Quantitative Chest CT Analysis in Relationships between CT Patterns, Virus Load, and Pathophysiological States in SARS-CoV-2 infected Patients: ACross-Sectional Observational Study
Wang Yan1*, Meng Haining2*, Wang Sumei1, Jia Chao1, Lian Zhiyuan1, Xie Weifeng1, Qu Yan1#
1.Department of Intensive Care Unit, Qingdao Hospital, University of Health, and Rehabilitation Sciences (Qingdao Municipal Hospital).
Address: No.5 Zhonghai East Road, Southern District, Qingdao City, Shandong Province, China.
Wang Yan: E-mailkeliven@163.net
Qu Yan E-mail:qdquyan@aliyun.com #Qu Yan is the corresponding author.
2.Department of Emergency Medicine, Medical College of Qingdao University, Qingdao, Shandong, China;
Meng Haining: E-mailmenghaining0307@163.com
*These authors contributed to the work equllly and should be regarded as co-first authors.
Abstract
CT imaging is often used to confirm COVID-19, playing a crucial role in the diagnosis and assessment due to its high sensitivity. The purpose of this study is to investigate results of quantitative CT analysis for CT patterns in SARS-CoV-2 infected patients, and how these relate to viral load and pathophysiological states. We recruited patients who had confirmed SARS-CoV-2 infection and undergone chest CT within 24 hours of confirmation. By quantitative CT analysis, and collecting clinical data, we explored correlations between those variables. Our research included 253 patients, after screening by exclusion criteria, 171 patients were included in final cohort. The incidence of SARS-CoV-2 associated pneumonia was 74.3%. The ROC test results showed AUCs for leukomonocyte count, and virus genes were 0.703, 0.562, 0.567, and 0.582, respectively. GGO pattern in CT was correlated PaO2/FiO2 ratio. Multiple linear regression results indicated GGO was associated with PaO2/FiO2. Meanwhile, the consolidation was correlated with PaCO2 level. Additionally, consolidation was also associated with neutrophil–lymphocyte ratio. Conclusion: Lymphocyte count may be a potential marker for predicting SARS-CoV-2 pneumonia, independent of virus load. Additionally, GGO is correlated with hypoxia, while consolidation is associated with PaCO2 levels and inflammation, which may affect aeration in the lungs.
Key words: Quantitative CT analysis, SARS-CoV-2 infection, GGO, consolidation, PaO2/FiO2 ratio, consolidation.
Introduction
The World Health Organization (WHO) has report (Available from:https://covid19.who.int/. ) that pneumonia caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread to 223 countries with more than 186 million confirmed cases and more than 4 million deaths[1]. This has become a public health issue of international concern until now. The proportion of hospitalized patients diagnosed with severe pneumonia and infected with SARS-CoV-2 who develop ARDS, based on oxygenation criteria, ranges from 20% to 67%[2, 3].
Computed tomography (CT) imaging is often used to confirm COVID-19 and plays a crucial role in the diagnosis and treatment assessment of COVID-19 due to its high sensitivity [4, 5]. The main characteristics of COVID-19 observed through CT imaging include bilateral pulmonary ground-glass opacity (GGO), a ”crazy paving” pattern, airway changes, and a reversed halo sign. Other manifestations may include consolidation and interlobular septal thickening. GGO is the earliest and most prominent pulmonary abnormality observed, while consolidation appears in the later stages [6, 7]. Furthermore, several studies have indicated that CT features are associated with the level of plasma cytokines [8, 9], and even with the prognosis of pneumonia associated with SARS-CoV-2[6, 10]. Based on these results, we want to make it clear whether a heavy virus load could result in significant alteration in CT manifestation and quantitative CT analysis of different CT patterns can provide more information about patients to help clinicians make better clinical interventions. However, there are a few questions that need to be answered. First, there is limited research available that links the physiological, laboratory [such as arterial blood gas (ABG), complete blood count (CBC), etc.], and imaging features of patients with SARS-CoV-2[2]. Second, the understanding of CT imaging reports is subjective and relies on clinical experience. Therefore, it is necessary to clarify the relationship between CT patterns and pathophysiological states.
The purpose of this study is to investigate the results of quantitative CT analysis for different CT patterns in SARS-CoV-2 infected patients, and how these relate to viral load and pathophysiological states.
Method
Study population
The local ethics board has approved this cross-sectional clinical cohort study. Patients infected with SARS-CoV-2 who were in charge in Qingdao Hospital, University of Health, and Rehabilitation Sciences (Qingdao Municipal Hospital) from Jun. 9th to 15th, 2023 (the initial week of deblocking for isolation in Qingdao region, China) were eligible to participate in this research. SARS-CoV-2 was detected by polymerase chain reaction (PCR) with a sample of throat swab or sputum specimen and the Cycle threshold (Ct) value below 40 was regarded as positive. Pneumonia was diagnosed according to the American Thoracic Society guidelines for community-acquired pneumonia[11]. The inclusion criteria was adult patients, who had a confirmed SARS-CoV-2 infection and undergone chest CT within 24 hours of admission. Patients with a.) history of pulmonary surgery; b.) thoracocyllosis or rib fracture; c.) pneumothorax with/without drainage; d.) Massive pleural effusion can’t be analysis by CT; e.) clinical diagnosed pneumonia without PCR validation; f.) clinical data missing were excluded. According to CT manifestation, patients were divided into SARS-CoV-2 infection without pneumonia and pneumonia associated with SARS-CoV-2. Clinical data were extracted from the hospital information system (HIS), and CT raw data (The digital imaging and communications in medicine, DICOM files) for the final target patients were downloaded from the picture archiving and communication system (PACS) in Qingdao Hospital, University of Health, and Rehabilitation Sciences.
Clinical laboratory data
The medical records were reviewed to record the patient’s medical history and interventions upon admission. The virus load was detected by the PCR Ct value of swab or sputum specimens for each patient. To describe the oxygenation situation and inflammatory response of these patients, results for ABG, fraction of inspired oxygen (FiO2), method of oxygen inhalation, and CBC were recorded. The ratio of partial pressure of arterial oxygen to fractional concentration of oxygen in inspired air (PaO2/FiO2 ratio) was also calculated. All laboratory records were collected within a 12-hour window around the date of the CT test.
CT examination protocol
CT examinations were performed using a multidetector scanner without the use of contrast medium for enhancement. Image raw data were collected by the Somatom Sensation 64 scanner (SIEMENS, Germany). The tube voltage used was 120 KV and the tube current was set to automatic. The pitch was 1.375 and the slice thickness was 5mm. All cases used the classic filtered back projection method with a soft tissue kernel of B20 and a lung kernel of B60. In all patients, a spiral acquisition was obtained from the apex to the bottom of the lungs at the stage of end inspiratory hold breathing. Coronal and sagittal multiplanar reconstructions were also available in all cases. The raw data were concluded into a 250-330 mm field of view and a 512×512 reconstruction matrix. The data were then downloaded, saved as DICOM files, and made available for analysis.
Imaging analysis
The CT scan data were analyzed by 3D slicer software[12] (version 5.0.3, http://www.slicer.org/ ) to estimate the affected lung area (GGO + consolidation + other patterns). The DICOM files were imported into a dedicated medical imaging software that includes a semi-automated segmentation algorithm for lung segmentation. Two senior doctors reviewed the results independently, without access to patients’ clinical information such as therapy or primary disease. In case of discrepancies, consensus was reached after consulting to another senior radiologist, and the results were saved as regions of interest (ROI) for quantitative analysis. Chest CT scans measured the density of the pulmonary voxel within ROI in Hounsfield units (HUs). Depending on the range of HU, ROI was divided into ground-glass opacity (GGO) regions (-500HU to -100HU), consolidations (-100HU to 100HU), and others (aerated lung: <-500HU, etc. ). Volumetric analysis and/or visualization in 3D Slicer via the Lung CT Analyzer project (https://github.com/rbumm/SlicerLungCTAnalyzer/ ) were performed. The total lung and affected volumes were estimated by reconstructing each marked slice using Lung CT Analyzer, a plug-in integrated into 3D Slicer[13]. (Data processing procedure was shown in Fig. 1).