Integrating Blood-Based Immune-Inflammation Biomarkers into Artificial Intelligence-Driven Prognostic Models in...

Area of Science:

• Oncology
• Biomarkers
• Artificial Intelligence

Background:

• Cancer stratification typically relies on tumor-intrinsic factors, neglecting systemic host biology.
• The Systemic Immune-Inflammation Index (SII) is a blood-based biomarker reflecting immune and inflammatory status.
• Artificial intelligence (AI) offers advanced capabilities for survival prediction by integrating complex data.

Purpose of the Study:

• To review the prognostic role of SII in solid tumors.
• To examine AI-based survival modeling in oncology.
• To propose a framework for integrating SII into explainable AI for enhanced cancer prognostication.

Main Methods:

• A structured narrative review of PubMed/MEDLINE and Scopus databases.
• Prioritization of methodological quality, biological plausibility, validation, and explainability.
• Focus on the prognostic significance of SII and AI in oncology survival prediction.

Main Results:

• Elevated SII is consistently linked to poorer survival across various solid tumors.
• AI models can effectively integrate diverse data, capture nonlinear relationships, and model tumor-host interactions.
• Methodological heterogeneity exists, but SII demonstrates consistent prognostic value.

Conclusions:

• Integrating SII into explainable AI survival models offers a promising avenue for precision oncology.
• A proposed framework emphasizes continuous modeling, feature engineering, and robust validation.
• This approach may enhance personalized risk stratification for cancer patients, pending further validation.