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COVID-19 Chest Computed Tomography to Stratify Severity and Disease Extension by Artificial Neural Network Computer-Aided Diagnosis

2020-12-04Journal article Open access
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dc.contributor.authorCarvalho, Alysson Roncally S.
dc.contributor.authorGuimarães, Alan
dc.contributor.authorWerberich, Gabriel Madeira
dc.contributor.authorde Castro, Stephane Nery
dc.contributor.authorPinto, Joana Sofia F.
dc.contributor.authorSchmitt, Willian Rebouças
dc.contributor.authorFrança, Manuela
dc.contributor.authorBozza, Fernando Augusto
dc.contributor.authorGuimarães, Bruno Leonardo da Silva
dc.contributor.authorZin, Walter Araujo
dc.contributor.authorRodrigues, Rosana Souza
dc.date.accessioned2021-11-23T15:27:27Z
dc.date.available2021-11-23T15:27:27Z
dc.date.issued2020-12-04
dc.description.abstractPurpose: This work aims to develop a computer-aided diagnosis (CAD) to quantify the extent of pulmonary involvement (PI) in COVID-19 as well as the radiological patterns referred to as lung opacities in chest computer tomography (CT). Methods: One hundred thirty subjects with COVID-19 pneumonia who underwent chest CT at hospital admission were retrospectively studied (141 sets of CT scan images). Eighty-eight healthy individuals without radiological evidence of acute lung disease served as controls. Two radiologists selected up to four regions of interest (ROI) per patient (totaling 1,475 ROIs) visually regarded as well-aerated regions (472), ground-glass opacity (GGO, 413), crazy paving and linear opacities (CP/LO, 340), and consolidation (250). After balancing with 250 ROIs for each class, the density quantiles (2.5, 25, 50, 75, and 97.5%) of 1,000 ROIs were used to train (700), validate (150), and test (150 ROIs) an artificial neural network (ANN) classifier (60 neurons in a single-hidden-layer architecture). Pulmonary involvement was defined as the sum of GGO, CP/LO, and consolidation volumes divided by total lung volume (TLV), and the cutoff of normality between controls and COVID-19 patients was determined with a receiver operator characteristic (ROC) curve. The severity of pulmonary involvement in COVID-19 patients was also assessed by calculating Z scores relative to the average volume of parenchymal opacities in controls. Thus, COVID-19 cases were classified as mild (<cutoff of normality), moderate (cutoff of normality ≤ pulmonary involvement < Z score 3), and severe pulmonary involvement (Z score ≥3). Results: Cohen's kappa agreement between CAD and radiologist classification was 81% (79-84%, 95% CI). The ROC curve of PI by the ANN presented a threshold of 21.5%, sensitivity of 0.80, specificity of 0.86, AUC of 0.90, accuracy of 0.82, F score of 0.85, and 0.65 Matthews' correlation coefficient. Accordingly, 77 patients were classified as having severe pulmonary involvement reaching 55 ± 13% of the TLV (Z score related to controls ≥3) and presented significantly higher lung weight, serum C-reactive protein concentration, proportion of hospitalization in intensive care units, instances of mechanical ventilation, and case fatality. Conclusion: The proposed CAD aided in detecting and quantifying the extent of pulmonary involvement, helping to phenotype patients with COVID-19 pneumonia.pt_PT
dc.description.sponsorshipThis research was supported by the Brazilian Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq, Grant No. 302839/2017-8) and the Rio de Janeiro State Research Supporting Foundation (Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro—FAPERJ, Grant No. E-26/203.001/2018)pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCarvalho ARS, Guimarães A, Werberich GM, et al. COVID-19 Chest Computed Tomography to Stratify Severity and Disease Extension by Artificial Neural Network Computer-Aided Diagnosis. Front Med (Lausanne). 2020;7:577609. doi:10.3389/fmed.2020.577609pt_PT
dc.identifier.doi10.3389/fmed.2020.577609pt_PT
dc.identifier.issn2296-858X
dc.identifier.urihttp://hdl.handle.net/10400.16/2630
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherFrontiers Mediapt_PT
dc.relation.publisherversionhttps://www.frontiersin.org/articles/10.3389/fmed.2020.577609/fullpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCOVID-19 pneumoniapt_PT
dc.subjectcomputer-aided diagnosispt_PT
dc.subjectdeep learningpt_PT
dc.subjectquantitative chest CT-analysispt_PT
dc.subjectradiomicspt_PT
dc.titleCOVID-19 Chest Computed Tomography to Stratify Severity and Disease Extension by Artificial Neural Network Computer-Aided Diagnosispt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceSwitzerlandpt_PT
oaire.citation.startPage577609pt_PT
oaire.citation.titleFrontiers in Medicinept_PT
oaire.citation.volume7pt_PT
person.familyNameFrança
person.givenNameManuela
person.identifier.ciencia-idEA1B-D7C4-C8CE
person.identifier.orcid0000-0003-1068-8577
person.identifier.scopus-author-id24466466300
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationd731cc71-98f2-45c3-82f4-030b27c145b6
relation.isAuthorOfPublication.latestForDiscoveryd731cc71-98f2-45c3-82f4-030b27c145b6

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