Abstract
BACKGROUND AND AIM: Artificial intelligence (AI) networks offer significant potential for predicting immunotherapy outcomes in gastrointestinal cancers by analyzing genetic mutation profiles. Their application in prognosis remains underexplored. This systematic review and meta-analysis aim to evaluate the effectiveness of AI-based models, which refers to systems utilizing artificial intelligence to analyze data and make predictions, in predicting immunotherapy responses in gastrointestinal cancers using genetic mutation features. METHODS: This study, adhering to PRISMA guidelines, aimed to evaluate AI networks for predicting gastrointestinal cancer prognosis in response to immunotherapy using genetic mutation features. A search in PubMed, WOS, and Scopus identified relevant studies. Data extraction and quality assessment were conducted, and statistical analysis included pooled estimates for sensitivity, specificity, accuracy, and AUC. Regression models and imputation methods addressed missing values, ensuring accurate and robust results. STATA version 18 was used to analyze the data. RESULT: A total of 45 studies, all published in 2024, involving 14,047 participants in training sets and 10,885 participants in test sets, were included. The pooled results of AI model performance for gastrointestinal cancers based on genetic mutation features were: AUC = 0.86 (95% CI: 0.86-0.87), Sensitivity = 83% (95% CI: 83%-84%), Specificity = 72% (95% CI: 72%-73%), and Accuracy = 82% (95% CI: 82%-83%). Heterogeneity was low to moderate, and no publication bias was detected. Subgroup analysis showed higher AUC for gastric cancer models (AUC: 0.87) and lower for pancreatic cancer models (AUC: 0.52). CONCLUSION: AI networks demonstrate promising potential in predicting immunotherapy outcomes for gastrointestinal cancers based on genetic mutation features. This systematic review highlights their effectiveness in stratifying patients and optimizing treatment decisions. However, further large-scale studies are needed to validate AI models and integrate them into clinical practice for improved precision in cancer immunotherapy.