Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia

Abstract

Objectives To evaluate a semi-automated segmentation and ventilated lung quantification on chest computed tomography (CT) to assess lung involvement in patients affected by SARS-CoV-2. Results were compared with clinical and functional parameters and outcomes. Methods All images underwent quantitative analyses with a dedicated workstation using a semi-automatic lung segmentation software to compute ventilated lung volume (VLV), Ground-glass opacity (GGO) volume (GGO-V), and consolidation volume (CONS-V) as absolute volume and as a percentage of total lung volume (TLV). The ratio between CONS-V, GGO-V, and VLV (CONS-V/VLV and GGO-V/VLV, respectively), TLV (CONS-V/TLV, GGO-V/TLV, and GGO-V + CONS-V/TLV respectively), and the ratio between VLV and TLV (VLV/TLV) were calculated. Results A total of 108 patients were enrolled. GGO-V/TLV significantly correlated with WBC (r = 0.369), neutrophils (r = 0.446), platelets (r = 0.182), CRP (r = 0.190), PaCO2 (r = 0.176), HCO3− (r = 0.284), and PaO2/FiO2 (P/F) values (r = − 0.344). CONS-V/TLV significantly correlated with WBC (r = 0.294), neutrophils (r = 0.300), lymphocytes (r = −0.225), CRP (r = 0.306), PaCO2 (r = 0.227), pH (r = 0.162), HCO3− (r = 0.394), and P/F (r = − 0.419) values. Statistically significant differences between CONS-V, GGO-V, GGO-V/TLV, CONS-V/TLV, GGO-V/VLV, CONS-V/VLV, GGO-V + CONS-V/TLV, VLV/TLV, CT score, and invasive ventilation by ET were found (all p < 0.05). Conclusion The use of quantitative semi-automated algorithm for lung CT elaboration effectively correlates the severity of SARS-CoV-2-related pneumonia with laboratory parameters and the need for invasive ventilation. Key Points • Pathological lung volumes, expressed both as GGO-V and as CONS-V, can be considered a useful tool in SARS-CoV-2-related pneumonia. • All lung volumes, expressed themselves and as ratio with TLV and VLV, correlate with laboratory data, in particular C-reactive protein and white blood cell count. • All lung volumes correlate with patient’s outcome, in particular concerning invasive ventilation.

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

“…Their semi-automatic software showed a strong negative correlation between P/F ratio or hypercapnia, expression of hypoxia, and analyzed CT volumes. [108] Moreover, the Dense-UNet used by Mergen et al further confirmed the previously described positive correlations about CRP and leukocytes. Authors underlined the negative correlation between percentage of opacity (PO) or percentage of high opacity (PHO, consolidations) with SO 2 as an additional demonstration that patients in need for supplemental oxygen have a higher proportion of involved lungs [109].…”

Prognostic value of CT integrated with clinical and laboratory data during the first peak of the COVID-19 pandemic in Northern Italy: A nomogram to predict unfavorable outcome

“…In other words, the most useful prognostic CT sign in predicting the outcome of COVID-19 patients was the expression of global lung involvement, regardless of the type of alteration and the consolidation density of the images. Although some recent papers [25][26][27] described some results on quantification based on open-source software for semi-automated pulmonary segmentation, in our experience, the visual analysis of lung involvement proved to be a quick, easy-to-use and reliable method for the evaluation and quantitation of lung involvement. This procedure can also be used also in an emergency scenario, independently of sophisticated, different, and still not fully comparable software-based methods for the interpretation of CT images.…”

“…The Fleischner Society issued a consensus statement in order to explore the best application of imaging, primarily CT, for the evaluation and risk stratification of patients [10], acknowledging that, in addition to supporting diagnosis, CT has also revealed its usefulness for providing diagnostic information. This approach requires the quantification of abnormalities that currently can be reached through visual analysis [19][20][21][22][23], or more recently using a software-based assessment [24][25][26][27] or, possibly in the future, artificial intelligence (A.I.) [28][29][30].…”

Pulmonary function and COVID-19

“…Certainly, pulmonary function impairment prior to hospitalisation predicts COVID-19 disease outcomes in sarcoidosis [ 25 ] and other chronic comorbid pulmonary conditions have been associated with poor outcome in COVID-19 such as COPD and ILD [ 26 , 27 , 28 , 29 ]. Ippolito et al [ 30 ], using quantitative CT analysis, found that disease severity and need for invasive ventilation was correlated with measures of lung volume. If lung volume measurements and pulmonary function can predict severity, it may be that static lung volumes and/or diffusing capacity abnormalities are not the consequence of COVID-19 disease severity, but that pre-existing abnormality contributes to COVID-19 disease severity.…”