Ailevi Akdeniz Ateşi Olan Hastalarda Mandibula’nın Morfolojik, Fraktal ve Dokusal Özellikleri: Olgu Kontrol Çalışması
PDF
Atıf
Paylaş
Talep
P: 246-255
Nisan 2024

Ailevi Akdeniz Ateşi Olan Hastalarda Mandibula’nın Morfolojik, Fraktal ve Dokusal Özellikleri: Olgu Kontrol Çalışması

Bezmialem Science 2024;12(2):246-255
Bilgi mevcut değil.
Bilgi mevcut değil
Alındığı Tarih: 29.09.2023
Kabul Tarihi: 01.02.2024
Yayın Tarihi: 03.05.2024
PDF
Atıf
Paylaş
Talep

ÖZET

Amaç:

Ailevi Akdeniz ateşi (FMF) inflamatuar bir hastalıktır ve kronik enflamasyon kemik döngüsünü ve metabolizmasını etkileyebilir. Bu çalışmanın amacı panoramik radyografiler üzerinde mandibular kemiğin morfolojik, fraktal ve dokusal özelliklerini FMF hastaları ve sağlıklı bireylerle karşılaştırmaktır.

Yöntemler:

Çalışmaya FMF tanısı alan 50 hasta ve yaş ve cinsiyet açısından uyumlu 50 sağlıklı kontrol dahil edildi. Toplam 100 hastanın dijital panoramik görüntüleri üzerinde mandibular korteksin morfolojik değerlendirmesi mandibular kortikal indeks (MKI) kullanılarak yapıldı. Trabeküler kemiğe ait fraktal boyut (FB) ve doku analizi için, ikinci küçük azı ve birinci büyük azı dişlerinin kökleri arasındaki trabeküler kemik bölgesinden 50x50 piksel büyüklüğünde ilgi alanları seçildi. FB hesaplanmasında kutu sayma yöntemi uygulandı. Bu bölgelerin piksel gri-skala düzeyleri farklı dağılımlar gösterdiğinden doku analizi için histogram eşitleme ile ön işleme yapıldı. Panoramik görüntülerin birinci derece ve gri seviye eş oluşum matrisi tabanlı ikinci derece özellikleri hesaplanarak dokusal karakterizasyonları elde edildi.

Bulgular:

Mandibular korteks MKI değerleri olgu ve kontrol grupları arasında anlamlı farklılık göstermedi (p>0,05). Trabeküler kemiğe ait FB değerleri olgu grubunda 1,43, kontrol grubunda 1,44 olup aralarında anlamlı fark yoktu (p>0,05). Trabeküler kemiğin birinci ve ikinci derece dokusal özellikleri olgu ve kontrol grupları arasında istatistiksel olarak anlamlı farklılık göstermedi (p>0,05).

Sonuç:

Mandibular kemiğin morfolojik, fraktal ve dokusal özellikleri FMF hastalarında ve sağlıklı kontrollerde panoramik radyografiler üzerinde farklılık göstermemektedir.

References

1
Ozen S, Batu ED. The myths we believed in familial Mediterranean fever: what have we learned in the past years? Semin Immunopathol 2015;37:363-9.
2
Zadeh N, Getzug T, Grody WW. Diagnosis and management of familial Mediterranean fever: integrating medical genetics in a dedicated interdisciplinary clinic. Genet Med 2011;13:263-9.
3
Basaran O, Uncu N, Celikel BA, Aydın F, Cakar N. Assessment of neutrophil to lymphocyte ratio and mean platelet volume in pediatric familial Mediterranean fever patients. J Res Med Sci 2017;22:35.
4
Kastner DL. Familial Mediterranean fever: the genetics of inflammation. Hosp Pract (1995) 1998;33:131-4, 139-40, 143-6 passim.
5
Cantarini L, Rigante D, Brizi MG, Lucherini OM, Sebastiani GD, Vitale A, et al. Clinical and biochemical landmarks in systemic autoinflammatory diseases. Ann Med 2012;44:664-73.
6
Bostancı V, Toker H, Senel S, Sahin S. Prevalence of periodontal disease in patients with Familial Mediterranean Fever: a cohort study from central Türkiye. Quintessence Int 2014;45:743-8.
7
Aypar E, Ozen S, Okur H, Kutluk T, Besbas N, Bakkaloglu A. Th1 polarization in familial Mediterranean fever. J Rheumatol 2003;30:2011-3.
8
Esmeray P, Keçeli Tİ, Tekçiçek M, Batu ED, Arıcı ZS, Ünlü HK, et al. Oral health status in children with familial Mediterranean fever. Turk J Pediatr 2021;63:443-9.
9
Ak KB, Suzen M, Uçkan S. Aseptic Arthritis of the Temporomandibular Joint with Severe Inflammation in an FMF Patient: A Case Report. Journal of Anatolian Medical Research 2022;7:35-40.
10
Kwon AY, Huh KH, Yi WJ, Lee SS, Choi SC, Heo MS. Is the panoramic mandibular index useful for bone quality evaluation? Imaging Sci Dent 2017;47:87-92.
11
Klemetti E, Kolmakov S, Kröger H. Pantomography in assessment of the osteoporosis risk group. Scand J Dent Res 1994;102:68-72.
12
Güleç M, Taşsöker M, Şener S. Tıpta ve Diş Hekimliğinde Fraktal Analiz. Ege Üniversitesi Diş Hekimliği Fakültesi Dergisi 2019;40:17-31.
13
Demirhan A, Güler İ. Image Segmentatıon Usıng Self-Organızıng Maps And Gray Level Co-Occurrence Matrıces. J. Fac. Eng. Arch. Gazi Univ 2010;25:285-91.
14
Aggarwal N, Agrawal R. First and second order statistics features for classification of magnetic resonance brain images. Journal of Signal and Information Processing 2012.
15
Yusof NSM, Dewi DEO, Faudzi AAM, Salih NM, Bakar NA, Hamid HA. Ultrasound imaging characterization on tissue mimicking materials for cardiac tissue phantom: Texture analysis perspective. Malaysian Journal of Fundamental and Applied Sciences 2017;17:4-2.
16
Cai JH, He Y, Zhong XL, Lei H, Wang F, Luo GH, et al. Magnetic Resonance Texture Analysis in Alzheimer’s disease. Acad Radiol 2020;27:1774-83.
17
Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev 2016;1:207-26.
18
Materka A, Strzelecki M. Texture analysis methods-a review. Technical university of lodz, institute of electronics, COST B11 report, Brussels 1998;10:4968.
19
Haralick RM, Shanmugam K, Dinstein I. Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics 1973;SMC-3:610-21.
20
Wang Y, Yu B, Zhong F, Guo Q, Li K, Hou Y, et al. MRI-based texture analysis of the primary tumor for pre-treatment prediction of bone metastases in prostate cancer. Magn Reson Imaging 2019;60:76-84.
21
Suhas M, Swathi B. Significance of haralick features in bone tumor classification using support vector machine. Paper presented at: Engineering Vibration, Communication and Information Processing: ICoEVCI 2018, India2019.
22
Yildirim K, Karatay S, Cetinkaya R, Uzkeser H, Erdal A, Capoglu I, et al. Bone mineral density in patients with familial Mediterranean fever. Rheumatol Int 2010;30:305-8.
23
White SC, Rudolph DJ. Alterations of the trabecular pattern of the jaws in patients with osteoporosis. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 1999;88:628-35.
24
Soh L-K, Tsatsoulis C. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on geoscience and remote sensing. 1999;37:780-95.
25
Brynolfsson P, Nilsson D, Torheim T, Asklund T, Karlsson CT, Trygg J, et al. Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters. Sci Rep 2017;7:4041.
26
Löfstedt T, Brynolfsson P, Asklund T, Nyholm T, Garpebring A. Gray-level invariant Haralick texture features. PloS one 2019;14:e0212110.
27
Clausi DA. An analysis of co-occurrence texture statistics as a function of grey level quantization. Canadian Journal of remote sensing 2002;28:45-62.
28
Uppuluri A. GLCM texture features MATLAB Central File Exchange 2023; https://www.mathworks.com/matlabcentral/fileexchange/22187-glcm-texture-features. Accessed Retrieved June 24, 2023.
29
Bayrak S, Göller Bulut D, Orhan K, Sinanoğlu EA, Kurşun Çakmak EŞ, Mısırlı M, et al. Evaluation of osseous changes in dental panoramic radiography of thalassemia patients using mandibular indexes and fractal size analysis. Oral Radiol 2020;36:18-24.
30
Altunok Ünlü N, Coşgun A, Altan H. Evaluation of bone changes on dental panoramic radiography using mandibular indexes and fractal dimension analysis in children with familial Mediterranean fever. Oral Radiol 2023;39:312-20.
31
Coşgunarslan A, Canger EM, Soydan Çabuk D, Kış HC. The evaluation of the mandibular bone structure changes related to lactation with fractal analysis. Oral Radiol 2020;36:238-47.
32
Zulpe N, Pawar V. GLCM textural features for brain tumor classification. International Journal of Computer Science Issues (IJCSI) 2012;9:354-59.
33
Durgamahanthi V, Anita Christaline J, Shirly Edward A. GLCM and GLRLM Based Texture Analysis: Application to Brain Cancer Diagnosis Using Histopathology Images. Intelligent Computing and Applications, Proceedings of ICICA 2019 2020:691-706.
34
Xian G-m. An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Systems with Applications 2010;37:6737-41.
35
Mohanty AK, Beberta S, Lenka SK. Classifying benign and malignant mass using GLCM and GLRLM based texture features from mammogram. International Journal of Engineering Research and Applications 2011;1:687-93.
36
Htay TT, Maung SS. Early stage breast cancer detection system using glcm feature extraction and k-nearest neighbor (k-NN) on mammography image. 2018 18th International Symposium on Communications and Information Technologies (ISCIT) 2018.
37
Arabi PM, Joshi G, Vamsha Deepa N. Performance evaluation of GLCM and pixel intensity matrix for skin texture analysis. Perspectives in Science 2016;8:203-6.
38
Yang X, Tridandapani S, Beitler JJ, Yu DS, Yoshida EJ, Curran WJ, et al. Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity. Med Phys 2012;39:5732-9.
39
Tahir MA, Bouridane A, Kurugollu F. An FPGA Based Coprocessor for GLCM and Haralick Texture Features and their Application in Prostate Cancer Classification. Analog Integrated Circuits and Signal Processing 2005;43:205-15.
40
Althubiti SA, Paul S, Mohanty R, Mohanty SN, Alenezi F, Polat K. Ensemble Learning Framework with GLCM Texture Extraction for Early Detection of Lung Cancer on CT Images. Comput Math Methods Med 2022;2022:2733965.
41
Sheha MA, Mabrouk MS, Sharawy A. Automatic detection of melanoma skin cancer using texture analysis. International Journal of Computer Applications 2012;42:22-6.
42
Doumou G, Siddique M, Tsoumpas C, Goh V, Cook GJ. The precision of textural analysis in 18F-FDG-PET scans of oesophageal cancer. European Radiology 2015;25:2805-12.
43
Song G, Xue F, Zhang C. A Model Using Texture Features to Differentiate the Nature of Thyroid Nodules on Sonography. Journal of Ultrasound in Medicine 2015;34:1753-60.
44
Novitasari DCR, Asyhar AH, Thohir M, Arifin AZ, Mu’jizah H, Foeady AZ. Cervical Cancer Identification Based Texture Analysis Using GLCM-KELM on Colposcopy Data. 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). 2020.
45
Kavitha MS, An SY, An CH, Huh KH, Yi WJ, Heo MS, et al. Texture analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women. Oral Surg Oral Med Oral Pathol Oral Radiol 2015;119:346-56.
46
Geetha V, Aprameya K. Textural analysis based classification of digital X-ray images for dental caries diagnosis. Int J Eng Manuf (IJEM) 2019;9:44-5.
47
Veena DK, Jatti A, Joshi R, Deepu KS. Characterization of dental pathologies using digital panoramic X-ray images based on texture analysis. Annu Int Conf IEEE Eng Med Biol Soc 2017;2017:592-5.
48
Kawashima Y, Fujita A, Buch K, Li B, Qureshi MM, Chapman MN, et al. Using texture analysis of head CT images to differentiate osteoporosis from normal bone density. Eur J Radiol 2019;116:212-8.
49
Huber MB, Carballido-Gamio J, Fritscher K, Schubert R, Haenni M, Hengg C, et al. Development and testing of texture discriminators for the analysis of trabecular bone in proximal femur radiographs. Med Phys 2009;36:5089-98.
50
Hwang JJ, Lee JH, Han SS, Kim YH, Jeong HG, Choi YJ, et al. Strut analysis for osteoporosis detection model using dental panoramic radiography. Dentomaxillofac Radiol 2017;46:20170006.
51
Kinalski MA, Boscato N, Damian MF. The accuracy of panoramic radiography as a screening of bone mineral density in women: a systematic review. Dentomaxillofac Radiol 2020;49:20190149.
52
Pacheco-Pereira C, Silvestre-Barbosa Y, Almeida FT, Geha H, Leite AF, Guerra ENS. Trabecular and cortical mandibular bone investigation in familial adenomatous polyposis patients. Sci Rep 2021;11:9143.
53
Ersan N, Özel B. Fractal dimension analysis of different mandibular regions in familial Mediterranean fever patients: A cross-sectional retrospective study. PLoS One 2023;18:e0288170.
54
Aral CA, Aral K, Yay A, Özçoban Ö, Berdeli A, Saraymen R. Effects of colchicine on gingival inflammation, apoptosis, and alveolar bone loss in experimental periodontitis. J Periodontol 2018;89:577-85.
2024 ©️ Galenos Publishing House