Lung cancer remains one of the most prevalent cancers globally and the leading cause of cancer-related deaths, accounting for nearly one-fifth of all cancer fatalities. Fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography ([18F]FDG PET/CT) plays a vital role in assessing lung cancer and managing disease progression. While traditional PET/CT imaging relies on qualitative analysis and basic quantitative parameters, radiomics offers a more advanced approach to analyzing tumor phenotypes.

Recently, radiomics has gained attention for its potential to enhance the prognostic and diagnostic capabilities of [18F]FDG PET/CT in various cancers. This review explores the expanding role of [18F]FDG PET/CT-based radiomics, particularly when integrated with artificial intelligence (AI), in managing lung cancer, especially non-small cell lung cancer (NSCLC).

We review how radiomics and AI improve diagnostics, staging, tumor subtype identification, and molecular marker detection, which influence treatment decisions. Additionally, we address challenges in clinical integration, such as imaging protocol standardization, feature reproducibility, and the need for extensive prospective studies. Ultimately, radiomics and AI hold great promise for enabling more personalized and effective lung cancer treatments, potentially transforming disease management.