Lung cancer is the leading cause of cancer-related death in Europe. Pathological staging is the gold standard, but it can be influenced by neo-adjuvant treatment and number of sampled lymph nodes; it is not feasible in advanced stages and in patients with high-risk comorbidities. Therefore, patients with tumors of the same stage can experience variations in the incidence of recurrence and survival since suboptimal staging leads to inappropriate treatment that result in poorer outcomes. It is still undetermined what are the tumor characteristics that can accurately assess tumor burden and predict patient outcome.Our central hypothesis is that image-derived and genetic characteristics are consistent with disease stage and patient outcome. Combining through artificial intelligence techniques data coming from imaging and circulating cell-free tumor DNA (ctDNA) can provide accurate staging and predict outcome. This hypothesis has been formulated based on preliminary data and on the evidence that image-derived biomarkers by means of image mining (radiomics and deep learning algorithms) are able to provide phenotype and prognostic information. On the other hand, the analysis of ctDNA isolated from the plasma of patients has been proposed as an alternative method to assess the disease in the different phases, in particular, at diagnosis and after surgery, for detection of residual disease.