1. Assessment of synthetic MRI to distinguish Warthin's tumor from pleomorphic adenoma in the parotid gland: comparison of two methods of positioning the region of interest for synthetic relaxometry measurement.
- Author
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Jiabin Sun, Xinping Kuai, Dawei Huang, Xinghua Ji, Chuanhai Jia, and Shengyu Wang
- Subjects
PLEOMORPHIC adenoma ,MAGNETIC resonance imaging ,MASSETER muscle ,LOGISTIC regression analysis ,PAROTID glands ,PAROTID gland tumors - Abstract
Purpose: To assess the diagnostic potential of the synthetic MRI (SyMRI) for differentiating Warthin's tumors (WT) from pleomorphic adenomas (PA). Materials and methods: 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 t 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. Results: 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. Conclusion: The SyMRI-derived quantitative parameters demonstrated excellent performance for discriminating PA from WT in the parotid gland. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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