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Updated: Jun 10, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
U-Net-based computed tomography quantification of viral pneumonia can predict fibrotic interstitial lung
Zhoumeng Ying1,2, Zhenchen Zhu1, Ge Hu3
1Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background:
Given the high prevalence of fibrotic interstitial lung abnormalities (ILAs) post-COVID-19, this study aims to evaluate the effectiveness of quantitative CT features in predicting fibrotic ILAs at 3-month follow-up.
Methods:
This retrospective study utilized cohorts from distinct clinical settings: the training dataset comprised individuals presenting at the fever clinic and emergency department, while the validation dataset included patients hospitalized with COVID-19 pneumonia. They were classified into fibrotic group and nonfibrotic group based on whether the fibrotic ILAs were present at follow-up. A U-Net-based AI tool was used for quantification of both pneumonia lesions and pulmonary blood volumes. Receiver operating characteristic (ROC) curve analysis and multivariate analysis were used to assess their predictive abilities for fibrotic ILAs.
Results:
Among the training dataset, 122 patients (mean age of 68 years ±16 [standard deviation], 73 men), 55.74% showed fibrotic ILAs at 3-month follow-up. The multivariate analysis identified the pneumonia volume [PV, odd ratio (OR) 3.28, 95% confidence interval (CI): 1.20-9.31, p = 0.02], consolidation volume (CV, OR 3.77, 95% CI: 1.37-10.75, p = 0.01), ground-glass opacity volume (GV, OR 3.38, 95% CI: 1.26-9.38, p = 0.02), pneumonia mass (PM, OR 3.58, 95% CI: 1.28-10.46, p = 0.02), and the CT score (OR 12.06, 95% CI: 3.15-58.89, p < 0.001) as independent predictors of fibrotic ILAs, and all quantitative parameters were as effective as CT score (all p > 0.05). And the area under the curve (AUC) values were PV (0.79), GV (0.78), PM (0.79), CV (0.80), and the CT score (0.77). The validation dataset, comprising 45 patients (mean age 67.29 ± 14.29 years, 25 males) with 57.78% showing fibrotic ILAs at follow-up, confirmed the predictive validity of these parameters with AUC values for PV (0.86), CV (0.90), GV (0.83), PM (0.88), and the CT score (0.85). Additionally, the percentage of blood volume in vessels <5mm2 relative to the total pulmonary blood volume (BV5%) was significantly lower in patients with fibrotic ILAs (p = 0.048) compared to those without.
Conclusion:
U-Net based quantification of pneumonia lesion and BV5% on baseline CT scan has the potential to predict fibrotic ILAs at follow-up in COVID-19 patients.
