Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software

Objective: To investigate two commercial software and their efficacy in the assessment of chest CT sequelae in patients affected by COVID-19 pneumonia, comparing the consistency of tools. Materials and Methods: Included in the study group were 120 COVID-19 patients (56 women and 104 men; 61 years of...

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Autores principales:	Vincenza Granata, Stefania Ianniello, Roberta Fusco, Fabrizio Urraro, Davide Pupo, Simona Magliocchetti, Fabrizio Albarello, Paolo Campioni, Massimo Cristofaro, Federica Di Stefano, Nicoletta Fusco, Ada Petrone, Vincenzo Schininà, Alberta Villanacci, Francesca Grassi, Roberta Grassi, Roberto Grassi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	COVID-19 post COVID-19 sequelae computed tomography quantitative analysis artificial intelligence Medicine R
Acceso en línea:	https://doaj.org/article/9fdba9a48e594fb790f2ba8a1decbbad
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spelling	oai:doaj.org-article:9fdba9a48e594fb790f2ba8a1decbbad2021-11-25T18:07:13ZQuantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software10.3390/jpm111111032075-4426https://doaj.org/article/9fdba9a48e594fb790f2ba8a1decbbad2021-10-01T00:00:00Zhttps://www.mdpi.com/2075-4426/11/11/1103https://doaj.org/toc/2075-4426Objective: To investigate two commercial software and their efficacy in the assessment of chest CT sequelae in patients affected by COVID-19 pneumonia, comparing the consistency of tools. Materials and Methods: Included in the study group were 120 COVID-19 patients (56 women and 104 men; 61 years of median age; range: 21–93 years) who underwent chest CT examinations at discharge between 5 March 2020 and 15 March 2021 and again at a follow-up time (3 months; range 30–237 days). A qualitative assessment by expert radiologists in the infectious disease field (experience of at least 5 years) was performed, and a quantitative evaluation using thoracic VCAR software (GE Healthcare, Chicago, Illinois, United States) and a pneumonia module of ANKE ASG-340 CT workstation (HTS Med & Anke, Naples, Italy) was performed. The qualitative evaluation included the presence of ground glass opacities (GGOs) consolidation, interlobular septal thickening, fibrotic-like changes (reticular pattern and/or honeycombing), bronchiectasis, air bronchogram, bronchial wall thickening, pulmonary nodules surrounded by GGOs, pleural and pericardial effusion, lymphadenopathy, and emphysema. A quantitative evaluation included the measurements of GGOs, consolidations, emphysema, residual healthy parenchyma, and total lung volumes for the right and left lung. A chi-square test and non-parametric test were utilized to verify the differences between groups. Correlation coefficients were used to analyze the correlation and variability among quantitative measurements by different computer tools. A receiver operating characteristic (ROC) analysis was performed. Results: The correlation coefficients showed great variability among the quantitative measurements by different tools when calculated on baseline CT scans and considering all patients. Instead, a good correlation (≥0.6) was obtained for the quantitative GGO, as well as the consolidation volumes obtained by two tools when calculated on baseline CT scans, considering the control group. An excellent correlation (≥0.75) was obtained for the quantitative residual healthy lung parenchyma volume, GGO, consolidation volumes obtained by two tools when calculated on follow-up CT scans, and for residual healthy lung parenchyma and GGO quantification when the percentage change of these volumes were calculated between a baseline and follow-up scan. The highest value of accuracy to identify patients with RT-PCR positive compared to the control group was obtained by a GGO total volume quantification by thoracic VCAR (accuracy = 0.75). Conclusions: Computer aided quantification could be an easy and feasible way to assess chest CT sequelae due to COVID-19 pneumonia; however, a great variability among measurements provided by different tools should be considered.Vincenza GranataStefania IannielloRoberta FuscoFabrizio UrraroDavide PupoSimona MagliocchettiFabrizio AlbarelloPaolo CampioniMassimo CristofaroFederica Di StefanoNicoletta FuscoAda PetroneVincenzo SchininàAlberta VillanacciFrancesca GrassiRoberta GrassiRoberto GrassiMDPI AGarticleCOVID-19post COVID-19 sequelaecomputed tomographyquantitative analysisartificial intelligenceMedicineRENJournal of Personalized Medicine, Vol 11, Iss 1103, p 1103 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 post COVID-19 sequelae computed tomography quantitative analysis artificial intelligence Medicine R
spellingShingle	COVID-19 post COVID-19 sequelae computed tomography quantitative analysis artificial intelligence Medicine R Vincenza Granata Stefania Ianniello Roberta Fusco Fabrizio Urraro Davide Pupo Simona Magliocchetti Fabrizio Albarello Paolo Campioni Massimo Cristofaro Federica Di Stefano Nicoletta Fusco Ada Petrone Vincenzo Schininà Alberta Villanacci Francesca Grassi Roberta Grassi Roberto Grassi Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software
description	Objective: To investigate two commercial software and their efficacy in the assessment of chest CT sequelae in patients affected by COVID-19 pneumonia, comparing the consistency of tools. Materials and Methods: Included in the study group were 120 COVID-19 patients (56 women and 104 men; 61 years of median age; range: 21–93 years) who underwent chest CT examinations at discharge between 5 March 2020 and 15 March 2021 and again at a follow-up time (3 months; range 30–237 days). A qualitative assessment by expert radiologists in the infectious disease field (experience of at least 5 years) was performed, and a quantitative evaluation using thoracic VCAR software (GE Healthcare, Chicago, Illinois, United States) and a pneumonia module of ANKE ASG-340 CT workstation (HTS Med & Anke, Naples, Italy) was performed. The qualitative evaluation included the presence of ground glass opacities (GGOs) consolidation, interlobular septal thickening, fibrotic-like changes (reticular pattern and/or honeycombing), bronchiectasis, air bronchogram, bronchial wall thickening, pulmonary nodules surrounded by GGOs, pleural and pericardial effusion, lymphadenopathy, and emphysema. A quantitative evaluation included the measurements of GGOs, consolidations, emphysema, residual healthy parenchyma, and total lung volumes for the right and left lung. A chi-square test and non-parametric test were utilized to verify the differences between groups. Correlation coefficients were used to analyze the correlation and variability among quantitative measurements by different computer tools. A receiver operating characteristic (ROC) analysis was performed. Results: The correlation coefficients showed great variability among the quantitative measurements by different tools when calculated on baseline CT scans and considering all patients. Instead, a good correlation (≥0.6) was obtained for the quantitative GGO, as well as the consolidation volumes obtained by two tools when calculated on baseline CT scans, considering the control group. An excellent correlation (≥0.75) was obtained for the quantitative residual healthy lung parenchyma volume, GGO, consolidation volumes obtained by two tools when calculated on follow-up CT scans, and for residual healthy lung parenchyma and GGO quantification when the percentage change of these volumes were calculated between a baseline and follow-up scan. The highest value of accuracy to identify patients with RT-PCR positive compared to the control group was obtained by a GGO total volume quantification by thoracic VCAR (accuracy = 0.75). Conclusions: Computer aided quantification could be an easy and feasible way to assess chest CT sequelae due to COVID-19 pneumonia; however, a great variability among measurements provided by different tools should be considered.
format	article
author	Vincenza Granata Stefania Ianniello Roberta Fusco Fabrizio Urraro Davide Pupo Simona Magliocchetti Fabrizio Albarello Paolo Campioni Massimo Cristofaro Federica Di Stefano Nicoletta Fusco Ada Petrone Vincenzo Schininà Alberta Villanacci Francesca Grassi Roberta Grassi Roberto Grassi
author_facet	Vincenza Granata Stefania Ianniello Roberta Fusco Fabrizio Urraro Davide Pupo Simona Magliocchetti Fabrizio Albarello Paolo Campioni Massimo Cristofaro Federica Di Stefano Nicoletta Fusco Ada Petrone Vincenzo Schininà Alberta Villanacci Francesca Grassi Roberta Grassi Roberto Grassi
author_sort	Vincenza Granata
title	Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software
title_short	Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software
title_full	Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software
title_fullStr	Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software
title_full_unstemmed	Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software
title_sort	quantitative analysis of residual covid-19 lung ct features: consistency among two commercial software
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9fdba9a48e594fb790f2ba8a1decbbad
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Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software

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