Immunotherapy · 2026
The role of artificial intelligence in predicting immunotherapy treatment outcomes based on gut microbiota composition in cancer patients: a meta-analysis.
СтатьяМетаанализСистематический обзорНазвание и аннотация: Перевод в очереди
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Alhassan Leen, Alrabadi Bassel, Batarseh Suhel F, Alghzawi Yousef, Bandak Natalie, Badwan Aseel, Al Kayed Hadeel
Аннотация
BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, but response varies across patients. Gut microbiota may influence immunotherapy outcomes, while artificial intelligence (AI) can help characterize complex microbiome-host interactions. This systematic review and meta-analysis evaluated AI models for predicting immunotherapy outcomes using gut microbiota data.
METHODS: PubMed, Scopus, and Cochrane Library were searched through January 2025. The primary outcome was pooled area under the curve (AUC).
RESULTS: Nine studies were included in the systematic review, with eight eligible for meta-analysis. The pooled AUC was 0.85 (95% CI: 0.78-0.91), indicating strong predictive performance. Several studies identified microbial taxa, including Bacteroides and Porphyromonadaceae, associated with treatment outcomes.
CONCLUSION: AI-based models show promising potential for predicting immunotherapy response using gut microbiota data. However, limited evidence, substantial methodological heterogeneity, and restricted patient populations limit the reliability and generalizability of current estimates. Larger multicenter studies with standardized AI pipelines and external validation are needed before clinical implementation.
METHODS: PubMed, Scopus, and Cochrane Library were searched through January 2025. The primary outcome was pooled area under the curve (AUC).
RESULTS: Nine studies were included in the systematic review, with eight eligible for meta-analysis. The pooled AUC was 0.85 (95% CI: 0.78-0.91), indicating strong predictive performance. Several studies identified microbial taxa, including Bacteroides and Porphyromonadaceae, associated with treatment outcomes.
CONCLUSION: AI-based models show promising potential for predicting immunotherapy response using gut microbiota data. However, limited evidence, substantial methodological heterogeneity, and restricted patient populations limit the reliability and generalizability of current estimates. Larger multicenter studies with standardized AI pipelines and external validation are needed before clinical implementation.