OBJECTIVE:To evaluate the utility of radiomics analysis for differentiating benign and malignant epithelial salivary gland tumors on diffusion-weighted imaging (DWI). METHODS:A retrospective dataset involving 218 and 51 patients with histology-confirmed benign and malignant epithelial salivary gland tumors was used in this study. A total of 396 radiomic features were extracted from the DW images. Analysis of variance (ANOVA) and least-absolute shrinkage and selection operator regression (LASSO) were used to select optimal radiomic features. The selected features were used to build three classification models namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN) by using a five-fold cross validation strategy on the training dataset. The diagnostic performance of each classification model was quantified by receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) in the training and validation datasets. RESULTS:Eight most valuable features were selected by LASSO. LR and SVM models yielded optimally diagnostic performance. In the training dataset, LR and SVM yielded AUC values of 0.886 and 0.893 via five-fold cross validation, respectively, while KNN model showed relatively lower AUC (0.796). In the testing dataset, a similar result was found, where AUC values for LR, SVM, and KNN were 0.876, 0.870, and 0.791, respectively. CONCLUSIONS:Classification models based on optimally selected radiomics features computed from DW images present a promising predictive value in distinguishing benign and malignant epithelial salivary gland tumors and thus have potential to be used for preoperative auxiliary diagnosis.

中文翻译：

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上皮唾液腺肿瘤：基于扩散加权成像的放射组学分析用于区分良恶性肿瘤。

目的：评估放射组学分析在弥散加权成像 (DWI) 上区分良恶性上皮性唾液腺肿瘤的效用。方法：本研究使用回顾性数据集，包括 218 和 51 名组织学证实为良性和恶性上皮性唾液腺肿瘤的患者。从 DW 图像中提取了总共 396 个放射组学特征。方差分析 (ANOVA) 和最小绝对收缩和选择算子回归 (LASSO) 用于选择最佳放射组学特征。通过在训练数据集上使用五重交叉验证策略，选择的特征用于构建三种分类模型，即逻辑回归方法 (LR)、支持向量机 (SVM) 和 K-最近邻 (KNN)。每个分类模型的诊断性能通过训练和验证数据集中的受试者工作特征 (ROC) 曲线和 ROC 曲线下面积 (AUC) 进行量化。结果：LASSO 选择了八个最有价值的特征。LR 和 SVM 模型产生了最佳的诊断性能。在训练数据集中，LR 和 SVM 通过五重交叉验证分别产生 0.886 和 0.893 的 AUC 值，而 KNN 模型显示相对较低的 AUC（0.796）。在测试数据集中，发现了类似的结果，其中 LR、SVM 和 KNN 的 AUC 值分别为 0.876、0.870 和 0.791。结论：