PDF(Pubmed)
Abstract：
UNASSIGNED: To differentiate parotid pleomorphic adenoma (PA) from adenolymphoma (AL) using radiomics of grayscale ultrasonography in combination with clinical features.
UNASSIGNED: This retrospective study aimed to analyze the clinical and radiographic characteristics of 162 cases from December 2019 to March 2023. The study population consisted of a training cohort of 113 patients and a validation cohort of 49 patients. Grayscale ultrasonography was processed using ITP-Snap software and Python to delineate regions of interest (ROIs) and extract radiomic features. Univariate analysis, Spearman\'s correlation, greedy recursive elimination strategy, and least absolute shrinkage and selection operator (LASSO) correlation were employed to select relevant radiographic features. Subsequently, eight machine learning methods (LR, SVM, KNN, RandomForest, ExtraTrees, XGBoost, LightGBM, and MLP) were employed to build a quantitative radiomic model using the selected features. A radiomic nomogram was developed through the utilization of multivariate logistic regression analysis, integrating both clinical and radiomic data. The accuracy of the nomogram was assessed using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test.
UNASSIGNED: To differentiate PA from AL, the radiomic model using SVM showed optimal discriminatory ability (accuracy = 0.929 and 0.857, sensitivity = 0.946 and 0.800, specificity = 0.921 and 0.897, positive predictive value = 0.854 and 0.842, and negative predictive value = 0.972 and 0.867 in the training and validation cohorts, respectively). A nomogram incorporating rad-Signature and clinical features achieved an area under the ROC curve (AUC) of 0.983 (95% confidence interval [CI]: 0.965-1) and 0.910 (95% CI: 0.830-0.990) in the training and validation cohorts, respectively. Decision curve analysis showed that the nomogram and radiomic model outperformed the clinical-factor model in terms of clinical usefulness.
UNASSIGNED: A nomogram based on grayscale ultrasonic radiomics and clinical features served as a non-invasive tool capable of differentiating PA and AL.
UNASSIGNED: This retrospective study aimed to analyze the clinical and radiographic characteristics of 162 cases from December 2019 to March 2023. The study population consisted of a training cohort of 113 patients and a validation cohort of 49 patients. Grayscale ultrasonography was processed using ITP-Snap software and Python to delineate regions of interest (ROIs) and extract radiomic features. Univariate analysis, Spearman\'s correlation, greedy recursive elimination strategy, and least absolute shrinkage and selection operator (LASSO) correlation were employed to select relevant radiographic features. Subsequently, eight machine learning methods (LR, SVM, KNN, RandomForest, ExtraTrees, XGBoost, LightGBM, and MLP) were employed to build a quantitative radiomic model using the selected features. A radiomic nomogram was developed through the utilization of multivariate logistic regression analysis, integrating both clinical and radiomic data. The accuracy of the nomogram was assessed using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test.
UNASSIGNED: To differentiate PA from AL, the radiomic model using SVM showed optimal discriminatory ability (accuracy = 0.929 and 0.857, sensitivity = 0.946 and 0.800, specificity = 0.921 and 0.897, positive predictive value = 0.854 and 0.842, and negative predictive value = 0.972 and 0.867 in the training and validation cohorts, respectively). A nomogram incorporating rad-Signature and clinical features achieved an area under the ROC curve (AUC) of 0.983 (95% confidence interval [CI]: 0.965-1) and 0.910 (95% CI: 0.830-0.990) in the training and validation cohorts, respectively. Decision curve analysis showed that the nomogram and radiomic model outperformed the clinical-factor model in terms of clinical usefulness.
UNASSIGNED: A nomogram based on grayscale ultrasonic radiomics and clinical features served as a non-invasive tool capable of differentiating PA and AL.
摘要：
■使用灰阶超声影像组学结合临床特征来区分腮腺多形性腺瘤（PA）和腺淋巴瘤（AL）。
■这项回顾性研究旨在分析2019年12月至2023年3月162例病例的临床和影像学特征。研究人群由113名患者的训练队列和49名患者的验证队列组成。使用ITP-Snap软件和Python处理灰度超声以描绘感兴趣区域(ROI)并提取影像组学特征。单变量分析,斯皮尔曼的相关性，贪婪递归消除策略，和最小绝对收缩和选择算子（LASSO）相关性被用来选择相关的射线照相特征。随后，八种机器学习方法(LR，SVM,KNN,RandomForest,ExtraTrees,XGBoost,LightGBM,和MLP）用于使用选定的特征建立定量放射学模型。通过利用多变量逻辑回归分析开发了放射学列线图，整合临床和影像数据。使用受试者工作特征（ROC）曲线分析评估列线图的准确性，校准,决策曲线分析(DCA)，还有Hosmer-Lemeshow测试.
■为了区分PA和AL，在训练和验证队列中，使用SVM的影像组学模型显示出最佳的辨别能力（准确性=0.929和0.857，敏感性=0.946和0.800，特异性=0.921和0.897，阳性预测值=0.854和0.842，阴性预测值=0.972和0.867，分别)。在训练和验证队列中，包含rad-Signature和临床特征的列线图的ROC曲线下面积（AUC）为0.983（95％置信区间[CI]：0.965-1）和0.910（95％CI：0.830-0.990），分别。决策曲线分析表明，在临床有用性方面，列线图和影像组学模型优于临床因素模型。
■基于灰度超声影像组学和临床特征的列线图用作能够区分PA和AL的非侵入性工具。
■这项回顾性研究旨在分析2019年12月至2023年3月162例病例的临床和影像学特征。研究人群由113名患者的训练队列和49名患者的验证队列组成。使用ITP-Snap软件和Python处理灰度超声以描绘感兴趣区域(ROI)并提取影像组学特征。单变量分析,斯皮尔曼的相关性，贪婪递归消除策略，和最小绝对收缩和选择算子（LASSO）相关性被用来选择相关的射线照相特征。随后，八种机器学习方法(LR，SVM,KNN,RandomForest,ExtraTrees,XGBoost,LightGBM,和MLP）用于使用选定的特征建立定量放射学模型。通过利用多变量逻辑回归分析开发了放射学列线图，整合临床和影像数据。使用受试者工作特征（ROC）曲线分析评估列线图的准确性，校准,决策曲线分析(DCA)，还有Hosmer-Lemeshow测试.
■为了区分PA和AL，在训练和验证队列中，使用SVM的影像组学模型显示出最佳的辨别能力（准确性=0.929和0.857，敏感性=0.946和0.800，特异性=0.921和0.897，阳性预测值=0.854和0.842，阴性预测值=0.972和0.867，分别)。在训练和验证队列中，包含rad-Signature和临床特征的列线图的ROC曲线下面积（AUC）为0.983（95％置信区间[CI]：0.965-1）和0.910（95％CI：0.830-0.990），分别。决策曲线分析表明，在临床有用性方面，列线图和影像组学模型优于临床因素模型。
■基于灰度超声影像组学和临床特征的列线图用作能够区分PA和AL的非侵入性工具。
