Lung cancer is the leading cause of cancer death among both men and women. To date, radiomics has been used to aid in lung cancer diagnosis, histologic classification and mutation detection, as well as to predict oncologic outcomes. At the IEO, lung cancer has been the focus of several efforts by the radiomics team. The role of radiomics and artificial intelligence in the early detection of lung cancer using low-dose CT (computed tomography) has been studied, with the goal of developing predictive models for the detection of malignant nodules and early prediction of response to therapy. Other projects have evaluated the possibility of predicting lung cancer mutation status and overall survival based on radiomic analysis of CT images. The radiomic team was also involved in investigating the more methodological aspects of these studies. In particular, the various classification methods and reproducibility of radiomic features in CT images were studied. The IEO is actively involved in the international multicenter BLUESKY study, which aims to evaluate the prognostic role of radiomics in predicting the efficacy of chemo-immunotherapy for locally advanced and inoperable non-small cell lung cancer. Alongside h CT, the possibility of predicting overall survival and disease-free survival by radiomic analysis of 18F-FDG PET images is being investigated. The analysis relies on methodological evaluations, including the evolution of the robustness of radiomic features according to image acquisition parameters changes and comparing different tumor lesion segmentation techniques.