Advances in artificial intelligence to predict cancer immunotherapy efficacy

Area of Science:

• Oncology
• Immunology
• Medical Artificial Intelligence

Background:

• Tumor immunotherapy, especially immune checkpoint inhibitors, shows significant clinical success.
• Accurate prediction of immunotherapy sensitivity and efficacy is crucial for patient outcomes.
• Artificial intelligence (AI) is increasingly applied in medicine to enhance treatment prediction.

Purpose of the Study:

• To review current AI-based prediction models for tumor immunotherapy efficacy.
• To explore AI applications using histopathological slides, imaging-omics, genomics, and proteomics.
• To discuss challenges and future directions for AI in immunotherapy.

Main Methods:

• Review of AI prediction models utilizing histopathological slide data.
• Analysis of AI models incorporating imaging-omics, genomics, and proteomics.
• Synthesis of research progress and applications of AI in immunotherapy prediction.

Main Results:

• AI models demonstrate potential in forecasting immunotherapy efficacy.
• Multi-modal data integration (histopathology, imaging-omics, genomics, proteomics) enhances prediction accuracy.
• AI facilitates the advancement of precision medicine in oncology.

Conclusions:

• AI is a powerful tool for predicting immunotherapy response and guiding treatment decisions.
• Further research is needed to overcome current challenges and optimize AI implementation.
• AI-assisted systems hold promise for early diagnosis and treatment in immunotherapy.