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

Imagem de Miniatura
Arquivos
art_ROSA_Differentiation_of_periapical_granuloma_from_radicular_cyst_using_2020.PDF (2 MB)
Citações na Scopus
19
Tipo de produção
article
Data de publicação
2020
DOI
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Editora
ELSEVIER SCI LTD
Autores
ROSA, Catharina Simioni De
BERGAMINI, Mariana Lobo
PALMIERI, Michelle
SARMENTO, Dmitry Jose de Santana
CARVALHO, Marcia Oliveira de
RICARDO, Ana Lucia Franco
HASSEUS, Bengt
JONASSON, Peter
COSTA, Andre Luiz Ferreira
Citação
HELIYON, v.6, n.10, article ID e05194, 9p, 2020
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
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 supple-mentary information.
Palavras-chave
Pathology, Dentistry, Oral medicine, Eye-ear-nose-throat, Radiology, Medical imaging, L cone beam computed tomography, Computer-assisted diagnosis, Image processing, Computer-assisted, Periapical diseases
URI
https://observatorio.fm.usp.br/handle/OPI/39610
Referências
  1. Andrade Ana Luiza Dias Leite de, 2013, J Endod, V39, P990, DOI 10.1016/j.joen.2013.04.028
  2. BENJAMINI Y, 1995, J R STAT SOC B, V57, P289, DOI 10.1111/j.2517-6161.1995.tb02031.x
  3. Bracks IV, 2014, J ENDODONT, V40, P63, DOI 10.1016/j.joen.2013.09.037
  4. Braz-Silva PH, 2019, ACTA ODONTOL SCAND, V77, P173, DOI 10.1080/00016357.2018.1521005
  5. de Albuquerque M, 2016, J NEUROIMAGING, V26, P201, DOI 10.1111/jon.12262
  6. de Oliveira MS, 2012, J NEUROIMAGING, V22, P46, DOI 10.1111/j.1552-6569.2010.00553.x
  7. de Oliveira MS, 2013, EPILEPSY BEHAV, V27, P22, DOI 10.1016/j.yebeh.2012.12.009
  8. FLORES FGP, 2009, MED PHYS, V42
  9. de Paula-Silva FWG, 2009, J ENDODONT, V35, P1009, DOI 10.1016/j.joen.2009.04.006
  10. Goncalves BC, 2020, J PERIODONTOL, V91, P1159, DOI 10.1002/JPER.19-0477
  11. Guo J, 2013, J ENDODONT, V39, P1485, DOI 10.1016/j.joen.2013.08.019
  12. HARALICK RM, 1979, P IEEE, V67, P786, DOI 10.1109/PROC.1979.11328
  13. HARALICK RM, 1973, IEEE T SYST MAN CYB, VSMC3, P610, DOI 10.1109/TSMC.1973.4309314
  14. Huang YH, 2013, COMPUT METH PROG BIO, V112, P508, DOI 10.1016/j.cmpb.2013.08.016
  15. Juerchott A, 2018, INT J ORAL SCI, V10, DOI 10.1038/s41368-018-0017-y
  16. Mazziotti S, 2015, CAN ASSOC RADIOL J, V66, P212, DOI 10.1016/j.carj.2014.12.004
  17. Mungai F., 2019, RADIOL MED
  18. Nair PNR, 1998, INT ENDOD J, V31, P155
  19. Oda M, 2019, EUR J RADIOL, V120, DOI 10.1016/j.ejrad.2019.108654
  20. Okada K, 2015, MED PHYS, V42, P1653, DOI 10.1118/1.4914418
  21. Oliveira MS, 2009, BRAZ J MED BIOL RES, V42, P1076, DOI 10.1590/S0100-879X2009005000034
  22. Pathak A., 2014, GRAY LEVEL COOCCURRE, P348
  23. Peyreti R, 2015, IEEE ENG MED BIO, P7218, DOI 10.1109/EMBC.2015.7320057
  24. Phillips I, 2018, BRIT J RADIOL, V91, DOI 10.1259/bjr.20170267
  25. Raja JV, 2012, DENTOMAXILLOFAC RAD, V41, P475, DOI 10.1259/dmfr/83345935
  26. Ramachandran PN, 1996, ORAL SURG ORAL MED O, V81, P93, DOI 10.1016/S1079-2104(96)80156-9
  27. Ricucci D, 2006, ORAL SURG ORAL MED O, V101, P389, DOI 10.1016/j.tripleo.2005.08.026
  28. Rozylo-Kalinowska Ingrid, 2007, Med Sci Monit, V13 Suppl 1, P129
  29. SHROUT MK, 1993, ORAL SURG ORAL MED O, V76, P356, DOI 10.1016/0030-4220(93)90268-9
  30. Silva TA, 2005, ORAL MICROBIOL IMMUN, V20, P310, DOI 10.1111/j.1399-302X.2005.00232.x
  31. Silva VKS, 2020, DENTOMAXILLOFAC RAD, V49, DOI 10.1259/dmfr.20190204
  32. Sonmez G, 2019, DENTOMAXILLOFAC RAD, V48, DOI 10.1259/dmfr.20190082
  33. Strzelecki M, 2013, NUCL INSTRUM METH A, V702, P137, DOI 10.1016/j.nima.2012.09.006
  34. Szczypinski PM, 2009, COMPUT METH PROG BIO, V94, P66, DOI 10.1016/j.cmpb.2008.08.005
  35. van der Borden WG, 2013, J ENDODONT, V39, P1245, DOI 10.1016/j.joen.2013.07.001
  36. van Timmeren JE, 2017, RADIOTHER ONCOL, V123, P363, DOI 10.1016/j.radonc.2017.04.016
  37. WHITE SC, 1994, ORAL SURG ORAL MED O, V78, P650, DOI 10.1016/0030-4220(94)90180-5
  38. Wu HF, 2013, J DIGIT IMAGING, V26, P797, DOI 10.1007/s10278-012-9547-6
  39. Yilmaz E, 2017, COMPUT METH PROG BIO, V146, P91, DOI 10.1016/j.cmpb.2017.05.012

Coleções
Artigos e Materiais de Revistas Científicas - LIM/52