Radiogenomics: transforming lung cancer care through non-invasive imaging and genomic integration.

Radiogenomics links quantitative features from routine CT and PET/CT with tumor genomics to non-invasively profile non-small cell lung cancer (NSCLC). This review synthesizes the current workflow-from image acquisition and segmentation to feature extraction and modeling-and emphasizes clinical use cases: triage when tissue is limited, risk stratification, therapy selection (including immunotherapy), and longitudinal monitoring. Despite promising results, clinical translation is constrained by non-standardized imaging/feature pipelines, limited multi-center validation and calibration, vulnerability to data leakage, and challenges in interpretability, uncertainty handling, and software and model governance. We advocate treating genomic burden-tumor mutational burden (TMB), intratumor heterogeneity (ITH), and copy-number alterations (CNA)-as first-class endpoints and covariates, and outline a pattern-aware framework for EGFR T790M to inform surveillance and treatment sequencing. We also provide a practical reporting checklist and a pitfall-to-remedy table to support reproducible, multi-center studies and regulatory-grade documentation. Radiogenomics is best viewed as a complement to biopsy rather than a replacement. Real-world impact now depends on harmonized protocols, leakage-free external validation, explainable and uncertainty-aware models, and integration with multi-omics decision support to deliver reliable, patient-centered lung cancer care.