Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019

Abstract

To examine the feasibility of using a computer tool for stratifying the severity of Coronavirus Disease 2019 (COVID-19) based on computed tomography (CT) images. We retrospectively examined 44 confirmed COVID-19 cases. All cases were evaluated separately by radiologists (visually) and through an in-house computer software. The degree of lesions was visually scored by the radiologist, as follows, for each of the 5 lung lobes: 0, no lesion present; 1, <1/3 involvement; 2, >1/3 and < 2/3 involvement; and 3, >2/3 involvement. Lesion density was assessed based on the proportion of ground-glass opacity (GGO), consolidation and fibrosis of the lesions. The parameters obtained using the computer tool included lung volume (mL), lesion volume (mL), lesion percentage (%), and mean lesion density (HU) of the whole lung, right lung, left lung, and each lobe. The scores obtained by the radiologists and quantitative results generated by the computer software were tested for correlation. A Chi-square test was used to test the consistency of radiologist- and computer-derived lesion percentage in the right/left lung, upper/lower lobe, and each of the 5 lobes. The results showed a strong to moderate correlation between lesion percentage scores obtained by radiologists and the computer software (r ranged from 0.7679 to 0.8373, P < 0.05), and a moderate correlation between the proportion of GGO and mean lesion density (r = -0.5894, P < 0.05), and proportion of consolidation and mean lesion density (r = 0.6282, P < 0.05). Computer-aided quantification showed a statistical significant higher lesion percentage for lower lobes than that assessed by the radiologists (χ² = 8.160, P = 0.004). Our experiments demonstrated that the computer tool could reliably and accurately assess the severity and distribution of pneumonia on CT scans.

Keywords: Coronavirus disease 2019 (COVID-19); Quantitative computed tomography (QCT); Severity stratification.

Graphical abstract

Fig. 1

Study flowchart.

Fig. 2

Examples of severity evaluation by a radiologist. (A, B): Images from a 60-year-old female patient with confirmed COVID-19. A pure GGO lesion can be seen in the left upper lobe (A, red arrow). As the lesion occupied <1/3 of the left upper lobe, the lesion percentage score was 1. Another GGO can be seen in the right lower lobe (B, red arrow); this also had a lesion percentage score of 1. The proportion of GGO, consolidation, and fibrosis were 10, 0, and 0, respectively. (C, D): Images from a 66-year-old female patient with confirmed COVID-19. GGO (C, red arrow; D, red arrow) and bilateral multifocal consolidation were observed (C, green arrow; D, green arrow). The scores of GGO, consolidation, and fibrosis were 3, 7, and 0, respectively. GGO, ground glass opacity.

Fig. 3

Illustration of lesion identification. The first step was the segmentation of the bilateral lung, and the results are displayed a three-dimensional model (shown in A, the right lung is colored green and the left lung is colored blue). The second step was the segmentation of pulmonary vessels (shown in B, the vessels are colored blue). After the subtraction of the pulmonary vessels from the lung regions, the fourth and final step was the segmentation of pneumonia lesions (shown in C). The red irregular nodular shapes were observed as a result of this lesion segmentation.

Fig. 4

Illustration of lesion segmentation by the computer software. (A, B, C): Images from a 60 year-old female patient with early stage COVID-19. Multifocal pure ground glass opacity (GGO) can be seen in the bilateral lung (A, red arrows). All GGO lesions were segmented, as shown on the same coronal view (B, within red line). All lesions (C, red nodular) can be seen on the three-dimensional reconstruction view. (D, E, F): Images from a 40-year-old female patient in the progressing stage of COVID-19. Multifocal GGOs (D, red arrows), patchy consolidation (D, green arrow), and fibrosis (D, blue arrows) can be observed. All lesions were segmented, as shown on the same coronal view (E, within red line). F represents the three-dimensional reconstruction of the lesion. (G, H, I): Images from a 50-year-old male patient in the severe stage of COVID-19. Diffused GGO (G, red arrows) and consolidation (G, green arrow) can be seen in the bilateral lungs. All lesions were segmented, as shown on the same coronal view (H, red color). I represent the three-dimensional reconstruction of the lesion.

Fig. 5

Correlation of lesion percentage derived by radiologists and the computer software. The scatter diagram (A-H, plotted with red reversed triangles) shows the correlation between lesion percentages assessed by the radiologist and the computer software for the whole lung (A), right lung (B), left lung (C), right upper lobe (D), right middle lobe (E), right lower lobe (F), left upper lobe (G), and left lower lobe (H). WL, whole lung; RL, right lung; LL, left lung; RUL, right upper lobe; RML, right middle lobe; LUL, left upper lobe; LLL, left lower lobe. RLP, radiologist-assessed lesion percentage; CLP, computer-assessed lesion percentage.

Fig. 6

Correlation of lesion density derived by radiologists and the computer software. The scatter diagram (A-B, plotted with red filled circles) shows a moderate negative correlation between the proportion of GGO and mean lesion density (shown in A), and a moderate positive correlation between the proportion of consolidation and mean lesion density (shown in B). P_GGO, proportion of ground glass opacity to the overall lesion; P_{consolidation}, proportion of consolidation to the overall lesion.