PDF(Pubmed)
Abstract：
OBJECTIVE: To investigate the added value of extracellular volume fraction (ECV) and arterial enhancement fraction (AEF) derived from enhanced CT to conventional image and clinical features for differentiating between pleomorphic adenoma (PA) and atypical parotid adenocarcinoma (PCA) pre-operation.
METHODS: From January 2010 to October 2023, a total of 187 cases of parotid tumors were recruited, and divided into training cohort (102 PAs and 51 PCAs) and testing cohort (24 PAs and 10 atypical PCAs). Clinical and CT image features of tumor were assessed. Both enhanced CT-derived ECV and AEF were calculated. Univariate analysis identified variables with statistically significant differences between the two subgroups in the training cohort. Multivariate logistic regression analysis with the forward variable selection method was used to build four models (clinical model, clinical model+ECV, clinical model+AEF, and combined model). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. Delong\'s test compared model differences, and calibration curve and decision curve analysis (DCA) assessed calibration and clinical application.
RESULTS: Age and boundary were chosen to build clinical model, and to construct its ROC curve. Amalgamating the clinical model, ECV, and AEF to establish a combined model demonstrated superior diagnostic effectiveness compared to the clinical model in both the training and test cohorts (AUC = 0.888, 0.867). There was a significant statistical difference between the combined model and the clinical model in the training cohort (p = 0.0145).
CONCLUSIONS: ECV and AEF are helpful in differentiating PA and atypical PCA, and integrating clinical and CT image features can further improve the diagnostic performance.
METHODS: From January 2010 to October 2023, a total of 187 cases of parotid tumors were recruited, and divided into training cohort (102 PAs and 51 PCAs) and testing cohort (24 PAs and 10 atypical PCAs). Clinical and CT image features of tumor were assessed. Both enhanced CT-derived ECV and AEF were calculated. Univariate analysis identified variables with statistically significant differences between the two subgroups in the training cohort. Multivariate logistic regression analysis with the forward variable selection method was used to build four models (clinical model, clinical model+ECV, clinical model+AEF, and combined model). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. Delong\'s test compared model differences, and calibration curve and decision curve analysis (DCA) assessed calibration and clinical application.
RESULTS: Age and boundary were chosen to build clinical model, and to construct its ROC curve. Amalgamating the clinical model, ECV, and AEF to establish a combined model demonstrated superior diagnostic effectiveness compared to the clinical model in both the training and test cohorts (AUC = 0.888, 0.867). There was a significant statistical difference between the combined model and the clinical model in the training cohort (p = 0.0145).
CONCLUSIONS: ECV and AEF are helpful in differentiating PA and atypical PCA, and integrating clinical and CT image features can further improve the diagnostic performance.
摘要：
目的：探讨多形性腺瘤(PA)和不典型腮腺腺癌(PCA)术前CT增强后的细胞外体积分数(ECV)和动脉强化分数(AEF)对常规图像和临床特征的增加价值。
方法：2010年1月至2023年10月，共收集187例腮腺肿瘤患者，分为训练队列(102个PA和51个PCAs)和测试队列(24个PA和10个非典型PCAs)。评估肿瘤的临床和CT图像特征。计算了增强CT衍生的ECV和AEF。单变量分析确定的变量在训练队列中的两个亚组之间具有统计学上的显着差异。采用正向变量选择方法进行多因素logistic回归分析,建立4个模型(临床模型,临床模型+ECV，临床模型+AEF，和组合模型)。使用受试者工作特征（ROC）曲线分析评估诊断性能。德隆检验比较了模型的差异，和校准曲线和决策曲线分析（DCA）评估校准和临床应用。
结果：选择年龄和边界建立临床模型，并构建其ROC曲线。合并临床模型，ECV,和AEF建立组合模型在训练和测试队列中与临床模型相比显示出优异的诊断有效性(AUC=0.888,0.867)。在训练队列中组合模型和临床模型之间存在显著的统计学差异(p=0.0145)。
结论：ECV和AEF有助于区分PA和非典型PCA，整合临床和CT图像特征可以进一步提高诊断性能。
方法：2010年1月至2023年10月，共收集187例腮腺肿瘤患者，分为训练队列(102个PA和51个PCAs)和测试队列(24个PA和10个非典型PCAs)。评估肿瘤的临床和CT图像特征。计算了增强CT衍生的ECV和AEF。单变量分析确定的变量在训练队列中的两个亚组之间具有统计学上的显着差异。采用正向变量选择方法进行多因素logistic回归分析,建立4个模型(临床模型,临床模型+ECV，临床模型+AEF，和组合模型)。使用受试者工作特征（ROC）曲线分析评估诊断性能。德隆检验比较了模型的差异，和校准曲线和决策曲线分析（DCA）评估校准和临床应用。
结果：选择年龄和边界建立临床模型，并构建其ROC曲线。合并临床模型，ECV,和AEF建立组合模型在训练和测试队列中与临床模型相比显示出优异的诊断有效性(AUC=0.888,0.867)。在训练队列中组合模型和临床模型之间存在显著的统计学差异(p=0.0145)。
结论：ECV和AEF有助于区分PA和非典型PCA，整合临床和CT图像特征可以进一步提高诊断性能。
