High Field MRI in Parotid Gland Tumors: A Diagnostic Algorithm

<b>Backgrounds:</b> Imaging of parotid tumors is crucial for surgery planning, but it cannot distinguish malignant from benign lesions with absolute reliability. The aim of the study was to establish a diagnostic MRI algorithm to differentiate parotid tumors. <b>Methods:</b> A retrospective study was conducted including all patients with parotid tumors, who underwent 3T-MRI and surgery. Morphological characteristics and normalized T2 and late postcontrast T1 signal intensities (SI) were assessed. "Ghosting sign" on late postcontrast T1 sequence was defined as indistinguishability of the tumor except for a thin peripheral enhancement. Patients were divided according to histology and imaging data were compared. A diagnostic MRI algorithm was established. <b>Results:</b> Thirty-six patients were included. The combination of normalized late T1 postcontrast SI, normalized T2 SI and "ghosting sign" allowed for the distinguishing of malignant from benign parotid tumors with high sensitivity (100%), specificity (93%), positive predictive value (80%), negative predictive value, (100%) and accuracy (94%). Moreover, pleomorphic adenomas often showed a homogeneous T2 signal and a complete capsule (<i>p</i> < 0.01), Warthin tumors protein-rich cysts and calcifications (<i>p</i> < 0.005 and <i>p</i> < 0.05), and malignant tumors an inhomogeneous contrast enhancement (<i>p</i> < 0.01). <b>Conclusions:</b> High field MRI represents a promising tool in parotid tumors, allowing for an accurate differentiation of malignant and benign lesions.

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Publication Details

Journal
Cancers
Published
2024-12-29
DOI
https://doi.org/10.3390/cancers17010071
Citations
4
Primary Topic
Salivary Gland Tumors Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
2.29
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article

High Field MRI in Parotid Gland Tumors: A Diagnostic Algorithm

Chiara Gaudino, Francesca De Felice, Resi Pucci, Patrizià Pantano et al.
4 citations
Cancers
Salivary Gland Tumors Diagnosis and Treatment
2.29
article

High Field MRI in Parotid Gland Tumors: A Diagnostic Algorithm

Chiara Gaudino, Francesca De Felice, Resi Pucci, Patrizià Pantano, Valentino Valentini, Martina Lucia Pisciotti, Andrea Cassoni, Cira Di Gioia, Chiara Veneroso
article en
4 citations

Abstract

<b>Backgrounds:</b> Imaging of parotid tumors is crucial for surgery planning, but it cannot distinguish malignant from benign lesions with absolute reliability. The aim of the study was to establish a diagnostic MRI algorithm to differentiate parotid tumors. <b>Methods:</b> A retrospective study was conducted including all patients with parotid tumors, who underwent 3T-MRI and surgery. Morphological characteristics and normalized T2 and late postcontrast T1 signal intensities (SI) were assessed. "Ghosting sign" on late postcontrast T1 sequence was defined as indistinguishability of the tumor except for a thin peripheral enhancement. Patients were divided according to histology and imaging data were compared. A diagnostic MRI algorithm was established. <b>Results:</b> Thirty-six patients were included. The combination of normalized late T1 postcontrast SI, normalized T2 SI and "ghosting sign" allowed for the distinguishing of malignant from benign parotid tumors with high sensitivity (100%), specificity (93%), positive predictive value (80%), negative predictive value, (100%) and accuracy (94%). Moreover, pleomorphic adenomas often showed a homogeneous T2 signal and a complete capsule (<i>p</i> < 0.01), Warthin tumors protein-rich cysts and calcifications (<i>p</i> < 0.005 and <i>p</i> < 0.05), and malignant tumors an inhomogeneous contrast enhancement (<i>p</i> < 0.01). <b>Conclusions:</b> High field MRI represents a promising tool in parotid tumors, allowing for an accurate differentiation of malignant and benign lesions.

CancersVol. 17(1)
Policlinico Umberto I (IT), Istituto Neurologico Mediterraneo (IT), Azienda Ospedaliera San Camillo-Forlanini (IT), Sapienza University of Rome (IT)
Openalex Percentile: Top 11%
Salivary Gland Tumors Diagnosis and Treatment
2.29
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