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We propose a solution for automatic classification of lung nodules in an environment with heterogeneous computed tomography (CT) acquisition parameters. Such a classification system needs to take into account the differences in CT acquisition parameters used when obtaining and processing each medical image. Using analysis of variance (ANOVA), our current research proposes to better understand the effects of CT acquisition parameters on predicting various semantic characteristics (such as spiculation, subtlety, and margin) used in the diagnosis interpretation process. All of the parameters were found to affect the low-level image features used in the classification models of these semantic characteristics. When this knowledge is used to normalize those parameters, the final semantic model will become unaffected by the CT acquisition parameters.