ObjectiveThis study assessed whether radiomics features could stratify parotid gland tumours accurately based on only noncontrast CT images and validated the best classifier of different radiomics models.MethodsIn this single-centre study, we retrospectively recruited 249 patients with a diagnosis of pleomorphic adenoma (PA), Warthin tumour (WT), basal cell adenoma (BCA) or malignant parotid gland tumours (MPGTs) from June 2020 to August 2022. Each patient was randomly classified into training and testing cohorts at a ratio of 7:3, and then, pairwise comparisons in different parotid tumour groups were performed. CT images were transferred to 3D-Slicer software and the region of interest was manually drawn for feature extraction. Feature selection methods were performed using the intraclass correlation coefficient, t test and least absolute shrinkage and selection operator. Five common classifiers, namely, random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbours (KNN) and general Bayesian network (Gnb), were selected to build different radiomics models. The receiver operating characteristic curve, area under the curve (AUC), accuracy, sensitivity, specificity and F-1 score were used to assess the prediction performances of these models. The calibration of the model was calculated by the Hosmer–Lemeshow test. DeLong’s test was utilized for comparing the AUCs.ResultsThe radiomics model based on the RF, SVM, Gnb, LR, LR and RF classifiers obtained the highest AUC in differentiating PA from MPGTs, WT from MPGTs, BCA from MPGTs, PA from WT, PA from BCA, and WT from BCA, respectively. Accordingly, the AUC and the accuracy of the model for each classifier were 0.834 and 0.71, 0.893 and 0.79, 0.844 and 0.79, 0.902 and 0.88, 0.602 and 0.68, and 0.861 and 0.94, respectively.ConclusionOur study demonstrated that noncontrast CT-based radiomics could stratify refined pathological types of parotid tumours well but could not sufficiently differentiate PA from BCA. Different classifiers had the best diagnostic performance for different parotid tumours. Our study findings add to the current knowledge on the differential diagnosis of parotid tumours.

中文翻译：

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具有各种分类器的基于 CT 的放射组学用于腮腺肿瘤的组织学分化

ObjectiveThis study assessed whether radiomics features could straining parotid gland tumors accurately based on only noncontrast CT images and validate the best classifier of different radiomics models.MethodsIn this single-center study,我们回顾性地招募了249名诊断为多形性腺瘤（PA）的患者， 2020年6月至2022年8月的Warthin肿瘤（WT）、基底细胞腺瘤（BCA）或恶性腮腺肿瘤（MPGTs）。每位患者按7:3的比例随机分为训练组和测试组，然后进行两两比较在不同的腮腺肿瘤组中进行。将 CT 图像传输到 3D-Slicer 软件，并手动绘制感兴趣区域以进行特征提取。使用类内相关系数进行特征选择方法，吨测试和最小绝对收缩和选择运算符。选择随机森林 (RF)、支持向量机 (SVM)、逻辑回归 (LR)、K 最近邻 (KNN) 和一般贝叶斯网络 (Gnb) 五种常用分类器来构建不同的放射组学模型。受试者工作特征曲线、曲线下面积（AUC）、准确性、敏感性、特异性和 F-1 评分用于评估这些模型的预测性能。通过 Hosmer-Lemeshow 检验计算模型的校准。DeLong's test用于比较AUC。结果基于RF，SVM，Gnb，LR，LR和RF分类器的放射组学模型在区分PA与MPGTs，WT与MPGTs，BCA与MPGTs，PA与WT，PA时获得了最高的AUC分别来自 BCA 和来自 BCA 的 WT。因此，每个分类器的 AUC 和模型的准确性分别为 0.834 和 0.71、0.893 和 0.79、0.844 和 0.79、0.902 和 0.88、0.602 和 0.68，以及 0.861 和 0.94。很好地细化了腮腺肿瘤的病理类型，但不能充分区分 PA 和 BCA。不同的分类器对不同的腮腺肿瘤具有最佳的诊断性能。我们的研究结果增加了目前关于腮腺肿瘤鉴别诊断的知识。结论我们的研究表明，基于非对比 CT 的放射组学可以很好地对腮腺肿瘤的精细病理类型进行分层，但不能充分区分 PA 和 BCA。不同的分类器对不同的腮腺肿瘤具有最佳的诊断性能。我们的研究结果增加了目前关于腮腺肿瘤鉴别诊断的知识。结论我们的研究表明，基于非对比 CT 的放射组学可以很好地对腮腺肿瘤的精细病理类型进行分层，但不能充分区分 PA 和 BCA。不同的分类器对不同的腮腺肿瘤具有最佳的诊断性能。我们的研究结果增加了目前关于腮腺肿瘤鉴别诊断的知识。