Clinical and Radiologic Findings of COVID-19 Pneumonia: South Korean Experience From Three Cases

Coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Several articles have so far reported on radiological findings of COVID-19 pneumonia. Herein, we present three cases of COVID-19 pneumonia in South Korea, and provide clinical information as well as chest radiograph and chest CT findings.

Role of Chest Radiographs and CT Scans and the Application of Artificial Intelligence in Coronavirus Disease 2019

코로나바이러스감염증-19 (coronavirus disease 2019; 이하 COVID-19)는 전 세계적 대유행 질환으로 인류 보건을 위협하고 있다. 흉부 CT 및 흉부X선사진은 COVID-19의 표준 진단검사인 역전사 중합효소 연쇄반응에 더하여 COVID-19 진단 및 중증도 평가에서 중요한 역할을 하고 있다. 본 종설에서는 흉부 CT 및 흉부X선사진의 COVID-19 폐렴에 대한 현재 역할에 대하여 살펴보고 인공지능을 적용한 대표적 초기 연구들과 저자들의 경험을 소개함으로써 향후 활용가치에 대해 살펴보고자 한다.

Dynamic CT assessment of disease change and prognosis of patients with moderate COVID-19 pneumonia

OBJECTIVES

To assess prognosis or dynamic change from initial diagnosis until recovery of the patients with moderate coronavirus disease (COVID-19) pneumonia using chest CT images.

MATERIALS AND METHODS

In this retrospective study, 33 patients (18 men, 15 women; median age, 49.0 years) with confirmed with moderate COVID-19 pneumonia in a multicenter hospital were included. The patients underwent at least four chest non-contrast-enhanced computed tomography (CT) scans at approximately 5-day intervals. We analyzed the clinical and CT characteristics of the patients. Moreover, the total CT score and the sum of lung involvement were determined for every CT scan.

RESULTS

The most widespread presenting symptoms were fever (32/33, 97.0%) and cough (17/33, 51.5%), which were often accompanied by decreased lymphocyte count (15/33, 45.5%) and increased C-reactive protein levels (18/33, 54.6%). Bilateral, multifocal ground glass opacities (32/33, 97.0%), consolidation (25/33, 75.8%), vascular thickening (23/33, 69.7%), and bronchial wall thickening (21/33, 63.6%) with peripheral distribution were the most frequent CT findings during moderate COVID-19 pneumonia. In patients recovering from moderate COVID-19 pneumonia, four stages (stages 1-4) of evolution were identified on chest CT with average CT scores of 3.4±2.3, 6.0±4.4, 5.6±3.8, and 4.9±3.2, respectively, from the onset of symptoms. For most patients, the peak of average total CT score increased for approximately 8 days after the onset of symptoms, after which it decreased gradually. The mean CT score of all patients was 4.7 at the time of discharge.

CONCLUSION

The moderate COVID-19 pneumonia CT score increased rapidly in a short period of time initially, followed by a slow decline over a relatively long time. The peak of the course occurred in stage 2. Complete recovery of patients with moderate COVID-19 pneumonia with high mean CT score at the time of discharge requires longer time.

Clinical and computed tomographic (CT) images characteristics in the patients with COVID-19 infection: What should radiologists need to know?

OBJECTIVE

To evaluate the clinical and computed tomographic (CT) features in the patients with COVID-19 pneumonia confirmed by the real-time reverse transcriptase polymerase chain reaction (rRT-PCR) amplification of the viral DNA from a sputum sample.

MATERIAL AND METHODS

Clinical information and CT findings of a total of 14 patients with COVID-19 infection (age range, 12-83 years; females 6) were analyzed retrospectively. The clinical information includes the history of exposure, laboratory results, and the symptoms (such as fever, cough, headache, etc.); CT findings of chest include the extension and distribution of lesion, the ground-glass opacity (GGO), consolidation, bronchovascular enlarged, irregular linear appearances, pleural effusion, and lymphadenopathy.

RESULTS

Eight patients had the exposure history for recent travel to Wuhan of Hubei province (8/14, 57%), 6 had the exposure to patients with COVID-19 infection. Significant statistical differences were observed in lymphocyte percentage decreased and C-reactive protein elevated (p = 0.015). Seven patients had fever, 7 had cough, 2 had headache, 3 had fatigue, 1 had body soreness, 3 had diarrhea, and 2 had no obvious symptoms. In chest CT examination, 10 patients were positive (10/14, 71.43%). Among these patients, 9 had lesions involving both lungs (9/10, 90%), 8 had lesions involving 4 to 5 lobes (8/10, 80%). Most of lesions were distributed peripherally and the most significant lesions were observed in the right lower lobe in 9 patients (9/10, 90%). Nodules were observed in 5 patients (5/10, 50%); GGO, consolidation, and bronchovascular enlarged were shown in 9 patients (9/10, 90%); irregular linear appearances were revealed in 7 patients (7/10, 70%); and pleural effusions were exhibited in 2 patients (2/10, 20%). Last, no patients showed lymphadenopathy.

CONCLUSION

There were some typical CT features for diagnosis of COVID-19 pneumonia. The radiologists should know these CT findings and clinical information, which could help for accurate analysis in the patients with 2019 novel coronavirus infection.

KSR/KSTR Guidelines for the Use of Diagnostic Imaging for COVID-19

The Korean Society of Radiology and the Korean Society of Thoracic Radiology have prepared recommendations for the use of diagnostic imaging for COVID-19 in various clinical scenarios. We have tried to grasp the situation in the real world, aggregated opinions from the chest radiologists, and reviewed available references, in order to suggest the most reasonable recommendations possible at this moment. As circumstances change and new evidences emerge, the recommendations should immediately be modified accordingly.

Factors associated with disease severity and mortality among patients with COVID-19: A systematic review and meta-analysis

BACKGROUND

Understanding the factors associated with disease severity and mortality in Coronavirus disease (COVID-19) is imperative to effectively triage patients. We performed a systematic review to determine the demographic, clinical, laboratory and radiological factors associated with severity and mortality in COVID-19.

METHODS

We searched PubMed, Embase and WHO database for English language articles from inception until May 8, 2020. We included Observational studies with direct comparison of clinical characteristics between a) patients who died and those who survived or b) patients with severe disease and those without severe disease. Data extraction and quality assessment were performed by two authors independently.

RESULTS

Among 15680 articles from the literature search, 109 articles were included in the analysis. The risk of mortality was higher in patients with increasing age, male gender (RR 1.45, 95%CI 1.23-1.71), dyspnea (RR 2.55, 95%CI 1.88-2.46), diabetes (RR 1.59, 95%CI 1.41-1.78), hypertension (RR 1.90, 95%CI 1.69-2.15). Congestive heart failure (OR 4.76, 95%CI 1.34-16.97), hilar lymphadenopathy (OR 8.34, 95%CI 2.57-27.08), bilateral lung involvement (OR 4.86, 95%CI 3.19-7.39) and reticular pattern (OR 5.54, 95%CI 1.24-24.67) were associated with severe disease. Clinically relevant cut-offs for leukocytosis(>10.0 x109/L), lymphopenia(< 1.1 x109/L), elevated C-reactive protein(>100mg/L), LDH(>250U/L) and D-dimer(>1mg/L) had higher odds of severe disease and greater risk of mortality.

CONCLUSION

Knowledge of the factors associated of disease severity and mortality identified in our study may assist in clinical decision-making and critical-care resource allocation for patients with COVID-19.

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

COVID-19 gender susceptibility and outcomes: A systematic review

BACKGROUND

Epidemiological differences between men and women have been reported with regards to sepsis, influenza and severe coronavirus infections including SARS-CoV and MERS-CoV.

AIM

To systematically review the literature relating to men versus women on SARS-CoV-2 in order to seek differences in disease characteristics (e.g. infectivity, severity) and outcomes (e.g. mortality).

METHODS

We searched 3 electronic databases up or observational studies reporting differences between men and women in the SARS-CoV-2 disease characteristics stated. We identified and included 47 studies, reporting data for 21,454 patients mainly from China.

RESULTS

The unadjusted mortality rates of men were higher than those of women, with a mortality OR 0.51 [0.42, 0.61] (p<0.001) for women. The proportion of men presenting with severe disease and admitted to the intensive care unit (ICU) was also higher than that of women (OR 0.75 [0.60-0.93] p<0.001 and OR 0.45 [0.40-0.52] p<0.001 respectively). Adjusted analyses could not be conducted due to lack of data.

CONCLUSION

COVID-19 may be associated with worse outcomes in males than in females. However, until more detailed data are provided in further studies enabling adjusted analysis, this remains an unproven assumption.