Performance of Chest Computed Tomography in Differentiating Coronavirus Disease 2019 From Other Viral Infections Using a Standardized Classification

Diagnostic performance of standardized typical CT findings for COVID-19: a systematic review and meta-analysis

OBJECTIVE

To meta-analyze diagnostic performance measures of standardized typical CT findings for COVID-19 and examine these measures by region and national income.

METHODS

MEDLINE and Embase were searched from January 2020 to April 2022 for diagnostic studies using the Radiological Society of North America (RSNA) classification or the COVID-19 Reporting and Data System (CO-RADS) for COVID-19. Patient and study characteristics were extracted. We pooled the diagnostic performance of typical CT findings in the RSNA and CO-RADS systems and interobserver agreement. Meta-regression was performed to examine the effect of potential explanatory factors on the diagnostic performance of the typical CT findings.

RESULTS

We included 42 diagnostic performance studies with 6777 PCR-positive and 9955 PCR-negative patients from 18 developing and 24 developed countries covering the Americas, Europe, Asia, and Africa. The pooled sensitivity was 70% (95% confidence interval [CI]: 65%, 74%; I² = 92%), and the pooled specificity was 90% (95% CI 86%, 93%; I² = 94%) for the typical CT findings of COVID-19. The sensitivity and specificity of the typical CT findings did not differ significantly by national income and the region of the study (p > 0.1, respectively). The pooled interobserver agreement from 19 studies was 0.72 (95% CI 0.63, 0.81; I² = 99%) for the typical CT findings and 0.67 (95% CI 0.61, 0.74; I² = 99%) for the overall CT classifications.

CONCLUSION

The standardized typical CT findings for COVID-19 provided moderate sensitivity and high specificity globally, regardless of region and national income, and were highly reproducible between radiologists.

CRITICAL RELEVANCE STATEMENT

Standardized typical CT findings for COVID-19 provided a reproducible high diagnostic accuracy globally.

KEY POINTS

Standardized typical CT findings for COVID-19 provide high sensitivity and specificity. Typical CT findings show high diagnosability regardless of region or income. The interobserver agreement for typical findings of COVID-19 is substantial.

The deadliest lung lobe in COVID-19: a retrospective cohort study of elderly patients hospitalized for COVID-19

INTRODUCTION

The relationship between which lung lobe is affected on the chest X-ray (CXR) performed at the Emergency Department (ED) and mortality from COVID-19 has not been studied. With this in mind, the present study aimed to discern which lung lobe was the one with the highest associated mortality rate in the elderly population.

METHODS

Information was collected from the 2020 hospital admissions records of our hospital. Our cohort consists of 300 admissions.

RESULTS

The presence of the left lower lobe (RR = 1.6; 95% CI: 1.1-2.4) and right middle lobe involvement (RR =1.8; 95% CI: 1.2-2.7) on CXR at the ED were both predictive factors of in-hospital mortality. Right middle lobe involvement on CXR at the ED was the risk factor with the highest relative risk value (RR = 1.8). Furthermore, right middle lobe involvement on CXR at ED was a predictor for persistent organ failure (RR = 1.7; 95% CI: 1.2-2.3), respiratory failure (RR = 1.7; 95% CI: 1.2-2.4) and acute kidney injury (RR = 1.5; 95% CI: 1.2-2). The isolated right middle lobe involvement on CXR at ED was a risk factor for in-hospital mortality (RR = 2.6; 95% CI = 1.8 - 3.7). However, the affected right middle lobe along with another/other lobe/s was a null factor.

CONCLUSION

Right middle lobe involvement was an independent predictor of in-hospital mortality.

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality

Rationale and Objectives

The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural network (dCNN) to predict inpatient outcomes associated with COVID-19 pneumonia.

Materials and Methods

A previously trained dCNN was tested on an external validation cohort of 241 patients who presented to the emergency department and received a chest computed tomography scan, 93 with COVID-19 and 168 without. Airspace opacity scoring systems were defined by the extent of airspace opacity in each lobe, totaled across the entire lungs. Expert and dCNN scores were concurrently evaluated for interobserver agreement, while both dCNN identified airspace opacity scoring and raw opacity values were used in the prediction of COVID-19 diagnosis and inpatient outcomes.

Results

Interobserver agreement for airspace opacity scoring was 0.892 (95% CI 0.834-0.930). Probability of each outcome behaved as a logistic function of the opacity scoring (25% intensive care unit admission at score of 13/25, 25% intubation at 17/25, and 25% mortality at 20/25). Length of hospitalization, intensive care unit stay, and intubation were associated with larger airspace opacity score (p = 0.032, 0.039, 0.036, respectively).

Conclusion

The tested dCNN was highly predictive of inpatient outcomes, performs at a near expert level, and provides added value for clinicians in terms of prognostication and disease severity.

[Korean Clinical Imaging Guidelines for Justification of Diagnostic Imaging Study for COVID-19]

To develop Korean coronavirus disease (COVID-19) chest imaging justification guidelines, eight key questions were selected and the following recommendations were made with the evidence-based clinical imaging guideline adaptation methodology. It is appropriate not to use chest imaging tests (chest radiograph or CT) for the diagnosis of COVID-19 in asymptomatic patients. If reverse transcription-polymerase chain reaction testing is not available or if results are delayed or are initially negative in the presence of symptoms suggestive of COVID-19, chest imaging tests may be considered. In addition to clinical evaluations and laboratory tests, chest imaging may be contemplated to determine hospital admission for asymptomatic or mildly symptomatic unhospitalized patients with confirmed COVID-19. In hospitalized patients with confirmed COVID-19, chest imaging may be advised to determine or modify treatment alternatives. CT angiography may be considered if hemoptysis or pulmonary embolism is clinically suspected in a patient with confirmed COVID-19. For COVID-19 patients with improved symptoms, chest imaging is not recommended to make decisions regarding hospital discharge. For patients with functional impairment after recovery from COVID-19, chest imaging may be considered to distinguish a potentially treatable disease.

The role of chest imaging in the diagnosis, management, and monitoring of coronavirus disease 2019 (COVID-19)

Coronavirus disease 2019 (COVID-19) pandemic has posed a major public health crisis all over the world. The role of chest imaging, especially computed tomography (CT), has evolved during the pandemic paralleling the accumulation of scientific evidence. In the early stage of the pandemic, the performance of chest imaging for COVID-19 has widely been debated especially in the context of comparison to real-time reverse transcription polymerase chain reaction. Current evidence is against the use of chest imaging for routine screening of COVID-19 contrary to the initial expectations. It still has an integral role to play, however, in its work up and staging, especially when assessing complications or disease progression. Chest CT is gold standard imaging modality for COVID-19 pneumonia; in some situations, chest X-ray or ultrasound may be an effective alternative. The most important role of radiologists in this context is to be able to identify those patients at greatest risk of imminent clinical decompensation by learning to stratify cases of COVID-19 on the basis of radiologic imaging in the most efficient and timely fashion possible. The present availability of multiple and more refined CT grading systems and classification is now making this task easier and thereby contributing to the recent improvements achieved in COVID-19 treatment and outcomes. In this article, evidence of chest imaging regarding diagnosis, management and monitoring of COVID-19 will be chronologically reviewed.

IONIZING RADIATION EXPOSURE IN PATIENTS WITH COVID-19: MORE THAN NEEDED

OBJECTIVE

The aim of the study was to evaluate the ionizing radiation exposure in patients with Coronavirus disease 2019 (COVID-19).

MATERIALS AND METHODS

This was a retrospective study in which all patients presented with suggestive symptoms of COVID-19 were included. The study was carried out in a university-affiliated private hospital in Istanbul, Turkey. Biological radiation dose exposure (cumulative effective dose: CED) was evaluated in millisievert (mSv) units.

RESULTS

A total of 1410 patients were included in the study. Of all study subjects, 804 patients (57%) underwent only one chest computed tomography (CT) procedure. Six hundred and six patients (43%) had two or more chest CT procedures. Median CED was 6.02 (min-max:1.67-16.27) mSv. The number of patients who were exposed to ≤ 5 mSv were 149 (24.6%), whereas 457 patients (75.4%) were exposed to >5 mSv.

CONCLUSION

The radiation exposure in COVID-19 patients seems unjustifiably high. Awareness should be increased as to the proper use of chest CT in COVID-19 as per to the society recommendations.

Impact of the COVID-19 pandemic on the selection of chest imaging modalities and reporting systems: a survey of Italian radiologists

PURPOSE

Chest imaging modalities play a key role for the management of patient with coronavirus disease (COVID-19). Unfortunately, there is no consensus on the optimal chest imaging approach in the evaluation of patients with COVID-19 pneumonia, and radiology departments tend to use different approaches. Thus, the main objective of this survey was to assess how chest imaging modalities have been used during the different phases of the first COVID-19 wave in Italy, and which diagnostic technique and reporting system would have been preferred based on the experience gained during the pandemic.

MATERIAL AND METHODS

The questionnaire of the survey consisted of 26 questions. The link to participate in the survey was sent to all members of the Italian Society of Medical and Interventional Radiology (SIRM).

RESULTS

The survey gathered responses from 716 SIRM members. The most notable result was that the most used and preferred chest imaging modality to assess/exclude/monitor COVID-19 pneumonia during the different phases of the first COVID-19 wave was computed tomography (51.8% to 77.1% of participants). Additionally, while the narrative report was the most used reporting system (55.6% of respondents), one-third of participants would have preferred to utilize structured reporting systems.

CONCLUSION

This survey shows that the participants' responses did not properly align with the imaging guidelines for managing COVID-19 that have been made by several scientific, including SIRM. Therefore, there is a need for continuing education to keep radiologists up to date and aware of the advantages and limitations of the chest imaging modalities and reporting systems.