Explore the vast range of titles available.

The sixth edition of the Japanese Gastric Cancer Treatment Guidelines was completed in July 2021, incorporating new evidence that emerged after publication of the previous edition. It consists of a text-based “Treatments” part and a “Clinical Questions” part including recommendations and explanations for clinical questions. The treatments parts include a comprehensive description regarding surgery, endoscopic resection and chemotherapy for gastric cancer. The clinical question part is based on the literature search and evaluation by an independent systematic review team. Consequently, not only evidence for each therapeutic recommendation was clearly shown, but it also identified the research fields that require further evaluation to provide appropriate recommendations.

BackgroundImage recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images.MethodsA CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer. To evaluate the diagnostic accuracy, an independent test set of 2296 stomach images collected from 69 consecutive patients with 77 gastric cancer lesions was applied to the constructed CNN.ResultsThe CNN required 47 s to analyze 2296 test images. The CNN correctly diagnosed 71 of 77 gastric cancer lesions with an overall sensitivity of 92.2%, and 161 non-cancerous lesions were detected as gastric cancer, resulting in a positive predictive value of 30.6%. Seventy of the 71 lesions (98.6%) with a diameter of 6 mm or more as well as all invasive cancers were correctly detected. All missed lesions were superficially depressed and differentiated-type intramucosal cancers that were difficult to distinguish from gastritis even for experienced endoscopists. Nearly half of the false-positive lesions were gastritis with changes in color tone or an irregular mucosal surface.ConclusionThe constructed CNN system for detecting gastric cancer could process numerous stored endoscopic images in a very short time with a clinically relevant diagnostic ability. It may be well applicable to daily clinical practice to reduce the burden of endoscopists.

Background: Recently the American Society for Gastrointestinal Endoscopy addressed the ‘resect and discard’ strategy, determining that accurate in vivo differentiation of colorectal polyps (CP) is necessary. Previous studies have suggested a promising application of artificial intelligence (AI), using deep learning in object recognition. Therefore, we aimed to construct an AI system that can accurately detect and classify CP using stored still images during colonoscopy. Methods: We used a deep convolutional neural network (CNN) architecture called Single Shot MultiBox Detector. We trained the CNN using 16,418 images from 4752 CPs and 4013 images of normal colorectums, and subsequently validated the performance of the trained CNN in 7077 colonoscopy images, including 1172 CP images from 309 various types of CP. Diagnostic speed and yields for the detection and classification of CP were evaluated as a measure of performance of the trained CNN. Results: The processing time of the CNN was 20 ms per frame. The trained CNN detected 1246 CP with a sensitivity of 92% and a positive predictive value (PPV) of 86%. The sensitivity and PPV were 90% and 83%, respectively, for the white light images, and 97% and 98% for the narrow band images. Among the correctly detected polyps, 83% of the CP were accurately classified through images. Furthermore, 97% of adenomas were precisely identified under the white light imaging. Conclusions: Our CNN showed promise in being able to detect and classify CP through endoscopic images, highlighting its high potential for future application as an AI-based CP diagnosis support system for colonoscopy.

Gastric mucins serve as a protective barrier on the stomach's surface, protecting from external stimuli including gastric acid and gut microbiota. Their composition typically changes in response to the metaplastic sequence triggered by Helicobacter pylori infection. This alteration in gastric mucins is also observed in cases of gastric cancer, although the precise connection between mucin expressions and gastric carcinogenesis remains uncertain. This review first introduces the relationship between mucin expressions and gastric metaplasia or cancer observed in humans and mice. Additionally, we discuss potential pathogenic mechanisms of how aberrant mucins and their glycans affect gastric carcinogenesis. Finally, we summarize challenges to target tumor‐specific glycans by utilizing lectin‐drug conjugates that can bind to specific glycans. Understanding the correlation and mechanism between these mucin expressions and gastric carcinogenesis could pave the way for new strategies in gastric cancer treatment. Gastric mucins shield the stomach lining from acid and bacteria, with alterations seen in Helicobacter pylori infection and gastric cancer. The review explores this relationship, potential mechanisms in carcinogenesis, and challenges in targeting tumor‐specific glycans for treatment, offering insights into novel approaches for gastric cancer therapy.

Nonalcoholic steatohepatitis (NASH) is a hepatic phenotype of the metabolic syndrome, and increases the risk of cirrhosis and hepatocellular carcinoma (HCC). Although increasing evidence points to the therapeutic implications of certain types of anti-diabetic agents in NASH, it remains to be elucidated whether their effects on NASH are independent of their effects on diabetes. Genetically obese melanocortin 4 receptor–deficient (MC4R-KO) mice fed Western diet are a murine model that sequentially develops hepatic steatosis, NASH, and HCC in the presence of obesity and insulin resistance. In this study, we investigated the effect of the dipeptidyl peptidase-4 (DPP-4) inhibitor anagliptin on NASH and HCC development in MC4R-KO mice. Anagliptin treatment effectively prevented inflammation, fibrosis, and carcinogenesis in the liver of MC4R-KO mice. Interestingly, anagliptin only marginally affected body weight, systemic glucose and lipid metabolism, and hepatic steatosis. Histological data and gene expression analysis suggest that anagliptin treatment targets macrophage activation in the liver during the progression from simple steatosis to NASH. As a molecular mechanism underlying anagliptin action, we showed that glucagon-like peptide-1 suppressed proinflammatory and profibrotic phenotypes of macrophages in vitro . This study highlights the glucose metabolism–independent effects of anagliptin on NASH and HCC development.

Previous therapeutic attempts to deplete cancer-associated fibroblasts (CAFs) or inhibit their proliferation in pancreatic ductal adenocarcinoma (PDAC) were not successful in mice or patients. Thus, CAFs may be tumor suppressive or heterogeneous, with distinct cancer-restraining and -promoting CAFs (rCAFs and pCAFs, respectively). Here, we showed that induced expression of the glycosylphosphatidylinositol-anchored protein Meflin, a rCAF-specific marker, in CAFs by genetic and pharmacological approaches improved the chemosensitivity of mouse PDAC. A chemical library screen identified Am80, a synthetic, nonnatural retinoid, as a reagent that effectively induced Meflin expression in CAFs. Am80 administration improved the sensitivity of PDAC to chemotherapeutics, accompanied by increases in tumor vessel area and intratumoral drug delivery. Mechanistically, Meflin was involved in the suppression of tissue stiffening by interacting with lysyl oxidase to inhibit its collagen crosslinking activity. These data suggested that modulation of CAF heterogeneity may represent a strategy for PDAC treatment.

Gastric neoplasms exhibiting oxyntic gland differentiation typically are composed of cells with mild cytonuclear atypia differentiating to chief cells and to a lesser extent, parietal cells. Such tumors with atypical features have been reported also and terminology for this entity remains a matter of considerable debate. We analyzed and classified 26 tumors as oxyntic gland neoplasms within mucosa (group A, eight tumors) and with submucosal invasion. The latter was divided further into those with typical histologic features (group B, 14 tumors) and atypical features, including high-grade nuclear or architectural abnormality and presence of atypical cellular differentiation (group C, four tumors). Groups A and B tumors shared similar histologic features displaying either a chief cell predominant pattern characterized by monotonous chief cell proliferation, or a well-differentiated mixed cell pattern showing admixture of chief and parietal cells resembling fundic gland. In addition, group C tumors displayed atypical cellular differentiation, including mucous neck cell and foveolar epithelium. Moderate or even marked cytological atypia was noted in group C, whereas it was usually mild in the other groups except for three group B tumors with focal moderate atypia. More than 1000 μm submucosal invasion and lymphovascular invasions were recognized only in group C. Mutation analyses identified KRAS mutation in one group C tumor as well as GNAS mutation in in one group A and group B tumors. Intramucosal tumors appear to behave biologically benign and should be classified as “oxyntic gland adenoma”. Those with submucosal invasion also have low malignant potential; however, a subset will have atypical features associated with aggressive histologic features and should be designated as “adenocarcinoma of fundic gland type”. Especially, we suggest “adenocarcinoma of fundic gland mucosa type” for tumors with submucosal invasion exhibiting atypical cellular differentiation, because the feature is likely to be a sign of aggressive phenotype.

The use of convolutional neural networks (CNNs) has dramatically advanced our ability to recognize images with machine learning methods. We aimed to construct a CNN that could recognize the anatomical location of esophagogastroduodenoscopy (EGD) images in an appropriate manner. A CNN-based diagnostic program was constructed based on GoogLeNet architecture, and was trained with 27,335 EGD images that were categorized into four major anatomical locations (larynx, esophagus, stomach and duodenum) and three subsequent sub-classifications for stomach images (upper, middle, and lower regions). The performance of the CNN was evaluated in an independent validation set of 17,081 EGD images by drawing receiver operating characteristics (ROC) curves and calculating the area under the curves (AUCs). ROC curves showed high performance of the trained CNN to classify the anatomical location of EGD images with AUCs of 1.00 for larynx and esophagus images, and 0.99 for stomach and duodenum images. Furthermore, the trained CNN could recognize specific anatomical locations within the stomach, with AUCs of 0.99 for the upper, middle, and lower stomach. In conclusion, the trained CNN showed robust performance in its ability to recognize the anatomical location of EGD images, highlighting its significant potential for future application as a computer-aided EGD diagnostic system.

Background Anti-programmed cell death-1 (PD-1) antibodies can cause thyroid dysfunction. However, no predictive biomarkers enabling stratification of thyroid dysfunction risk have been identified. Methods A total of 209 patients treated with an anti-PD-1 antibody were evaluated for anti-thyroid antibodies at baseline and prospectively for thyroid function every 6 weeks for 24 weeks after treatment initiation, and then observed until the visits stopped. Thyroid ultrasonography was performed if the patient was positive for anti-thyroid antibodies at baseline. Results Of the 209 patients, 19 (9.1%) developed thyroid dysfunction (destructive thyroiditis or hypothyroidism). The cumulative incidence of thyroid dysfunction was significantly higher in patients who were positive vs. negative for anti-thyroid antibodies (15/44 [34.1%] vs. 4/165 [2.4%], p  < 0.001). Forty-two patients positive for anti-thyroid antibodies at baseline were divided into two groups according to the presence of an irregular echo pattern. The cumulative incidence of thyroid dysfunction was significantly higher in those with an irregular vs. a regular echo pattern (13/23 [56.5%] vs. 1/19 [5.3%], p  = 0.001). None of the patients developed thyroid dysfunction after the initial 24-week period. Conclusions The risk of thyroid dysfunction induced by anti-PD-1 antibodies can be predicted by evaluation of anti-thyroid antibodies and the thyroid echo pattern at baseline. Trial registration UMIN000019024.