Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images. (2022)

• Record Type: Journal Article
• Authors: Faggioni, Lorenzo; Gabelloni, Michela; De Vietro, Fabrizio; Frey, Jessica; Mendola, Vincenzo; Cavallero, Diletta; Borgheresi, Rita; Tumminello, Lorenzo; Shortrede, Jorge; Morganti, Riccardo; Seccia, Veronica; Coppola, Francesca; Cioni, Dania; Neri, Emanuele
• Is Part Of: European journal of radiology open. Volume 9(2022)
• Publication Date: 2022
• Subjects: ADC apparent diffusion coefficient -- AUC area under the curve -- FNAC fine needle aspiration cytology -- GLCM gray level co-occurrence matrix -- GLDM gray level dependence matrix -- GLRLM gray level run length matrix -- GLSZM gray level size zone matrix -- IBSI Image Biomarker Standardization Initiative -- NGTDM neighboring gray tone difference matrix -- PA pleomorphic adenoma -- PcfsT1W post-contrast fat-suppressed T1-weighted -- ROC receiver operating characteristics -- WT Warthin tumor; Warthin tumor -- Pleomorphic adenoma -- Head and neck cancer -- Parotid neoplasm -- Radiomics -- Magnetic resonance imaging; Medical radiology -- Periodicals; 616.075705
• DOI: 10.1016/j.ejro.2022.100429
• Languages: English
• ISSNs: 2352-0477

Abstract

Abstract: Purpose: Differentiating Warthin tumor (WT) from pleomorphic adenoma (PA) is of primary importance due to differences in patient management, treatment and outcome. We sought to evaluate the performance of MRI-based radiomic features in discriminating PA from WT in the preoperative setting. Methods: We retrospectively evaluated 81 parotid gland lesions (48 PA and 33 WT) on T2-weighted (T2w) images and 52 of them on post-contrast fat-suppressed T1-weighted (pcfsT1w) images. All MRI examinations were carried out on a 1.5-Tesla MRI scanner, and images were segmented manually using the software ITK-SNAP (www.itk-snap.org). Results: The most discriminative feature on pcfsT1w images was GLCM_InverseVariance, yielding area under the curve (AUC), sensitivity and specificity of 0.9, 86 % and 87 %, respectively. Skewness was the feature extracted from T2w images with the highest specificity (88 %) in discriminating WT from PA. Conclusion: Radiomic analysis could be an important tool to improve diagnostic accuracy in differentiating PA from WT.