Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1277, Wuhan, 430022, China.
Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
Sci Rep. 2021 Jan 11;11(1):417. doi: 10.1038/s41598-020-80261-w.
This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman's correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.
本研究旨在探索和比较一种新的基于深度学习的定量方法与传统的 COVID-19 胸部 CT 扫描半定量评分。共纳入 95 例确诊 COVID-19 患者，共计 465 次连续胸部 CT 扫描，其中 61 例为中度患者（中度组，319 次胸部 CT 扫描），34 例为重度患者（重度组，146 次胸部 CT 扫描）。对所有胸部 CT 扫描进行常规 CT 评分和基于深度学习的定量评估，以实现两个研究目标：（1）两种估计方法之间的相关性；（2）在中度和重度组之间使用这两种估计方法探索动态模式。两种评估方法之间的 Spearman 相关系数为 0.920（p<0.001）。基于深度学习的定量评估预测肺受累（使用基于深度学习的定量评估计算的 CT 评分和肺病变百分比）在症状出现后第 23 天在重度组中增加得更快，达到高峰，而在中度组中则在第 18 天达到高峰，病变吸收更快。COVID-19 的基于深度学习的定量评估与传统 CT 评分具有良好的相关性，并在评估 COVID-19 疾病严重程度方面具有潜在的优势。
