Engineered Biomaterials and AI Imaging for Immunotherapy Monitoring

Integrating engineered biomaterials with deep learning-driven molecular imaging promises non-invasive monitoring and prediction of cancer immunotherapy response, but clinical translation faces hurdles like probe synthesis, tissue penetration, and model generalizability.

• The review details chemical and biomolecular strategies to boost imaging probe sensitivity and specificity for visualizing immune cells and signals in the tumor microenvironment.
• Deep learning models enable predictive analysis of immune dynamics, though limited generalizability across patient populations remains a critical barrier.
• Combined, these technologies could enable personalized immunotherapy, but high probe costs, deep-tissue penetration, and off-target toxicity currently impede clinical use.

Automated summary

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