An increasing number of advances have been achieved in the field of medical imaging, namely the conversion of standardof- care images into mineable data. Radiomics refers to the high-throughput extraction of quantitative image features from medical images (watch the video on https://youtu.be/ Tq980GEVP0Y and visit www.radiomics.world). These image features can be divided into four groups depending on the tumor characteristic they describe: tumor intensity, tumor shape, tumor texture, or wavelets. This study consisted in a radiomics analysis of 18’026 features extracted from standard-of-care, pretreatment, 4D computed tomography (CT) images from a cohort of 44 non-small-cell lung cancer (NSCLC) patients treated with chemoradiotherapy at the Cliniques Universitaires St-Luc. A radiomics signature was created using machine learning algorithms in order to preselect a small group of radiomics features based on their correlation with the studied endpoint (survival, histological types, etc.). The signature was created by analyzing the features of a patient cohort, and its robustness was then tested and validated on an external independent dataset.