Radiomics and deep learning for immune checkpoint inhibitor outcomes: imaging-derived immune phenotyping and clinical translation across solid tumors.

Immune checkpoint inhibitors (ICIs) have transformed the treatment of multiple solid tumors, but durable benefit remains restricted to a subset of patients and conventional size-based imaging does not fully capture immune-related response patterns. Radiomics and deep learning offer a route to convert routine CT, MRI, and PET/CT into quantitative biomarkers of tumor phenotype, whole-body heterogeneity, and temporal treatment response. This narrative review critically synthesizes the evidence across solid tumors with an emphasis on four questions that are often separated in the literature: what added information is provided by different AI approaches, whether imaging signatures can be interpreted as non-invasive immune phenotypes, how methodological choices determine reproducibility, and what evidence is required for clinical translation. CT currently has the broadest outcome-prediction literature, particularly in non-small cell lung cancer; MRI provides complementary diffusion, perfusion, and soft-tissue information in hepatocellular carcinoma and brain metastases; and PET/CT adds metabolic and whole-body measures that may reflect both tumor burden and immune-metabolic state. Associations with CD8-positive T-cell infiltration, PD-L1 expression, tumor immune microenvironment states, response, progression-free survival, and overall survival support biological plausibility, but they do not establish treatment-specific predictive utility. We compare handcrafted radiomics, classical machine learning, convolutional neural networks, transformer-based approaches, self-supervised learning, and emerging foundation models, and discuss explainable AI, longitudinal imaging, pseudoprogression, and hyperprogressive disease. Major barriers remain small retrospective cohorts, high-dimensional feature selection, data leakage, scanner and protocol effects, treatment heterogeneity, selective reporting, weak calibration, and limited external or prospective validation. At present, AI-derived imaging biomarkers are best considered investigational decision-support tools. Their most credible near-term role is to complement, rather than replace, established clinical and molecular biomarkers; multimodal strategies that combine imaging with tissue, circulating, and clinical data remain promising but require demonstration of incremental and prospective clinical utility.