Department of Radiology, The First People's Hospital of Foshan, Foshan, China.
Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China.
Cancer Med. 2024 Jun;13(12):e7407. doi: 10.1002/cam4.7407.
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.
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.
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).
ECV and AEF are helpful in differentiating PA and atypical PCA, and integrating clinical and CT image features can further improve the diagnostic performance.
探讨增强 CT 衍生的细胞外容积分数（ECV）和动脉增强分数（AEF）对鉴别多形性腺瘤（PA）和非典型腮腺腺癌（PCA）术前的常规影像和临床特征的附加价值。
2010 年 1 月至 2023 年 10 月，共纳入 187 例腮腺肿瘤患者，分为训练队列（102 例 PA 和 51 例 PCA）和测试队列（24 例 PA 和 10 例非典型 PCA）。评估肿瘤的临床和 CT 图像特征。计算增强 CT 衍生的 ECV 和 AEF。单变量分析确定了训练队列中两组间有统计学差异的变量。采用向前变量选择法的多变量逻辑回归分析构建四个模型（临床模型、临床模型+ECV、临床模型+AEF 和联合模型）。使用受试者工作特征（ROC）曲线分析评估诊断性能。Delong 检验比较模型差异，校准曲线和决策曲线分析（DCA）评估校准和临床应用。
年龄和边界被选择构建临床模型，并构建其 ROC 曲线。将临床模型、ECV 和 AEF 结合起来建立联合模型，与训练和测试队列中的临床模型相比，其诊断效果均更优（AUC=0.888，0.867）。在训练队列中，联合模型与临床模型之间存在显著的统计学差异（p=0.0145）。
ECV 和 AEF 有助于鉴别 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。
