Department of Radiology, Nihon University School of Dentistry at Matsudo 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, Japan.
Dentomaxillofac Radiol. 2023 Apr;52(4):20220404. doi: 10.1259/dmfr.20220404. Epub 2023 Apr 4.
Warthin's tumors (WT) and pleomorphic adenomas (PA) are the commonest parotid gland tumors; however, their differentiation remains difficult. This study aimed to investigate the utility of the apparent diffusion coefficient (ADC) value, texture features, and their combination for the differential diagnosis of parotid gland tumors.
Patients who underwent magnetic resonance imaging (MRI) between April 2008 and March 2021 for parotid gland tumors were included and divided into two groups according to the tumor type: WT and PA. The tumor types were used as predictor variables, while the ADC value, texture features, and their combination were the outcome variables. Texture features were measured on short tau inversion recovery (STIR) images and selected using the Fisher's coefficient method and probability of error, and average correlation coefficients. The Mann-Whitney U-test was used to analyze bivariate statistics. Receiver operating characteristic curve analysis was used to assess the ability of the ADC value, texture features, and their combination to distinguishing between the two tumor types.
A total of 22 patients were included, 11 in each group. The ADC value, 10 texture features, and their combination were significantly different between the two groups ( < .001). Moreover, all three variables had high area under the curve values of 0.93-0.96.
The ADC value, texture features, and their combination demonstrated good diagnostic ability to distinguish between WTs and PAs. This method may be used to aid the differential diagnosis of parotid gland tumors, thereby promoting timely and adequate treatment.
沃辛氏瘤(WT)和多形性腺瘤(PA)是最常见的腮腺肿瘤,但两者的鉴别仍具有一定难度。本研究旨在探讨表观扩散系数(ADC)值、纹理特征及其联合应用在腮腺肿瘤鉴别诊断中的价值。
选取 2008 年 4 月至 2021 年 3 月间因腮腺肿瘤接受磁共振成像(MRI)检查的患者,并根据肿瘤类型分为 WT 组和 PA 组。以肿瘤类型为预测变量,ADC 值、纹理特征及其联合应用为结局变量。在短回波时间反转恢复(STIR)图像上测量纹理特征,并采用 Fisher 系数法、错误概率和平均相关系数选择特征。采用 Mann-Whitney U 检验进行双变量统计学分析。采用受试者工作特征曲线分析评估 ADC 值、纹理特征及其联合应用区分两种肿瘤类型的能力。
共纳入 22 例患者,每组 11 例。两组间 ADC 值、10 个纹理特征及其联合应用均存在显著差异(<.001)。此外,所有三个变量的曲线下面积均较高,为 0.93-0.96。
ADC 值、纹理特征及其联合应用对鉴别 WT 和 PA 具有良好的诊断能力。该方法可能有助于腮腺肿瘤的鉴别诊断,从而促进及时、充分的治疗。