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

Stratification in oncology remains largely based on tumor-intrinsic characteristics, with limited integration of systemic host biology. The Systemic Immune-Inflammation Index (SII), derived from peripheral blood counts, reflects interactions between inflammation, thrombosis, and immune competence and has emerged as a simple, accessible prognostic biomarker. At the same time, artificial intelligence (AI)–based survival models enable integration of heterogeneous data and nonlinear relationships in outcome prediction. However, the structured incorporation of biologically interpretable host-derived markers into explainable AI frameworks remains insufficiently defined. A structured narrative review of PubMed/MEDLINE and Scopus was conducted to examine (i) the prognostic role of SII in solid tumors and (ii) AI-based survival modeling in oncology. Rather than quantitative synthesis, the review prioritized methodological quality, biological plausibility, validation strategies, and explainability relevant to clinical translation. Elevated SII is consistently associated with poorer survival across multiple solid malignancies and treatment contexts, although effect sizes vary and methodological heterogeneity remains considerable. AI-based survival models can incorporate continuous and multimodal variables, capture nonlinear associations, and model complex tumor–host interactions. We propose a translational framework integrating SII into explainable AI survival pipelines through continuous modeling, interaction-aware feature engineering, rigorous internal and external validation, and interpretable modeling approaches. Embedding SII as a biologically grounded continuous variable within multivariable prognostic architectures may enhance personalized risk stratification in precision oncology, pending prospective validation and standardized reporting.