Therefore, in this study, we evaluated whether the optimization of reconstruction settings (i.e. Thin/Thick slice thicknesses, Sharp/Smooth convolution kernels) could allow the construction of a better radiomic signature, derived from a large number of quantitative image features, to predict the EGFR mutation status in primary lung adenocarcinoma (LAC) using standard of care CT imaging. Here, EGFR is linked to lung adenocarcinoma.