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 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.