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Erschienen in: Oral Radiology 2/2023

14.06.2022 | Original Article

Magnetic resonance image texture analysis of the lateral pterygoid muscle in patients with rheumatoid arthritis: a preliminary report

verfasst von: Hirotaka Muraoka, Takashi Kaneda, Naohisa Hirahara, Kotaro Ito, Shunya Okada, Takumi Kondo

Erschienen in: Oral Radiology | Ausgabe 2/2023

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Abstract

Objectives

Magnetic resonance imaging (MRI) is useful for assessing temporomandibular disorders (TMDs). However, few studies have attempted texture analysis of the lateral pterygoid muscle in patients with rheumatoid arthritis (RA). This study aims to investigate the usefulness of MRI texture analysis of the lateral pterygoid muscle of patients with RA of the temporomandibular joint (TMJ).

Methods

We analyzed the data from 36 patients (18 non-RA patients and 18 RA patients) who complained of pain and underwent MRI between April 2008 and August 2021. From the MRI scans of these patients, 279 radiomics features were extracted using STIR image data of the ROIs on the lateral pterygoid muscle of patients with RA and analyzed using MaZda ver. 3.3. Seven gray-level co-occurrence matrix features (Sum entropy, Sum variance) were picked up using the Fisher coefficient, for comparison between the RA and non-RA groups. Data analysis was performed using the Mann–Whitney U test A P value of < 0.05 was considered as statistically significant.

Results

All seven lateral pterygoid muscle radiomic features indicated significant differences between the non-RA and RA groups (P < 0.05).

Conclusion

MRI texture analysis shows potential for application in radiomics diagnosis of RA in TMJ.
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Metadaten
Titel
Magnetic resonance image texture analysis of the lateral pterygoid muscle in patients with rheumatoid arthritis: a preliminary report
verfasst von
Hirotaka Muraoka
Takashi Kaneda
Naohisa Hirahara
Kotaro Ito
Shunya Okada
Takumi Kondo
Publikationsdatum
14.06.2022
Verlag
Springer Nature Singapore
Erschienen in
Oral Radiology / Ausgabe 2/2023
Print ISSN: 0911-6028
Elektronische ISSN: 1613-9674
DOI
https://doi.org/10.1007/s11282-022-00625-y

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