We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
Thermo Fisher Scientific - Direct Effect Media

Download Mobile App

Radiologists Accurately Differentiate Between COVID-19 and Other Atypical Pneumonias on Chest CT, Finds Study

By HospiMedica International staff writers
Posted on 21 Oct 2021
Print article

Radiologists can differentiate COVID-19 from other atypical pneumonias on chest computed tomography (CT) but performed worse in the early and late CT stage of COVID 19 pneumonia, according to the findings of a new study.

The study was conducted by researchers at Heidelberg University Hospital (Heidelberg, Germany) to examine the performance of radiologists in differentiating COVID-19 from non-COVID-19 atypical pneumonia and to perform an analysis of CT patterns in a study cohort including viral, fungal and atypical bacterial pathogens.

Nucleic acid tests, most commonly via reverse transcription polymerase chain reaction (RT-PCR) assay, represent the standardized test for the detection of SARS-CoV-2 RNA from respiratory clinical specimens with a specificity reaching 100%. Besides RT-PCR, chest CT has turned out to be a helpful and fast tool in diagnosing COVID-19 pneumonia, with a moderate to high overall sensitivity of 75-88%. However, compared to the highly specific RT-PCR, the specificity of chest CT in diagnosing COVID-19 is lower, with a reported overall specificity of 46-80%. This can be explained by the fact that typical signs of COVID-19 pneumonia partially overlap with that of other acute and chronic pulmonary conditions. Some of the findings frequently encountered in COVID-19 pneumonia are: ground glass opacities (GGO), consolidation, crazy paving and enlargement of sub-segmental vessels (diameter greater than 3 mm) in areas of GGO.

The aim of this study was to investigate the diagnostic performance of radiologists with different level of experience in differentiating COVID-19 pneumonia from other atypical bacterial, fungal and viral pneumonias. Furthermore, the ability of radiologists to correctly classify infiltrates as COVID-19 pneumonia was tested for every one of the described CT stages of the disease. In addition, the study performed a detailed analysis of infiltrate patterns of all pneumonias included, aiming at identifying those atypical pneumonias most similar to COVID-19 pneumonia and defining imaging markers that might help distinguish COVID-19 pneumonia from its top differential diagnoses. Patients with positive RT-PCR tests for COVID-19 pneumonia and non-COVID-19 atypical pneumonia were retrospectively included. Five radiologists, blinded to the pathogen test results, assessed the CT scans and classified them as COVID-19 or non-COVID-19 pneumonia. For both groups specific CT features were recorded and a multivariate logistic regression model was used to calculate their ability to predict COVID-19 pneumonia.

The radiologists differentiated between COVID-19 and non-COVID-19 pneumonia with an overall accuracy, sensitivity, and specificity of 88%, 79%, and 90%, respectively. The percentage of correct ratings was lower in the early and late stage of COVID-19 pneumonia compared to the progressive and peak stage (68% and 71% vs. 85% and 89%). The variables associated with the most increased risk of COVID-19 pneumonia were band like subpleural opacities, vascular enlargement, and subpleural curvilinear lines. Bronchial wall thickening and centrilobular nodules were associated with decreased risk of COVID-19 pneumonia with OR of 0.30 and 0.10, respectively.

The study concluded that radiologists can differentiate between COVID-19 and non-COVID-19 atypical pneumonias at chest CT with high overall accuracy, although a lower performance was observed in the early and late stage of COVID 19 pneumonia. Specific CT features might help to make the correct diagnosis. The diagnostic accuracy of radiologists in this study was higher compared to earlier studies which the researchers have attributed to the continuous growing experience of radiologists with the imaging findings of COVID-19 pneumonia since the detection of SARS-CoV-2 in December 2019. The study was the first to examine the radiologists’ performance in relation to the stage of the COVID-19 pneumonia and the first to search for the atypical pneumonias most often misdiagnosed as COVID-19.

Related Links:
Heidelberg University Hospital 

Print article


Critical Care

view channel
Image: Triage Cardiac Panel is a rapid, POC fluorescence immunoassay used with Triage MeterPro (Photo courtesy of Quidel)

Quidel Triage Cardiac Panel Facilitates Rapid POC Diagnosis of Chest Pain Patients in ED

Chest and abdominal pain are the most common reasons that persons aged 15 years and over visit the emergency department (ED). Because both emergency and non-emergency care are provided, symptoms vary widely... Read more

Surgical Techniques

view channel
Image: Resolute Onyx DES helps address all DES needs and numerous patient anatomies (Photo courtesy of Medtronic)

Medtronic’s Latest Generation Drug-Eluting Coronary Stent System Offers Dual-Layer Balloon Technology

Coronary artery disease (CAD) is one of the leading causes of death and is caused by plaque buildup on the inside of the coronary arteries. These plaque deposits can narrow or clog the inside of the arteries,... Read more

Patient Care

view channel
Image: Future wearable health tech could measure gases released from skin (Photo courtesy of Pexels)

Wearable Health Tech Could Measure Gases Released From Skin to Monitor Metabolic Diseases

Most research on measuring human biomarkers, which are measures of a body’s health, rely on electrical signals to sense the chemicals excreted in sweat. But sensors that rely on perspiration often require... Read more

Health IT

view channel
Image: AI can reveal a patient`s heart health (Photo courtesy of Mayo Clinic)

AI Trained for Specific Vocal Biomarkers Could Accurately Predict Coronary Artery Disease

Earlier studies have examined the use of voice analysis for identifying voice markers associated with coronary artery disease (CAD) and heart failure. Other research groups have explored the use of similar... Read more
Copyright © 2000-2022 Globetech Media. All rights reserved.