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.
Authors
- Dario Plataroti
- Annalisa De Liperi
- Chiara Airoldi (ORCID: https://orcid.org/0000-0002-9114-5816)
- Chiara Romei (ORCID: https://orcid.org/0000-0003-3023-8784)
- Michele Tonerini
- Federica Volpi
- Lisa Novaria
- Salvatore Claudio Fanni (ORCID: https://orcid.org/0000-0002-4003-3320)
- Emanuele Neri (ORCID: https://orcid.org/0000-0001-7950-4559)
- Brian J. Bartholmai (ORCID: https://orcid.org/0000-0001-7834-6579)
- Leonardo Colligiani (ORCID: https://orcid.org/0000-0002-6217-4933)
Institutions
- University of Pisa (IT)
- Università degli Studi del Piemonte Orientale “Amedeo Avogadro” (IT)
- Mayo Clinic (US)
- WinnMed (US)
Publication Details
- Journal
- Journal of Clinical Medicine
- Published
- 2024-12-01
- DOI
- https://doi.org/10.3390/jcm13237308
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00