Early-stage non-small-cell lung cancer (ES-NSCLC) represents 20-30% of all NSCLC and is characterised by a high survival rate after surgery, with variability in clinical outcome among patients sharing the same disease stage, suggesting that other factors could determine the risk of relapse. We hypothesize that multiple factors could influence the prognosis of resected ES-NSCLC patients, such as tumour tissue and tumour microenvironment (TME) characteristics, liquid biopsy, radiomics features and clinical-pathological factors. MIRACLE aims to develop and validate a machine learning algorithm acting as a clinical decision support tool for disease free survival prediction based on joint analysis of biological, clinical and radiologic features. A previously prospectively collected cohort of ES-NSCLC patients will be considered as a training set. Tumour tissue and TME characteristics will be analysed using DNA and RNA sequencing; liquid biopsy will be used to assess free circulating DNA and extracellular vesicles; radiomics parameters will be retrieved from computed tomography images. All these features, together with clinico-pathological factors, will be integrated in a model that will enable personalised patient treatment. The developed algorithm will be validated in a prospective cohort enrolled during the timeframe of project MIRACLE.