Sun Jiabin, Kuai Xinping, Huang Dawei, Ji Xinghua, Jia Chuanhai, Wang Shengyu
Department of Radiology, Changshu No.2 People's Hospital, the Fifth Affiliated Clinical Medical College of Yangzhou University, Changshu, Jiangsu, China.
Department of Radiology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Front Oncol. 2024 Oct 4;14:1446736. doi: 10.3389/fonc.2024.1446736. eCollection 2024.
To assess the diagnostic potential of the synthetic MRI (SyMRI) for differentiating Warthin's tumors (WT) from pleomorphic adenomas (PA).
Forty-nine individuals with parotid gland tumors (PA, n = 23; WT, n = 26) were recruited. Using two distinct regions of interest (ROI), SyMRI quantitative parameters of lesions were calculated, including mean and standard deviation (T1, T2, PD, T1sd, T2sd, and PDsd). Meanwhile, T1ratio, T2ratio, and PDratio (lesion/masseter muscle) were calculated based on the mean SyMRI quantitative parameters of masseter muscle (T1, T2, PD). Using the independent samples test, we compared PA and WT parameters, while comparing the areas under the curve (AUC) using the DeLong's test. A multi-parameter SyMRI model was constructed using logistic regression analysis.
In PA, the T1, T1sd, T2, PD, T1ratio, T2ratio, and PDratio derived from full and partial lesion ROIs were significantly higher than in WT. According to the receiver operating curve analysis, the AUC of the quantitative parameters derived from full-lesion and partial-lesion ROIs ranged from 0.722 to 0.983 for differentiating PA from WT. T1 values derived from partial-lesion ROI delineation demonstrated the best diagnostic performance among all single parameters, achieving an AUC of 0.983. Using 1322 ms as a cutoff value, the sensitivity, specificity, and accuracy were 88.46%, 100% and 93.88%, respectively.
The SyMRI-derived quantitative parameters demonstrated excellent performance for discriminating PA from WT in the parotid gland.
评估合成磁共振成像（SyMRI）在鉴别沃辛瘤（WT）和多形性腺瘤（PA）方面的诊断潜力。
招募了49例腮腺肿瘤患者（PA，n = 23；WT，n = 26）。使用两个不同的感兴趣区域（ROI），计算病变的SyMRI定量参数，包括均值和标准差（T1、T2、PD、T1sd、T2sd和PDsd）。同时，根据咬肌的SyMRI定量参数均值（T1、T2、PD）计算T1比率、T2比率和PD比率（病变/咬肌）。使用独立样本t检验比较PA和WT参数，同时使用德龙检验比较曲线下面积（AUC）。采用逻辑回归分析构建多参数SyMRI模型。
在PA中，来自完整和部分病变ROI的T1、T1sd、T2、PD、T1比率、T2比率和PD比率显著高于WT。根据受试者工作特征曲线分析，对于鉴别PA和WT，来自完整病变和部分病变ROI的定量参数的AUC范围为0.722至0.983。在所有单参数中，部分病变ROI勾勒得出的T1值显示出最佳诊断性能，AUC为0.983。以1322 ms作为截断值，敏感性、特异性和准确性分别为88.46%、100%和93.88%。
SyMRI衍生的定量参数在鉴别腮腺中的PA和WT方面表现出色。
