Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis

Catharina Simioni De Rosa; Mariana Lobo Bergamini; Michelle Palmieri; Dmitry José de Santana Sarmento; Marcia Oliveira de Carvalho; Ana Lúcia Franco Ricardo; Bengt Hasseus; Peter Jonasson; Paulo Henrique Braz-Silva; Andre Luiz Ferreira Costa

doi:10.1016/j.heliyon.2020.e05194

Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis

Heliyon. 2020 Oct 9;6(10):e05194. doi: 10.1016/j.heliyon.2020.e05194. eCollection 2020 Oct.

Affiliations

¹ Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, Brazil.
² Department of Physics, Institute of Technology, Paulista University (UNIP), São Paulo, Brazil.
³ Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo, Brazil.
⁴ Department of Oral Medicine and Pathology, Institute of Odontology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
⁵ Department of Endodontology, Institute of Odontology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
⁶ Laboratory of Virology, Institute of Tropical Medicine of Sao Paulo, School of Medicine, University of São Paulo, São Paulo, Brazil.

Abstract

Objective: This study aimed to investigate the use of texture analysis for characterization of radicular cysts and periapical granulomas and to assess its efficacy to differentiate between both lesions with histological diagnosis.

Methods: Cone beam computed tomography (CBCT) images were obtained from 19 patients with 25 periapical lesions (14 radicular cysts and 11 periapical granulomas) confirmed by biopsy. Regions of interest were created in the lesions from which 11 texture parameters were calculated. Spearman's correlation analysis was performed and adjusted with Benjamini-Hochberg false discovery rate procedure (FDR <0.005).

Results: The texture parameters used to differentiate the lesions were assessed by using a receiver operating characteristic analysis. Five texture parameters were predictive of lesion differentiation for eight positions: angular second moment; sum of squares; sum of average; contrast; correlation.

Conclusion: Texture analysis of CBCT scans distinguishes radicular cysts from periapical granulomas and can be a promising diagnostic tool for periapical lesions.

Clinical significance: Texture analysis can be used in diagnostic and treatment monitoring to provide supplementary information.

Keywords: Computer-assisted; Computer-assisted diagnosis; Dentistry; Eye-ear-nose-throat; Image processing; L cone beam computed tomography; Medical imaging; Oral medicine; Pathology; Periapical diseases; Radiology.