Diagnostic and Interventional Radiology
Chest Imaging - Original Article

Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings

1.

Department of Radiology, Hacettepe University Faculty of Medicine, Ankara, Turkey

2.

Department of Pulmonary Medicine, Hacettepe University School of Medicine, Ankara, Turkey

3.

Division of Intensive Care, Department of Internal Disease, Hacettepe University Faculty of Medicine, Ankara, Turkey

4.

Department of Infectious Diseases, Hacettepe University Faculty of Medicine, Ankara, Turkey

Diagn Interv Radiol 2020; 26: 557-564
DOI: 10.5152/dir.2020.20407
Read: 429 Downloads: 97 Published: 02 September 2020

Purpose

The aim of this study was to evaluate visual and software-based quantitative assessment of parenchymal changes and normal lung parenchyma in patients with coronavirus disease 2019 (COVID-19) pneumonia. The secondary aim of the study was to compare the radiologic findings with clinical and laboratory data.

 

Methods

Patients with COVID-19 who underwent chest computed tomography (CT) between March 11, 2020 and April 15, 2020 were retrospectively evaluated. Clinical and laboratory findings of patients with abnormal findings on chest CT and PCR-evidence of COVID-19 infection were recorded. Visual quantitative assessment score (VQAS) was performed according to the extent of lung opacities. Software-based quantitative assessment of the normal lung parenchyma percentage (SQNLP) was automatically quantified by a deep learning software. The presence of consolidation and crazy paving pattern (CPP) was also recorded.  Statistical analyses were performed to evaluate the correlation between quantitative radiologic assessments, and clinical and laboratory findings, as well as to determine the predictive utility of radiologic findings for estimating severe pneumonia and admission to intensive care unit (ICU).

 

Results

A total of 90 patients were enrolled. Both VQAS and SQNLP were significantly correlated with multiple clinical parameters. While VQAS >8.5 (sensitivity, 84.2%; specificity, 80.3%) and SQNLP <82.45% (sensitivity, 83.1%; specificity, 84.2%) were related to severe pneumonia, VQAS >9.5 (sensitivity, 93.3%; specificity, 86.5%) and SQNLP <81.1% (sensitivity, 86.5%; specificity, 86.7%) were predictive of ICU admission.  Both consolidation and CPP were more commonly seen in patients with severe pneumonia than patients with nonsevere pneumonia (P = 0.197 for consolidation; P < 0.001 for CPP). Moreover, the presence of CPP showed high specificity (97.2%) for severe pneumonia.

 

Conclusion

Both SQNLP and VQAS were significantly related to the clinical findings, highlighting their clinical utility in predicting severe pneumonia, ICU admission, length of hospital stay, and management of the disease. On the other hand, presence of CPP has high specificity for severe COVID-19 pneumonia.

 

You may cite this article as: Durhan G, Ardalı Düzgün S, Başaran Demirkazık F, et al. Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings. Diagn Interv Radiol 2020; 26:557–564

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