Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1371/journal.pone.0251783 http://hdl.handle.net/11449/228960 |
Resumo: | In this work, we aimed to develop an automatic algorithm for the quantification of total volume and lung impairments in four different diseases. The quantification was completely automatic based upon high resolution computed tomography exams. The algorithm was capable of measuring volume and differentiating pulmonary involvement including inflammatory process and fibrosis, emphysema, and ground-glass opacities. The algorithm classifies the percentage of each pulmonary involvement when compared to the entire lung volume. Our algorithm was applied to four different patients groups: no lung disease patients, patients diagnosed with SARS-CoV-2, patients with chronic obstructive pulmonary disease, and patients with paracoccidioidomycosis. The quantification results were compared with a semi-automatic algorithm previously validated. Results confirmed that the automatic approach has a good agreement with the semi-automatic. Bland-Altman (B&A) demonstrated a low dispersion when comparing total lung volume, and also when comparing each lung impairment individually. Linear regression adjustment achieved an R value of 0.81 when comparing total lung volume between both methods. Our approach provides a reliable quantification process for physicians, thus impairments measurements contributes to support prognostic decisions in important lung diseases including the infection of SARS-CoV-2. |
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spelling |
Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patientsIn this work, we aimed to develop an automatic algorithm for the quantification of total volume and lung impairments in four different diseases. The quantification was completely automatic based upon high resolution computed tomography exams. The algorithm was capable of measuring volume and differentiating pulmonary involvement including inflammatory process and fibrosis, emphysema, and ground-glass opacities. The algorithm classifies the percentage of each pulmonary involvement when compared to the entire lung volume. Our algorithm was applied to four different patients groups: no lung disease patients, patients diagnosed with SARS-CoV-2, patients with chronic obstructive pulmonary disease, and patients with paracoccidioidomycosis. The quantification results were compared with a semi-automatic algorithm previously validated. Results confirmed that the automatic approach has a good agreement with the semi-automatic. Bland-Altman (B&A) demonstrated a low dispersion when comparing total lung volume, and also when comparing each lung impairment individually. Linear regression adjustment achieved an R value of 0.81 when comparing total lung volume between both methods. Our approach provides a reliable quantification process for physicians, thus impairments measurements contributes to support prognostic decisions in important lung diseases including the infection of SARS-CoV-2.Botucatu Medical School Clinics Hospital Medical Physics and Radioprotection NucleusInstitute of Bioscience Sao Paulo State University Julio de Mesquita FilhoRadiology and Medical Imaging State University of CampinasMedical School Sao Paulo State University Julio de Mesquita FilhoBotucatu Medical School Clinics Hospital Medical Physics and Radioprotection NucleusInstitute of Bioscience Sao Paulo State University Julio de Mesquita FilhoMedical School Sao Paulo State University Julio de Mesquita FilhoUniversidade Estadual Paulista (UNESP)Universidade Estadual de Campinas (UNICAMP)Alves, Allan Felipe Fattori [UNESP]Miranda, José Ricardo Arruda [UNESP]Reis, FabianoOliveira, Abner Alves [UNESP]Souza, Sérgio Augusto Santana [UNESP]Fortaleza, Carlos Magno Castelo Branco [UNESP]Tanni, Suzana Erico [UNESP]Castro, José Thiago SouzaPina, Diana Rodrigues [UNESP]2022-04-29T08:29:34Z2022-04-29T08:29:34Z2021-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1371/journal.pone.0251783PLoS ONE, v. 16, n. 6 June, 2021.1932-6203http://hdl.handle.net/11449/22896010.1371/journal.pone.02517832-s2.0-85107673816Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPLoS ONEinfo:eu-repo/semantics/openAccess2022-04-29T08:29:34Zoai:repositorio.unesp.br:11449/228960Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:24:41.972165Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients |
title |
Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients |
spellingShingle |
Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients Alves, Allan Felipe Fattori [UNESP] |
title_short |
Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients |
title_full |
Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients |
title_fullStr |
Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients |
title_full_unstemmed |
Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients |
title_sort |
Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients |
author |
Alves, Allan Felipe Fattori [UNESP] |
author_facet |
Alves, Allan Felipe Fattori [UNESP] Miranda, José Ricardo Arruda [UNESP] Reis, Fabiano Oliveira, Abner Alves [UNESP] Souza, Sérgio Augusto Santana [UNESP] Fortaleza, Carlos Magno Castelo Branco [UNESP] Tanni, Suzana Erico [UNESP] Castro, José Thiago Souza Pina, Diana Rodrigues [UNESP] |
author_role |
author |
author2 |
Miranda, José Ricardo Arruda [UNESP] Reis, Fabiano Oliveira, Abner Alves [UNESP] Souza, Sérgio Augusto Santana [UNESP] Fortaleza, Carlos Magno Castelo Branco [UNESP] Tanni, Suzana Erico [UNESP] Castro, José Thiago Souza Pina, Diana Rodrigues [UNESP] |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Universidade Estadual de Campinas (UNICAMP) |
dc.contributor.author.fl_str_mv |
Alves, Allan Felipe Fattori [UNESP] Miranda, José Ricardo Arruda [UNESP] Reis, Fabiano Oliveira, Abner Alves [UNESP] Souza, Sérgio Augusto Santana [UNESP] Fortaleza, Carlos Magno Castelo Branco [UNESP] Tanni, Suzana Erico [UNESP] Castro, José Thiago Souza Pina, Diana Rodrigues [UNESP] |
description |
In this work, we aimed to develop an automatic algorithm for the quantification of total volume and lung impairments in four different diseases. The quantification was completely automatic based upon high resolution computed tomography exams. The algorithm was capable of measuring volume and differentiating pulmonary involvement including inflammatory process and fibrosis, emphysema, and ground-glass opacities. The algorithm classifies the percentage of each pulmonary involvement when compared to the entire lung volume. Our algorithm was applied to four different patients groups: no lung disease patients, patients diagnosed with SARS-CoV-2, patients with chronic obstructive pulmonary disease, and patients with paracoccidioidomycosis. The quantification results were compared with a semi-automatic algorithm previously validated. Results confirmed that the automatic approach has a good agreement with the semi-automatic. Bland-Altman (B&A) demonstrated a low dispersion when comparing total lung volume, and also when comparing each lung impairment individually. Linear regression adjustment achieved an R value of 0.81 when comparing total lung volume between both methods. Our approach provides a reliable quantification process for physicians, thus impairments measurements contributes to support prognostic decisions in important lung diseases including the infection of SARS-CoV-2. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-01 2022-04-29T08:29:34Z 2022-04-29T08:29:34Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1371/journal.pone.0251783 PLoS ONE, v. 16, n. 6 June, 2021. 1932-6203 http://hdl.handle.net/11449/228960 10.1371/journal.pone.0251783 2-s2.0-85107673816 |
url |
http://dx.doi.org/10.1371/journal.pone.0251783 http://hdl.handle.net/11449/228960 |
identifier_str_mv |
PLoS ONE, v. 16, n. 6 June, 2021. 1932-6203 10.1371/journal.pone.0251783 2-s2.0-85107673816 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
PLoS ONE |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129517863043072 |