Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection

By Adam Bernheim, X. Mei, Mingqian Huang, Yang Yang, Z. Fayad, Ning Zhang, Kai-yue Diao, Bin Lin, Xiqi Zhu, Kunwei Li and 4 more. Radiology, 2020

2,536 citations recorded by the source corpus.

Abstract In this retrospective study, chest CTs of 121 symptomatic patients infected with coronavirus disease-19 (COVID-19) from four centers in China from January 18, 2020 to February 2, 2020 were reviewed for common CT findings in relationship to the time between symptom onset and the initial CT scan (i.e. early, 0-2 days (36 patients), intermediate 3-5 days (33 patients), late 6-12 days (25 patients)). The hallmarks of COVID-19 infection on imaging were bilateral and peripheral ground-glass and consolidative pulmonary opacities. Notably, 20/36 (56%) of early patients had a normal CT. With a longer time after the onset of symptoms, CT findings were more frequent, including consolidation, bilateral and peripheral disease, greater total lung involvement, linear opacities, “crazy-paving” pattern and the “reverse halo” sign. Bilateral lung involvement was observed in 10/36 early patients (28%), 25/33 intermediate patients (76%), and 22/25 late patients (88%).

Subfields: Infectious Diseases, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8e0d4b14fd0d56aba6d63ad6ad0708f07dc51ee7

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Citations in the corpus

Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices (2020, methodology): “Therefore, for COVID-19 detection, a CT scan of the chest is drawing the researchers’ attention [10], [11].”
The Role of Imaging in the Detection and Management of COVID-19: A Review (2020, background): “Follow-up CT checking images reveal temporal changes in imaging characteristics for COVID-19 [18], [22], [23], [32], [61]–[65].”
The Clinical and Chest CT Features Associated With Severe and Critical COVID-19 Pneumonia (2020, background): “Compared with the ordinary patients, the severe/critical patients have poor prognosis and high mortality.(12,13) Studying the clinical and imaging characteristics of them is helpful to deepen our understanding of the mechanism of severe/critical conditions and promote its clinical diagnosis and trea”
Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound (2020, background): “and multi-lobar glass ground opacifications (mainly posteriorly and/or peripherally distributed) [4], [5].”
Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020, background): “At the beginning of the pandemic, Chinese clinical centers had insufficient test kits, which are also producing a high rate of false-negative results, so doctors are encouraged to make a diagnosis only based on clinical and chest CT results [12,14].”
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation (2020, methodology): “[15] analyzed the 121 COVID19 patients and carried on a visual check by the experienced radiologist to categorize them as early, intermediate and late cases.”
Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks (2020, background): “[23] studied 121 infected patients’ chest CT from four different centers of China.”
Essentials for Radiologists on COVID-19: An Update—Radiology Scientific Expert Panel (2020, background): “The betacoronavirus was first reported in December 2019 in Wuhan, China.”
AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data (2020, neutral): “Besides, Electronic Health Record (EHRs), Computerized Tomography (CT) scans [19 – 21], Chest X-rays (CRRs, see Fig.”
Temporal Changes of CT Findings in 90 Patients with COVID-19 Pneumonia: A Longitudinal Study (2020, background): “Several other studies had also reported the sensitivities, with the percentages varying from 44% to 98%(18-21).”
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans (2020, background): “Several studies [1] have shown that computed tomography (CT) scans manifest clear radiological ﬁndings of COVID-19 patients and are promising in serving as a more efﬁcient and accessible testing manner due to the wide availability of CT devices that can generate results at a fast speed.”