Imaging Science in Dentistry

Korean Academy of Oral and Maxillofacial Radiology (대한영상치의학회)
권호 표지
DOI QR코드

DOI QR Code

Cone-beam computed tomography texture analysis can help differentiate odontogenic and non-odontogenic maxillary sinusitis

  • Received : 2022.09.22
  • Accepted : 2022.12.14
  • Published : 2023.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: This study aimed to assess texture analysis(TA) of cone-beam computed tomography (CBCT) images as a quantitative tool for the differential diagnosis of odontogenic and non-odontogenic maxillary sinusitis(OS and NOS, respectively). Materials and Methods: CBCT images of 40 patients diagnosed with OS (N=20) and NOS (N=20) were evaluated. The gray level co-occurrence (GLCM) matrix parameters, and gray level run length matrix texture (GLRLM) parameters were extracted using manually placed regions of interest on lesion images. Seven texture parameters were calculated using GLCM and 4 parameters using GLRLM. The Mann-Whitney test was used for comparisons between the groups, and the Levene test was performed to confirm the homogeneity of variance (α=5%). Results: The results showed statistically significant differences(P<0.05) between the OS and NOS patients regarding 3 TA parameters. NOS patients presented higher values for contrast, while OS patients presented higher values for correlation and inverse difference moment. Greater textural homogeneity was observed in the OS patients than in the NOS patients, with statistically significant differences in standard deviations between the groups for correlation, sum of squares, sum of entropy, and entropy. Conclusion: TA enabled quantitative differentiation between OS and NOS on CBCT images by using the parameters of contrast, correlation, and inverse difference moment.

Keywords

Acknowledgement

This study was supported by The São Paulo Research Foundation (FAPESP) n° 2020/08295-3 and n° 2019/00495-6.

References

  1. Nair UP, Nair MK. Maxillary sinusitis of odontogenic origin: cone-beam volumetric computerized tomography-aided diagnosis. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2010; 110: e53-7. https://doi.org/10.1016/j.tripleo.2010.06.020
  2. Maillet M, Bowles WR, McClanahan SL, John MT, Ahmad M. Cone-beam computed tomography evaluation of maxillary sinusitis. J Endod 2011; 37: 753-7. https://doi.org/10.1016/j.joen.2011.02.032
  3. Longhini AB, Branstetter BF, Ferguson BJ. Radiology quiz case 1. Acute maxillary sinusitis secondary to a migrated dental implant obstructing the ostiomeatal complex. Arch Otolaryngol Head Neck Surg 2011; 137: 823, 826.
  4. Ito K, Kondo T, Andreu-Arasa VC, Li B, Hirahara N, Muraoka H, et al. Quantitative assessment of the maxillary sinusitis using computed tomography texture analysis: odontogenic vs nonodontogenic etiology. Oral Radiol 2022; 38: 315-24. https://doi.org/10.1007/s11282-021-00558-y
  5. Tsuzuki K, Kuroda K, Hashimoto K, Okazaki K, Noguchi K, Kishimoto H, et al. Odontogenic chronic rhinosinusitis patients undergoing tooth extraction: oral surgeon and otolaryngologist viewpoints and appropriate management. J Laryngol Otol 2020; 134: 241-6. https://doi.org/10.1017/S0022215120000535
  6. Parks ET. Cone beam computed tomography for the nasal cavity and paranasal sinuses. Dent Clin North Am 2014; 58: 627-51. https://doi.org/10.1016/j.cden.2014.04.003
  7. Ritter L, Lutz J, Neugebauer J, Scheer M, Dreiseidler T, Zinser MJ, et al. Prevalence of pathologic findings in the maxillary sinus in cone-beam computerized tomography. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2011; 111: 634-40. https://doi.org/10.1016/j.tripleo.2010.12.007
  8. Abrahams JJ, Glassberg RM. Dental disease: a frequently unrecognized cause of maxillary sinus abnormalities? AJR Am J Roentgenol 1996; 166: 1219-23. https://doi.org/10.2214/ajr.166.5.8615273
  9. Zhu J, Lin W, Yuan W, Chen L. New insight on pathophysiology, diagnosis, and treatment of odontogenic maxillary sinusitis. J Nanomater 2021; 2021: 9997180.
  10. Shen Q, Shan Y, Hu Z, Chen W, Yang B, Han J, et al. Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement. Eur Radiol 2018; 28: 4389-96. https://doi.org/10.1007/s00330-018-5364-8
  11. Mungai F, Verrone GB, Pietragalla M, Berti V, Addeo G, Desideri I, 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; 124: 804-11. https://doi.org/10.1007/s11547-019-01028-6
  12. Tsai A, Buch K, Fujita A, Qureshi MM, Kuno H, Chapman MN, et al. Using CT texture analysis to differentiate between nasopharyngeal carcinoma and age-matched adenoid controls. Eur J Radiol 2018; 108: 208-14. https://doi.org/10.1016/j.ejrad.2018.09.012
  13. De Rosa CS, Bergamini ML, Palmieri M, Sarmento DJ, de Carvalho MO, Ricardo AL, et al. Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis. Heliyon 2020; 6: e05194.
  14. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 2017; 37: 1483-503. https://doi.org/10.1148/rg.2017170056
  15. Larroza A, Moratal D, Paredes-Sanchez A, Soria-Olivas E, Chust ML, Arribas LA, et al. Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI. J Magn Reson Imaging 2015; 42: 1362-8. https://doi.org/10.1002/jmri.24913
  16. Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol 2004; 59: 1061-9. https://doi.org/10.1016/j.crad.2004.07.008
  17. Huang YH, Chang YC, Huang CS, Wu TJ, Chen JH, Chang RF. Computer-aided diagnosis of mass-like lesion in breast MRI: differential analysis of the 3-D morphology between benign and malignant tumors. Comput Methods Programs Biomed 2013; 112: 508-17. https://doi.org/10.1016/j.cmpb.2013.08.016
  18. Buch K, Fujita A, Li B, Kawashima Y, Qureshi MM, Sakai O. Using texture analysis to determine human papillomavirus status of oropharyngeal squamous cell carcinomas on CT. AJNR Am J Neuroradiol 2015; 36: 1343-8. https://doi.org/10.3174/ajnr.A4285
  19. Raja JV, Khan M, Ramachandra VK, Al-Kadi O. Texture analysis of CT images in the characterization of oral cancers involving buccal mucosa. Dentomaxillofac Radiol 2012; 41: 475-80. https://doi.org/10.1259/dmfr/83345935
  20. Obuchowicz R, Nurzynska K, Obuchowicz B, Urbanik A, Piorkowski A. Caries detection enhancement using texture feature maps of intraoral radiographs. Oral Radiol 2020; 36: 275-87. https://doi.org/10.1007/s11282-018-0354-8
  21. Goncalves BC, de Araujo EC, Nussi AD, Bechara N, Sarmento D, Oliveira MS, et al. Texture analysis of cone-beam computed tomography images assists the detection of furcal lesion. J Periodontol 2020; 91: 1159-66. https://doi.org/10.1002/JPER.19-0477
  22. Costa AL, de Souza Carreira B, Fardim KA, Nussi AD, da Silva Lima VC, Miguel MM, et al. Texture analysis of cone beam computed tomography images reveals dental implant stability. Int J Oral Maxillofac Surg 2021; 50: 1609-16. https://doi.org/10.1016/j.ijom.2021.04.009
  23. Galloway MM. Texture analysis using gray level run lengths. Comput Graph Image Process 1975; 4: 172-9. https://doi.org/10.1016/S0146-664X(75)80008-6
  24. Whyte A, Boeddinghaus R. Imaging of odontogenic sinusitis. Clin Radiol 2019; 74: 503-16. https://doi.org/10.1016/j.crad.2019.02.012
  25. Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973; 3: 610-21. https://doi.org/10.1109/TSMC.1973.4309314
  26. Tonetti MS, Greenwell H, Kornman KS. Staging and grading of periodontitis: framework and proposal of a new classification and case definition. J Periodontol 2018; 89 Suppl 1: S159-72. https://doi.org/10.1002/JPER.18-0006
  27. Simuntis R, Kubilius R, Vaitkus S. Odontogenic maxillary sinusitis: a review. Stomatologija 2014; 16: 39-43.
  28. Fujimoto K, Tonan T, Azuma S, Kage M, Nakashima O, Johkoh T, et al. Evaluation of the mean and entropy of apparent diffusion coefficient values in chronic hepatitis C: correlation with pathologic fibrosis stage and inflammatory activity grade. Radiology 2011; 258: 739-48. https://doi.org/10.1148/radiol.10100853
  29. Aksoy U, Orhan K. Association between odontogenic conditions and maxillary sinus mucosal thickening: a retrospective CBCT study. Clin Oral Investig 2019; 23: 123-31. https://doi.org/10.1007/s00784-018-2418-x