CT Scans of Patients with 2019 Novel Coronavirus (COVID-19) Pneumonia

Detection method of viral pneumonia imaging features based on CT scan images in COVID-19 case study

This study aims to automatically analyze and extract abnormalities in the lung field due to Coronavirus Disease 2019 (COVID-19). Types of abnormalities that can be detected are Ground Glass Opacity (GGO) and consolidation. The proposed method can also identify the location of the abnormality in the lung field, that is, the central and peripheral lung area. The location and type of these abnormalities affect the severity and confidence level of a patient suffering from COVID-19. The detection results using the proposed method are compared with the results of manual detection by radiologists. From the experimental results, the proposed system can provide an average error of 0.059 for the severity score and 0.069 for the confidence level. This method has been implemented in a web-based application for general users.•A method to detect the appearance of viral pneumonia imaging features, namely Ground Glass Opacity (GGO) and consolidation on the chest Computed Tomography (CT) scan images.•This method can separate the lung field to the right lung and the left lung, and it also can identify the detected imaging feature's location in the central or peripheral of the lung field.•Severity level and confidence level of the patient's suffering are measured.

Dynamic white matter changes in recovered COVID-19 patients: a two-year follow-up study

Background and purpose: Long COVID with regard to the neurological system deserves more attention, as a surge of treated patients are being discharged from the hospital. As the dynamic changes in white matter after two years remain unknown, this characteristic was the focus of this study. Methods: We investigated 17 recovered COVID-19 patients at two years after discharge. Diffusion tensor imaging, neurite orientation dispersion and density imaging were performed to identify white matter integrity and changes from one to two years after discharge. Data for 13 revisited healthy controls were collected as a reference. Subscales of the Wechsler Intelligence scale were used to assess cognitive function. Repeated-measures ANOVA was used to detect longitudinal changes in 17 recovered COVID-19 patients and 13 healthy controls after one-year follow-up. Correlations between diffusion metrics, cognitive function, and other clinical characteristics (i.e., inflammatory factors) were also analyzed. Results: Longitudinal analysis showed the recovery trends of large-scale brain regions, with small-scale brain region deterioration from one year to two years after SARS-CoV-2 infection. However, persistent white matter abnormalities were noted at two years after discharge. Longitudinal changes of cognitive function showed no group difference. But cross-sectional cognitive difference between recovered COVID-19 patients and revisited HCs was detected. Inflammation levels in the acute stage correlated positively with white matter abnormalities and negatively with cognitive function. Moreover, the more abnormal the white matter was at two years, the greater was the cognitive deficit present. Conclusion: Recovered COVID-19 patients showed longitudinal recovery trends of white matter. But also had persistent white matter abnormalities at two years after discharge. Inflammation levels in the acute stage may be considered predictors of cognition and white matter integrity, and the white matter microstructure acts as a biomarker of cognitive function in recovered COVID-19 patients. These findings provide an objective basis for early clinical intervention.

Comparison of chest CT features between progressive and nonprogressive patients with COVID-19 pneumonia: A meta-analysis

OBJECTIVE

The aim of this study was to compare the radiographic features of patients with progressive and nonprogressive coronavirus disease 2019 (COVID-19) pneumonia.

METHODS

PubMed, Embase, and Cochrane Library databases were searched from January 1, 2020, to February 28, 2022, by using the keywords: "COVID-19", "novel Coronavirus", "2019-novel coronavirus", "CT", "radiology" and "imaging". We summarized the computed tomography manifestations of progressive and nonprogressive COVID-19 pneumonia. The meta-analysis was performed using the Stata statistical software version 16.0.

RESULTS

A total of 10 studies with 1092 patients were included in this analysis. The findings of this meta-analysis indicated that the dominating computed tomography characteristics of progressive patients were a crazy-paving pattern (odds ratio [OR] = 2.10) and patchy shadowing (OR = 1.64). The dominating lesions distribution of progressive patients were bilateral (OR = 11.62), central mixed subpleural (OR = 1.37), and central (OR = 1.36). The other dominating lesions of progressive patients were pleura thickening (OR = 2.13), lymphadenopathy (OR = 1.74), vascular enlargement (OR = 1.39), air bronchogram (OR = 1.29), and pleural effusion (OR = 1.29). Two patterns of lesions showed significant links with the progression of disease: nodule (P = .001) and crazy-paving pattern (P = .023). Four lesions distribution showed significant links with the progression of disease: bilateral (P = .004), right upper lobe (P = .003), right middle lobe (P = .001), and left upper lobe (P = .018).

CONCLUSION

Nodules, crazy-paving pattern, and/or new lesions in bilateral, upper and middle lobe of right lung, and lower lobe of left lung may indicate disease deterioration. Clinicians should formulate or modify treatment strategies in time according to these specific conditions.

Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images

Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency during the worldwide outbreak. However, radiologists have to distinguish COVID-19 pneumonia from other pneumonia in a large number of CT scans, which is tedious and inefficient. Thus, it is urgently and clinically needed to develop an efficient and accurate diagnostic tool to help radiologists to fulfill the difficult task. In this study, we proposed a deep supervised autoencoder (DSAE) framework to automatically identify COVID-19 using multi-view features extracted from CT images. To fully explore features characterizing CT images from different frequency domains, DSAE was proposed to learn the latent representation by multi-task learning. The proposal was designed to both encode valuable information from different frequency features and construct a compact class structure for separability. To achieve this, we designed a multi-task loss function, which consists of a supervised loss and a reconstruction loss. Our proposed method was evaluated on a newly collected dataset of 787 subjects including COVID-19 pneumonia patients, other pneumonia patients, and normal subjects without abnormal CT findings. Extensive experimental results demonstrated that our proposed method achieved encouraging diagnostic performance and may have potential clinical application for the diagnosis of COVID-19.

Computational studies suggest compounds restoring function of p53 cancer mutants can bind SARS-CoV-2 spike protein

It is reasonable to think that cancer patients undergoing chemotherapy or immunotherapy may have a more aggressive course if they are positive for the novel coronavirus disease. Their compulsive condition requires investigation into effective drugs. We applied computational techniques to a series of compounds known for restoring the function of p53 cancer mutant p53^R175H and p53^G245S. Two potent inhibitors, 1-(3-chlorophenyl)-3-(1, 3 -thiazol-2-yl) urea (CTU, PubChem NSC321792) with the highest binding affinity -6.92 kcal/mol followed by a thiosemicarbazone compound N'-(1-(Pyridin-2-yl)ethylidene) azetidine - 1 -carbothiohydrazide (NPC, PubChem NSC319726) with -6.75 kcal/mol were subjected to Molecular Dynamics simulation with receptor binding domain (RBD) and compared with control ligand dexamethasone. In particular, CTU adheres to pocket 1 with an average free energy of binding -21.65 ± 2.89 kcal/mol at the RBD - angiotensin-converting enzyme 2 binding region with the highest frequency of amino acid residues after reaching a local equilibrium in 100 ns MD simulation trajectory. A significant enthalpy contribution from the independent simulations unfolds the possibility of dual binding sites for NPC as shifted pocket 1 (-15.59 ± 5.98 kcal/mol) and pocket 2 (-18.90 ± 5.02 kcal/mol). The obtained results for these two compounds are in good agreement with dexamethasone (-18.45 ± 2.42 kcal/mol). Taken together our findings could facilitate the discovery of small molecules that restore the function of p53 cancer mutants newly against COVID-19 in cancer patients.

Investigating of the role of CT scan for cancer patients during the first wave of COVID-19 pandemic

Introduction

Amidst this current COVID-19 pandemic, we undertook this systematic review to determine the role of medical imaging, with a special emphasis on computed tomography (CT), on guiding the care and management of oncologic patients.

Material and Methods

Study selection focused on articles from 01/02/2020 to 04/23/2020. After removal of irrelevant articles, all systematic or non-systematic reviews, comments, correspondence, editorials, guidelines and meta-analysis and case reports with less than 5 patients were also excluded. Full-text articles of eligible publications were reviewed to select all imaging-based publications, and the existence or not of an oncologic population was reported for each publication. Two independent reviewers collected the following information: ( 1) General publication data; (2) Study design characteristics; (3) Demographic, clinical and pathological variables with percentage of cancer patients if available; (4) Imaging performances. The sensitivity and specificity of chest CT (C-CT) were pooled separately using a random-effects model. The positive predictive value (PPV) and negative predictive value (NPV) of C-CT as a test was estimated for a wide range of disease prevalence rates.

Results

A total of 106 publications were fully reviewed. Among them, 96 were identified to have extractable data for a two-by-two contingency table for CT performance. At the end, 53 studies (including 6 that used two different populations) were included in diagnosis accuracy analysis (N = 59). We identified 53 studies totaling 11,352 patients for whom the sensitivity (95CI) was 0.886 (0.880; 0.894), while specificity remained low: in 93% of cases (55/59), specificity was ≤ 0.5. Among all the 106 reviewed studies, only 7 studies included oncologic patients and were included in the final analysis for C-CT performances. The percentage of patients with cancer in these studies was 0.3% (34/11352 patients), lower than the global prevalence of cancer. Among all these studies, only 1 (0.9%, 1/106) reported performance specifically in a cohort of cancer patients, but it however only reported true positives.

Discussion

There is a concerning lack of COVID-19 studies involving oncologic patients, showing there is a real need for further investigation and evaluation of the performance of the different medical imaging modalities in this specific patient population.

Deep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction

The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to health-care organizations worldwide. To combat the global crisis, the use of thoracic imaging has played a major role in diagnosis, prediction, and management for COVID-19 patients with moderate to severe symptoms or with evidence of worsening respiratory status. In response, the medical image analysis community acted quickly to develop and disseminate deep learning models and tools to meet the urgent need of managing and interpreting large amounts of COVID-19 imaging data. This review aims to not only summarize existing deep learning and medical image analysis methods but also offer in-depth discussions and recommendations for future investigations. We believe that the wide availability of high-quality, curated, and benchmarked COVID-19 imaging data sets offers the great promise of a transformative test bed to develop, validate, and disseminate novel deep learning methods in the frontiers of data science and artificial intelligence. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 24 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Serum Lactate Dehydrogenase Level as a Prognostic Factor for COVID-19: A Retrospective Study Based on a Large Sample Size

Background: In this study, we investigated the relationship between serum lactate dehydrogenase (LDH) level and disease progression and prognosis of patients with COVID-19.

Methods: We retrospectively reviewed the information of 1,751 patients with COVID-19 from Leishenshan Hospital in Wuhan, China. Univariate and multivariate Cox regression analyses as well as Logistics regression analyses, and Kaplan-Meier curves were used to determine the association between LDH levels and the prognosis of COVID-19 patients.

Results: LDH was an independent risk factor for in-hospital death no matter it was taken as classified variable and continuous variable (all P = 0.001) but not for severe or critical illness status. The Kaplan-Meier curves for LDH level showed that an elevated level of LDH was associated with in-hospital death.

Conclusions: In patients with COVID-19, the increased LDH level is associated with a higher risk of negative clinical prognosis and higher mortality. This will provide a reference for clinicians and researchers to understand, diagnose, and treat patients with COVID-19. Further prospective studies with larger sample sizes are needed to verify these findings.

COVID-CT-Mask-Net: prediction of COVID-19 from CT scans using regional features

We present COVID-CT-Mask-Net model that predicts COVID-19 in chest CT scans. The model works in two stages: in the first stage, Mask R-CNN is trained to localize and detect two types of lesions in images. In the second stage, these detections are fused to classify the whole input image. To develop the solution for the three-class problem (COVID-19, Common Pneumonia and Control), we used the COVIDx-CT data split derived from the dataset of chest CT scans collected by China National Center for Bioinformation. We use 3000 images (about 5% of the train split of COVIDx-CT) to train the model. Without any complicated data normalization, balancing and regularization, and training only a small fraction of the model's parameters, we achieve a 9 0 . 8 0 % COVID-19 sensitivity, 9 1 . 6 2 % Common Pneumonia sensitivity and 9 2 . 1 0 % true negative rate (Control sensitivity), an overall accuracy of 9 1 . 6 6 % and F1-score of 9 1 . 5 0 % on the test data split with 21192 images, bringing the ratio of test to train data to 7.06. We also establish an important result that regional predictions (bounding boxes with confidence scores) detected by Mask R-CNN can be used to classify whole images. The full source code, models and pretrained weights are available on https://github.com/AlexTS1980/COVID-CT-Mask-Net.

Clustering cases of Chlamydia psittaci pneumonia mimicking COVID-19 pneumonia

BACKGROUND

The onset symptoms of people infected by Chlamydia psittaci can mimic the coronavirus disease 2019 (COVID-19). However, the differences in laboratory tests and imaging features between psittacosis and COVID-19 remain unknown.

AIM

To better understand the two diseases and then make an early diagnosis and treatment.

METHODS

Six patients from two institutions confirmed as psittacosis by high-throughput genetic testing and 31 patients confirmed as COVID-19 were retrospectively included. The epidemiology, clinical characteristics, laboratory tests and computed tomography (CT) imaging features were collected and compared between the two groups. The follow-up CT imaging findings of patients with psittacosis were also investigated.

RESULTS

The white blood cell count (WBC), neutrophil count and calcium were more likely to be decreased in patients with COVID-19 but were increased in patients with psittacosis (all P = 0.000). Lymphocyte count and platelet count were higher in patients with psittacosis than in those with COVID-19 (P = 0.044, P = 0.035, respectively). Lesions in patients with psittacosis were more likely to be unilateral (P = 0.001), involve fewer lung lobes (P = 0.006) and have pleural effusions (P = 0.002). Vascular enlargement was more common in patients with COVID-19 (P = 0.003). Consolidation in lung CT images was absorbed in all 6 patients.

CONCLUSION

Psittacosis has the potential for human-to-human transmission. Patients with psittacosis present increased WBC count and neutrophil count and have specific CT imaging findings, including unilateral distribution, less involvement of lung lobes and pleural effusions, which might help us to differentiate it from COVID-19.

DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images

BACKGROUND AND OBJECTIVE

Given that the novel coronavirus disease 2019 (COVID-19) has become a pandemic, a method to accurately distinguish COVID-19 from community-acquired pneumonia (CAP) is urgently needed. However, the spatial uncertainty and morphological diversity of COVID-19 lesions in the lungs, and subtle differences with respect to CAP, make differential diagnosis non-trivial.

METHODS

We propose a deep represented multiple instance learning (DR-MIL) method to fulfill this task. A 3D volumetric CT scan of one patient is treated as one bag and ten CT slices are selected as the initial instances. For each instance, deep features are extracted from the pre-trained ResNet-50 with fine-tuning and represented as one deep represented instance score (DRIS). Each bag with a DRIS for each initial instance is then input into a citation k-nearest neighbor search to generate the final prediction. A total of 141 COVID-19 and 100 CAP CT scans were used. The performance of DR-MIL is compared with other potential strategies and state-of-the-art models.

RESULTS

DR-MIL displayed an accuracy of 95% and an area under curve of 0.943, which were superior to those observed for comparable methods. COVID-19 and CAP exhibited significant differences in both the DRIS and the spatial pattern of lesions (p<0.001). As a means of content-based image retrieval, DR-MIL can identify images used as key instances, references, and citers for visual interpretation.

CONCLUSIONS

DR-MIL can effectively represent the deep characteristics of COVID-19 lesions in CT images and accurately distinguish COVID-19 from CAP in a weakly supervised manner. The resulting DRIS is a useful supplement to visual interpretation of the spatial pattern of lesions when screening for COVID-19.

The Differences and Changes of Semi-Quantitative and Quantitative CT Features of Coronavirus Disease 2019 Pneumonia in Patients With or Without Smoking History

Objective: To assess CT features of COVID-19 patients with different smoking status using quantitative and semi-quantitative technologies and to investigate changes of CT features in different disease states between the two groups.

Methods: 30 COVID-19 patients with current smoking status (29 men, 1 woman) admitted in our database were enrolled as smoking group and 56 COVID-19 patients without smoking history (24 men, 32 women) admitted during the same period were enrolled as a control group. Twenty-seven smoking cases and 55 control cases reached recovery standard and were discharged. Initial and follow-up CT during hospitalization and follow-up CT after discharge were acquired. Thirty quantitative features, including the ratio of infection volume and visual-assessed interstitial changes score including total score, score of ground glass opacity, consolidation, septal thickening, reticulation and honeycombing sign, were analyzed.

Results: Initial CT images of the smoking group showed higher scores of septal thickening [4.5 (0–5) vs. 0 (0–4), p = 0.001] and reticulation [0 (0–5.25) vs 0 (0–0), p = 0.001] as well as higher total score [7 (5–12.25) vs. 6 (5–7), p = 0.008] with statistical significance than in the control group. The score of reticulation was higher in the smoking group than in the control group when discharged [0.89 (0–0) vs. 0.09 (0–0), p = 0.02]. The score of septal thickening tended to be higher in the smoking group than the control group [4 (0–4) vs. 0 (0–4), p = 0.007] after being discharged. Quantitative CT features including infection ratio of whole lung and left lung as well as infection ratio within HU (−750, −300) and within HU (−300, 49) were higher in the control group of initial CT with statistical differences. The infection ratio of whole lung and left lung, infection ratio within HU (−750), and within HU (−750, −300) were higher in the control group with statistical differences when discharged. This trend turned adverse after discharge and the values of quantitative features were generally higher in the smoking group than in the control group without statistical differences.

Conclusions: Patients with a history of smoking presented more severe interstitial manifestations and more residual lesion after being discharged. More support should be given for COVID-19 patients with a smoking history during hospitalization and after discharge.

Risk factors for recurrent positive results of the nucleic acid amplification test for COVID-19 patients: a retrospective study

Positive retests of COVID-19 represent a public health concern because of the increased risk of transmission. This study explored whether factors other than the nucleic acid amplification test (NAAT) contribute to positive retest results. Patients with COVID-19 admitted to the Guanggu district of the Hubei Maternal and Child Health Hospital between February 17 and March 28, 2020, were retrospectively included. The patients were grouped into the negative (n = 133) and positive (n = 51) retest groups. The results showed that the proportion of patients presenting with cough was higher (P < 0.001) and the proportion of patients with dyspnea was lower (P = 0.018) in the positive than in the negative retest group. The positive retest group showed shorter durations between symptom onset and hospitalization (P < 0.001) and symptom onset and the first positive NAAT (P = 0.033). The positive retest group had higher basophil counts (P = 0.023) and direct bilirubin (P = 0.032) and chlorine concentrations (P = 0.023) but lower potassium concentrations (P = 0.001) than the negative retest group. Multivariable regression analysis showed that coughing (OR = 7.59, 95% CI 2.28–25.32, P = 0.001) and serum chloride concentrations (OR = 1.38, 95% CI 1.08–1.77, P = 0.010) were independently associated with a positive retest result. Coughing and serum chloride concentrations were independent risk factors for positive NAAT retest results. Patients with a hospital stay of < 2 weeks or a short incubation period should stay in isolation and be monitored to reduce transmission. These results could help identify patients who require closer surveillance.

Efficacy and safety assessment of severe COVID-19 patients with Chinese medicine: A retrospective case series study at early stage of the COVID-19 epidemic in Wuhan, China

ETHNOPHARMACOLOGICAL RELEVANCE

The coronavirus disease 2019 (COVID-19) has formed a global pandemic since late 2019. Benefitting from the application experience of Chinese Medicine (CM) for influenza and SARS, CM has been used to save patients at the early stage of COVID-19 outbreak in China.

AIM OF THE STUDY

In order to evaluate the efficacy and safety of CM, and compare with Western Medicine (WM) for COVID-19, we conducted a retrospective case series study based on the patients in Wuhan Jinyintan Hospital, Wuhan, China.

METHODS

The inclusion and exclusion criteria of data extraction were set for this retrospective study. All patients who were admitted by the Wuhan Jinyintan Hospital between January 17th and February 25th 2020 were considered. In addition, patients enrolled met the severe defined by the guidelines released by the National Health Commission of the People's Republic of China. In these cases included in the study, CM or WM treatment was selected according to the wishes of the patients at the beginning of hospitalization. The patients in CM group were treated with Huashi Baidu granule (137 g po, bid) combined with the injections of Xiyanping (100 mg iv, bid), Xuebijing (100 ml iv, bid) and Shenmai (60 ml iv, qd) according to the syndrome of epidemic toxin blocking the lung in the theory of Traditional Chinese Medicine. The WM group received antiviral therapy (including abidor capsule 0.2 g po, tid; Lopinavir-Ritonavir tablets, 500 mg po, bid), antibiotics (such as cefoperazone 2 g iv, bid; moxifloxacin hydrochloride tablets, 0.4 g po, qd) or corticosteroid therapy (such as methylprednisolone succinate sodium 40 mg iv, qd; prednisone, 30 mg po, qd). In addition, patients in both groups received routine supportive treatment, including oxygen inhalation, symptomatic therapy, and/or human intravenous immunoglobulin, and/or serum albumin, and treatment for underlying diseases. The clinical outcomes were evaluated based on changes related with clinical manifestations, computer tomography (CT) scan images, and laboratory examinations before and after the treatment.

RESULTS

55 severe COVID-19 patients, with 23 in CM group and 32 in WM group, were included for analyzed. There was no case of death, being transferred to ICU, or receiving invasive mechanical ventilation in two groups during hospitalization. The median time of SARS-CoV-2 RNA clearance in CM and WM group were 12 days and 15.5 days respectively, the ratio of nucleic acid negative conversion of CM group at different follow-up time points was significantly higher than that of WM group (HR: 2.281, P = 0.018). Further, the chest CT imaging showed more widely lung lesion opacity absorbed in the CM group. The high sensitivity C-reactive protein and serum ferritin decreased significantly in the CM group (P<0.05). There was no significant difference in adverse events in terms of liver function and renal function between the two groups.

CONCLUSION

Based on this retrospective analysis from Wuhan Jinyintan Hospital, CM has better effects in SARS-CoV-2 RNA clearance, promoting lung lesion opacity absorbed and reducing inflammation in severe COVID-19 patients, which is effective and safe therapy for treating severe COVID-19 and reducing mortality.

Chest Radiological Findings and Clinical Characteristics of Laboratory-Confirmed COVID-19 Patients from Saudi Arabia

Background

Coronavirus disease 2019 (COVID-19) is a viral respiratory disease that first emerged in China in December 2019 and quickly spread worldwide. As the prevalence of COVID-19 increases, radiological examination is becoming an essential diagnostic tool for identifying and managing the disease’s progression. Therefore, we aimed to identify the chest imaging features and clinical characteristics of patients with laboratory-confirmed COVID-19 in Saudi Arabia.

Material/Methods

In this retrospective study, data of laboratory-confirmed COVID-19 patients were collected from 4 hospitals in Jeddah, Saudi Arabia. Their common clinical characteristics, as well as imaging features of chest X-rays and computed tomography (CT) images, were analyzed.

Results

A total of 297 patients with laboratory-confirmed COVID-19 who underwent chest imaging were investigated in this study. Of these patients, 77.9% were male and 22.2% were female. Their mean age was 48 years old. The most common clinical symptoms were fever (187 patients; 63%) and cough (174 patients; 58.6%). The predominant descriptive chest imaging findings were ground-glass opacities and consolidation. Locations of abnormalities were bilateral, mainly distributed peripherally, in the lower lung zones, and in the middle lung zones.

Conclusions

This study provides an understanding of the most common clinical and radiological features of patients with laboratory-confirmed COVID-19 in Saudi Arabia. The majority of COVID-19 patients in our study cohort had either stable or worse progression of lung lesions during follow-ups; thus, they presented moderate disease cases. Elderly males were more affected by COVID-19 than females, with fever and cough being the most common clinical symptoms.

Chest CT findings in patients with coronavirus disease 2019 (COVID-19): a comprehensive review

The objective of this review was to summarize the most pertinent CT imaging findings in patients with coronavirus disease 2019 (COVID-19). A literature search retrieved eligible studies in PubMed, EMBASE, Cochrane Library and Web of Science up to June 1, 2020. A comprehensive review of publications of the Chinese Medical Association about COVID-19 was also performed. A total of 84 articles with more than 5340 participants were included and reviewed. Chest CT comprised 92.61% of abnormal CT findings overall. Compared with real-time polymerase chain reaction result, CT findings has a sensitivity of 96.14% but a low specificity of 40.48% in diagnosing COVID-19. Ground glass opacity (GGO), pure (57.31%) or mixed with consolidation (41.51%) were the most common CT features with a majority of bilateral (80.32%) and peripheral (66.21%) lung involvement. The opacity might associate with other imaging features, including air bronchogram (41.07%), vascular enlargement (54.33%), bronchial wall thickening (19.12%), crazy-paving pattern (27.55%), interlobular septal thickening (42.48%), halo sign (25.48%), reverse halo sign (12.29%), bronchiectasis (32.44%), and pulmonary fibrosis (26.22%). Other accompanying signs including pleural effusion, lymphadenopathy and pericardial effusion were rare, but pleural thickening was common. The younger or early stage patients tended to have more GGOs, while extensive/multilobar involvement with consolidation was prevalent in the older or severe population. Children with COVID-19 showed significantly lower incidences of some ancillary findings than those of adults and showed a better performance on CT during follow up. Follow-up CT showed GGO lesions gradually decreased, and the consolidation lesions first increased and then remained relatively stable at 6-13 days, and then absorbed and fibrosis increased after 14 days. Chest CT imaging is an important component in the diagnosis, staging, disease progression and follow-up of patients with COVID-19.

Pharmacological interventions for COVID-19: a systematic review of observational studies and clinical trials

Introduction: Currently, there is no approved therapeutic entity for coronavirus disease 2019 (COVID-19) and clinicians are primarily relying on drug repurposing. However, findings across studies are widely disparate, making it difficult to draw firm conclusions. Since clinicians need accurate evidence to treat COVID-19, this manuscript systematically analyzed the published and ongoing studies evaluating the pharmacological interventions for COVID-19.Areas Covered: A systematic search of observational studies and Clinical Trials on the treatment and prevention of COVID-19 was performed by using various databases from inception to 2 December 2020.Expert Opinion: A total of 460 studies met the inclusion criteria. Of these, 37 were research studies, 386 were ongoing trials, and 37 were completed trials. Anti-virals, steroids, anti-malarial, plasma exchange, and monoclonal antibodies were the most common treatment modalities used alone or in combination in these studies. However, tocilizumab, plasma exchange, and steroids have shown significant improvements in patient's clinical and radiological status. Tocilizumab reported minimum hospital stay of 2 days along with maximum recovery and patient's stability rate. Existing literature demonstrate promising results of tocilizumab, plasma exchange, and steroids among COVID-19 patients. Nevertheless, these studies are accompanied by several methodological disparities which should be considered while interpreting the results.

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

COVID-19 and SARS-CoV-2 Variants: Current Challenges and Health Concern

The ongoing coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China, was triggered and unfolded quickly throughout the globe by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The new virus, transmitted primarily through inhalation or contact with infected droplets, seems very contagious and pathogenic, with an incubation period varying from 2 to 14 days. The epidemic is an ongoing public health problem that challenges the present global health system. A worldwide social and economic stress has been observed. The transitional source of origin and its transport to humans is unknown, but speedy human transportation has been accepted extensively. The typical clinical symptoms of COVID-19 are almost like colds. With case fatality rates varying from 2 to 3 percent, a small number of patients may experience serious health problems or even die. To date, there is a limited number of antiviral agents or vaccines for the treatment of COVID-19. The occurrence and pathogenicity of COVID-19 infection are outlined and comparatively analyzed, given the outbreak's urgency. The recent developments in diagnostics, treatment, and marketed vaccine are discussed to deal with this viral outbreak. Now the scientist is concerned about the appearance of several variants over the globe and the efficacy of the vaccine against these variants. There is a need for consistent monitoring of the virus epidemiology and surveillance of the ongoing variant and related disease severity.

Monitoring Coronavirus Disease 2019: A Review of Available Diagnostic Tools

Coronavirus disease 2019 (COVID-19) pneumonia is caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has rapidly become a global public health concern. As the new type of betacoronavirus, SARS-CoV-2 can spread across species and between populations and has a greater risk of transmission than other coronaviruses. To control the spread of SARS-CoV-2, it is vital to have a rapid and effective means of diagnosing asymptomatic SARS-CoV-2-positive individuals and patients with COVID-19, an early isolation protocol for infected individuals, and effective treatments for patients with COVID-19 pneumonia. In this review, we will summarize the novel diagnostic tools that are currently available for coronavirus, including imaging examinations and laboratory medicine by next-generation sequencing (NGS), real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) analysis, immunoassay for COVID-19, cytokine and T cell immunoassays, biochemistry and microbiology laboratory parameters in the blood of the patients with COVID-19, and a field-effect transistor-based biosensor of COVID-19. Specifically, we will discuss the effective detection rate and assay time for the rRT-PCR analysis of SARS-CoV-2 and the sensitivity and specificity of different antibody detection methods, such as colloidal gold and ELISA using specimen sources obtained from the respiratory tract, peripheral serum or plasma, and other bodily fluids. Such diagnostics will help scientists and clinicians develop appropriate strategies to combat COVID-19.

Health Protection of CT Radiographers During the Outbreak of COVID-19: Application of Automatic Positioning Technology for Relocatable CT in the Fang Cang Hospital

Background: To investigate the value of automatic positioning technology in improving the protection of radiographers in the relocatable CT room of a Fang Cang hospital during the outbreak of coronavirus disease 2019 (COVID-19).

Methods: The National Emergency Medical Team of our hospital assumed command of Wuchang Fang Cang Hospital and treated confirmed COVID-19 patients with mild symptoms. Relocatable CT was used to examine patients in this hospital. Automatic positioning technology was applied to avoid close contact between medical staff and patients and to protect medical staff more effectively.

Results: Seven hundred lung CT scans acquired from 269 patients were completed from February 17 to 26, 2020 with automatic positioning technology for relocatable CT in a Fang Cang hospital. All scans were conducted successfully using automatic positioning technology. All patients entered the scanning room from a separate door. All the position lines were accurate, and all images met the requirement for diagnosis of COVID-19, with satisfied quality. None of our medical staff had any close contact with patients.

Conclusion: Automatic positioning technology applied to relocatable CT can minimize the close contact between technologists and patients and effectively improve the protection of medical staff without sacrificing image quality.

Study on the prognosis predictive model of COVID-19 patients based on CT radiomics

Making timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set and 64 in the test set) diagnosed with COVID-19. Patients were divided into aggravation and improvement groups according to the disease progression. Three kinds of models were established, including the radiomics, clinical, and combined model. Receiver operating characteristic curves, decision curves, and Delong’s test were used to evaluate and compare the models. Our analysis showed that all the established prediction models had good predictive performance in predicting the progress and outcome of COVID-19.

Evaluating clinical characteristics studies produced early in the Covid-19 pandemic: A systematic review

OBJECTIVES

Clinical characterisation studies have been essential in helping inform research, diagnosis and clinical management efforts, particularly early in a pandemic. This systematic review summarises the early literature on clinical characteristics of patients admitted to hospital, and evaluates the quality of evidence produced during the initial stages of the pandemic.

METHODS

MEDLINE, EMBASE and Global Health databases were searched for studies published from January 1st 2020 to April 28th 2020. Studies which reported on at least 100 hospitalised patients with Covid-19 of any age were included. Data on clinical characteristics were independently extracted by two review authors. Study design specific critical appraisal tools were used to evaluate included studies: the Newcastle Ottawa scale for cohort and cross sectional studies, Joanna Briggs Institute checklist for case series and the Cochrane collaboration tool for assessing risk of bias in randomised trials.

RESULTS

The search yielded 78 studies presenting data on 77,443 people. Most studies (82%) were conducted in China. No studies included patients from low- and middle-income countries. The overall quality of included studies was low to moderate, and the majority of studies did not include a control group. Fever and cough were the most commonly reported symptoms early in the pandemic. Laboratory and imaging findings were diverse with lymphocytopenia and ground glass opacities the most common findings respectively. Clinical data in children and vulnerable populations were limited.

CONCLUSIONS

The early Covid-19 literature had moderate to high risk of bias and presented several methodological issues. Early clinical characterisation studies should aim to include different at-risk populations, including patients in non-hospital settings. Pandemic preparedness requires collection tools to ensure observational studies are methodologically robust and will help produce high-quality data early on in the pandemic to guide clinical practice and public health policy.

REVIEW REGISTRATION

Available at https://osf.io/mpafn.

Typical Covid-19 case with primary pneumomediastinum in a 37 year old male

We report the case of a 37-year-old man who was admitted to Baqiyatallah hospital in Tehran (Iran) for retrosternal pain, fever, fatigue, dyspnoea and severe non-productive cough. The patient was subsequently confirmed as positive for COVID-19 at real-time polymerase chain reaction (RT-PCR) test. Chest computed tomography (CT) revealed also the presence of pneumomediastinum. This case highlights the importance of chest CT imaging for COVID-19 pneumonia to detect co-existing conditions as pneumomediastinum.

Central nervous system manifestations in COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

At the end of December 2019, a novel respiratory infection, initially reported in China, known as COVID-19 initially reported in China, and later known as COVID-19, led to a global pandemic. Despite many studies reporting respiratory infections as the primary manifestations of this illness, an increasing number of investigations have focused on the central nervous system (CNS) manifestations in COVID-19. In this study, we aimed to evaluate the CNS presentations in COVID-19 patients in an attempt to identify the common CNS features and provide a better overview to tackle this new pandemic.

METHODS

In this systematic review and meta-analysis, we searched PubMed, Web of Science, Ovid, EMBASE, Scopus, and Google Scholar. Included studies were publications that reported the CNS features between 1 January 2020 and 20 April 2020. The data of selected studies were screened and extracted independently by four reviewers. Extracted data analyzed by using STATA statistical software. The study protocol registered with PROSPERO (CRD42020184456).

RESULTS

Of 2,353 retrieved studies, we selected 64 studies with 11,687 patients after screening. Most of the studies were conducted in China (58 studies). The most common CNS symptom of COVID-19 was headache (8.69%, 95%CI: 6.76%-10.82%), dizziness (5.94%, 95%CI: 3.66%-8.22%), and impaired consciousness (1.90%, 95%CI: 1.0%-2.79%).

CONCLUSIONS

The growing number of studies has reported COVID-19, CNS presentations as remarkable manifestations that happen. Hence, understanding the CNS characteristics of COVID-19 can help us for better diagnosis and ultimately prevention of worse outcomes.

Comparing the Value of Cystatin C and Serum Creatinine for Evaluating the Renal Function and Predicting the Prognosis of COVID-19 Patients

Background: Coronavirus disease- (COVID-19-) related renal function abnormality is associated with poor prognosis. However, the clinical significance of dynamic changes in renal function indicators has not been studied, and no studies have evaluated the renal function in COVID-19 patients by cystatin C. Objective: This study aimed to evaluate the effect of abnormal renal function on admission on prognosis of COVID-19 patients and the prognostic value of various renal function indicators. Methods: A total of 1,764 COVID-19 patients without a history of chronic kidney disease were categorized into two groups, an elevated cystatin C group and a normal cystatin C group, based on the results of renal function tests on admission. The clinical characteristics were compared between the two groups, and logistic or Cox regression analyses were performed to explore the associations between elevated cystatin C/serum creatinine levels and disease severity and survival. We also performed receiver operating characteristic (ROC) curve, Kaplan-Meier survival, and curve fitting analyses. Results: When adjusted for several significant clinical variables, elevated cystatin C levels on admission were independent predictors of disease severity (p < 0.001), and elevated creatinine levels were independent predictors of death (p = 0.020). Additionally, the ROC curve analysis shows that elevated cystatin C levels [area under the curve (AUC): 0.656] have a better predictive value for disease severity than elevated creatinine levels (AUC: 0.540). The survival curves of patients with elevated cystatin C/creatinine levels show a sharper decline than those of patients with normal cystatin C/creatinine levels (p < 0.001). The curve fitting analysis revealed that, compared to the flat curves of cystatin C and creatinine levels for patients who survived, the curves for patients who died kept rising, and cystatin C levels rose above the normal range earlier than creatinine. Conclusions: Elevated cystatin C, which occurs earlier than serum creatinine, is useful for the early detection of renal function abnormality and might have better predictive value for disease severity in COVID-19 patients, while elevated serum creatinine may have a better predictive value for risks of death.

The value of repeated CT in monitoring the disease progression in moderate COVID-19 pneumonia: A single-center, retrospective study

ABSTRACT

The role of thoracic CT (computerized tomography) in monitoring disease course of COVID-19 is controversial. The purpose of this study is to investigate the risk factors and predictive value of deterioration on repeatedly performed CT scan during hospitalization.All COVID-19 patients treated in our isolation ward, from January 22, 2020 to February 7, 2020, were reviewed. Patients included were categorized into RD (Radiological Deterioration) group or NRD (No Radiological Deterioration) group according to the manifestation on the CT routinely performed during the hospitalization. All clinical data and CT images were analyzed.Forty three patients were included in our study. All are moderate cases with at least 4 CT scans each. Eighteen (42.9%) patients had radiological deteriorations which were all identified in CT2 (the first CT after admission). Patients in RD group had lower leukocyte count (P = .003), lymphocyte count (P = .030), and higher prevalence (P = .012) of elevated C-reactive protein (CRP) at admission. NRD patients had a lower prevalence of reticulations (P = .034) on baseline CT (CT1, performed within 2 days before admission) and a longer duration between symptom onset and the time of CT2 (P < .01). There was no significant difference in hospital stay or fibrotic change on CT4 (follow-up CT scan performed 4 weeks after discharge) between 2 groups. Shorter duration between symptom onset and CT2 time (odds ratio [OR], 0.436; 95% confidence interval: 0.233-0.816; P < .01) and lower leukocyte count in baseline evaluation (OR, 0.316; 95% CI: 0.116-0.859; P < .05) were associated with increased odds of radiological deterioration on CT image during hospitalization.For moderate COVID-19 patients, the value of routinely performed CT during the treatment is limited. We recommend avoiding using CT as a routine monitor in moderate COVID-19 patients.

Clinical features and outcomes of adults with coronavirus disease 2019: A systematic review and pooled analysis of the literature

BACKGROUND

The 2019 coronavirus disease (COVID-19) has become a global pandemic and the published literature describing the virus has grown exponentially.

METHODS

We conducted a systematic review of the literature to identify the symptoms, comorbidities present, radiological features and outcomes for adults testing positive for COVID-19 admitted to hospital. The results across multiple studies were numerically pooled to yield total estimated.

RESULTS

A total of 45 studies were included in this review with 14 358 adult participants (average age 51 years, male 51%). The pooled findings suggest that the most common symptom among patients was fever (81.2%) followed by cough (62.9%), fatigue (38.0%) and anorexia/loss of appetite (33.7%). The comorbidities that were most prevalent among patients with the virus were hypertension (19.1%), cardiovascular disease (17.9%), endocrine disorder (9.3%) and diabetes (9.2%). Abnormal chest X-ray findings were present in 27.7% of patients and ground-glass opacity was demonstrated on chest computerized tomography in 63.0% of patients. The most frequent adverse outcomes were acute respiratory distress syndrome (27.4%), acute cardiac injury (16.2%) and acute kidney injury (12.6%). Death occurred in 8.2% of patients and 16.3% required intensive care admission and 11.7% had mechanical ventilation. Bacterial or secondary infections affected 8.5% of patients and 6.9% developed shock.

CONCLUSIONS

COVID-19 most commonly presents with fever, cough, fatigue and anorexia and among patients with existing hypertension and cardiovascular disease. It is important as serious adverse outcomes can develop such as acute respiratory distress syndrome, acute cardiac injury, acute kidney injury and death.

Proactive anti-inflammatory therapy with colchicine in the treatment of advanced stages of new coronavirus infection. The first results of the COLORIT study

Actuality The course of the novel coronavirus disease (COVID-19) is unpredictable. It manifests in some cases as increasing inflammation to even the onset of a cytokine storm and irreversible progression of acute respiratory syndrome, which is associated with the risk of death in patients. Thus, proactive anti-inflammatory therapy remains an open serious question in patients with COVID-19 and pneumonia, who still have signs of inflammation on days 7-9 of the disease: elevated C-reactive protein (CRP)&gt;60 mg/dL and at least two of the four clinical signs: fever &gt;37.5°C; persistent cough; dyspnea (RR &gt;20 brpm) and/or reduced oxygen blood saturation &lt;94% when breathing atmospheric air. We designed the randomized trial: COLchicine versus Ruxolitinib and Secukinumab in Open-label Prospective Randomized Trial in Patients with COVID-19 (COLORIT). We present here data comparing patients who received colchicine with those who did not receive specific anti-inflammatory therapy. Results of the comparison of colchicine, ruxolitinib, and secukinumab will be presented later.Objective Compare efficacy and safety of colchicine compared to the management of patients with COVID-19 without specific anti-inflammatory therapy.Material and Methods Initially, 20 people were expected to be randomized in the control group. However, enrollment to the control group was discontinued subsequently after the inclusion of 5 patients due to the risk of severe deterioration in the absence of anti-inflammatory treatment. Therefore, 17 patients, who had not received anti-inflammatory therapy when treated in the MSU Medical Research and Educational Center before the study, were also included in the control group. The effects were assessed on day 12 after the inclusion or at discharge if it occurred earlier than on day 12. The primary endpoint was the changes in the SHOCS-COVID score, which includes the assessment of the patient's clinical condition, CT score of the lung tissue damage, the severity of systemic inflammation (CRP changes), and the risk of thrombotic complications (D-dimer) [1].Results The median SHOCS score decreased from 8 to 2 (p = 0.017), i.e., from moderate to mild degree, in the colchicine group. The change in the SHOCS-COVID score was minimal and statistically insignificant in the control group. In patients with COVID-19 treated with colchicine, the CRP levels decreased rapidly and normalized (from 99.4 to 4.2 mg/dL, p&lt;0.001). In the control group, the CRP levels decreased moderately and statistically insignificantly and achieved 22.8 mg/dL by the end of the follow-up period, which was still more than four times higher than normal. The most informative criterion for inflammation lymphocyte-to-C-reactive protein ratio (LCR) increased in the colchicine group by 393 versus 54 in the control group (p = 0.003). After treatment, it was 60.8 in the control group, which was less than 100 considered safe in terms of systemic inflammation progression. The difference from 427 in the colchicine group was highly significant (p = 0.003).The marked and rapid decrease in the inflammation factors was accompanied in the colchicine group by the reduced need for oxygen support from 14 (66.7%) to 2 (9.5%). In the control group, the number of patients without anti-inflammatory therapy requiring oxygen support remained unchanged at 50%. There was a trend to shorter hospital stays in the group of specific anti-inflammatory therapy up to 13 days compared to 17.5 days in the control group (p = 0.079). Moreover, two patients died in the control group, and there were no fatal cases in the colchicine group. In the colchicine group, one patient had deep vein thrombosis with D-dimer elevated to 5.99 µg/mL, which resolved before discharge.Conclusions Colchicine 1 mg for 1-3 days followed by 0.5 mg/day for 14 days is effective as a proactive anti-inflammatory therapy in hospitalized patients with COVID-19 and viral pneumonia. The management of such patients without proactive anti-inflammatory therapy is likely to be unreasonable and may worsen the course of COVID-19. However, the findings should be treated with caution, given the small size of the trial.

Chest CT findings of coronavirus disease 2019 (COVID-19): A comprehensive meta-analysis of 9907 confirmed patients

OBJECTIVES

We performed a systematic review and meta-analysis of the prevalence of chest CT findings in patients with confirmed COVID-19 infection.

METHODS

Systematic review of the literature was performed using PubMed, Scopus, Embase, and Google Scholar to retrieve original studies on chest CT findings of patients with confirmed COVID-19, available up to 10 May 2020. Data on frequency and distribution of chest CT findings were extracted from eligible studies, pooled and meta-analyzed using random-effects model to calculate the prevalence of chest CT findings.

RESULTS

Overall, 103 studies (pooled population: 9907 confirmed COVID-19 patients) were meta-analyzed. The most common CT findings were ground-glass opacities (GGOs) (77.18%, 95%CI = 72.23-81.47), reticulations (46.24%, 95%CI = 38.51-54.14), and air bronchogram (41.61%, 95%CI = 32.78-51.01). Pleural thickening (33.35%, 95%CI = 21.89-47.18) and bronchial wall thickening (15.48%, 95%CI = 8.54-26.43) were major atypical and airway findings. Lesions were predominantly distributed bilaterally (75.72%, 95%CI = 70.79-80.06) and peripherally (65.64%, 95%CI = 58.21-72.36), while 8.20% (95%CI = 6.30-10.61) of patients had no abnormal findings and pre-existing lung diseases were present in 6.01% (95%CI = 4.37-8.23).

CONCLUSIONS

The most common CT findings in COVID-19 are GGOs with/without consolidation, reticulations, and air bronchogram, which often involve both lungs with peripheral distribution. However, COVID-19 might present with atypical manifestations or no abnormal findings in chest CT, which deserve clinicians' notice.

Targeted Nanotherapeutics for Respiratory Diseases: Cancer, Fibrosis, and Coronavirus

Systemic delivery of therapeutics for treatment of lung diseases has several limitations including poor organ distribution of delivered payload with relatively low accumulation of active substances in the lungs and severe adverse side effects. In contrast, nanocarrier based therapeutics provide a broad range of opportunities due to their ability to encapsulate substances with different aqueous solubility, transport distinct types of cargo, target therapeutics specifically to the deceased organ, cell, or cellular organelle limiting adverse side effects and increasing the efficacy of therapy. Moreover, many nanotherapeutics can be delivered by inhalation locally to the lungs avoiding systemic circulation. In addition, nanoscale based delivery systems can be multifunctional, simultaneously carrying out several tasks including diagnostics, treatment and suppression of cellular resistance to the treatment. Nanoscale delivery systems improve the clinical efficacy of conventional therapeutics allowing new approaches for the treatment of respiratory diseases which are difficult to treat or possess intrinsic or acquired resistance to treatment. The present review summarizes recent advances in the development of nanocarrier based therapeutics for local and targeted delivery of drugs, nucleic acids and imaging agents for diagnostics and treatment of various diseases such as cancer, cystic fibrosis, and coronavirus.

The role of CT imaging for management of COVID-19 in epidemic area: early experience from a University Hospital

BACKGROUND

To evaluate the clinical value of the chest CT scan compared to the reference standard real-time polymerase chain reaction (RT-PCR) in COVID-19 patients.

METHODS

From March 29th to April 15th of 2020, a total of 240 patients with respiratory distress underwent both a low-dose chest CT scan and RT-PCR tests. The performance of chest CT in diagnosing COVID-19 was assessed with reference to the RT-PCR result. Two board-certified radiologists (mean 24 years of experience chest CT), blinded for the RT-PCR result, reviewed all scans and decided positive or negative chest CT findings by consensus.

RESULTS

Out of 240 patients, 60% (144/240) had positive RT-PCR results and 89% (213/240) had a positive chest CT scans. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of chest CT in suggesting COVID-19 were 100% (95% CI: 97-100%, 144/240), 28% (95% CI: 19-38%, 27/240), 68% (95% CI: 65-70%) and 100%, respectively. The diagnostic accuracy of the chest CT suggesting COVID-19 was 71% (95% CI: 65-77%). Thirty-three patients with positive chest CT scan and negative RT-PCR test at baseline underwent repeat RT-PCR assay. In this subgroup, 21.2% (7/33) cases became RT-PCR positive.

CONCLUSION

Chest CT imaging has high sensitivity and high NPV for diagnosing COVID-19 and can be considered as an alternative primary screening tool for COVID-19 in epidemic areas. In addition, a negative RT-PCR test, but positive CT findings can still be suggestive of COVID-19 infection.

A practical approach to imaging characteristics and standardized reporting of COVID-19: a radiologic review

Novel coronavirus (2019-nCoV), also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a pathogen that has caused a rapidly spreading pandemic all over the world. The primary mean of transmission is inhalation with a predilection for respiratory system involvement, especially in the distal airways. The disease that arises from this novel coronavirus is named coronavirus disease 2019 (COVID-19). COVID-19 may have a rapid and devastating course in some cases leading to severe complications and death. Radiological imaging methods have an invaluable role in diagnosis, follow-up, and treatment. In this review, radiological imaging findings of COVID-19 have been systematically reviewed based on the published literature so far. Radiologic reporting templates are also emphasized from a different point of view, considering specific distinctive patterns of involvement.

Deep CNN models for predicting COVID-19 in CT and x-ray images

Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images. We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ray images. Approach: Regions of interest (ROI) corresponding to ground-glass opacities (GGO), consolidations, and pleural effusions were labeled in 100 axial lung CT images from 60 COVID-19-infected subjects. These segmented regions were then employed as an additional input to six deep convolutional neural network (CNN) architectures (AlexNet, DenseNet, GoogleNet, NASNet-Mobile, ResNet18, and DarkNet), pretrained on natural images, to differentiate between COVID-19 and normal CT images. We also explored the model's ability to classify x-ray images as COVID-19, non-COVID-19 pneumonia, or normal. Performance on test images was measured with global accuracy and area under the receiver operating characteristic curve (AUC). Results: When using raw CT images as input to the tested models, the highest accuracy of 82% and AUC of 88.16% is achieved. Incorporating the three ROIs as an additional model inputs further boosts performance to an accuracy of 82.30% and an AUC of 90.10% (DarkNet). For x-ray images, we obtained an outstanding AUC of 97% for classifying COVID-19 versus normal versus other. Combing chest CT and x-ray images, DarkNet architecture achieves the highest accuracy of 99.09% and AUC of 99.89% in classifying COVID-19 from non-COVID-19. Our results confirm the ability of deep CNNs with transfer learning to predict COVID-19 in both chest CT and x-ray images. Conclusions: The proposed method could help radiologists increase the accuracy of their diagnosis and increase efficiency in COVID-19 management.

COVID-19 and Cancer Comorbidity: Therapeutic Opportunities and Challenges

The coronavirus disease 2019 (COVID-19) is a viral disease caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that affects the respiratory system of infected individuals. COVID-19 spreads between humans through respiratory droplets produced when an infected person coughs or sneezes. The COVID-19 outbreak originated in Wuhan, China at the end of 2019. As of 29 Sept 2020, over 235 countries, areas or territories across the globe reported a total of 33,441,919 confirmed cases, and 1,003,497 confirmed deaths due to COVID-19. Individuals of all ages are at risk for infection, but in most cases disease severity is associated with age and pre-existing diseases that compromise immunity, like cancer. Numerous reports suggest that people with cancer can be at higher risk of severe illness and related deaths from COVID-19. Therefore, managing cancer care under this pandemic is challenging and requires a collaborative multidisciplinary approach for optimal care of cancer patients in hospital settings. In this comprehensive review, we discuss the impact of the COVID-19 pandemic on cancer patients, their care, and treatment. Further, this review covers the SARS-CoV-2 pandemic, genome characterization, COVID-19 pathophysiology, and associated signaling pathways in cancer, and the choice of anticancer agents as repurposed drugs for treating COVID-19.

Exploring the magic bullets to identify Achilles' heel in SARS-CoV-2: Delving deeper into the sea of possible therapeutic options in Covid-19 disease: An update

The symptoms associated with Covid-19 caused by SARS-CoV-2 in severe conditions can cause multiple organ failure and fatality via a plethora of mechanisms, and it is essential to discover the efficacious and safe drug. For this, a successful strategy is to inhibit in different stages of the SARS-CoV-2 life cycle and host cell reactions. The current review briefly put forth the summary of the SARS-CoV-2 pandemic and highlight the critical areas of understanding in genomics, proteomics, medicinal chemistry, and natural products derived drug discovery. The review further extends to briefly put forth the updates in the drug testing system, biologics, biophysics, and their advances concerning SARS-CoV-2. The salient features include information on SARS-CoV-2 morphology, genomic characterization, and pathophysiology along with important protein targets and how they influence the drug design and development against SARS-CoV-2 and a concerted and integrated approach to target these stages. The review also gives the status of drug design and discovery to identify the drugs acting on critical targets in SARS-CoV-2 and host reactions to treat Covid-19.

Prognostic value of bedside lung ultrasound score in patients with COVID-19

Background

Bedside lung ultrasound (LUS) has emerged as a useful and non-invasive tool to detect lung involvement and monitor changes in patients with coronavirus disease 2019 (COVID-19). However, the clinical significance of the LUS score in patients with COVID-19 remains unknown. We aimed to investigate the prognostic value of the LUS score in patients with COVID-19.

Method

The LUS protocol consisted of 12 scanning zones and was performed in 280 consecutive patients with COVID-19. The LUS score based on B-lines, lung consolidation and pleural line abnormalities was evaluated.

Results

The median time from admission to LUS examinations was 7 days (interquartile range [IQR] 3–10). Patients in the highest LUS score group were more likely to have a lower lymphocyte percentage (LYM%); higher levels of D-dimer, C-reactive protein, hypersensitive troponin I and creatine kinase muscle-brain; more invasive mechanical ventilation therapy; higher incidence of ARDS; and higher mortality than patients in the lowest LUS score group. After a median follow-up of 14 days [IQR, 10–20 days], 37 patients developed ARDS, and 13 died. Patients with adverse outcomes presented a higher rate of bilateral involvement; more involved zones and B-lines, pleural line abnormalities and consolidation; and a higher LUS score than event-free survivors. The Cox models adding the LUS score as a continuous variable (hazard ratio [HR]: 1.05, 95% confidence intervals [CI] 1.02 ~ 1.08; P < 0.001; Akaike information criterion [AIC] = 272; C-index = 0.903) or as a categorical variable (HR 10.76, 95% CI 2.75 ~ 42.05; P = 0.001; AIC = 272; C-index = 0.902) were found to predict poor outcomes more accurately than the basic model (AIC = 286; C-index = 0.866). An LUS score cut-off > 12 predicted adverse outcomes with a specificity and sensitivity of 90.5% and 91.9%, respectively.

Conclusions

The LUS score devised by our group performs well at predicting adverse outcomes in patients with COVID-19 and is important for risk stratification in COVID-19 patients.

Chest CT Imaging Signature of Coronavirus Disease 2019 Infection: In Pursuit of the Scientific Evidence

BACKGROUND

Chest CT may be used for the diagnosis of coronavirus disease 2019 (COVID-19), but clear scientific evidence is lacking. Therefore, we systematically reviewed and meta-analyzed the chest CT imaging signature of COVID-19.

RESEARCH QUESTION

What is the chest CT imaging signature of COVID-19 infection?

STUDY DESIGN AND METHODS

A systematic literature search was performed for original studies on chest CT imaging findings in patients with COVID-19. Methodologic quality of studies was evaluated. Pooled prevalence of chest CT imaging findings were calculated with the use of a random effects model in case of between-study heterogeneity (predefined as I2 ≥50); otherwise, a fixed effects model was used.

RESULTS

Twenty-eight studies were included. The median number of patients with COVID-19 per study was 124 (range, 50-476), comprising a total of 3,466 patients. Median prevalence of symptomatic patients was 99% (range, >76.3%-100%). Twenty-seven of the studies (96%) had a retrospective design. Methodologic quality concerns were present with either risk of or actual referral bias (13 studies), patient spectrum bias (eight studies), disease progression bias (26 studies), observer variability bias (27 studies), and test review bias (14 studies). Pooled prevalence was 10.6% for normal chest CT imaging findings. Pooled prevalences were 90.0% for posterior predilection, 81.0% for ground-glass opacity, 75.8% for bilateral abnormalities, 73.1% for left lower lobe involvement, 72.9% for vascular thickening, and 72.2% for right lower lobe involvement. Pooled prevalences were 5.2% for pleural effusion, 5.1% for lymphadenopathy, 4.1% for airway secretions/tree-in-bud sign, 3.6% for central lesion distribution, 2.7% for pericardial effusion, and 0.7% for cavitation/cystic changes. Pooled prevalences of other CT imaging findings ranged between 10.5% and 63.2%.

INTERPRETATION

Studies on chest CT imaging findings in COVID-19 suffer from methodologic quality concerns. More high-quality research is necessary to establish diagnostic CT criteria for COVID-19. Based on the available evidence that requires cautious interpretation, several chest CT imaging findings appear to be suggestive of COVID-19, but normal chest CT imaging findings do not exclude COVID-19, not even in symptomatic patients.

Evaluation of the clinical performance of single-, dual-, and triple-target SARS-CoV-2 RT-qPCR methods

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Evaluating the single-, dual-, triple-target detection kits based on RT-qPCR.

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The number of targets in a detection kit is not a key factor affecting sensitivity.

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The amount of sample loading may influence the performance of a detection kit.

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Providing a guide when developing a nucleic acid detection kit for SARS-CoV-2.

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Digital PCR can be used to review clinically suspected samples.

Background

The coronavirus disease 2019 (COVID-19) has become a pandemic. Reverse transcription quantitative PCR (RT-qPCR) has played a vital role in the diagnosis of COVID-19, but the rates of false negatives is not ideal in dealing with this highly infectious virus. It is thus necessary to systematically evaluate the clinical performance of the single-, dual-, triple-target detection kits to guide the clinical diagnosis of this disease.

Methods

A series of reference materials calibrated by droplet digital PCR (ddPCR) and 57 clinical samples were used to evaluate the clinical performance of six single-, dual-, triple-target SARS-CoV-2 nucleic acid detection kits based on RT-qPCR.

Results

The dual-target kits, kit B and kit C had the highest and the lowest detection sensitivity, which was 125 copies/mL and 4000 copies/mL, respectively. Among the 57 clinical samples from patients with COVID-19, 47 were tested positive by the kit B, while 35, 29, 28, 30, and 29 were found positive by the kits A, C, D, E, and F, respectively. The number of targets in a detection kit is not a key factor affecting sensitivity, while the amount of sample loading may influence the performance of a detection kit.

Conclusions

This study provides a guide when choosing or developing a nucleic acid detection kit for the diagnosis of COVID-19. Also, the absolute-quantification feature and high-sensitivity performance of ddPCR, suggesting that it can be used to review clinically suspected samples.

The lingering manifestations of COVID-19 during and after convalescence: update on long-term pulmonary consequences of coronavirus disease 2019 (COVID-19)

The long-term sequelae of coronavirus disease 2019 (COVID-19) are still unknown. Lessons from past viral epidemics reveal that, after recovery, patients with viral pulmonary infections can suffer from irreversible pulmonary dysfunction and demonstrate residual imaging or functional abnormalities. Residual ground glass opacities, consolidations, reticular and linear opacities, residual crazy paving pattern, melted sugar sign, and parenchymal fibrotic bands are several features found in the late or remission stages of COVID-19. These radiologic findings have been observed weeks after symptom onset, even after hospital discharge, and they may or may not correlate with clinical manifestations. High-resolution CT may be indicated to establish new baselines and track changes in residual impairments. In our previous review, we observed significant pulmonary sequelae in some COVID-19 survivors at follow-up. In this update, we review the current literature on the clinical and radiologic manifestations of post-recovery COVID-19 toward the end of hospital admission and after discharge.

The Relationship Between Chest Imaging Findings and the Viral Load of COVID-19

Purpose: We aimed to investigate the relationship between clinical characteristics, radiographic features, and the viral load of patients with coronavirus disease 2019 (COVID-19). Methods and Materials: We retrospectively collected 56 COVID-19 cases from two institutions in Hunan province, China. The basal clinical characteristics, detail imaging features and follow-up CT changes were evaluated and the relationship with the viral load was analyzed. Results: GGO (48, 85.7%) and vascular enlargement (44, 78.6%) were the most frequent signs in COVID-19 patients. Of the lesions, 64.3% of the margins were uneasily differentiated. However, no significant correlations were found in terms of leucocytes, neutrophils, lymphocytes, platelets, and C-reactive protein (all P > 0.05). In contrast, the uneasily differentiated margin was negatively correlated with the Ct value (r = -0.283, P = 0.042), that is, an uneasily differentiated margin indicated a lower Ct value (P = 0.043). Patients with a lower Ct value were likely to present a progress follow-up change (P = 0.022). The Ct value at baseline could predict a progress follow-up change with an AUC of 0.685 (Cut-off value = 29.48). All four patients with normal CT findings presented new lesion(s) on follow-up CT scans. Conclusion: The viral load of COVID-19 is negatively correlated with an uneasily differentiated lesion margin on initial CT scan images and the Ct value should noted when making a diagnosis. In addition, following-up CT scans are necessary for patients who presented a normal CT at the initial diagnosis, especially for those with a low Ct value.

The importance of distinguishing COVID-19 from more common respiratory illnesses

We recruited 1591 patients who presented to our fever clinics from 23 January 2020 to 16 February 2020. The different imaging findings between COVID-19 pneumonia and influenza A viruses, influenza B virus pneumonia were also investigated. Most patients were infected by influenza A and B viruses in the flu-season. A laboratory kit is urgently needed to test different viruses simultaneously. Computed tomography can help early screen suspected patients with COVID-19 and differentiate different virus-related pneumonia.

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

OBJECTIVE

To investigate in the CT manifestations of severe and critical Coronavirus Disease 2019 (COVID-19) patients.

METHODS

Medical data was collected for 2 severe patients and 4 critical COVID-19 patients from onset to their recovery. Three or four CT scans for each patient were taken. The semi-quantitative analysis method was introduced for lesion and its distribution area.

RESULTS

The ground-glass opacities (GGO) and mixed GGO with consolidation were found as the most frequent features. Consolidation followed, and the appearance of stripes which showed an increasing trend before the patient was discharged. Consolidation was associated with clinical severity and disease progression, and the rapid change of the lesion in a short period of time was also a notable feature within 2-3 weeks. After being discharged, the efficacy of treatment could be demonstrated by a follow up CT scan. The distribution of lesion also showed dynamic progress in the follow up CT scan.

CONCLUSION

CT scans in the whole course provided the entire inflammation information to assess clinical severity, disease progression and the treatment efficacy for COVID-19.

Development of minimal basic data set to report COVID-19

Background: Effective surveillance of COVID-19 highlights the importance of rapid, valid, and standardized information to crisis monitoring and prompts clinical interventions. Minimal basic data set (MBDS) is a set of metrics to be collated in a standard approach to allow aggregated use of data for clinical purposes and research. Data standardization enables accurate comparability of collected data, and accordingly, enhanced generalization of findings. The aim of this study is to establish a core set of data to characterize COVID-19 to consolidate clinical practice. Methods: A 3-step sequential approach was used in this study: (1) an elementary list of data were collected from the existing information systems and data sets; (2) a systematic literature review was conducted to extract evidence supporting the development of MBDS; and (3) a 2-round Delphi survey was done for reaching consensus on data elements to include in COVID-19 MBDS and for its robust validation. Results: In total, 643 studies were identified, of which 38 met the inclusion criteria, where a total of 149 items were identified in the data sources. The data elements were classified by 3 experts and validated via a 2-round Delphi procedure. Finally, 125 data elements were confirmed as the MBDS. Conclusion: The development of COVID-19 MBDS could provide a basis for meaningful evaluations, reporting, and benchmarking COVID-19 disease across regions and countries. It could also provide scientific collaboration for care providers in the field, which may lead to improved quality of documentation, clinical care, and research outcomes.

Diagnostic Value of Imaging Modalities for COVID-19: Scoping Review

BACKGROUND

Coronavirus disease (COVID-19) is a serious infectious disease that causes severe respiratory illness. This pandemic represents a serious public health risk. Therefore, early and accurate diagnosis is essential to control disease progression. Radiological examination plays a crucial role in the early identification and management of infected patients.

OBJECTIVE

The aim of this review was to identify the diagnostic value of different imaging modalities used for diagnosis of COVID-19.

METHODS

A comprehensive literature search was conducted using the PubMed, Scopus, Web of Science, and Google Scholar databases. The keywords diagnostic imaging, radiology, respiratory infection, pneumonia, coronavirus infection and COVID-19 were used to identify radiology articles focusing on the diagnosis of COVID-19 and to determine the diagnostic value of various imaging modalities, including x-ray, computed tomography (CT), ultrasound, and nuclear medicine for identification and management of infected patients.

RESULTS

We identified 50 articles in the literature search. Studies that investigated the diagnostic roles and imaging features of patients with COVID-19, using either chest CT, lung ultrasound, chest x-ray, or positron emission topography/computed tomography (PET/CT) scan, were discussed. Of these imaging modalities, chest x-ray and CT scan are the most commonly used for diagnosis and management of COVID-19 patients, with chest CT scan being more accurate and sensitive in identifying COVID-19 at early stages. Only a few studies have investigated the roles of ultrasound and PET/CT scan in diagnosing COVID-19.

CONCLUSIONS

Chest CT scan remains the most sensitive imaging modality in initial diagnosis and management of suspected and confirmed patients with COVID-19. Other diagnostic imaging modalities could add value in evaluating disease progression and monitoring critically ill patients with COVID-19.

Organ-specific manifestations of COVID-19 infection

Although COVID-19 presents primarily as a lower respiratory tract infection transmitted via air droplets, increasing data suggest multiorgan involvement in patients that are infected. This systemic involvement is postulated to be mainly related to the SARS-CoV-2 virus binding on angiotensin-converting enzyme 2 (ACE2) receptors located on several different human cells. Lung involvement is the most common serious manifestation of the disease, ranging from asymptomatic disease or mild pneumonia, to severe disease associated with hypoxia, critical disease associated with shock, respiratory failure and multiorgan failure or death. Among patients with COVID-19, underlying cardiovascular comorbidities including hypertension, diabetes and especially cardiovascular disease, has been associated with adverse outcomes, whereas the emergence of cardiovascular complications, including myocardial injury, heart failure and arrhythmias, has been associated with poor survival. Gastrointestinal symptoms are also frequently encountered and may persist for several days. Haematological complications are frequent as well and have been associated with poor prognosis. Furthermore, recent studies have reported that over a third of infected patients develop a broad spectrum of neurological symptoms affecting the central nervous system, peripheral nervous system and skeletal muscles, including anosmia and ageusia. The skin, the kidneys, the liver, the endocrine organs and the eyes are also affected by the systemic COVID-19 disease. Herein, we provide a comprehensive overview of the organ-specific systemic manifestations of COVID-19.

Development and Validation of a Prognostic Nomogram Based on Clinical and CT Features for Adverse Outcome Prediction in Patients with COVID-19

Objective

The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT) features for outcome prediction in patients with coronavirus disease (COVID-19).

Materials and Methods

The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitals were retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in the training cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in the validation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, or death. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. A nomogram was constructed based on the combination of clinical and CT features, and its prognostic performance was externally tested in the validation group. The predictive value of the combined model was compared with models built on the clinical and radiological attributes alone.

Results

Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohort experienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67–6.71; p < 0.001), lymphocyte count (HR, 0.12; 95% CI, 0.04–0.38; p < 0.001) and crazy-paving sign (HR, 2.15; 95% CI, 1.03–4.48; p = 0.042) were the independent factors. The nomogram displayed a concordance index (C-index) of 0.82 (95% CI, 0.76–0.88), and its prognostic value was confirmed in the validation cohort with a C-index of 0.89 (95% CI, 0.82–0.96). The combined model provided the best performance over the clinical or radiological model (p < 0.050).

Conclusion

Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverse outcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predicting adverse outcomes of patients with COVID-19.