Quantitative computed tomography of the coronavirus disease 2019 (COVID-19) pneumonia

Abstract

Objective

To quantify coronavirus diseases 2019 (COVID-19) pneumonia and to explore whether quantitative computer tomography (CT) could be used to assess severity on admission.

Materials and methods

From January 17 to February 9, 2020, 38 hospitalized patients with COVID-19 pneumonia were consecutively enrolled in our hospitals. All clinical data and the chest CT on admission were retrospectively reviewed and analyzed. Firstly, a quantitative method based on multi-scale convolutional neural networks was used to assess the infected lung segments and this was compared with the semi-quantitative method. Secondly, the quantitative method was tested with laboratory results and the pneumonia severity index (PSI) by correlation analyses. Thirdly, both quantitative and semi-quantitative parameters between patients with different PSI were compared.

Results

Thirty cases were finally enrolled: 16 (53.33%) of them were male, and the mean age was 48 years old. The interval from onset symptoms to first chest CT scan was 8 days. The proportion of ground glass opacity (GGO), consolidation and the total lesion based on the quantitative method was positively correlated with the semi-quantitative CT score (P < 0.001 for all; rs = 0.88, 0.87, 0.90), CRP (P = 0.0278, 0.0168, 0.0078; rs = 0.40, 0.43, 0.48) and ESR (P = 0.0296, 0.0408, 0.0048; rs = 0.46, 0.44, 0.58), respectively, and was negatively correlated with the lymphocyte count (P = 0.0222, 0.0024, 0.0068; rs = −0.42, −0.53, −0.48). There was a positive correlation trend between the proportion of total infection and the pneumonia severity index (P = 0.0994; rs = 0.30) and a tendency that patients with severe COVID-19 pneumonia had higher percentage of consolidation and total infection (P = 0.0903, 0.0989).

Conclusions

Quantitative CT may have potential in assessing the severity of COVID-19 pneumonia on admission.