scholarly journals A retrospective study of the initial chest CT imaging findings in 50 COVID-19 patients stratified by gender and age

2020 ◽  
Vol 28 (5) ◽  
pp. 875-884
Author(s):  
Qianbiao Gu ◽  
Xin Ouyang ◽  
An Xie ◽  
Xianzheng Tan ◽  
Jianbin Liu ◽  
...  
Keyword(s):  
Clinical Manifestations ◽  
Disease Diagnosis ◽  
Ct Images ◽  
Ct Imaging ◽  
Chest Ct ◽  
Elderly Group ◽  
Imaging Features ◽  
Imaging Findings ◽  
Gender And Age ◽  
Honeycomb Pattern

OBJECTIVE: To retrospectively analyze and stratify the initial clinical features and chest CT imaging findings of patients with COVID-19 by gender and age. METHODS: Data of 50 COVID-19 patients were collected in two hospitals. The clinical manifestations, laboratory examination and chest CT imaging features were analyzed, and a stratification analysis was performed according to gender and age [younger group: <50 years old, elderly group ≥50 years old]. RESULTS: Most patients had a history of epidemic exposure within 2 weeks (96%). The main clinical complaints are fever (54%) and cough (46%). In chest CT images, ground-glass opacity (GGO) is the most common feature (37/38, 97%) in abnormal CT findings, with the remaining 12 patients (12/50, 24%) presenting normal CT images. Other concomitant abnormalities include dilatation of vessels in lesion (76%), interlobular thickening (47%), adjacent pleural thickening (37%), focal consolidation (26%), nodules (16%) and honeycomb pattern (13%). The lesions were distributed in the periphery (50%) or mixed (50%). Subgroup analysis showed that there was no difference in the gender distribution of all the clinical and imaging features. Laboratory findings, interlobular thickening, honeycomb pattern and nodules demonstrated remarkable difference between younger group and elderly group. The average CT score for pulmonary involvement degree was 5.0±4.7. Correlation analysis revealed that CT score was significantly correlated with age, body temperature and days from illness onset (p < 0.05). CONCLUSIONS: COVID-19 has various clinical and imaging appearances. However, it has certain characteristics that can be stratified. CT plays an important role in disease diagnosis and early intervention.

scholarly journals Key Points of Clinical and CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV) Imported Pneumonia Based On 21 Cases Analysis

2020 ◽  
Author(s):  
Z.F. Xu ◽  
W.X. Wu ◽  
Y.B. Jin ◽  
A.Z. Pan
Keyword(s):  
Clinical Manifestations ◽  
Ct Imaging ◽  
Chest Ct ◽  
Imaging Features ◽  
Shortness Of Breath ◽  
Multiple Lesions ◽  
Reticular Pattern ◽  
Severe Group ◽  
Novel Coronavirus ◽  
Ct Features

AbstractBackground and ObjectiveWHO Director-General declared that the 2019-nCoV outbreak constitutes a Public Health Emergency of International Concern,and the outbreak is still on-going.Chest CT had been a key component of the diagnostic workup for patients with suspected infection. In this retrospective study, we attempt to summarize and analyze the chest CT features of 2019-nCov infections, and to identify the typical features to improved the diagnostic accuracy of new coronavirus pneumonia (NCP).MethodsChest CT scans and Clinical data of 21 patients confirmed NCP in our hospital were enrolled.These patients were divided into mild and sever group according to clinical manifestations described by the 6th clinical practice guideline of NCP in China. Main clinical and chest CT features were analyzed and identify.ResultsFever (85.7%) and cough (80.9%) were the two main symptoms of NCP patients.More significantly higher incidence (85.7%) of shortness of breath in the severe cases. Multiple lesions in both lungs and with incidence of GGO(100%),vascular enlargement (76.5%) and cobblestone/reticular pattern(70.6%) were the major feature.The incidence of consolidation, mixed pattern and vascular enlargement features were up to 100% in the severe group, significantly higher than that of patients in mild group. In addition, the incidence of air-bronchogram, dilated bronchi with thickened wall and fibrosis in the severe group was significantly higher than that in the mild group.ConclusionsFever and cough are the typical clinical features of NCP patients, and chest CT mainly manifested as multiple lesions in both lungs, often accompanied by GGO, vascular enlargement and cobblestone/reticular pattern.Changes in these main CT features can indicate development of the diseaseSummary2019 novel Coronavirus (2019-nCov) had typical clinical manifestations (fever and cough), and presented with characteristic chest CT imaging features (multiple lesions in both lungs, often accompanied by GGO, vascular enlargement and cobblestone/reticular pattern), which are helpful to the radiologist in the early detection and diagnosis of this emerging global health emergency. In addition, changes in these main CT features can indicate development of the disease.HighlightsFever (85.7%) and cough (80.9%) were the two main symptoms of NCP patients.The incidence of shortness of breath was 85.7% in the severe cases, significantly higher than 21.4% in the mild cases.Multiple lesions in both lungs and with incidence of GGO (100%), vascular enlargement (76.5%) and cobblestone/reticular pattern (70.6%) were the major features of NCP patients. 85.7% of cases in serve group displayed 4-5 lobes were involved simultaneously.Changes in these main CT imaging features can indicate development of the disease. About 19.1% of patients (4 of 21) presented with a normal CT.

scholarly journals COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

2020 ◽  
Vol 7 ◽  
Author(s):  
Hayden Gunraj ◽  
Linda Wang ◽  
Alexander Wong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Ct Images ◽  
Ct Imaging ◽  
Chest Ct ◽  
Deep Convolutional Neural Network ◽  
Screening Tools ◽  
Screening Methods ◽  
Rt Pcr

The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients have worsening respiratory status or developing complications that require expedited care, or patients are suspected to be COVID-19-positive but have negative RT-PCR test results. Early studies on CT-based screening have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, in this study we introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach. Additionally, we introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation comprising 104,009 images across 1,489 patient cases. Furthermore, in the interest of reliability and transparency, we leverage an explainability-driven performance validation strategy to investigate the decision-making behavior of COVIDNet-CT, and in doing so ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images. Both COVIDNet-CT and the COVIDx-CT dataset are available to the general public in an open-source and open access manner as part of the COVID-Net initiative. While COVIDNet-CT is not yet a production-ready screening solution, we hope that releasing the model and dataset will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.

scholarly journals Non-neoplastic conditions mimicking peritoneal carcinomatosis at CT imaging

2020 ◽  
Vol 93 (1113) ◽  
pp. 20200401
Author(s):  
Mohab M Elmohr ◽  
Khaled M Elsayes ◽  
Perry J Pickhardt
Keyword(s):  
Peritoneal Carcinomatosis ◽  
Ct Imaging ◽  
Imaging Features ◽  
Imaging Findings ◽  
Abdominal Ct ◽  
Cross Sectional ◽  
Specific Patient ◽  
Cross Sectional Imaging ◽  
Sectional Imaging ◽  
General Appearance

The general appearance of peritoneal carcinomatosis at abdominal CT and other cross-sectional imaging modalities consists of varying amounts of peritoneal-based soft tissue implants (mass-forming or infiltrative), peritoneal fluid, and occasionally calcification. However, a wide variety of common and uncommon neoplastic and non-neoplastic conditions can closely mimic peritoneal carcinomatosis at imaging. Neoplastic mimics of peritoneal carcinomatosis include primary peritoneal and sub peritoneal tumors, as well as peritoneal lymphomatosis and sarcomatosis. Broad categories of non-neoplastic mimics of peritoneal carcinomatosis include tumor-like conditions, systemic processes, atypical infections, and fat-based conditions. For many entities, suggestive or specific patient information and/or CT imaging findings exist that may allow the radiologist to narrow the differential diagnosis. In this article, we review the salient clinical and cross-sectional imaging features of non-neoplastic mimics of peritoneal carcinomatosis and malignancy, with emphasis on the CT findings and the additional clues that may suggest the correct benign diagnosis.

scholarly journals Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation

2020 ◽  
Author(s):  
A. Amyar ◽  
R. Modzelewski ◽  
S. Ruan
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Ct Imaging ◽  
Chest Ct ◽  
The Novel ◽  
Imaging Analysis ◽  
Proposed Model ◽  
Fast Spreading ◽  
Novel Coronavirus ◽  
Deep Learning Model

ABSTRACTThe fast spreading of the novel coronavirus COVID-19 has aroused worldwide interest and concern, and caused more than one million and a half confirmed cases to date. To combat this spread, medical imaging such as computed tomography (CT) images can be used for diagnostic. An automatic detection tools is necessary for helping screening COVID-19 pneumonia using chest CT imaging. In this work, we propose a multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Our motivation is to leverage useful information contained in multiple related tasks to help improve both segmentation and classification performances. Our architecture is composed by an encoder and two decoders for reconstruction and segmentation, and a multi-layer perceptron for classification. The proposed model is evaluated and compared with other image segmentation and classification techniques using a dataset of 1044 patients including 449 patients with COVID-19, 100 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.78 for the segmentation and an area under the ROC curve higher than 93% for the classification.

scholarly journals Clinical characteristics and chest CT imaging features of critically ill COVID-19 patients

2020 ◽  
Vol 30 (11) ◽  
pp. 6151-6160 ◽  
Author(s):  
Nan Zhang ◽  
Xunhua Xu ◽  
Ling-Yan Zhou ◽  
Gang Chen ◽  
Yu Li ◽  
...  
Keyword(s):  
Critically Ill ◽  
Clinical Characteristics ◽  
Ct Imaging ◽  
Chest Ct ◽  
Imaging Features

scholarly journals Semi-quantitative analysis for the dynamic chest CT imaging features from onset to recovery in severe and critical COVID-19

2020 ◽  
Vol 7 (3) ◽  
pp. 114-122
Author(s):  
Ruxiu Liu ◽  
Chaoqi Lei ◽  
Xiang Liao ◽  
Shan Shi ◽  
Jun Li ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Ct Imaging ◽  
Chest Ct ◽  
Imaging Features
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