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Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis
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 fr...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7560585/ https://www.ncbi.nlm.nih.gov/pubmed/33088959 http://dx.doi.org/10.1016/j.heliyon.2020.e05194 |
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author | De Rosa, Catharina Simioni Bergamini, Mariana Lobo Palmieri, Michelle Sarmento, Dmitry José de Santana de Carvalho, Marcia Oliveira Ricardo, Ana Lúcia Franco Hasseus, Bengt Jonasson, Peter Braz-Silva, Paulo Henrique Ferreira Costa, Andre Luiz |
author_facet | De Rosa, Catharina Simioni Bergamini, Mariana Lobo Palmieri, Michelle Sarmento, Dmitry José de Santana de Carvalho, Marcia Oliveira Ricardo, Ana Lúcia Franco Hasseus, Bengt Jonasson, Peter Braz-Silva, Paulo Henrique Ferreira Costa, Andre Luiz |
author_sort | De Rosa, Catharina Simioni |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7560585 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-75605852020-10-20 Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis De Rosa, Catharina Simioni Bergamini, Mariana Lobo Palmieri, Michelle Sarmento, Dmitry José de Santana de Carvalho, Marcia Oliveira Ricardo, Ana Lúcia Franco Hasseus, Bengt Jonasson, Peter Braz-Silva, Paulo Henrique Ferreira Costa, Andre Luiz Heliyon Research Article 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. Elsevier 2020-10-09 /pmc/articles/PMC7560585/ /pubmed/33088959 http://dx.doi.org/10.1016/j.heliyon.2020.e05194 Text en © 2020 The Authors. Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article De Rosa, Catharina Simioni Bergamini, Mariana Lobo Palmieri, Michelle Sarmento, Dmitry José de Santana de Carvalho, Marcia Oliveira Ricardo, Ana Lúcia Franco Hasseus, Bengt Jonasson, Peter Braz-Silva, Paulo Henrique Ferreira Costa, Andre Luiz Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis |
title | Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis |
title_full | Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis |
title_fullStr | Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis |
title_full_unstemmed | Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis |
title_short | Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis |
title_sort | differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7560585/ https://www.ncbi.nlm.nih.gov/pubmed/33088959 http://dx.doi.org/10.1016/j.heliyon.2020.e05194 |
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