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Background The number of researches on the application of artificial intelligence (AI) to diagnosis and treatment of gastric cancer has been increasing in recent years, but no researcher has systematically analyzed it using bibliometric analysis. Objective To analyze the researches on the application of AI to diagnosis and treatment of gastric cancer, explore the research hotspots and development trends from 2003 to 2022. Methods On November 06, 2022, Web of Science (WOS) core collection database was searched by computer to obtain studies on the application of AI to gastric cancer diagnosis and treatment, and VOSviewer 1.6.18 software was used to visualize and analyze inter-country (region), inter-institution, and inter-author collaborations, co-cited authors, keyword co-occurrences and overlays through bibliometric analysis. CiteSpace 5.7.R5 software was used to perform institutional betweenness centrality analysis, journal biplot overlay, cluster analysis of co-cited literature for the last 6 years, co-cited literature clustering timeline graph analysis and reference bursting analysis. Excel 2019 software was used to plot bar graphs of the volume of publications and descriptive analysis tables of countries (regions), institutions, journals, authors, cited references and keywords. Results A total of 703 papers were included, and the annual publication volume of the application of AI to gastric cancer diagnosis and treatment showed an overall increasing trend from 2003-2022, with a rapid increase after 2017 and the most rapid growth from 2019-2021. The top publishing country, institution and author was China, Chinese Academy of Sciences and TADA TOMOHIRO, respectively. The top three co-cited authors of BRAY FREDDIE, HIRASAWA TOSHIAKI and JIANG YUMING had made significant contributions to the field. Frontiers in Oncology was the journal with the highest publication volume, and Gastrointestinal Endoscopy was the most influential journal among the top ten journals for researches related to the application of AI to the diagnosis and treatment of gastric cancer. The citing journals mainly focused on the two fields of "Medicine, Medical, Clinical" and "Molecular, Biology, Immunology". And the cited journals mainly focused on the two fields of "Molecular, Biology, Genetics" and "Health, Nursing, Medicine". The top-ranked literature in terms of total citations titled Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. All keywords were classified into 4 categories based on keyword clustering results, including AI-assisted biological research of gastric cancer, AI-assisted endoscopic diagnosis of gastric cancer, AI-assisted pathological diagnosis of gastric cancer, and AI-assisted non-endoscopic treatment and prognosis prediction of gastric cancer. Deep learning, convolutional neural network, imaging histology, gastrointestinal endoscopy, pathology and immunotherapy were the current research hotspots. Conclusion AI has a broad application prospect in gastric cancer diagnosis and treatment, and more and more scholars are devoted to AI in gastric cancer diagnosis and treatment. Currently, AI has been widely studied in the biology, diagnosis, staging, efficacy assessment and prognosis prediction of gastric cancer. The results of this study can provide a reference for scholars engaged in research work related to AI and gastric cancer.

Objective To establish a prediction model for non-curative resection in patients with early gastric cancer (EGC) who underwent endoscopic submucosal dissection (ESD), and to evaluate its predictive value. Methods Clinical data of EGC patients in the Second Hospital & Clinical Medical School, Lanzhou University from January 2014 to July 2023 were retrospectively collected. According to the postoperative pathological results of ESD, the patients were divided into curative resection group and non-curative resection group. Multifactorial Logistic regression analysis was used to screen the risk factors for non-curative resection after ESD surgery and establish a prediction model, and the model was evaluated using receiver operating characteristic(ROC) curves, calibration curves and clinical decision curve analysis. Results A total of 479 EGC patients who underwent ESD were included, with 60 cases in the non-curative resection group and 419 cases in the curative resection group. The results of multifactorial Logistic regression analysis showed that the lesion diameter > 2 cm (OR=3.017, 95% CI: 1.483-6.136, P=0.002), flat lesion morphology (OR=2.712, 95% CI: 0.774-9.497, P=0.043), undifferentiated/mixed histologic type (OR= 4.199, 95% CI: 1.621-10.872, P=0.003), and submucosal infiltration (OR=30.329, 95% CI: 13.059-70.436, P < 0.001) were independent risk factors for non-curative resection after ESD in EGC patients. The area under the curve of ROC validated within the column-line graph prediction model constructed accordingly was 0.867 (95% CI: 0.811-0.923), the calibration curve showed that the model had good calibration, and decision curve analysis showed the model had a good clinical usefulness. Conclusions The prediction model constructed based on lesion diameter, lesion morphology, histologic type, and depth of mucosal infiltration has good differentiation, calibration, and clinical utility. This model is expected to assist in the early clinical screening of the population at high risk for noncurative resection after ESD in patients with EGC, and to provide a basis for the development of optimal clinical decisions.

Radiotherapy is an important treatment method for malignant tumors. Radiation resistance is the main obstacle to the therapeutic effect of radiotherapy. Cellular metabolic reprogramming is one of the main features of cancer, and it may have an important effect on the therapeutic effect of radiotherapy. Glutamine is closely related to tumor cell biosynthesis and growth. It affects radiotherapy sensitivity by producing antioxidants through decomposition. In addition, the expression patterns and functions of two isoenzymes of glutamine, namely, glutaminase (GLS) and glutaminase 2 (GLS2), are different and have an important influence on the sensitivity of radiotherapy. The utilization of glutamine metabolism in the tumor microenvironment has great research value to improve the efficacy of radiotherapy. This review describes the metabolic characteristics of glutamine in malignant tumors and the sensitization effect of glutamine inhibitors on the efficacy of radiotherapy.

To optimize the mechanical arm target capture and classification of the open multiple-view (MV) visualization program, the open MV visualization programming and deep learning detection method combined with the different capture strategies of robotic arm, a method to extend the research is proposed. For the proposed sorting robot’s multi-cargo grasping, the analysis required to detect a wide variety of goods in a storage environment that lacks color or structural features uniformly. On the basis of SSD target detection method regression, the object’s 3D position information is reconstructed by default preselected cell selection. 3D coordinate accuracy of binocular navigation system was verified as 8% when the target cargo location distance is more than 5 cm, and binoculars matching success rate is 89.7%. The success rate of Sorting and hoarding is increased from 6% to 85% by adding a change to the scoring points of the target products of uneven quality, with this we have achieved efficient and accurate import.