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In vivo study of cone beam computed tomography texture analysis of mandibular condyle and its correlation with gender and age

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Abstract

Objective

Texture analysis is an image processing method that aims to assess the distribution of gray-level intensity and spatial organization of the pixels in the image. The purpose of this study was to investigate whether the texture analysis applied to cone beam computed tomography (CBCT) images could detect variation in the condyle trabecular bone of individuals from different age groups and genders.

Methods

The sample consisted of imaging exams from 63 individuals divided into three groups according to age groups of 03–13, 14–24 and 25–34. For texture analysis, the MaZda® software was used to extract the following parameters: second angular momentum, contrast, correlation, sum of squares, inverse difference moment, sum entropy and entropy. Statistical analysis was performed using Mann–Whitney test for gender and Kruskal–Wallis test for age (P = 5%).

Results

No statistically significant differences were found between age groups for any of the parameters. Males had lower values for the parameter correlation than those of females (P < 0.05).

Conclusion

Texture analysis proved to be useful to discriminate mandibular condyle trabecular bone between genders.

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References

  1. de Oliveira MS, Balthazar ML, D’Abreu A, et al. MR imaging texture analysis of the corpus callosum and thalamus in amnestic mild cognitive impairment and mild Alzheimer disease. AJNR Am J Neuroradiol. 2011;32(1):60–6. https://doi.org/10.3174/ajnr.A2232.

    Article  Google Scholar 

  2. Caruso D, Zerunian M, Ciolina M, et al. Haralick’s texture features for the prediction of response to therapy in colorectal cancer: a preliminary study. Radiol Med. 2018;123(3):161–7. https://doi.org/10.1007/s11547-017-0833-8.

    Article  Google Scholar 

  3. Maas M, Lambregts DM, Lahaye MJ, et al. T-staging of rectal cancer: accuracy of 3.0 Tesla MRI compared with 1.5 Tesla. Abdom Imaging. 2012;37(3):475–81. https://doi.org/10.1007/s00261-011-9770-5.

    Article  Google Scholar 

  4. De Rosa CS, Bergamini ML, Palmieri M, et al. Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis. Heliyon. 2020;6(10): e05194. https://doi.org/10.1016/j.heliyon.2020.e05194.

    Article  Google Scholar 

  5. de Albuquerque M, Anjos LG, Tavares M, de Andrade H, et al. MRI texture analysis reveals deep gray nuclei damage in amyotrophic lateral sclerosis. J Neuroimaging. 2016;26(2):201–6. https://doi.org/10.1111/jon.12262.

    Article  Google Scholar 

  6. Bonilha L, Kobayashi E, Castellano G, et al. Texture analysis of hippocampal sclerosis. Epilepsia. 2003;44(12):1546–50.

    Article  Google Scholar 

  7. Raja JV, Khan M, Ramachandra VK, et al. Texture analysis of CT images in the characterization of oral cancers involving buccal mucosa. Dentomaxillofac Radiol. 2012;41(6):475–80. https://doi.org/10.1259/dmfr/83345935.

    Article  Google Scholar 

  8. Ramkumar S, Ranjbar S, Ning S, et al. MRI-based texture analysis to differentiate sinonasal squamous cell carcinoma from inverted papilloma. AJNR Am J Neuroradiol. 2017;38(5):1019–25. https://doi.org/10.3174/ajnr.A5106.

    Article  Google Scholar 

  9. MacKay JW, Kapoor G, Driban JB, et al. Association of subchondral bone texture on magnetic resonance imaging with radiographic knee osteoarthritis progression: data from the Osteoarthritis Initiative Bone Ancillary Study. Eur Radiol. 2018;28(11):4687–95. https://doi.org/10.1007/s00330-018-5444-9.

    Article  Google Scholar 

  10. Fruehwald-Pallamar J, Czerny C, Holzer-Fruehwald L, et al. Texture-based and diffusion-weighted discrimination of parotid gland lesions on MR images at 3.0 Tesla. NMR Biomed. 2013;26(11):1372–9. https://doi.org/10.1002/nbm.2962.

    Article  Google Scholar 

  11. Miles KA, Ganeshan B, Hayball MP. CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging. 2013;13(3):400–6. https://doi.org/10.1102/1470-7330.2013.9045.

    Article  Google Scholar 

  12. Costa ALF, de Souza CB, Fardim KAC, et al. Texture analysis of cone beam computed tomography images reveals dental implant stability. Int J Oral Maxillofac Surg. 2021. https://doi.org/10.1016/j.ijom.2021.04.009.

    Article  Google Scholar 

  13. Castellano G, Bonilha L, Li LM, et al. Texture analysis of medical images. Clin Radiol. 2004;59(12):1061–9. https://doi.org/10.1016/j.crad.2004.07.008.

    Article  Google Scholar 

  14. De Cecco CN, Ganeshan B, Ciolina M, et al. Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Invest Radiol. 2015;50(4):239–45. https://doi.org/10.1097/RLI.0000000000000116.

    Article  Google Scholar 

  15. Bianchi J, Goncalves JR, Ruellas ACO, et al. Software comparison to analyze bone radiomics from high resolution CBCT scans of mandibular condyles. Dentomaxillofac Radiol. 2019;48(6):20190049. https://doi.org/10.1259/dmfr.20190049.

    Article  Google Scholar 

  16. Zaccagna F, Ganeshan B, Arca M, et al. CT texture-based radiomics analysis of carotid arteries identifies vulnerable patients: a preliminary outcome study. Neuroradiology. 2021;63(7):1043–52. https://doi.org/10.1007/s00234-020-02628-0.

    Article  Google Scholar 

  17. Dieckmeyer M, Junker D, Ruschke S, et al. Vertebral bone marrow heterogeneity using texture analysis of chemical shift encoding-based mri: variations in age, sex, and anatomical location. Front Endocrinol (Lausanne). 2020;11: 555931. https://doi.org/10.3389/fendo.2020.555931.

    Article  Google Scholar 

  18. Ling H, Yang X, Li P, et al. Cross gender-age trabecular texture analysis in cone beam CT. Dentomaxillofac Radiol. 2014;43(4):20130324. https://doi.org/10.1259/dmfr.20130324.

    Article  Google Scholar 

  19. Vujasinovic T, Pribic J, Kanjer K, et al. Gray-level co-occurrence matrix texture analysis of breast tumor images in prognosis of distant metastasis risk. Microsc Microanal. 2015;21(3):646–54. https://doi.org/10.1017/S1431927615000379.

    Article  Google Scholar 

  20. Haralick RS. K; Dinstein I textural features for image classification. IEEE Trans Syst Man Cybern. 1973;3:610–21.

    Article  Google Scholar 

  21. Clarke B. Normal bone anatomy and physiology. Clin J Am Soc Nephrol. 2008;3(Suppl 3):S131–9. https://doi.org/10.2215/CJN.04151206.

    Article  Google Scholar 

  22. Amin S, Khosla S. Sex- and age-related differences in bone microarchitecture in men relative to women assessed by high-resolution peripheral quantitative computed tomography. J Osteoporos. 2012;2012: 129760. https://doi.org/10.1155/2012/129760.

    Article  Google Scholar 

  23. Dalzell N, Kaptoge S, Morris N, et al. Bone micro-architecture and determinants of strength in the radius and tibia: age-related changes in a population-based study of normal adults measured with high-resolution pQCT. Osteoporos Int. 2009;20(10):1683–94. https://doi.org/10.1007/s00198-008-0833-6.

    Article  Google Scholar 

  24. Widmalm SE, Westesson PL, Kim IK, et al. Temporomandibular joint pathosis related to sex, age, and dentition in autopsy material. Oral Surg Oral Med Oral Pathol. 1994;78(4):416–25. https://doi.org/10.1016/0030-4220(94)90031-0.

    Article  Google Scholar 

  25. Jiao K, Dai J, Wang MQ, et al. Age- and sex-related changes of mandibular condylar cartilage and subchondral bone: a histomorphometric and micro-CT study in rats. Arch Oral Biol. 2010;55(2):155–63. https://doi.org/10.1016/j.archoralbio.2009.11.012.

    Article  Google Scholar 

  26. Mulder L, Koolstra JH, Van Eijden TM. Accuracy of MicroCT in the quantitative determination of the degree and distribution of mineralization in developing bone. Acta Radiol. 2006;47(8):882–3. https://doi.org/10.1080/02841850600854944.

    Article  Google Scholar 

  27. Parfitt AM. Age-related structural changes in trabecular and cortical bone: cellular mechanisms and biomechanical consequences. Calcif Tissue Int. 1984;36(Suppl 1):S123–8. https://doi.org/10.1007/BF02406145.

    Article  Google Scholar 

  28. Goncalves BC, de Araujo EC, Nussi AD, et al. Texture analysis of cone-beam computed tomography images assists the detection of furcal lesion. J Periodontol. 2020. https://doi.org/10.1002/JPER.19-0477.

    Article  Google Scholar 

  29. Haralick RM. Texture features for image classification. IEEE Trans Syst Man Cybern. 1973;SMC-3(6):610–621.

    Article  Google Scholar 

  30. Lubner MG, Smith AD, Sandrasegaran K, et al. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics. 2017;37(5):1483–503. https://doi.org/10.1148/rg.2017170056.

    Article  Google Scholar 

  31. Ranjanomennahary P, Ghalila SS, Malouche D, et al. Comparison of radiograph-based texture analysis and bone mineral density with three-dimensional microarchitecture of trabecular bone. Med Phys. 2011;38(1):420–8. https://doi.org/10.1118/1.3528125.

    Article  Google Scholar 

  32. Ganeshan B, Skogen K, Pressney I, et al. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol. 2012;67(2):157–64. https://doi.org/10.1016/j.crad.2011.08.012.

    Article  Google Scholar 

  33. Mungai F, Verrone GB, Pietragalla M, et al. CT assessment of tumor heterogeneity and the potential for the prediction of human papillomavirus status in oropharyngeal squamous cell carcinoma. Radiol Med. 2019. https://doi.org/10.1007/s11547-019-01028-6.

    Article  Google Scholar 

  34. Gong H, Zhu D, Gao J, et al. An adaptation model for trabecular bone at different mechanical levels. Biomed Eng Online. 2010;2(9):32. https://doi.org/10.1186/1475-925X-9-32.

    Article  Google Scholar 

  35. Cosgunarslan A, SoydanCabuk D, Canger EM. Effect of total edentulism on the internal bone structure of mandibular condyle: a preliminary study. Oral Radiol. 2021;37(2):268–75. https://doi.org/10.1007/s11282-020-00444-z.

    Article  Google Scholar 

  36. Gulec M, Tassoker M, Ozcan S, et al. Evaluation of the mandibular trabecular bone in patients with bruxism using fractal analysis. Oral Radiol. 2021;37(1):36–45. https://doi.org/10.1007/s11282-020-00422-5.

    Article  Google Scholar 

  37. Harrison LC, Nikander R, Sikio M, et al. MRI texture analysis of femoral neck: detection of exercise load-associated differences in trabecular bone. J Magn Reson Imaging. 2011;34(6):1359–66. https://doi.org/10.1002/jmri.22751.

    Article  Google Scholar 

  38. Maciel JG, Araujo IM, Trazzi LC, et al. Association of bone mineral density with bone texture attributes extracted using routine magnetic resonance imaging. Clinics (Sao Paulo). 2020;75: e1766. https://doi.org/10.6061/clinics/2020/e1766.

    Article  Google Scholar 

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Funding

This study was supported by FAPESP (São Paulo Research Foundation) grants: 2017/09550-4 and 2019/00495-6.

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Authors and Affiliations

  1. Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), Rua Galvão Bueno, 868, Liberdade, São Paulo, SP, 01506-000, Brazil

    Amanda Drumstas Nussi, Celso Massahiro Ogawa & Andre Luiz Ferreira Costa

  2. Department of Diagnosis and Surgery, Science and Technology Institute, São Paulo State University (UNESP), São José dos Campos, São Paulo, Brazil

    Sérgio Lucio Pereira de Castro Lopes

  3. Division of General Pathology, Department of Stomatology, School of Dentistry, University of São Paulo (USP), São Paulo, SP, Brazil

    Catharina Simioni De Rosa, João Pedro Perez Gomes & Paulo Henrique Braz-Silva

  4. Laboratory of Virology, Institute of Tropical Medicine of São Paulo, School of Medicine, University of São Paulo, São Paulo, SP, Brazil

    Paulo Henrique Braz-Silva

Authors
  1. Amanda Drumstas Nussi
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  7. Andre Luiz Ferreira Costa
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Corresponding author

Correspondence to Andre Luiz Ferreira Costa.

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Conflict of interest

All the authors of this work declare no conflict of interest.

Ethics approval

The study was approved by the Institutional Review Board of USP, according to protocol number 31575020.8.0000.0075. All the procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008. Informed consent was obtained from all patients for being included in the study.

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Written informed consent was obtained from the guardian of each participant, after informed about the study.

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Nussi, A.D., de Castro Lopes, S.L.P., De Rosa, C.S. et al. In vivo study of cone beam computed tomography texture analysis of mandibular condyle and its correlation with gender and age. Oral Radiol 39, 191–197 (2023). https://doi.org/10.1007/s11282-022-00620-3

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  • DOI: https://doi.org/10.1007/s11282-022-00620-3

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