Temporal changes of COVID-19 pneumonia by mass evaluation using CT: a retrospective multi-center study

Abstract

Background: Coronavirus disease 2019 (COVID-19) has widely spread worldwide and caused a pandemic. Chest CT has been found to play an important role in the diagnosis and management of COVID-19. However, quantitatively assessing temporal changes of COVID-19 pneumonia over time using CT has still not been fully elucidated. The purpose of this study was to perform a longitudinal study to quantitatively assess temporal changes of COVID-19 pneumonia.

Methods: This retrospective and multi-center study included patients with laboratory-confirmed COVID-19 infection from 16 hospitals between January 19 and March 27, 2020. Mass was used as an approach to quantitatively measure dynamic changes of pulmonary involvement in patients with COVID-19. Artificial intelligence (AI) was employed as image segmentation and analysis tool for calculating the mass of pulmonary involvement.

Results: A total of 581 confirmed patients with 1,309 chest CT examinations were included in this study. The median age was 46 years (IQR, 35-55; range, 4-87 years), and 311 (53.5%) patients were male. The mass of pulmonary involvement peaked on day 10 after the onset of initial symptoms. Furthermore, the mass of pulmonary involvement of older patients (>45 years) was significantly severer (P<0.001) and peaked later (day 11 vs. day 8) than that of younger patients (≤45 years). In addition, there were no significant differences in the peak time (day 10 vs. day 10) and median mass (P=0.679) of pulmonary involvement between male and female.

Conclusions: Pulmonary involvement peaked on day 10 after the onset of initial symptoms in patients with COVID-19. Further, pulmonary involvement of older patients was severer and peaked later than that of younger patients. These findings suggest that AI-based quantitative mass evaluation of COVID-19 pneumonia hold great potential for monitoring the disease progression.

Keywords: Coronavirus disease 2019 (COVID-19); artificial intelligence (AI); chest CT; temporal changes.

Figure 1

Flow diagram of the study population.

Figure 2

Dynamic changes in mass of pulmonary involvement in a 29-year-old woman with COVID-19 pneumonia presenting with persistent fever (38.0 °C) for two days. (A) At presentation (day 2), a small nodule of GGO with partial consolidation was demonstrated in the left lower lobe; after segmentation, the volume mean CT value of lesions were calculated by artificial intelligence (AI). Day 2, the volume of lesion was 6.51 cm³ and mean CT value of lesion was −487.08 HU. Mass = 6.51×(−487.08+1000)=3339. (B) Scan obtained on day 4 showed semi-consolidation with increased extent with superimposed inter- and intralobular septal thickening (crazy-paving pattern); day 4, the volume of lesions was 12.05 cm³ and mean CT value of lesions was −355.86 HU. Mass = 12.05×(−355.86+1000)=7762. (C) Scan obtained on day 12 showed multiple semi-consolidation with increased extent in both lower lobe; day 12, the volume of lesions was 206.19 cm³ and mean CT value of lesions was −449.08 HU. Mass = 206.19×(−449.08+1000)=113594. (D) Scan obtained on day 27 showed obvious absorption of abnormalities and only small pure GGO could be observed in the left lower lobe; day 27, the volume of lesion was 9.42 cm³ and mean CT value of lesion was −637.33 HU. Mass = 9.42×(−637.33+1000)=3416. COVID-19, coronavirus disease 2019; GGO, ground-glass opacity.

Figure 3

Dynamic changes in mass of pulmonary involvement over time after the onset of initial symptoms. (A) Dynamic changes in mass of pulmonary involvement over weeks. Peak in mass of pulmonary involvement occurred on week 2. Coloured bars represent medians and black bars represent interquartile ranges. (B) Dynamic changes in mass of pulmonary involvement over days. Peak in mass of pulmonary involvement occurred on day 10 (curve fitting equation: y = −0.4301×x⁴+54.45×x³−2272×x²+32018x−7808, in which x = day from the onset of initial symptoms, y = total mass of the pneumonia lesions; R²=0.08, P<0.001).

Figure 4

(A,B) Dynamic changes in mass of involved pneumonia lesions from the onset of initial symptoms in the group of patients ≤45 and ≥46 years, respectively. (A) In the group of patients ≤45 years, the peak in mass of pulmonary involvement occurred on day 8 (red arrow) (curve fitting equation: y= −0.3906×x⁴+46.35×x³−1748×x²+20762x+16527, in which x = day from the onset of initial symptoms, y = mass of the pneumonia lesions; R²=0.07, P<0.001); (B) in the group of patients ≥46 years, the peak in mass of pulmonary involvement occurred on day 11 (red arrow) (curve fitting equation: y = −0.5749×x⁴+73.15×x³−3099×x²+45349x−37890, in which x = day from the onset of initial symptoms, y = mass of the pneumonia lesions; R²=0.11, P<0.001). (C,D) Dynamic changes in mass of involved pneumonia lesions from the onset of initial symptoms in the group of male and female patients, respectively. (C) In the group of male patients, the peak in mass of pulmonary involvement occurred on day 10 (red arrow) (curve fitting equation: y = −0.5323×x⁴+65.99×x³−2729×x²+38794x−20950, in which x = day from the onset of initial symptoms, y = mass of the pneumonia lesions; R²=0.08, P<0.001); (D) in the group of female patients, the peak in mass of pulmonary involvement occurred on day 10 (red arrow) (curve fitting equation: y = −0.3505×x⁴+44.29×x³−1837×x²+25470x+3803, in which x = day from the onset of initial symptoms, y = mass of the pneumonia lesions; R²=0.1, P<0.001).

Figure 5

Association between age (A) and sex (B) and the mass of pneumonia lesions. Coloured bars represent medians and black bars represent interquartile ranges.

Figure S1

The number distribution of CT follow-up exams.

Figure S2

The quantitative evaluation system of CT for COVID-19 with artificial intelligence (AI). COVID-19, coronavirus disease 2019.