Most Computer-Aided Diagnosis (CAD) research studies are performed using a single type of Computer Tomography (CT) scanner and therefore, do not take into account the effect of differences in the imaging acquisition scanner parameters. In this paper, we present a study on the effect of the CT parameters on the low-level image features automatically extracted from CT images for lung nodule interpretation. The study is an extension of our previous study where we showed that image features can be used to predict semantic characteristics of lung nodules such as margin, lobulation, spiculation, and texture. Using the Lung Image Data Consortium (LIDC) dataset, we propose to integrate the imaging acquisition parameters with the low-level image features to generate classification models for the nodules' semantic characteristics. Our preliminary results identify seven CT parameters (convolution kernel, reconstruction diameter, exposure, nodule location along the z-axis, distance source to patient, slice thickness, and kVp) as influential in producing classification rules for the LIDC semantic characteristics. Further post-processing analysis, which included running box plots and binning of values, identified four CT parameters: distance source to patient, kVp, nodule location, and rescale intercept. The identification of these parameters will create the premises to normalize the image features across different scanners and, in the long run, generate automatic rules for lung nodules interpretation independently of the CT scanner types.