Efficacy of Magnetic Resonance Imaging Texture Features of the Lateral Pterygoid Muscle in Distinguishing Rheumatoid Arthritis and Osteoarthritis of the Temporomandibular Joint

Overview

Journal Dentomaxillofac Radiol

Publisher Oxford University Press

Date 2023 Jan 3

PMID 36594821

Authors

Hirotaka Muraoka

Takashi Kaneda

Naohisa Hirahara

Kotaro Ito

Shunya Okada

Takumi Kondo

Affiliations

Soon will be listed here.

Abstract

Objectives: The aim of this study was to assess whether magnetic resonance imaging (MRI) texture features of the lateral pterygoid muscle can distinguish between rheumatoid arthritis (RA) and osteoarthritis (OA) of the temporomandibular joint (TMJ).

Methods: The authors extracted 279 texture features from 36 patients with RA and OA from the region of interest set for the lateral pterygoid muscle on short tau inversion recovery (STIR) images using MaZda Ver.3.3. A total of 10 texture features were selected using Fisher's coefficients, as well as probability of error and average correlation coefficients. Data observed to have a non-normal distribution using the Kolmogorov-Smirnov test were compared using the Mann-Whitney U-test. Receiver operating characteristic (ROC) curves were used to assess the ability of the 10 texture features to distinguish RA and OA of the TMJ.

Results: A total of 10 features (5 Correlation, 3 Run Length Nonuniformity, 1 Sigma, and 1 Teta) were selected from 279 texture features. These texture features revealed significant differences between the RA and OA groups ( < 0.01). The sensitivity, specificity, accuracy, and area under the ROC curve of the texture features for distinguishing RA from OA were 0.78-0.94, 0.89-1.0, 0.86-0.92, and 0.89-0.95, respectively.

Conclusion: MRI texture analysis of the lateral pterygoid muscle may be useful for distinguishing between RA and OA of the TMJ.

Citing Articles

Evaluation of masticatory muscles in temporomandibular joint disorder patients using quantitative MRI fat fraction analysis-Could it be a biomarker?.

Jeon K, Choi Y, Lee C, Kim H, Han S PLoS One. 2024; 19(1):e0296769.

PMID: 38241266 PMC: 10798479. DOI: 10.1371/journal.pone.0296769.

References

Cevidanes L, Hajati A, Paniagua B, Lim P, Walker D, Palconet G . Quantification of condylar resorption in temporomandibular joint osteoarthritis. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2010; 110(1):110-7. PMC: 2900430. DOI: 10.1016/j.tripleo.2010.01.008. View

Power M, Fell G, Wright M . Principles for high-quality, high-value testing. Evid Based Med. 2012; 18(1):5-10. PMC: 3585491. DOI: 10.1136/eb-2012-100645. View

van der Linden M, Knevel R, Huizinga T, van der Helm-van Mil A . Classification of rheumatoid arthritis: comparison of the 1987 American College of Rheumatology criteria and the 2010 American College of Rheumatology/European League Against Rheumatism criteria. Arthritis Rheum. 2010; 63(1):37-42. DOI: 10.1002/art.30100. View

. Treatment of temporomandibular disorders in children: summary statements and recommendations. American Academy of Pediatric Dentistry University of Texas Health Science Center at San Antonio Dental School. J Am Dent Assoc. 1990; 120(3):265, 267, 269. View

Szczypinski P, Strzelecki M, Materka A, Klepaczko A . MaZda--a software package for image texture analysis. Comput Methods Programs Biomed. 2008; 94(1):66-76. DOI: 10.1016/j.cmpb.2008.08.005. View

Liu Y, Xu X, Yin L, Zhang X, Li L, Lu H . Relationship between Glioblastoma Heterogeneity and Survival Time: An MR Imaging Texture Analysis. AJNR Am J Neuroradiol. 2017; 38(9):1695-1701. PMC: 7963694. DOI: 10.3174/ajnr.A5279. View

Arnett F, Edworthy S, Bloch D, McShane D, Fries J, Cooper N . The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum. 1988; 31(3):315-24. DOI: 10.1002/art.1780310302. View

Fujima N, Homma A, Harada T, Shimizu Y, Tha K, Kano S . The utility of MRI histogram and texture analysis for the prediction of histological diagnosis in head and neck malignancies. Cancer Imaging. 2019; 19(1):5. PMC: 6360729. DOI: 10.1186/s40644-019-0193-9. View

Hochberg M, Chang R, Dwosh I, Lindsey S, Pincus T, Wolfe F . The American College of Rheumatology 1991 revised criteria for the classification of global functional status in rheumatoid arthritis. Arthritis Rheum. 1992; 35(5):498-502. DOI: 10.1002/art.1780350502. View

10.

Wiberg B, Wanman A . Signs of osteoarthrosis of the temporomandibular joints in young patients: a clinical and radiographic study. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 1998; 86(2):158-64. DOI: 10.1016/s1079-2104(98)90118-4. View

11.

Muraoka H, Ito K, Hirahara N, Okada S, Kondo T, Kaneda T . Quantitative Assessment of the Apparent Diffusion Coefficient Values of the Inflammatory Connective Tissue Around the Mandibular Condyle in Rheumatoid Arthritis. J Oral Maxillofac Surg. 2021; 79(6):1230-1235. DOI: 10.1016/j.joms.2021.01.014. View

12.

Aletaha D, Neogi T, Silman A, Funovits J, Felson D, Bingham 3rd C . 2010 rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Ann Rheum Dis. 2010; 69(9):1580-8. DOI: 10.1136/ard.2010.138461. View

13.

Brooks S, Westesson P, Eriksson L, Hansson L, Barsotti J . Prevalence of osseous changes in the temporomandibular joint of asymptomatic persons without internal derangement. Oral Surg Oral Med Oral Pathol. 1992; 73(1):118-22. DOI: 10.1016/0030-4220(92)90168-p. View

14.

Ren J, Qi M, Yuan Y, Duan S, Tao X . Machine Learning-Based MRI Texture Analysis to Predict the Histologic Grade of Oral Squamous Cell Carcinoma. AJR Am J Roentgenol. 2020; 215(5):1184-1190. DOI: 10.2214/AJR.19.22593. View

15.

Kretapirom K, Okochi K, Nakamura S, Tetsumura A, Ohbayashi N, Yoshino N . MRI characteristics of rheumatoid arthritis in the temporomandibular joint. Dentomaxillofac Radiol. 2012; 42(4):31627230. PMC: 3667508. DOI: 10.1259/dmfr/31627230. View

16.

Goncalves B, de Araujo E, Nussi A, Bechara N, Sarmento D, Oliveira M . Texture analysis of cone-beam computed tomography images assists the detection of furcal lesion. J Periodontol. 2020; 91(9):1159-1166. DOI: 10.1002/JPER.19-0477. View

17.

Bessa-Nogueira R, Vasconcelos B, Duarte A, Goes P, Bezerra T . Targeted assessment of the temporomandibular joint in patients with rheumatoid arthritis. J Oral Maxillofac Surg. 2008; 66(9):1804-11. DOI: 10.1016/j.joms.2007.08.037. View

18.

Nagawa K, Suzuki M, Yamamoto Y, Inoue K, Kozawa E, Mimura T . Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies. Sci Rep. 2021; 11(1):9821. PMC: 8110584. DOI: 10.1038/s41598-021-89311-3. View

19.

Seymour R, CROUSE V, IRBY W . Temporomandibular ankylosis secondary to rheumatoid arthritis. Report of a case. Oral Surg Oral Med Oral Pathol. 1975; 40(5):584-9. DOI: 10.1016/0030-4220(75)90367-9. View

20.

Yuan Y, Ren J, Tao X . Machine learning-based MRI texture analysis to predict occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma. Eur Radiol. 2021; 31(9):6429-6437. DOI: 10.1007/s00330-021-07731-1. View