O-CNN by estimating the overall classification accuracy. RESULTS: The sensitivity, specificity, and PPV of the A-CNN in classifying gastric cancer at the lesion level were 99.0 % (95 % confidence interval [CI] 94.6 %-100 %), 93.3 % (95 %CI 87.3 %-97.1 %), and 92.5 % (95 %CI 85.8 %-96.7 %), respectively, and for classifying gastric ulcers were 93.3 % (95 %CI 87.3 %-97.1 %), 99.0 % (95 %CI 94.6 %-100 %), and 99.1 % (95 %CI 95.2 %-100 %), respectively. At the lesion level, the overall accuracies of the O- and A-CNN for classifying gastric cancers and gastric ulcers were 45.9 % (gastric cancers 100 %, gastric ulcers 0.8 %) and 95.9 % (gastric cancers 99.0 %, gastric ulcers 93.3 %), respectively. CONCLUSION: The newly developed AI-based diagnostic system can effectively classify gastric cancers and gastric ulcers.

[Endoscopic Diagnosis Using Artificial Intelligence].

Image recognition using artificial intelligence(AI)has developed dramatically with innovative technologies such as machine learning and deep learning. Currently, it is considered that AI has exceeded human ability in image recognition. In the field of endoscopic diagnosis, development of computer-aided diagnosis(CAD)systems using AI is progressing. The CAD is expected to help endoscopists improve detection and characterization of polyp, cancer, and inflamation in all digestive area. Some CAD systemes showing ability better than endoscopists have been reported. It may be well applicable to daily clinical practice as real time endoscopic diagnosis in the near future.