Lung cancer is one of the most common malignant tumors in the world. Early detection and precise treatment are of great significance to clinical decision-making and patient prognosis. As an emerging imaging technology, dual-energy computed tomography (DECT) has increasingly prominent advantages in multi-parameter and quantitative analysis in assessing the benign and malignant, classification, and prognosis of lung cancer. Radiomics uses an automated high-throughput method to extract a large number of quantitative features from medical images, quantify tumor heterogeneity, monitor tumor development and prognosis, and provide new ideas for the diagnosis and identification of lung cancer. This article will review the application progress of DECT post-processing technology combined with radiomics in lung cancer diagnosis, identification, biomarker and gene prediction, and prognosis assessment.