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Texture analysis of low-flow vascular malformations in the oral and maxillofacial region: venous malformation vs. lymphatic malformation

PURPOSE: It is challenging for radiologists to distinguish between venous malformations (VMs) and lymphatic malformations (LMs) using magnetic resonance imaging (MRI). Thus, this study aimed to differentiate VMs from LMs using non-contrast-enhanced MRI texture analysis. MATERIAL AND METHODS: This re...

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Detalles Bibliográficos
Autores principales: Ito, Kotaro, Muraoka, Hirotaka, Hirahara, Naohisa, Sawada, Eri, Tokunaga, Satoshi, Kaneda, Takashi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Termedia Publishing House 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9536206/
https://www.ncbi.nlm.nih.gov/pubmed/36250141
http://dx.doi.org/10.5114/pjr.2022.119473
Descripción
Sumario:PURPOSE: It is challenging for radiologists to distinguish between venous malformations (VMs) and lymphatic malformations (LMs) using magnetic resonance imaging (MRI). Thus, this study aimed to differentiate VMs from LMs using non-contrast-enhanced MRI texture analysis. MATERIAL AND METHODS: This retrospective case-control study included 12 LM patients (6 men and 6 women; mean age 43.58, range 7-85 years) and 29 VM patients (7 men and 22 women; mean age 53.10, range 19-76 years) who underwent MRI for suspected vascular malformations. LM and VM patients were identified by histopathological examination of tissues excised during surgery. The texture features of VM and LM were analysed using the open-access software MaZda version 3.3. Seventeen texture features were selected using the Fisher and probability of error and average correlation coefficient methods in MaZda from 279 original parameters calculated for VM and LM. RESULTS: Among 17 selected texture features, the patients with LM and VM revealed significant differences in 1 histogram feature, 8 grey-level co-occurrence matrix (GLCM) features, and 1 grey-level run-length matrix feature. At the cut-off values of the histogram feature [skewness ≤ –0.131], and the GLCM features [S(0, 2) correlation ≥ 0.667, S(0, 3) correlation ≥ 0.451, S(0, 4) correlation ≥ 0.276, S(0, 5) correlation ≥ 0.389, S(1, 1) correlation ≥ 0.739, S(2, 2) correlation ≥ 0.446, S(2, –2) correlation ≥ 0.299, S(3, –3) correlation ≥ 0.091] had area under the curves of 0.724, 0.764, 0.773, 0.747, 0.733, 0.759, 0.730, 0.744 and 0.727, respectively. CONCLUSIONS: Non-contrast-enhanced MRI texture analysis allows us to differentiate between LMs and VMs.