{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T12:07:33Z","timestamp":1789992453441,"version":"4.0.1"},"reference-count":62,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/fimmu.2026.1931388","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T11:16:23Z","timestamp":1789989383000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiomics and deep learning for immune checkpoint inhibitor outcomes: imaging-derived immune phenotyping and clinical translation across solid tumors"],"prefix":"10.3389","volume":"17","author":[{"given":"Hangyu","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Radiology, Ningbo Hangzhou Bay Hospital (Ningbo Branch of Renji Hospital, Shanghai Jiao Tong University School of 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