Artificial intelligence in endoscopic ultrasonography: risk stratification of gastric gastrointestinal stromal tumors

Exploring the immune escape mechanisms in gastric cancer patients based on the deep AI algorithms and single-cell sequencing analysis

Gastric cancer is a prevalent and deadly malignancy, and the response to immunotherapy varies among patients. This study aimed to develop a prognostic model for gastric cancer patients and investigate immune escape mechanisms using deep machine learning and single-cell sequencing analysis. Data from public databases were analysed, and a prediction model was constructed using 101 algorithms. The high-AIDPS group, characterized by increased AIDPS expression, exhibited worse survival, genomic variations and immune cell infiltration. These patients also showed immunotherapy tolerance. Treatment strategies targeting the high-AIDPS group identified three potential drugs. Additionally, distinct cluster groups and upregulated AIDPS-associated genes were observed in gastric adenocarcinoma cell lines. Inhibition of GHRL expression suppressed cancer cell activity, inhibited M2 polarization in macrophages and reduced invasiveness. Overall, AIDPS plays a critical role in gastric cancer prognosis, genomic variations, immune cell infiltration and immunotherapy response, and targeting GHRL expression holds promise for personalized treatment. These findings contribute to improved clinical management in gastric cancer.

Endoscopic resection for the treatment of gastric gastrointestinal stromal tumors: a retrospective study from a large tertiary hospital in China

BACKGROUND AND AIMS

With the continuous development of endoscopic technology, endoscopic resection (ER) has gradually become an optional method for the treatment of gastric gastrointestinal stromal tumors (GISTs). However, studies with a large sample or a long follow-up are lacking. Therefore, this research aims to evaluate the efficacy and safety of ER for gastric GISTs in the real-world setting with more than 300 enrolled patients and a follow-up period longer than 45 months.

METHODS

From January 2013 to February 2023, 409 patients with a pathological diagnosis of GISTs after ER were retrospectively enrolled in this study. After excluding 86 patients with non-gastric GISTs, we assessed 323 patients with gastric GISTs. The main outcome measures were en bloc resection, complete resection, residual disease, recurrence, and complications.

RESULTS

There were 194 (60.06%) females and 129 (39.94%) males, and the median age of the included patients was 58 years (51, 63). The median tumor size was 15.0 (10.0, 20.0) mm. According to the modified NIH criteria, 246 (75.85%) patients were classified as very low risk, 62 (19.20%) were classified as low risk, 12 (3.72%) were classified as moderate risk, and 3 (0.93%) were classified as high risk. A total of 287 (88.85%) patients achieved en bloc resection, and 287 (88.85%) also achieved complete resection. Only one patient showed residual and no recurrent lesions were noted during the follow-up. Regarding complications, three patients had complications, with a complication rate of 0.93%, and no severe complications requiring surgical intervention occurred.

CONCLUSION

ER is an appropriate alternative method for the treatment of gastric GISTs, with an en bloc resection rate of 88.85% and a complication rate of 0.93%. No recurrence was noted during follow-up, even for GISTs with piecemeal resection.

Applications of artificial intelligence in gastroscopy: a narrative review

Gastroscopy, a critical tool for the diagnosis of upper gastrointestinal diseases, has recently incorporated artificial intelligence (AI) technology to alleviate the challenges involved in endoscopic diagnosis of some lesions, thereby enhancing diagnostic accuracy. This narrative review covers the current status of research concerning various applications of AI technology to gastroscopy, then discusses future research directions. By providing this review, we hope to promote the integration of gastroscopy and AI technology, with long-term clinical applications that can assist patients.