Quantitative Chest CT Analysis: Three Different Approaches to Quantify the Burden of Viral Interstitial Pneumonia Using COVID-19 as a Paradigm

<b>Objectives:</b> To investigate the relationship between COVID-19 pneumonia outcomes and three chest CT analysis approaches. <b>Methods:</b> Patients with COVID-19 pneumonia who underwent chest CT were included and divided into survivors/non-survivors and intubated/not-intubated. Chest CTs were analyzed through a (1) Total Severity Score visually quantified by an emergency (TSS1) and a thoracic radiologist (TSS2); (2) density mask technique quantifying normal parenchyma (DM_Norm 1) and ground glass opacities (DM_GGO1) repeated after the manual delineation of consolidations (DM_Norm2, DM_GGO2, DM_Consolidation); (3) texture analysis quantifying normal parenchyma (TA_Norm) and interstitial lung disease (TA_ILD). Association with outcomes was assessed through Chi-square and the Mann-Whitney test. The TSS inter-reader variability was assessed through intraclass correlation coefficient (ICC) and Bland-Altman analysis. The relationship between quantitative variables and outcomes was investigated through multivariate logistic regression analysis. Variables correlation was investigated using Spearman analysis. <b>Results:</b> Overall, 192 patients (mean age, 66.8 ± 15.4 years) were included. TSS was significantly higher in intubated patients but only TSS1 in survivors. TSS presented an ICC of 0.83 (0.76; 0.88) and a bias (LOA) of 1.55 (-4.69, 7.78). DM_Consolidation showed the greatest median difference between survivors/not survivors (<i>p</i> = 0.002). The strongest independent predictor for mortality was DM_Consolidation (AUC 0.688), while the strongest independent predictor for the intensity of care was TSS2 (0.7498). DM_Norm 2 was the singular feature independently associated with both the outcomes. DM_GGO1 strongly correlated with TA_ILD (ρ = 0.977). <b>Conclusions:</b> The DM technique and TA achieved consistent measurements and a better correlation with patient outcomes.

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Journal
Journal of Clinical Medicine
Published
2024-12-01
DOI
https://doi.org/10.3390/jcm13237308
Primary Topic
COVID-19 diagnosis using AI
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article
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article

Quantitative Chest CT Analysis: Three Different Approaches to Quantify the Burden of Viral Interstitial Pneumonia Using COVID-19 as a Paradigm

Dario Plataroti, Annalisa De Liperi, Chiara Airoldi, Chiara Romei et al.
Journal of Clinical Medicine
COVID-19 diagnosis using AI
article

Quantitative Chest CT Analysis: Three Different Approaches to Quantify the Burden of Viral Interstitial Pneumonia Using COVID-19 as a Paradigm

Dario Plataroti, Annalisa De Liperi, Chiara Airoldi, Chiara Romei, Michele Tonerini, Federica Volpi, Lisa Novaria, Salvatore Claudio Fanni, Emanuele Neri, Brian J. Bartholmai, Leonardo Colligiani
article en

Abstract

<b>Objectives:</b> To investigate the relationship between COVID-19 pneumonia outcomes and three chest CT analysis approaches. <b>Methods:</b> Patients with COVID-19 pneumonia who underwent chest CT were included and divided into survivors/non-survivors and intubated/not-intubated. Chest CTs were analyzed through a (1) Total Severity Score visually quantified by an emergency (TSS1) and a thoracic radiologist (TSS2); (2) density mask technique quantifying normal parenchyma (DM_Norm 1) and ground glass opacities (DM_GGO1) repeated after the manual delineation of consolidations (DM_Norm2, DM_GGO2, DM_Consolidation); (3) texture analysis quantifying normal parenchyma (TA_Norm) and interstitial lung disease (TA_ILD). Association with outcomes was assessed through Chi-square and the Mann-Whitney test. The TSS inter-reader variability was assessed through intraclass correlation coefficient (ICC) and Bland-Altman analysis. The relationship between quantitative variables and outcomes was investigated through multivariate logistic regression analysis. Variables correlation was investigated using Spearman analysis. <b>Results:</b> Overall, 192 patients (mean age, 66.8 ± 15.4 years) were included. TSS was significantly higher in intubated patients but only TSS1 in survivors. TSS presented an ICC of 0.83 (0.76; 0.88) and a bias (LOA) of 1.55 (-4.69, 7.78). DM_Consolidation showed the greatest median difference between survivors/not survivors (<i>p</i> = 0.002). The strongest independent predictor for mortality was DM_Consolidation (AUC 0.688), while the strongest independent predictor for the intensity of care was TSS2 (0.7498). DM_Norm 2 was the singular feature independently associated with both the outcomes. DM_GGO1 strongly correlated with TA_ILD (ρ = 0.977). <b>Conclusions:</b> The DM technique and TA achieved consistent measurements and a better correlation with patient outcomes.

Journal of Clinical MedicineVol. 13(23)
University of Pisa (IT), Università degli Studi del Piemonte Orientale “Amedeo Avogadro” (IT), Mayo Clinic (US), WinnMed (US)
Good health and well-being
Openalex Percentile: Top 62%
COVID-19 diagnosis using AI
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