Texture analysis of cone-beam computed tomography images assists the detection of furcal lesion

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dc.contributor.authorGonçalves, Bianca C. [UNESP]
dc.contributor.authorde Araújo, Elaine C. [UNESP]
dc.contributor.authorNussi, Amanda D.
dc.contributor.authorBechara, Naira [UNESP]
dc.contributor.authorSarmento, Dmitry
dc.contributor.authorOliveira, Marcia S.
dc.contributor.authorSantamaria, Mauro P. [UNESP]
dc.contributor.authorCosta, Andre Luiz F.
dc.contributor.authorLopes, Sérgio [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionSao Paulo
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.date.accessioned2020-12-12T01:38:21Z
dc.date.available2020-12-12T01:38:21Z
dc.date.issued2020-09-01
dc.description.abstractBackground: The aim of this study was to apply texture analysis (TA) to cone-beam computed tomography (CBCT) scans of patients with grade C periodontitis for detection of non-visible changes in the image. Methods: TA was performed on CBCT scans of 34 patients with grade C periodontitis. Axial sections of CBCT were divided into three groups as follows: Group L (lesion) in which there is a furcal lesion with periodontal bone loss; Group I (intermediate) in which the border of the furcal lesion has normal characteristics; and Group C (control) in which the area is healthy. Eleven texture parameters were extracted from the region of interest. Mann-Whitney U test was used to assess the differences in the texture between the three groups as follows: L versus I; L versus C, and I versus C. Results: Statistically significant differences (P <0.05) were observed in almost all parameters in the intergroup analyses (i.e., L versus I and L versus C). However, statistical differences were smaller in groups I versus C in which only entropy of sum, entropy of difference, mean of sum, and variance of difference were statistically different (P < 0.05). Conclusion: TA can potentially provide prognostic information to improve the diagnostic accuracy in the grading of the tissue around the furcal lesion, thus potentially accelerating the treatment decision-making process.en
dc.description.affiliationDepartment of Diagnosis and Surgery São José dos Campos School of Dentistry São Paulo State University (UNESP)
dc.description.affiliationPostgraduate Program in Dentistry Cruzeiro do Sul University (UNICSUL) Sao Paulo
dc.description.affiliationDepartment of Stomatology School of Dentistry University of São Paulo Sao Paulo
dc.description.affiliationDepartment of Physics Institute of Exact Sciences and Technology Paulista University (UNIP) Sao Paulo
dc.description.affiliationUnespDepartment of Diagnosis and Surgery São José dos Campos School of Dentistry São Paulo State University (UNESP)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 2017/09550-4
dc.description.sponsorshipIdFAPESP: 2018/17850-0
dc.format.extent1159-1166
dc.identifierhttp://dx.doi.org/10.1002/JPER.19-0477
dc.identifier.citationJournal of Periodontology, v. 91, n. 9, p. 1159-1166, 2020.
dc.identifier.doi10.1002/JPER.19-0477
dc.identifier.issn0022-3492
dc.identifier.scopus2-s2.0-85090778300
dc.identifier.urihttp://hdl.handle.net/11449/199384
dc.language.isoeng
dc.relation.ispartofJournal of Periodontology
dc.sourceScopus
dc.subjectaggressive periodontitis
dc.subjectdiagnostic imaging
dc.subjectperiodontal diseases
dc.subjecttexture analysis
dc.titleTexture analysis of cone-beam computed tomography images assists the detection of furcal lesionen
dc.typeArtigo
unesp.author.orcid0000-0003-4856-5417[8]

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