目的:使用预处理2-脱氧-2-[18F]氟-D-葡萄糖([18F]-FDG)-正电子发射断层扫描(PET)为基础的影像组学特征来开发和识别机器学习(ML)模型,以区分良性和恶性腮腺疾病(PGD)。
方法:这项回顾性研究包括62例患者,63例PGDs接受预处理[18F]-FDG-PET/计算机断层扫描(CT)。病变被分配到训练(n=44)和测试(n=19)组。总的来说,49[18F]-基于FDG-PET的放射组学特征被用于使用五种不同的常规ML算法模型(随机森林,神经网络,k-最近的邻居,逻辑回归,和支持向量机)和基于深度学习(DL)的集成ML模型。在训练组中,每个常规ML模型是使用递归特征消除方法和10倍交叉验证和合成少数过采样技术选择的5个最重要特征构建的.基于DL的集成ML模型是使用装袋和多层堆叠方法的五个最重要特征构建的。接收器工作特征曲线下的面积(AUC)和准确性用于比较预测性能。
结果:总计,确定了24个良性和39个恶性PGD。代谢性肿瘤体积和四个GLSZM特征(GLSZM_ZSE,GLSZM_SZE,GLSZM_GLNU,和GLSZM_ZSNU)是五个最重要的放射学特征。除GLSZM_SZE外,恶性PGD的所有五个特征均显着高于良性PGD(每个p<0.05)。基于DL的集成ML模型在训练和测试队列中具有表现最好的分类器(AUC=1.000,准确度=1.000vsAUC=0.976,准确度=0.947)。
结论:使用基于[18F]-FDG-PET的影像组学特征的基于DL的集成ML模型可用于区分良性和恶性PGD。使用基于[18F]-FDG-PET的放射组学特征的基于DL的集成ML模型可以克服先前报道的[18F]-FDG-PET/CT扫描用于区分良性和恶性PGD的局限性。使用基于[18F]-FDG-PET的放射组学特征的基于DL的集成ML方法可以为管理PGD提供有用的信息。
OBJECTIVE: To develop and identify machine learning (ML) models using pretreatment 2-deoxy-2-[18F]fluoro-D-glucose ([18F]-FDG)-positron emission tomography (PET)-based radiomic features to differentiate benign from malignant parotid gland diseases (PGDs).
METHODS: This retrospective study included 62 patients with 63 PGDs who underwent pretreatment [18F]-FDG-PET/computed tomography (CT). The lesions were assigned to the training (n = 44) and testing (n = 19) cohorts. In total, 49 [18F]-FDG-PET-based radiomic features were utilized to differentiate benign from malignant PGDs using five different conventional ML algorithmic models (random forest, neural network, k-nearest neighbors, logistic regression, and support vector machine) and the deep learning (DL)-based ensemble ML model. In the training cohort, each conventional ML model was constructed using the five most important features selected by the recursive feature elimination method with the tenfold cross-validation and synthetic minority oversampling technique. The DL-based ensemble ML model was constructed using the five most important features of the bagging and multilayer stacking methods. The area under the receiver operating characteristic curves (AUCs) and accuracies were used to compare predictive performances.
RESULTS: In total, 24 benign and 39 malignant PGDs were identified. Metabolic tumor volume and four GLSZM features (GLSZM_ZSE, GLSZM_SZE, GLSZM_GLNU, and GLSZM_ZSNU) were the five most important radiomic features. All five features except GLSZM_SZE were significantly higher in malignant PGDs than in benign ones (each p < 0.05). The DL-based ensemble ML model had the best performing classifier in the training and testing cohorts (AUC = 1.000, accuracy = 1.000 vs AUC = 0.976, accuracy = 0.947).
CONCLUSIONS: The DL-based ensemble ML model using [18F]-FDG-PET-based radiomic features can be useful for differentiating benign from malignant PGDs. The DL-based ensemble ML model using [18F]-FDG-PET-based radiomic features can overcome the previously reported limitation of [18F]-FDG-PET/CT scan for differentiating benign from malignant PGDs. The DL-based ensemble ML approach using [18F]-FDG-PET-based radiomic features can provide useful information for managing PGD.