To reduce the variability of radiomics features caused by computed tomography (CT) imaging protocols through using a generative adversarial network (GAN) method. In this study, we defined a set of images acquired with a certain imaging protocol as a domain, and a total of four domains (A, B, C, and T [target]) from three different scanners was included. In data set#1, 60 patients for each domain were collected. Data sets#2 and #3 included 40 slices of spleen for each of the domains. In data set#4, the slices of three colorectal cancer groups (n = 28, 38 and 32) were separately retrieved from three different scanners, and each group contained short-term and long-term survivors. Seventy-seven features were extracted for evaluation by comparing the feature distributions. First, we trained the GAN model on data set#1 to learn how to normalize images from domains A, B and C to T. Next, by comparing feature distributions between normalized images of the different domains, we identified the appropriate model and assessed it, in data set#2 and data set#3, respectively. Finally, to investigate whether our proposed method could facilitate multicenter radiomics analysis, we built the least absolute shrinkage and selection operator classifier to distinguish short-term from long-term survivors based on a certain group in data set#4, and validate it in another two groups, which formed a cross-validation between groups in data set#4. After normalization, the percentage of aligned features between domains A versus T, B versus T, and C versus T increased from 10.4 %, 18.2% and 50.1% to 93.5%, 89.6% and 77.9%, respectively. In the cross-validation results, the average improvement of the area under the receiver operating characteristic curve achieved 11% (3%-32%). Our proposed GAN-based normalization method could reduce the variability of radiomics features caused by different CT imaging protocols and facilitate multicenter radiomics analysis.