Abstract

OBJECTIVES:

This study aims to trace the dynamic lung changes of coronavirus disease 2019 (COVID-19) using computed tomography (CT) images by a quantitative method.

METHODS:

In this retrospective study, 28 confirmed COVID-19 cases with 145 CT scans are collected. The lesions are detected automatically and the parameters including lesion volume (LeV/mL), lesion percentage to lung volume (LeV%), mean lesion density (MLeD/HU), low attenuation area lower than – 400HU (LAA-400%), and lesion weight (LM/mL*HU) are computed for quantification. The dynamic changes of lungs are traced from the day of initial symptoms to the day of discharge. The lesion distribution among the five lobes and the dynamic changes in each lobe are also analyzed.

RESULTS:

LeV%, MLeD, and LM reach peaks on days 9, 6 and 8, followed by a decrease trend in the next two weeks. LAA-400% (mostly the ground glass opacity) declines to the lowest on days 4–5, and then increases. The lesion is mostly seen in the bilateral lower lobes, followed by the left upper lobe, right upper lobe and right middle lobe (p < 0.05). The right middle lobe is the earliest one (on days 6–7), while the right lower lobe is the latest one (on days 9–10) that reaches to peak among the five lobes.

CONCLUSIONS:

Severity of COVID-19 increases from the day of initial symptoms, reaches to the peak around on day 8, and then decreases. Lesion is more commonly seen in the bilateral lower lobes.

中文翻译：

---

通过定量计算机断层扫描技术评估冠状病毒病2019（COVID-19）期间的动态肺部变化。

摘要

目标：

这项研究旨在通过定量层析成像（CT）图像追踪冠状病毒疾病2019（COVID-19）的动态肺部变化。

方法：

在这项回顾性研究中，收集了28例经145次CT扫描确诊的COVID-19病例。自动检测病灶，其参数包括病灶体积（LeV / mL），病灶相对肺体积的百分比（LeV％），平均病灶密度（MLeD / HU），低于– 400HU的低衰减区域（LAA-400％），计算病变重量（LM / mL * HU）以进行定量。从最初症状出现之日到出院之日，可以追踪到肺的动态变化。还分析了五个肺叶之间的病变分布以及每个肺叶的动态变化。

结果：

LeV％，MLeD和LM在第9、6和8天达到峰值，然后在接下来的两周内下降。LAA-400％（大部分是毛玻璃不透明度）在第4-5天下降到最低，然后增加。病变多见于双侧下叶，其次为左上叶，右上叶和右中叶（p  <0.05）。右中叶是最早的（在第6-7天），而右下叶是最近的（在第9-10天），在五个叶之间达到峰值。

结论：

从最初出现症状之日起，COVID-19的严重度升高，在第8天左右达到峰值，然后降低。病变多见于双侧下叶。