Lung Volume Change Assessment in Moderate and Severe COVID-19 Using CT Volumetry
<b>Background/Objectives: Background</b>: COVID-19 pneumonia leads to alveolar collapse and parenchymal infiltration, contributing to lung volume loss and respiratory failure. <b>Objectives</b>: To quantify lung volume loss and recovery in moderate and severe cases, explore m...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/15/12/1465 |
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| Summary: | <b>Background/Objectives: Background</b>: COVID-19 pneumonia leads to alveolar collapse and parenchymal infiltration, contributing to lung volume loss and respiratory failure. <b>Objectives</b>: To quantify lung volume loss and recovery in moderate and severe cases, explore mechanisms of respiratory failure, and correlate imaging findings with histopathological changes. <b>Methods</b>: We retrospectively analyzed 43 patients with moderate/severe COVID-19. CT scans from the acute phase and at 3–12 months follow-ups were processed using 3D Slicer. Infiltrated (−650 to −200 HU) and collapsed (−200 to 0 HU) lung regions were quantified and summed to define the affected lung volume. CT severity scores and total affected percentage were compared with lung volume loss. Histopathological analysis of three autopsy cases was used to support imaging findings. <b>Results</b>: Median acute phase lung volume loss was 30.6%. Patients with <25%, 25–50%, and >50% affected lung had median losses of 6.5%, 35.7%, and 39.8%, respectively. Volume loss strongly correlated with affected lung percentage (r = 0.72, <i>p</i> < 0.000001) and moderately with CT severity score (r = 0.52, <i>p</i> < 0.01). Histology confirmed alveolar area reductions over 65% in infiltrated regions. <b>Conclusions</b>: Lung volume loss reflects both imaging severity and histopathological damage, offering insights into the mechanisms of COVID-19 respiratory failure. CT volumetry is a valuable tool for assessing parenchymal injury and monitoring recovery, and 3D Slicer provides an accessible platform for implementing this approach. |
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| ISSN: | 2075-4418 |