An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video)

Artificial intelligence in gastrointestinal surgery: A minireview of predictive models and clinical applications

BACKGROUND

Artificial intelligence (AI) is playing an increasingly significant role in predicting outcomes of gastrointestinal (GI) surgeries, improving preoperative risk assessment and post-surgical decision-making. AI models, particularly those based on machine learning, have demonstrated potential in predicting surgical complications and recovery trajectories.

AIM

To evaluate the role of AI in predicting outcomes for GI surgeries, focusing on its efficacy in enhancing surgical planning, predicting complications, and optimizing post-operative care.

METHODS

A systematic review of studies published up to March 2025 was conducted across databases such as PubMed, Scopus, and Web of Science. Studies were included if they utilized AI models for predicting surgical outcomes, including morbidity, mortality, and recovery. Data were extracted on the AI techniques, performance metrics, and clinical applicability.

RESULTS

Machine learning models demonstrated significantly better performance than logistic regression models, with an area under the curve difference of 0.07 (95%CI: 0.04–0.09; P < 0.001). Models focusing on variables such as patient demographics, nutritional status, and surgical specifics have shown improved accuracy. AI’s ability to integrate multifaceted data sources, such as imaging and genomics, contributes to its superior predictive power. AI has improved the early detection of gastric cancer, achieving 95% sensitivity in real-world settings.

CONCLUSION

AI has the potential to transform GI surgical practices by offering more accurate and personalized predictions of surgical outcomes. However, challenges related to data quality, model transparency, and clinical integration remain.

Identification and validation of serum MUC17 as a non-invasive early warning biomarker for screening of gastric intraepithelial neoplasia

BACKGROUND

The early diagnosis and treatment of Gastric Intraepithelial Neoplasia (GIN) are pivotal for improving the survival rates of patients with gastric cancer (GC). Regrettably, reliable noninvasive biomarkers for GIN screening are currently lacking.

METHODS

mRNA data from the GEO database, pan-cancer data from the TCGA database, and a gene list of exocrine proteins were subjected to integrated analysis to identify a noninvasive biomarker for GIN. The scRNA-seq data analysis, IHC and Elisa were employed to validate the expression of the biomarker in the serum and tissues of clinical patients across different pathological stages.

RESULTS

MUC17 has been identified as a non-invasive diagnostic marker for GIN. It is upregulated in GIN prior to the onset of gastric carcinogenesis and downregulated in other tumors, with high GC specificity. The area under the curve values of serum MUC17 for differentiating chronic gastritis (CG) from low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and early gastric cancer (EGC) were 0.8788, 0.8544, and 0.9513, respectively. Additionally, low plasma MUC17 levels were found to be significantly lower in gastric ulcer (GU), gastric neuroendocrine tumor (GNET), and gastrointestinal stromal tumor (GIST) compared to GIN. The AUC for differentiating between GIN and GU, GNET, or GIST was 0.7803, 0.9244 and 0.9796, respectively.

CONCLUSIONS

These findings suggest that plasma MUC17 levels hold substantial promise as a screening biomarker for individuals with GIN and EGC, effectively identifying high-risk groups that necessitate further gastroscopy.

Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging

Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of AI in abdominal imaging, with a focus on recent literature contributions. This work explores the diagnosis and characterization of hepatobiliary, pancreatic, gastric, colonic, and other pathologies. In addition, the role of AI has been observed to help differentiate renal, adrenal, and splenic disorders. Furthermore, workflow optimization strategies and quantitative imaging techniques used for the measurement and characterization of tissue properties, including radiomics and deep learning, are highlighted. An assessment of how these advancements enable more precise diagnosis, tumor description, and body composition evaluation is presented, which ultimately advances the clinical effectiveness and productivity of radiology. Despite the advancements of AI in abdominal imaging, technical, ethical, and legal challenges persist, and these challenges, as well as opportunities for future development, are highlighted.