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Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia
OBJECTIVES: To evaluate a semi-automated segmentation and ventilated lung quantification on chest computed tomography (CT) to assess lung involvement in patients affected by SARS-CoV-2. Results were compared with clinical and functional parameters and outcomes. METHODS: All images underwent quantita...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596627/ https://www.ncbi.nlm.nih.gov/pubmed/33125559 http://dx.doi.org/10.1007/s00330-020-07271-0 |
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author | Ippolito, Davide Ragusi, Maria Gandola, Davide Maino, Cesare Pecorelli, Anna Terrani, Simone Peroni, Marta Giandola, Teresa Porta, Marco Talei Franzesi, Cammillo Sironi, Sandro |
author_facet | Ippolito, Davide Ragusi, Maria Gandola, Davide Maino, Cesare Pecorelli, Anna Terrani, Simone Peroni, Marta Giandola, Teresa Porta, Marco Talei Franzesi, Cammillo Sironi, Sandro |
author_sort | Ippolito, Davide |
collection | PubMed |
description | OBJECTIVES: To evaluate a semi-automated segmentation and ventilated lung quantification on chest computed tomography (CT) to assess lung involvement in patients affected by SARS-CoV-2. Results were compared with clinical and functional parameters and outcomes. METHODS: All images underwent quantitative analyses with a dedicated workstation using a semi-automatic lung segmentation software to compute ventilated lung volume (VLV), Ground-glass opacity (GGO) volume (GGO-V), and consolidation volume (CONS-V) as absolute volume and as a percentage of total lung volume (TLV). The ratio between CONS-V, GGO-V, and VLV (CONS-V/VLV and GGO-V/VLV, respectively), TLV (CONS-V/TLV, GGO-V/TLV, and GGO-V + CONS-V/TLV respectively), and the ratio between VLV and TLV (VLV/TLV) were calculated. RESULTS: A total of 108 patients were enrolled. GGO-V/TLV significantly correlated with WBC (r = 0.369), neutrophils (r = 0.446), platelets (r = 0.182), CRP (r = 0.190), PaCO(2) (r = 0.176), HCO(3)(−) (r = 0.284), and PaO2/FiO2 (P/F) values (r = − 0.344). CONS-V/TLV significantly correlated with WBC (r = 0.294), neutrophils (r = 0.300), lymphocytes (r = −0.225), CRP (r = 0.306), PaCO(2) (r = 0.227), pH (r = 0.162), HCO(3)(−) (r = 0.394), and P/F (r = − 0.419) values. Statistically significant differences between CONS-V, GGO-V, GGO-V/TLV, CONS-V/TLV, GGO-V/VLV, CONS-V/VLV, GGO-V + CONS-V/TLV, VLV/TLV, CT score, and invasive ventilation by ET were found (all p < 0.05). CONCLUSION: The use of quantitative semi-automated algorithm for lung CT elaboration effectively correlates the severity of SARS-CoV-2-related pneumonia with laboratory parameters and the need for invasive ventilation. KEY POINTS: • Pathological lung volumes, expressed both as GGO-V and as CONS-V, can be considered a useful tool in SARS-CoV-2-related pneumonia. • All lung volumes, expressed themselves and as ratio with TLV and VLV, correlate with laboratory data, in particular C-reactive protein and white blood cell count. • All lung volumes correlate with patient’s outcome, in particular concerning invasive ventilation. |
format | Online Article Text |
id | pubmed-7596627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-75966272020-10-30 Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia Ippolito, Davide Ragusi, Maria Gandola, Davide Maino, Cesare Pecorelli, Anna Terrani, Simone Peroni, Marta Giandola, Teresa Porta, Marco Talei Franzesi, Cammillo Sironi, Sandro Eur Radiol Imaging Informatics and Artificial Intelligence OBJECTIVES: To evaluate a semi-automated segmentation and ventilated lung quantification on chest computed tomography (CT) to assess lung involvement in patients affected by SARS-CoV-2. Results were compared with clinical and functional parameters and outcomes. METHODS: All images underwent quantitative analyses with a dedicated workstation using a semi-automatic lung segmentation software to compute ventilated lung volume (VLV), Ground-glass opacity (GGO) volume (GGO-V), and consolidation volume (CONS-V) as absolute volume and as a percentage of total lung volume (TLV). The ratio between CONS-V, GGO-V, and VLV (CONS-V/VLV and GGO-V/VLV, respectively), TLV (CONS-V/TLV, GGO-V/TLV, and GGO-V + CONS-V/TLV respectively), and the ratio between VLV and TLV (VLV/TLV) were calculated. RESULTS: A total of 108 patients were enrolled. GGO-V/TLV significantly correlated with WBC (r = 0.369), neutrophils (r = 0.446), platelets (r = 0.182), CRP (r = 0.190), PaCO(2) (r = 0.176), HCO(3)(−) (r = 0.284), and PaO2/FiO2 (P/F) values (r = − 0.344). CONS-V/TLV significantly correlated with WBC (r = 0.294), neutrophils (r = 0.300), lymphocytes (r = −0.225), CRP (r = 0.306), PaCO(2) (r = 0.227), pH (r = 0.162), HCO(3)(−) (r = 0.394), and P/F (r = − 0.419) values. Statistically significant differences between CONS-V, GGO-V, GGO-V/TLV, CONS-V/TLV, GGO-V/VLV, CONS-V/VLV, GGO-V + CONS-V/TLV, VLV/TLV, CT score, and invasive ventilation by ET were found (all p < 0.05). CONCLUSION: The use of quantitative semi-automated algorithm for lung CT elaboration effectively correlates the severity of SARS-CoV-2-related pneumonia with laboratory parameters and the need for invasive ventilation. KEY POINTS: • Pathological lung volumes, expressed both as GGO-V and as CONS-V, can be considered a useful tool in SARS-CoV-2-related pneumonia. • All lung volumes, expressed themselves and as ratio with TLV and VLV, correlate with laboratory data, in particular C-reactive protein and white blood cell count. • All lung volumes correlate with patient’s outcome, in particular concerning invasive ventilation. Springer Berlin Heidelberg 2020-10-30 2021 /pmc/articles/PMC7596627/ /pubmed/33125559 http://dx.doi.org/10.1007/s00330-020-07271-0 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Imaging Informatics and Artificial Intelligence Ippolito, Davide Ragusi, Maria Gandola, Davide Maino, Cesare Pecorelli, Anna Terrani, Simone Peroni, Marta Giandola, Teresa Porta, Marco Talei Franzesi, Cammillo Sironi, Sandro Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia |
title | Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia |
title_full | Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia |
title_fullStr | Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia |
title_full_unstemmed | Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia |
title_short | Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia |
title_sort | computed tomography semi-automated lung volume quantification in sars-cov-2-related pneumonia |
topic | Imaging Informatics and Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596627/ https://www.ncbi.nlm.nih.gov/pubmed/33125559 http://dx.doi.org/10.1007/s00330-020-07271-0 |
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