Usefulness of texture and color enhancement imaging (TXI) in early gastric cancer found after Helicobacter pylori eradication.

Early-stage gastric cancer (EGC) found after Helicobacter pylori (Hp) eradication is often difficult to diagnose using conventional white light (WL) endoscopy. We aimed to evaluate whether Texture and Color Enhancement Imaging (TXI), a new image-enhanced endoscopy enhances the EGC lesions after Hp eradication. We also compared diagnostic accuracy and lesion detection time between WL and TXI in trainee endoscopists. 58 EGC lesions after successful Hp eradication were enrolled. Using endoscopic images in WLI, TXI mode 1 (TXI1), and TXI mode 2 (TXI2), visibility of EGC was assessed by six expert endoscopists using a subjective score. Mean color differences (ΔE) of four matched adjacent and intra-tumoral points were examined. Using randomly allocated images, diagnostic accuracy and lesion detection time were evaluated in three trainee endoscopists. Visibility score was unchanged (Score 0) in 20.7% (12/58) and 45.6% (26/57), slightly improved (Score 1) in 60.3% (35/58) and 52.6% (30/57), obviously improved (Score 2) in 45.6% (26/58) and 1.8% (1/57), in TXI1 and TXI2 compared to WL, respectively. Mean ΔE ± SEM in TXI1 (22.90 ± 0.96), and TXI2 (15.32 ± 0.71) were higher than that in WL (1.88 ± 0.26, both P < 0.0001). TXI1 presented higher diagnostic accuracy compared to WL, in two of three trainees (94.8% vs. 74.1%, 100% vs. 89.7%, P = 0.003; < 0.005, respectively). Lesion detection time was shorter in TXI1 in two of three trainees (P = 0.006, 0.004, respectively) compared to WL. TXI improves visibility of EGC after Hp eradication that may contribute to correct diagnosis.

A non-invasive combined strategy to improve the appropriateness of upper gastrointestinal endoscopy.

Background and aim Increasing the appropriateness of upper gastrointestinal endoscopy (UGIE) improves the quality of care while containing costs. The aim of this study was to improve the appropriateness of UGIE through a process involving evaluation of prescriptions and the use of a non-invasive alternative. Materials and methods A senior endoscopist evaluated the appropriateness of all outpatient referrals for UGIE and established the proper timing. Referrals were either accepted and programmed, canceled, or substituted by a non-invasive evaluation of gastric function, determining serum levels of gastrin-17 (G17), Pepsinogen I (PGI) and II (PGII), and antibodies against Helicobacter pylori. Results A total of 5102 requests for UGIE examinations were evaluated; 540 (10.4%) were inappropriate and had been prescribed for: gastroesophageal reflux disease (n=307), surveillance with erroneous timing (n=113), dyspepsia (n=66), other indications (n=20), and absence of written indication (n=34). Gastric function was evaluated in 282/540 patients; findings included normal values in 94 patients without proton-pump inhibitor therapy (PPI) and in 48 on PPI, active H pylori infection in 56, previous H pylori infection in 30, GERD in n=50, and atrophic gastritis in n=4. UGIE was performed in the latter 4 cases.  Within 2 years (range 1-22 months) of the initial refusal, 105/504 patients underwent UGIE, with normal endoscopic findings in 71/105 (67.5%), and with no cases of cancer. Conclusions This strategy, based on a strict control of prescriptions, is effective to increase the appropriateness while containing public health costs. The use of gastric function testing improves patient selection for UGIE endoscopy.

Unusual Presentation of Helicobacter pylori Infection as PSMA-Avid Gastric Mass.

ABSTRACT: 68 Ga-PSMA PET/CT is one of the most common imaging uses for prostate cancer imaging. Although initially thought to be specific for prostate cancer, there are many evidences that are coming of its concentration in many other neoplastic and nonneoplastic pathologies. Helicobacter pylori is the most common bacteria causing gastric inflammation and usually presents with gastroduodenal ulcer. Here we present one unusual case of benign gastric mass formation caused by H. pylori infection with PSMA concentration.

Intelligent Algorithm-Based Gastrointestinal X-Ray Examination in Evaluating the Therapeutic Effect of Probiotics Combined with Triple Therapy on Children with Helicobacter Infection.

In this study, gastrointestinal X-ray imaging was processed based on the Ncut algorithm, the gastric signs of the children after probiotics combined with triple therapy were examined, the therapeutic effect of probiotics combined with triple therapy was evaluated, and the correlation between Helicobacter pylori (HP) infection and gastric disease was analyzed. The included children were randomly divided into group A (treated with standard triple therapy) and group B (pretreated with probiotics on the basis of the treatment method of group A) with 48 cases in each group. The gastrointestinal angiography results of children were observed. The accuracy of the gastrointestinal angiography based on intelligent algorithms was evaluated by taking the results of the urea breath test (UBT) as the gold standard. The results were as follows: first, gastrointestinal X-ray imaging before and after treatment showed that the recovery of the gastric body and gastric antral mucosa for children in group B was better than that of group A (P < 0.05); second, the incidence of gastrointestinal diseases in HP-positive patients was 78% and the incidence of HP-negative patients was 32%; third, the sensitivity, specificity, and accuracy of gastrointestinal X-ray imaging based on intelligent algorithm were 76.47%, 93.67%, and 90.63%, respectively. After treatment, tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6) in group B were much lower than those in group A (P < 0.05), and the incidence of adverse reactions in group B was lower than that in group A (P < 0.05). In summary, gastrointestinal X-ray imaging based on intelligent algorithm had a reliable reference value for the judgment of gastrointestinal HP infection and probiotics combined with triple therapy was more effective for HP infection, which was worthy of clinical application.

Enhanced Visibility in Evaluating Gastric Cancer and Helicobacter pylori-Associated Gastritis Using Linked Color Imaging with a Light-Emitting Diode Light Source.

BACKGROUND: In Japan, laser light source (Laser) endoscopy is widely available, and the characteristics of light-emitting diode light source (LED) endoscopy have not been clarified. AIMS: We assessed the visibility of early gastric cancers (EGCs) and Helicobacter pylori (H. pylori)-associated gastritis for LED endoscopy compared with laser endoscopy using white-light imaging (WLI) and linked color imaging (LCI). METHODS: We assessed 99 lesions between February 2019 and March 2020. The visibility was scored from four (excellent visibility) to one (poor visibility) by evaluating videos including EGCs and gastric mucosa captured using WLI and LCI with LED endoscopy (LED-WLI and LED-LCI, respectively) and laser endoscopy (Laser-WLI and Laser-LCI, respectively). The primary end point was the non-inferiority of the visibility of EGCs and H. pylori-associated gastritis between LED-/Laser-WLI and LED-/Laser-LCI. RESULTS: The visibility scores of EGCs for LED-/Laser-WLI and LED-/Laser-LCI were 3.14/2.97 and 3.39/3.35, respectively. The visibility scores of H. pylori-associated gastritis [intestinal metaplasia (IM), diffuse redness (DR), regular arrangement of collecting venules (RAC) and map-like redness (MR)] for LED-/Laser-WLI and LED-/Laser-LCI were 3.05/2.85 and 3.60/3.50 (IM), 2.76/2.50 and 2.96/2.86 (DR), 2.69/2.44 and 2.77/2.62 (RAC) and 2.97/2.75 and 3.39/3.27 (MR). Non-inferiority was demonstrated for visualizing EGCs and H. pylori-associated gastritis. CONCLUSIONS: LED-WLI and LED-LCI can be used to visualize EGCs and H. pylori-associated gastritis with non-inferiority to Laser-WLI and Laser-LCI. Furthermore, even with LED, LCI was more effective than WLI for evaluating EGCs and H. pylori-associated gastritis. Therefore, LED endoscopy can be used to detect EGCs and evaluate H. pylori-associated gastritis accurately.

Linked Color Imaging (LCI) Emphasizes the Color Changes in the Gastric Mucosa After Helicobacter pylori Eradication.

BACKGROUND: Diffuse redness is a characteristic endoscopic finding that indicates current infection of Helicobacter pylori, which is reduced after successful eradication. Linked color imaging (LCI) has been reported to improve the visibility of diffuse redness compared to white light imaging (WLI); however, quantitative evaluation has not been reported. AIMS: This study aimed to objectively evaluate the color change of the gastric mucosa after H. pylori eradication. METHODS: Images of the greater curvature of the antrum and corpus were captured, and the sites were biopsied during esophagogastroduodenoscopy (EGD) before and 1 year after eradication. The region of interest (ROI) was set around the biopsied area on the images. The color difference (ΔE) before and after eradication was calculated using the CIE L*a*b* color space. The association between the histological evaluation and the color value of the corresponding ROI was determined. RESULTS: At the antrum, there was no significant color change with either mode. At the corpus, the a* value, which reflected redness, decreased significantly after eradication with both modes (WLI: 41.2 to 36.0, LCI: 37.5 to 25.5); the b* value, reflecting yellowish, decreased with WLI, but increased significantly with LCI (WLI: 44.6 to 41.6, LCI: 23.9 to 29.2). The ΔE was significantly larger with LCI than with WLI (16.5 vs. 8.6). The a* values at the corpus were generally associated with histological neutrophil infiltration. CONCLUSIONS: Quantitative evaluation revealed that LCI emphasizes the change in color of the gastric mucosa due to the reduction in diffuse redness.

Clinicopathological features and prognosis of developed gastric cancer based on the diagnosis of mucosal atrophy and enlarged folds of stomach by double-contrast upper gastrointestinal barium X-ray radiography.

BACKGROUND: Mucosal atrophy and enlarged folds of stomach by double-contrast upper gastrointestinal barium X-ray radiography (UGI-XR) are two major features of Helicobacter pylori-induced chronic gastritis. These were previously shown to be risk indicators of gastric cancer, but their predictability for clinicopathological characters of developed gastric cancer is unelucidated. In addition, evidence for decreasing the mortality of gastric cancer by appropriate follow-up of UGI screening is needed. METHODS: The 5134 generally healthy UGI-XR examinees, who underwent follow-up UGI-XR or upper gastrointestinal endoscopy (UGI-ES) more than once, were prospectively observed for 10 years. RESULTS: At the beginning of follow-up, 1515 (29.5%) had mucosal atrophy and 990 (19.5%) had enlarged folds. For the serum anti-H. pylori IgG, 1301 (25.3%) were positive, 177 (3.4%) were possibly positive, and 3656 (71.2%) were negative. During the 10-year observation period, gastric cancer developed in 15 subjects, among which 13 had mucosal atrophy and 10 had enlarged folds. These two features were expectedly useful indicators for gastric cancer incidence, but they showed no significant association with tumor stage or histological type of developed cancer. Only one of the 5134 subjects died of gastric cancer during 10 years, which was significantly lower than the predicted number of gastric cancer death (6.78 for 10 years) according to the mortality rate in Japan. CONCLUSIONS: Neither mucosal atrophy nor enlarged folds of stomach showed a significant association with clinicopathological features of developed gastric tumors. Appropriate follow-up of cancer screening by UGI-XR or UGI-ES can reduce the risk of gastric cancer-related death.

Standard vs magnifying narrow-band imaging endoscopy for diagnosis of Helicobacter pylori infection and gastric precancerous conditions.

BACKGROUND: Advances in endoscopic imaging enable the identification of patients at high risk of gastric cancer. However, there are no comparative data on the utility of standard and magnifying narrow-band imaging (M-NBI) endoscopy for diagnosing Helicobacter pylori (H. pylori) infection, gastric atrophy, and intestinal metaplasia. AIM: To compare the diagnostic performance of standard and M-NBI endoscopy for H. pylori gastritis and precancerous conditions. METHODS: In 254 patients, standard endoscopy findings were classified into mosaic-like appearance (type A), diffuse homogenous redness (type B), and irregular redness with groove (type C). Gastric mucosal patterns visualized by M-NBI were classified as regular round pits with polygonal sulci (type Z-1), more dilated and linear pits without sulci (type Z-2), and loss of gastric pits with coiled vessels (type Z-3). RESULTS: The diagnostic accuracy of standard and M-NBI endoscopy for H. pylori gastritis was 93.3% and 96.1%, respectively. Regarding gastric precancerous conditions, the accuracy of standard and M-NBI endoscopy was 72.0% vs 72.6% for moderate to severe atrophy, and 61.7% vs. 61.1% for intestinal metaplasia in the corpus, respectively. Compared to type A and Z-1, types B+C and Z-2+Z-3 were significantly associated with moderate to severe atrophy [odds ratio (OR) = 5.56 and 8.67] and serum pepsinogen I/II ratio of ≤ 3 (OR = 4.48 and 5.69). CONCLUSION: Close observation of the gastric mucosa by standard and M-NBI endoscopy is useful for the diagnosis of H. pylori gastritis and precancerous conditions.

Screening Esophagogastroduodenoscopy Before Laparoscopic Sleeve Gastrectomy: Results in 819 Patients.

Aim: The routine use of esophagogastroduodenoscopy (EGD) during the preoperative evaluation of surgical weight loss candidates is controversial. The aim of this study is to evaluate the findings of preoperative EGD in patients who are scheduled for a primary laparoscopic sleeve gastrectomy (LSG). The probable effect of these findings on the medical and surgical strategy that was followed is assessed. Methods: Findings of EGD obtained from consecutive LSG candidates and all data were prospectively recorded and retrieved from the database. Results: A total of 819 patients underwent EGD successfully. Mean age and body mass index were 38 ± 11.3 and 43.17 ± 7.2 kg/m2, respectively. Fifty-eight percent were female. EGD of 263 (32.1%) patients was normal and 687 (84%) patients were asymptomatic. At least one abnormal finding was detected in 65% of the asymptomatic patients. Abnormal findings that did not change the surgical strategy were found in 550 patients (67.2%). Findings such as gastritis or duodenitis that changed the medical management before surgery were found in 309 patients (38.2%). Helicobacter pylori was positive in 218 (26.6%) patients but eradication treatment was not applied in the preoperative period. No pathology was detected that would create absolute contraindication or change the type of surgery in any patient. Only technical modifications were required in 13% due to hiatal hernia. The timing of the planned surgery has changed in only 6 patients (0.74%) (early stage neuroendocrine tumor, leiomyoma, severe ulcer). Conclusions: Routine EGD performed before LSG did not change the planned bariatric option in any patient, but led to 13% rate of technical modifications due to the presence of hiatal hernia. At least one abnormal finding was detected in 65% of asymptomatic patients. Due to endoscopic findings, the rate of patients who started medical acid-suppression treatment in the preoperative period was 38%.

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.

Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions.

BACKGROUND: The risk of gastric cancer increases in patients with Helicobacter pylori-associated chronic atrophic gastritis (CAG). X-ray examination can evaluate the condition of the stomach, and it can be used for gastric cancer mass screening. However, skilled doctors for interpretation of X-ray examination are decreasing due to the diverse of inspections. AIM: To evaluate the effectiveness of stomach regions that are automatically estimated by a deep learning-based model for CAG detection. METHODS: We used 815 gastric X-ray images (GXIs) obtained from 815 subjects. The ground truth of this study was the diagnostic results in X-ray and endoscopic examinations. For a part of GXIs for training, the stomach regions are manually annotated. A model for automatic estimation of the stomach regions is trained with the GXIs. For the rest of them, the stomach regions are automatically estimated. Finally, a model for automatic CAG detection is trained with all GXIs for training. RESULTS: In the case that the stomach regions were manually annotated for only 10 GXIs and 30 GXIs, the harmonic mean of sensitivity and specificity of CAG detection were 0.955 ± 0.002 and 0.963 ± 0.004, respectively. CONCLUSION: By estimating stomach regions automatically, our method contributes to the reduction of the workload of manual annotation and the accurate detection of the CAG.

Utilizing artificial intelligence in endoscopy: a clinician's guide.

INTRODUCTION: Artificial intelligence (AI) that surpasses human ability in image recognition is expected to be applied in the field of gastrointestinal endoscopes. Accordingly, its research and development (R &D) is being actively conducted. With the development of endoscopic diagnosis, there is a shortage of specialists who can perform high-precision endoscopy. We will examine whether AI with excellent image recognition ability can overcome this problem. AREAS COVERED: Since 2016, papers on artificial intelligence using convolutional neural network (CNN in other word Deep Learning) have been published. CNN is generally capable of more accurate detection and classification than conventional machine learning. This is a review of papers using CNN in the gastrointestinal endoscopy area, along with the reasons why AI is required in clinical practice. We divided this review into four parts: stomach, esophagus, large intestine, and capsule endoscope (small intestine). EXPERT OPINION: Potential applications for the AI include colorectal polyp detection and differentiation, gastric and esophageal cancer detection, and lesion detection in capsule endoscopy. The accuracy of endoscopic diagnosis will increase if the AI and endoscopist perform the endoscopy together.

Validity of endoscopic features for the diagnosis of Helicobacter pylori infection status based on the Kyoto classification of gastritis.

OBJECTIVES: Evaluation of Helicobacter pylori infection status (non-infection, past infection, current infection) has become important. This study aimed to determine the usefulness of the Kyoto classification of gastritis for diagnosing H. pylori infection status by endoscopy. METHODS: In this prospective study, 498 subjects were recruited. Seven well-experienced endoscopists blinded to the history of eradication therapy performed the examinations. Endoscopic findings were assessed according to the Kyoto classification of gastritis: diffuse redness, regular arrangement of collecting venules (RAC), fundic gland polyp (FGP), atrophy, xanthoma, hyperplastic polyp, map-like redness, intestinal metaplasia, nodularity, mucosal swelling, white and flat elevated lesion, sticky mucus, depressive erosion, raised erosion, red streak, and enlarged folds. We established prediction models according to a machine learning procedure and compared them with general assessment by endoscopists using the Kyoto classification of gastritis. RESULTS: Significantly higher diagnostic odds were obtained for RAC (32.2), FGP (7.7), and red streak (4.7) in subjects with non-infection, map-like redness (12.9) in subjects with past infection, and diffuse redness (26.8), mucosal swelling (13.3), sticky mucus (10.2) and enlarged fold (8.6) in subjects with current infection. The overall diagnostic accuracy rate was 82.9% with the Kyoto classification of gastritis. The diagnostic accuracy of the prediction model was 88.6% for the model without H. pylori eradication history and 93.4% for the model with eradication history. CONCLUSIONS: The Kyoto classification of gastritis is useful for diagnosing H. pylori infection status based on endoscopic findings. Our prediction model is helpful for novice endoscopists. (UMIN000016674).

Comparative study of magnifying narrow-band imaging and conventional white light endoscopy in the diagnosis of Helicobacter pylori status after eradication therapy.

Predicting Helicobacter pylori (Hp) status by endoscopic finding would be useful in recent clinical condition that the use of proton-pump inhibitors, anti-platelet, and anti-coagulant have become widespread. We aimed to elucidate the diagnostic accuracy of magnifying narrow-band imaging (M-NBI) endoscopy in distinguishing Hp status in patients with or without history of successful Hp eradication and compare this accuracy to the diagnostic accuracy of conventional white light (WL) endoscopy.Two hundred seven endoscopic examinations before and after Hp eradication were performed in prospective 163 patients. Endoscopic images by using the M-NBI and conventional WL were stored electronically and randomly allocated to 2 readers for evaluation. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were assessed by reference to Hp status assessed by conventional clinical test.Sensitivity, specificity, PPV, NPV, and accuracy for predicting Hp status for the conventional WL was 72.2%, 75.5%, 72.2%, 75.5%, and 73.9% for the first reader; 86.6%, 57.3%, 64.1%, 82.9%, and 71.0% for the second reader. On the other hand, sensitivity, specificity, PPV, NPV, and accuracy for predicting Hp status for the M-NBI was 96.9%, 93.6%, 93.1%, 97.1%, and 95.2% for the first reader; 92.8%, 93.6%, 92.8%, 93.6%, and 93.2% for the second reader, respectively. The diagnostic accuracy of M-NBI was significantly higher than that of WL (P < .0001 for both readers). Inter-observer agreement of M-NBI (k = 0.83) was also better than that of WL (k = 0.53).M-NBI was capable of distinguishing Hp status before and after eradication therapy.

An unusual presentation of gastric mucosa-associated lymphoid tissue (MALT) lymphoma following blunt abdominal trauma.

A 79-year-old woman presented to the emergency department following a motor vehicle collision. As part of her workup she underwent a CT scan which identified a large mass containing calcifications centred around the gastric antrum, and while being assessed she produced 500 mL of haematemesis. An endoscopy revealed an area of friable mucosa the nature of which was uncertain, and multiple biopsies revealed amyloid deposition and active Helicobacter pylori gastritis. Following review of imaging and pathology, a diagnosis of gastric mucosa-associated lymphoid tissue (MALT) lymphoma was established. She was treated with quadruple therapy for the H. pylori and at 6-month follow-up she is asymptomatic with repeat endoscopy revealing healing of the ulceration and no biopsy evidence of H. pylori or MALT.

Exophytic primary gastric squamous cell carcinoma and H. pylori gastritis.

Primary gastric squamous cell carcinoma (SCC) is rare, and the simultaneous Helicobacter pylori infection has not been reported in the literature. Here, we presented a patient with concurrent H. pylori gastritis and primary gastric SCC. A 54-year-old Hispanic man presented with diarrhoea, chills, night sweats and weight loss of 16 lbs for the previous 6 weeks. Abdominal CT revealed large exophytic mass from the stomach infiltrating multiple organs. Biopsy was performed and histology showed squamoid features. Immunohistochemistry stain was positive for p40, CK5/6, CK7 and Helicobacter type organisms. Patient was diagnosed with primary gastric SCC and has been receiving chemotherapy. We also reviewed the diagnosis, prognosis and treatment of primary gastric SCC.

Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images.

BACKGROUND AND AIM: We recently reported the role of artificial intelligence in the diagnosis of Helicobacter pylori (H. pylori) gastritis on the basis of endoscopic images. However, that study included only H. pylori-positive and -negative patients, excluding patients after H. pylori-eradication. In this study, we constructed a convolutional neural network (CNN) and evaluated its ability to ascertain all H. pylori infection statuses. METHODS: A deep CNN was pre-trained and fine-tuned on a dataset of 98,564 endoscopic images from 5236 patients (742 H. pylori-positive, 3649 -negative, and 845 -eradicated). A separate test data set (23,699 images from 847 patients; 70 positive, 493 negative, and 284 eradicated) was evaluated by the CNN. RESULTS: The trained CNN outputs a continuous number between 0 and 1 as the probability index for H. pylori infection status per image (Pp, H. pylori-positive; Pn, negative; Pe, eradicated). The most probable (largest number) of the three infectious statuses was selected as the 'CNN diagnosis'. Among 23,699 images, the CNN diagnosed 418 images as positive, 23,034 as negative, and 247 as eradicated. Because of the large number of H. pylori negative findings, the probability of H. pylori-negative was artificially re-defined as Pn -0.9, after which 80% (465/582) of negative diagnoses were accurate, 84% (147/174) eradicated, and 48% (44/91) positive. The time needed to diagnose 23,699 images was 261 seconds. CONCLUSION: We used a novel algorithm to construct a CNN for diagnosing H. pylori infection status on the basis of endoscopic images very quickly. ABBREVIATIONS: H. pylori: Helicobacter pylori; CNN: convolutional neural network; AI: artificial intelligence; EGD: esophagogastroduodenoscopies.

FDG PET/CT as a diagnostic and prognostic tool for the evaluation of marginal zone lymphoma.

We evaluated the role of 18-fluoro-2-deoxy-d-glucose positron emission tomography ([18F] FDG-PET) with computed tomography (CT) (PET/CT) as a diagnostic and prognostic tool in newly diagnosed marginal zone lymphoma (MZL) patients. This is a retrospective cohort study of patients with newly diagnosed MZL, treated with immunotherapy, chemotherapy regimens, surgery, or Helicobacter pylori eradication between 2008 and 2016 in a single tertiary center. Only patients who had a pretreatment PET/CT (P-PET/CT) were included. P-PET/CT, interim (I-PET/CT), and end-of-treatment PET/CT (E-PET/CT) studies were reviewed. P-PET/CT results were reported using two methods of evaluation, qualitative and semi quantitative: visual assessment (VAS) and maximal standardized uptake value (SUVmax), and I-PET and E-PET results were reported by Deauville 5-point score (DS) evaluation as well. Avidity of PET/CT was defined as abnormal uptake in any of these methods. The primary outcome was the prognostic role of P-PET/CT, I-PET/CT, and E-PET/CT on progression-free survival (PFS) and overall survival (OS). Data of 196 patients with MZL were identified, 110 of which had P-PET/CT and were included in this analysis. Median age was 67 years (range 18-93). The median follow-up period was 63 months (range 3-278). The median OS and PFS for the whole cohort were 63 (interquartile range 39-85) and 60 (interquartile range 37-76) months, respectively. The avidity of PET at baseline for the whole cohort was 70% (77/110 patients), for MALT lymphoma, 62.5% (40/64 patients), for NMZL, 76.4% (13/17 patients), and for SMZL, 82.7% (24/29 patients). When adjusted for IPI, sex, and comorbidities, positive E-PET/CT was associated with reduced PFS with a hazard ratio (HR) of 3.4 (95% CI, 1.27-9.14, P = 0.02). Positive E-PET/CT did not correlate with OS. However, there were only three events. P-PET/CT was not predictive of PFS or OS. Our study demonstrates that above 70% of MZL are FDG avid. Positive E-PET/CT is a strong prognostic factor for PFS.

Grading of Atrophic Gastritis is Useful for Risk Stratification in Endoscopic Screening for Gastric Cancer.

OBJECTIVES: In order to screen for gastric cancer effectively, its interval should be set according to the risk. This study aimed to determine whether risk stratification is possible using the data obtained from medical examination or endoscopic findings. METHODS: First, subjects who underwent both cancer screening and medical examination from 2009 to 2015 and underwent cancer screening once more by 2016 were studied. Data such as the lipid profile and history of smoking obtained during the medical examination, and the grade of atrophy and presence of peptic ulcers were studied using multivariate analysis. Next, subjects who underwent cancer screening twice or more between 2009 and 2015 with or without medical examinations were studied to analyze any correlation between the grade of atrophy and cancer occurrence using univariate analysis. In both studies, the status of Helicobacter pylori (HP) infection was determined. RESULTS: In the multivariate analysis, 9378 subjects were included. Aging, advanced atrophy, presence of ulcers, and uric acid levels were identified as risk factors. Among subjects who underwent successful HP eradication therapy, advanced atrophy and aging were observed to be crucial risk factors. In the univariate analysis, there were 12,941 subjects. Gastric cancer occurred more frequently in the more severe atrophy group (P < 0.001). The annual rate of cancer occurrence in the most severe atrophy group was 0.31%, which was approximately thrice as that in the less atrophy group. CONCLUSIONS: Risk stratification was possible based on endoscopic examination alone. The interval should be set depending on each case.