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.

通过使用生成对抗网络 (GAN) 方法减少由计算机断层扫描 (CT) 成像协议引起的放射组学特征的可变性。在这项研究中，我们将使用特定成像协议获取的一组图像定义为一个域，包括来自三个不同扫描仪的总共四个域（A、B、C 和 T [目标]）。在数据集#1 中，每个域收集了 60 名患者。数据集#2 和#3 包括每个域的 40 个脾脏切片。在数据集#4 中，三个结直肠癌组的切片 ( n= 28、38 和 32）分别从三个不同的扫描仪中检索，每组包含短期和长期幸存者。通过比较特征分布，提取了 77 个特征进行评估。首先，我们在数据集#1 上训练了 GAN 模型，以学习如何将域 A、B 和 C 的图像归一化到 T。接下来，通过比较不同域的归一化图像之间的特征分布，我们确定了合适的模型并对其进行了评估, 分别在数据集#2 和数据集#3 中。最后，为了研究我们提出的方法是否可以促进多中心放射组学分析，我们构建了最小绝对收缩和选择算子分类器以基于数据集#4 中的某个组区分短期和长期幸存者，并在另一个两组，这形成了数据集#4 中组之间的交叉验证。归一化后，域 A 与 T、B 与 T、C 与 T 之间对齐特征的百分比分别从 10.4%、18.2% 和 50.1% 增加到 93.5%、89.6% 和 77.9%。在交叉验证结果中，受试者工作特征曲线下面积的平均改善达到了 11% (3%–32%)。我们提出的基于 GAN 的归一化方法可以减少由不同 CT 成像协议引起的放射组学特征的可变性，并促进多中心放射组学分析。受试者工作特征曲线下面积的平均改善达到 11% (3%–32%)。我们提出的基于 GAN 的归一化方法可以减少由不同 CT 成像协议引起的放射组学特征的可变性，并促进多中心放射组学分析。受试者工作特征曲线下面积的平均改善达到 11% (3%–32%)。我们提出的基于 GAN 的归一化方法可以减少由不同 CT 成像协议引起的放射组学特征的可变性，并促进多中心放射组学分析。