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Abstract：
UNASSIGNED: This study aimed to investigate the diagnostic value of machine-learning (ML) models with multiple classifiers based on non-enhanced CT Radiomics features for differentiating anterior mediastinal cysts (AMCs) from thymomas, and high-risk from low risk thymomas.
UNASSIGNED: In total, 201 patients with AMCs and thymomas from three centers were included and divided into two groups: AMCs vs. thymomas, and high-risk vs low-risk thymomas. A radiomics model (RM) was built with 73 radiomics features that were extracted from the three-dimensional images of each patient. A combined model (CM) was built with clinical features and subjective CT finding features combined with radiomics features. For the RM and CM in each group, five selection methods were adopted to select suitable features for the classifier, and seven ML classifiers were employed to build discriminative models. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of each combination.
UNASSIGNED: Several classifiers combined with suitable selection methods demonstrated good diagnostic performance with areas under the curves (AUCs) of 0.876 and 0.922 for the RM and CM in group 1 and 0.747 and 0.783 for the RM and CM in group 2, respectively. The combination of support vector machine (SVM) as the feature-selection method and Gradient Boosting Decision Tree (GBDT) as the classification algorithm represented the best comprehensive discriminative ability in both group. Comparatively, assessments by radiologists achieved a middle AUCs of 0.656 and 0.626 in the two groups, which were lower than the AUCs of the RM and CM. Most CMs exhibited higher AUC value compared to RMs in both groups, among them only a few CMs demonstrated better performance with significant difference in group 1.
UNASSIGNED: Our ML models demonstrated good performance for differentiation of AMCs from thymomas and low-risk from high-risk thymomas. ML based on non-enhanced CT radiomics may serve as a novel preoperative tool.
UNASSIGNED: In total, 201 patients with AMCs and thymomas from three centers were included and divided into two groups: AMCs vs. thymomas, and high-risk vs low-risk thymomas. A radiomics model (RM) was built with 73 radiomics features that were extracted from the three-dimensional images of each patient. A combined model (CM) was built with clinical features and subjective CT finding features combined with radiomics features. For the RM and CM in each group, five selection methods were adopted to select suitable features for the classifier, and seven ML classifiers were employed to build discriminative models. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of each combination.
UNASSIGNED: Several classifiers combined with suitable selection methods demonstrated good diagnostic performance with areas under the curves (AUCs) of 0.876 and 0.922 for the RM and CM in group 1 and 0.747 and 0.783 for the RM and CM in group 2, respectively. The combination of support vector machine (SVM) as the feature-selection method and Gradient Boosting Decision Tree (GBDT) as the classification algorithm represented the best comprehensive discriminative ability in both group. Comparatively, assessments by radiologists achieved a middle AUCs of 0.656 and 0.626 in the two groups, which were lower than the AUCs of the RM and CM. Most CMs exhibited higher AUC value compared to RMs in both groups, among them only a few CMs demonstrated better performance with significant difference in group 1.
UNASSIGNED: Our ML models demonstrated good performance for differentiation of AMCs from thymomas and low-risk from high-risk thymomas. ML based on non-enhanced CT radiomics may serve as a novel preoperative tool.
摘要：
UNASSIGNED：本研究旨在研究基于非增强CT影像组学特征的多分类器机器学习（ML）模型在区分前纵隔囊肿（AMC）和胸腺瘤中的诊断价值。低危胸腺瘤的高风险。
未经批准：总共，纳入来自三个中心的201例AMC和胸腺瘤患者，并分为两组：AMC与胸腺瘤,和高风险与低风险胸腺瘤。使用从每个患者的三维图像中提取的73个影像组学特征建立影像组学模型（RM）。建立了具有临床特征和主观CT发现特征以及影像组学特征的组合模型（CM）。对于每个组中的RM和CM，采用五种选择方法为分类器选择合适的特征，并采用7个ML分类器构建判别模型。使用受试者工作特征（ROC）曲线来评估每种组合的诊断性能。
UNASSIGNED：几种分类器结合合适的选择方法显示出良好的诊断性能，第1组RM和CM的曲线下面积（AUC）分别为0.876和0.922，第2组RM和CM分别为0.747和0.783。支持向量机（SVM）作为特征选择方法和梯度提升决策树（GBDT）作为分类算法的组合在两组中都表现出最佳的综合判别能力。相对而言,放射科医生的评估在两组中达到了0.656和0.626的中间AUC，低于RM和CM的AUC。与两组的RM相比，大多数CM表现出更高的AUC值，其中只有少数CMs表现出更好的性能，在第1组中存在显着差异。
UNASSIGNED：我们的ML模型在区分AMC与胸腺瘤以及低风险与高风险胸腺瘤方面表现良好。基于非增强CT影像组学的ML可能是一种新颖的术前工具。
未经批准：总共，纳入来自三个中心的201例AMC和胸腺瘤患者，并分为两组：AMC与胸腺瘤,和高风险与低风险胸腺瘤。使用从每个患者的三维图像中提取的73个影像组学特征建立影像组学模型（RM）。建立了具有临床特征和主观CT发现特征以及影像组学特征的组合模型（CM）。对于每个组中的RM和CM，采用五种选择方法为分类器选择合适的特征，并采用7个ML分类器构建判别模型。使用受试者工作特征（ROC）曲线来评估每种组合的诊断性能。
UNASSIGNED：几种分类器结合合适的选择方法显示出良好的诊断性能，第1组RM和CM的曲线下面积（AUC）分别为0.876和0.922，第2组RM和CM分别为0.747和0.783。支持向量机（SVM）作为特征选择方法和梯度提升决策树（GBDT）作为分类算法的组合在两组中都表现出最佳的综合判别能力。相对而言,放射科医生的评估在两组中达到了0.656和0.626的中间AUC，低于RM和CM的AUC。与两组的RM相比，大多数CM表现出更高的AUC值，其中只有少数CMs表现出更好的性能，在第1组中存在显着差异。
UNASSIGNED：我们的ML模型在区分AMC与胸腺瘤以及低风险与高风险胸腺瘤方面表现良好。基于非增强CT影像组学的ML可能是一种新颖的术前工具。
