Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management.

By Yan Li, L. Xia. AJR. American journal of roentgenology, 2020

1,084 citations recorded by the source corpus.

OBJECTIVE. The objective of our study was to determine the misdiagnosis rate of radiologists for coronavirus disease 2019 (COVID-19) and evaluate the performance of chest CT in the diagnosis and management of COVID-19. The CT features of COVID-19 are reported and compared with the CT features of other viruses to familiarize radiologists with possible CT patterns. MATERIALS AND METHODS. This study included the first 51 patients with a diagnosis of COVID-19 infection confirmed by nucleic acid testing (23 women and 28 men; age range, 26-83 years) and two patients with adenovirus (one woman and one man; ages, 58 and 66 years). We reviewed the clinical information, CT images, and corresponding image reports of these 53 patients. The CT images included images from 99 chest CT examinations, including initial and follow-up CT studies. We compared the image reports of the initial CT study with the laboratory test results and identified CT patterns suggestive of viral infection. RESULTS. COVID-19 was misdiagnosed as a common infection at the initial CT study in two inpatients with underlying disease and COVID-19. Viral pneumonia was correctly diagnosed at the initial CT study in the remaining 49 patients with COVID-19 and two patients with adenovirus. These patients were isolated and obtained treatment. Ground-glass opacities (GGOs) and consolidation with or without vascular enlargement, interlobular septal thickening, and air bronchogram sign are common CT features of COVID-19. The The "reversed halo" sign and pulmonary nodules with a halo sign are uncommon CT features. The CT findings of COVID-19 overlap with the CT findings of adenovirus infection. There are differences as well as similarities in the CT features of COVID-19 compared with those of the severe acute respiratory syndrome. CONCLUSION. We found that chest CT had a low rate of missed diagnosis of COVID-19 (3.9%, 2/51) and may be useful as a standard method for the rapid diagnosis of COVID-19 to optimize the management of patients. However, CT is still limited for identifying specific viruses and distinguishing between viruses.

Subfields: Biomedical Engineering, Critical Care and Intensive Care Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/56d7c31382d287ad08558326ea0b38612f7eae04

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Citations in the corpus

COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images (2020, positive): “Although the radiological images based diagnostic is a faster way and also it has some advances over the PCR testing in terms of the detection rate in earlier stages of the COVID-19, the backbone of the system is the need of experts in comprehending the images.”
A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images (2020, background): “Earlier works have shown that anomalies can be found in COVID-19 patients in chest CT scans in the shape of ground-glass opacities [8].”
Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, background): “1) Computed Tomography (CT) Scans: Recent studies have found that radiologists can diagnose COVID-19 using Chest CT scans with lower false positive rates [126], [127] than other imaging modalities such as X-ray and Ultrasound scans.”
Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection (2020, background): “Thus, in order to avoid COVID-19 misdiagnoses [15], it is necessary to estimate uncertainty in a model’s predictions.”
Coronavirus Diseases (COVID-19) Current Status and Future Perspectives: A Narrative Review (2020, background): “Less common findings include pleural thickening, pleural effusion, and lymphadenopathy [24].”
Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks (2020, background): “While the role of radiographic images is still under discussion as the crisis unfolds [6], they have several important advantages including fast turnaround time (e.g., 15 to 30 minutes for a CT scan) and wide availability at most hospitals.”
Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020, background): “9 Conv2d [128, 17, 17] 73,728 10 Conv2d [64, 19, 19] 8192”