Chest CT in COVID-19: What the Radiologist Needs to Know

The Short and Long of COVID-19: A Review of Acute and Chronic Radiologic Pulmonary Manifestations of SARS-2-CoV and Their Clinical Significance

Coronavirus disease 2019 (COVID-19) pneumonia has had catastrophic effects worldwide. Radiology, in particular computed tomography (CT) imaging, has proven to be valuable in the diagnosis, prognostication, and longitudinal assessment of those diagnosed with COVID-19 pneumonia. This article will review acute and chronic pulmonary radiologic manifestations of COVID-19 pneumonia with an emphasis on CT and also highlighting histopathology, relevant clinical details, and some notable challenges when interpreting the literature.

Prevalence and prognostic role of thoracic lymphadenopathy in Covid-19

PURPOSE

 The prevalent coronavirus disease 2019 (COVID-19) pandemic has spread throughout the world and is considered a serious threat to global health. The prognostic role of thoracic lymphadenopathy in COVID-19 is unclear. The aim of the present meta-analysis was to analyze the prognostic role of thoracic lymphadenopathy for the prediction of 30-day mortality in patients with COVID-19.

MATERIALS AND METHODS

 The MEDLINE library, Cochrane, and SCOPUS databases were screened for associations between CT-defined features and mortality in COVID-19 patients up to June 2021. In total, 21 studies were included in the present analysis. The quality of the included studies was assessed by the Newcastle-Ottawa Scale. The meta-analysis was performed using RevMan 5.3. Heterogeneity was calculated by means of the inconsistency index I2. DerSimonian and Laird random-effect models with inverse variance weights were performed without any further correction.

RESULTS

 The included studies comprised 4621 patients. The prevalence of thoracic lymphadenopathy varied between 1 % and 73.4 %. The pooled prevalence was 16.7 %, 95 % CI = (15.6 %; 17.8 %). The hospital mortality was higher in patients with thoracic lymphadenopathy (34.7 %) than in patients without (20.0 %). The pooled odds ratio for the influence of thoracic lymphadenopathy on mortality was 2.13 (95 % CI = [1.80-2.52], p < 0.001).

CONCLUSION

 The prevalence of thoracic lymphadenopathy in COVID-19 is 16.7 %. The presence of thoracic lymphadenopathy is associated with an approximately twofold increase in the risk for hospital mortality in COVID-19.

KEY POINTS

  · The prevalence of lymphadenopathy in COVID-19 is 16.7 %.. · Patients with lymphadenopathy in COVID-19 have a higher risk of mortality during hospitalization.. · Lymphadenopathy nearly doubles mortality and plays an important prognostic role..

CITATION FORMAT

· Bucher AM, Sieren M, Meinel F et al. Prevalence and prognostic role of thoracic lymphadenopathy in Covid-19. Rofo 2025; 197: 163 - 171.

Ventilatory variables and computed tomography features in COVID-19 ARDS and non-COVID-19-related ARDS: a prospective observational cohort study

BACKGROUND

This study compared the ventilatory variables and computed tomography (CT) features of patients with coronavirus disease 2019 (COVID-19) versus those of patients with pulmonary non-COVID-19-related acute respiratory distress syndrome (ARDS) during the early phase of ARDS.

METHODS

This prospective, observational cohort study of ARDS patients in Taiwan was performed between February 2017 and June 2018 as well as between October 2020 and January 2024. Analysis was performed on clinical characteristics, including consecutive ventilatory variables during the first week after ARDS diagnosis. Analysis was also performed on CT scans obtained within one week after ARDS onset.

RESULTS

A total of 222 ARDS patients were divided into a COVID-19 ARDS group (n = 44; 19.8%) and a non-COVID-19 group (all pulmonary origin) (n = 178; 80.2%). No significant difference was observed between the two groups in terms of all-cause hospital mortality (38.6% versus 47.8%, p = 0.277). Pulmonary non-COVID-19 patients presented higher values for mechanical power (MP), MP normalized to predicted body weight (MP/PBW), MP normalized to compliance (MP/compliance), ventilatory ratio (VR), peak inspiratory pressure (Ppeak), and dynamic driving pressure (∆P) as well as lower dynamic compliance from day 1 to day 7 after ARDS onset. In both groups, non-survivors exceeded survivors and presented higher values for MP, MP/PBW, MP/compliance, VR, Ppeak, and dynamic ∆P with lower dynamic compliance from day 1 to day 7 after ARDS onset. The CT severity score for each of the five lung lobes and total CT scores were all significantly higher in the non-COVID-19 group (all p < 0.05). Multivariable logistic regression models revealed that Sequential Organ Failure Assessment (SOFA) score was independently associated with mortality in the COVID-19 group. In the non-COVID-19 group, body mass index, immunocompromised status, SOFA score, MP/PBW and total CT severity scores were independently associated with mortality.

CONCLUSIONS

In the early course of ARDS, physicians should be aware of the distinctions between COVID-19-related ARDS and non-COVID-19-related ARDS in terms of ventilatory variables and CT imaging presentations. It is also important to tailor the mechanical ventilation settings according to these distinct subsets of ARDS.

Delta and Omicron SARS-CoV-2 pneumonia: Comparison of clinical and radiological features

BACKGROUND

Computed tomography (CT) is a critical tool for the diagnosis of pneumonia caused by SARS-CoV-2. The Delta and Omicron variants show distinct clinical features, but the radiological differences between pneumonia caused by these variants have not been extensively studied in patients with oxygen-dependent pneumonia.

OBJECTIVE

To compare the radiological and clinical features of pneumonia in patients hospitalized with oxygen-dependent SARS-CoV-2 infection caused by the Delta and Omicron variants.

METHODS

We performed a retrospective single-center study, including patients hospitalized with oxygen-dependent SARS-CoV-2 pneumonia between October 2021 and February 2022. Clinical and radiological data were collected and compared between patients infected with the Delta variant and those with the Omicron variant. CT scans were reviewed by a radiologist and a pulmonologist blinded to clinical and variant information.

RESULTS

A total of 135 patients with the Delta variant and 48 with the Omicron variant were included. Patients infected with Omicron were older (median age 75 years [68-83.2] vs 69 years [62-77.5], p = 0.004), more immunocompromised (52 % vs. 25 %, p < 0.001), and had higher vaccination rates (73 % vs. 51 %, p = 0.009). Radiologically, ground-glass opacities were present in 95 % of patients. There were no significant differences in the degree of lung involvement, type of lesions and their predominance. Unilateral lung involvement was more common in Omicron-infected patients (8.3 % vs 0.74 %, p = 0.02).

CONCLUSION

While Omicron oxygen-dependent pneumonia occurred in older and more comorbid patients, its clinical and radiological features were largely indistinguishable from those caused by the Delta variant, except for a higher rate of unilateral lung involvement.

The prognostic relevance of pleural effusion in patients with COVID-19 - A German multicenter study

PURPOSE

This study evaluates the prognostic significance of pleural effusion (PE) in COVID-19 patients across thirteen centers in Germany, aiming to clarify its role in predicting clinical outcomes.

METHODS

In this retrospective analysis within the RACOON project (Radiological Cooperative Network of the COVID-19 pandemic), 1183 patients (29.3 % women, 70.7 % men) underwent chest CT to assess PE. We investigated PE's association with 30-day mortality, ICU admission, and the need for mechanical ventilation.

RESULTS

PE was detected in 31.5 % of patients, showing a significant correlation with 30-day mortality (47.5 % in non-survivors vs. 27.3 % in survivors, p < 0.001), with a hazard ratio of 2.22 (95 % CI 1.65-2.99, p < 0.001). No significant association was found between PE volume or density and mortality. ICU admissions were noted in 46.8 % of patients, while mechanical ventilation was required for 26.7 %.

CONCLUSION

Pleural effusion is present in a significant portion of COVID-19 patients and independently predicts increased 30-day mortality, underscoring its value as a prognostic marker. Its identification, irrespective of volume or density, should be a priority in radiological reports to guide clinical decision-making.

Impact of COVID-19 pandemic on acute stroke care in a tertiary stroke centre

The aim of this study was to evaluate how COVID-19 affected acute stroke care and outcome in patients with acute ischemic or hemorrhagic stroke. We performed a retrospective analysis on patients who were admitted with acute ischemic (AIS) or hemorrhagic (ICH) stroke from September 2020 to May 2021 with and without COVID-19. We recorded demographic and clinical data, imaging parameters, functional outcome and mortality at one year. Beside descriptive statistics we performed χ2-probe, Mann-Witney U-test, Student t-probe and multivariate testing. We found a 29%-reduction in the number of AIS cases during the pandemic. The number of the large vessel occlusions /LVOs/ (N = 83, 41.7%), from them 37 (17.7%) had mechanical thrombectomy (MT), was higher than before the COVID-19 period (p = 0.02 and p = 0.001, respectively). From all patients needing acute revascularization therapy (N = 137) 118 patients received it, among them 20 (16.9%) had COVID-19. Those positive for COVID-19 were more likely to have a higher median NIHSS score at baseline and at 24 h (p = 0.02 and p = 0.03, respectively). They also had a lower rate of favourable outcome at discharge (15% vs. 41.8%; p = 0.024) and at three months (25% vs. 52%, p = 0.02), longer median hospitalization (p < 0.0001), and a higher mortality rate (52% vs. 25%; p = 0.03). The incidence of symptomatic intracerebral hemorrhage (sICH) did not differ between the groups. Regarding the ICH patients, NIHSS score at 24 h (p = 0.036), mortality at 3 months (p = 0.004) and at one year (p = 0.00) were higher in the COVID-19 group. We concluded that the pandemic resulted fewer admission due to AIS with an increased number of LVOs and MTs. AIS patients with concomitant SARS-CoV-2 infection have more severe strokes and unfavorable long term outcome. The risk of sICH was not increased in COVID-19 positive patients therefore reperfusion therapies appear to be safe and beneficial for some individuals. Patients with ICH and comorbid COVID-19 have a very poor prognosis.

Comparison of X-ray and CT Images of COVID-19 Caused by the Wild-Type and Alpha-Variant SARS-CoV-2

Introduction This study aimed to determine the characteristics of coronavirus disease 2019 (COVID-19) pneumonia caused by the wild type and the alpha variant in patients. This study included patients with COVID-19 admitted to Fukui General Hospital between October 31, 2020, and April 30, 2021. Methods Pneumonia occurrence rate, chest X-ray, and computed tomography (CT) findings were evaluated by two radiologists. The time since the onset and presence of pneumonia were also investigated. Results Out of 128 patients, 43 had pneumonia. The pneumonia detection rates using chest radiography were 15.6% (20/128) and 33.6% (43/128) using CT (p = 0.0008). Of the pneumonia cases detected by CT, 32.0% (8/25) of the wild type and 66.7% (12/18) of the alpha variant were detected by X-rays (p = 0.0246). The main finding of pneumonia was a higher percentage of ground-glass opacities than consolidation in both the wild type and alpha variant. In the alpha variant, multiple signs of air bubbles were observed in four patients on chest CT; however, these were not observed in the wild type (p = 0.014). Conclusion The imaging features of pneumonia may be different in variants of COVID-19 compared to those in the wild type. CT helps to detect pneumonia and identify features in patients with COVID-19 because it is difficult to detect COVID-19 pneumonia using plain chest radiographs.

An Overview of Pediatric Pulmonary Complications During COVID-19 Pandemic: A Lesson for Future

BACKGROUND

The pediatric community is considered a suitable target for controlling the spread and mortality of viral diseases. In late December 2019, a respiratory disease due to the novel coronavirus, later COVID-19, hit the globe. The COVID-19 global disruption had direct and indirect impacts on different aspects of child health. Therefore, surveillance, preventive approaches, and treatment plans for children came into the spotlight.

OBJECTIVE

This study aims to discuss the clinical pictures as well as laboratory and radiological findings of the infected children during the COVID-19 pandemic. The focus of this study is to express the clinical manifestations of respiratory disease in pediatric SARS-CoV-2, available therapeutic options, vaccine recommendations, and long COVID sequelae in affected children. This review could serve as a hint for upcoming challenges in pediatric care during future pandemics.

RESULTS

The clinical presentation of COVID-19 in pediatrics can range from mild pulmonary disease to acute respiratory distress syndrome (ARDS). Supportive care is a crucial component of the management of pediatric COVID-19. However, the importance of specializing in how to treat patients with more severe conditions cannot be overstated. Additionally, clinicians must consider prevention strategies as well as potential complications.

CONCLUSION

Although the infected patients are dipping day by day, there is a lack of clinical guidelines for pediatric SARS-CoV-2-associated pulmonary diseases. Understanding of the physicians about all aspects of pediatric care during the COVID-19 pandemic could lead to enhanced quality of future patient care and safety, reduced costs of health policies, and surveil the risk that patients with respiratory viruses can expose to society.

Increased luminal area of large conducting airways in patients with COVID-19 and post-acute sequelae of COVID-19: a retrospective case-control study

Coronavirus disease 2019 (COVID-19) is associated with enlarged luminal areas of large conducting airways. In 10-30% of patients with acute COVID-19 infection, symptoms persist for more than 4 wk (referred to as post-acute sequelae of COVID 19, or PASC), and it is unknown if airway changes are associated with this persistence. Thus, we aim to investigate whether luminal area of large conducting airways is different between patients with PASC and COVID-19 and healthy controls. In this retrospective case-control study, 75 patients with PASC (48 females) were age-, height-, and sex-matched to 75 patients with COVID-19 and 75 healthy controls. Using three-dimensional digital reconstruction from computed tomography imaging, we measured luminal areas of seven conducting airways, including trachea, right and left main bronchi, bronchus intermediate, right and left upper lobe, and left lower lobe bronchi. Kruskal-Wallis H test was used to compare measurements between the three groups, as appropriate. Airway luminal areas between COVID-19 and PASC groups were not different (all, P > 0.66). There were no group differences in airway luminal area (PASC vs. control) for trachea and right main bronchus. However, in the remaining five airways, airway luminal areas were 12-39% larger among patients with PASC than in controls (all, P < 0.05). Patients diagnosed with COVID-19 and PASC have greater airway luminal area in most large conducting airways compared with healthy controls. No differences in luminal area between patients with COVID-19 and PASC suggest persistence of changes or insufficient time for reversal of changes.NEW & NOTEWORTHY Three-dimensional reconstruction of airways has shown increased luminal area in patients with COVID-19 and post-acute sequelae of COVID-19 when compared with healthy controls. These findings suggest the role of large conducting airways in the pathogenesis of post-acute sequelae of COVID 19.

Coping with Unknown Health Crisis via Social Media: A Content Analysis of Online Mutual Aid Group in the Beginning of the COVID-19 Pandemic

OBJECTIVE

The initial emergence of SARS-CoV-2 created uncertainty for humanity, driving people to seek assistance on social media. This study aims to understand the role of social media in coping with crises and to offer guidance for future uncertainties by examining the experiences of Wuhan during the early stages of the pandemic.

METHODS

Using quantitative content analysis, this study investigated 2207 Weibo posts tagged with "COVID-19 Mutual Aid" from individuals located in Wuhan during the early lockdown period from January 23, 2020, to March 23, 2020.

RESULTS

At the start of pandemic, messages seeking tangible support were most common. A hurdle regression model showed that deeper self-disclosure led to more retransmission of help-seeking messages. The Chi-Square and Mann-Whitney U tests revealed that health professionals and laypeople had different self-disclosure strategies.

CONCLUSIONS

This study provides insight into the online social support exchange during the early stages of the COVID-19 pandemic in Wuhan, highlighting the importance of self-disclosure on message retransmission, and the differences in self-disclosure strategies between health professionals and laypeople in online help-seeking.

The Ambivalence of Post COVID-19 Vaccination Responses in Humans

The Coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has prompted a massive global vaccination campaign, leading to the rapid development and deployment of several vaccines. Various COVID-19 vaccines are under different phases of clinical trials and include the whole virus or its parts like DNA, mRNA, or protein subunits administered directly or through vectors. Beginning in 2020, a few mRNA (Pfizer-BioNTech BNT162b2 and Moderna mRNA-1273) and adenovirus-based (AstraZeneca ChAdOx1-S and the Janssen Ad26.COV2.S) vaccines were recommended by WHO for emergency use before the completion of the phase 3 and 4 trials. These vaccines were mostly administered in two or three doses at a defined frequency between the two doses. While these vaccines, mainly based on viral nucleic acids or protein conferred protection against the progression of SARS-CoV-2 infection into severe COVID-19, and prevented death due to the disease, their use has also been accompanied by a plethora of side effects. Common side effects include localized reactions such as pain at the injection site, as well as systemic reactions like fever, fatigue, and headache. These symptoms are generally mild to moderate and resolve within a few days. However, rare but more serious side effects have been reported, including allergic reactions such as anaphylaxis and, in some cases, myocarditis or pericarditis, particularly in younger males. Ongoing surveillance and research efforts continue to refine the understanding of these adverse effects, providing critical insights into the risk-benefit profile of COVID-19 vaccines. Nonetheless, the overall safety profile supports the continued use of these vaccines in combating the pandemic, with regulatory agencies and health organizations emphasizing the importance of vaccination in preventing COVID-19's severe outcomes. In this review, we describe different types of COVID-19 vaccines and summarize various adverse effects due to autoimmune and inflammatory response(s) manifesting predominantly as cardiac, hematological, neurological, and psychological dysfunctions. The incidence, clinical presentation, risk factors, diagnosis, and management of different adverse effects and possible mechanisms contributing to these effects are discussed. The review highlights the potential ambivalence of human response post-COVID-19 vaccination and necessitates the need to mitigate the adverse side effects.

Discovery of a new long COVID mouse model via systemic histopathological comparison of SARS-CoV-2 intranasal and inhalation infection

Intranasal infection is commonly used to establish a SARS-CoV-2 mouse model due to its non-invasive procedures and a minimal effect from the operation itself. However, mice intranasally infected with SARS-CoV-2 have a high mortality rate, which limits the utility of this model for exploring therapeutic strategies and the sequelae of non-fatal COVID-19 cases. To resolve these limitations, an aerosolised viral administration method has been suggested. However, an in-depth pathological analysis comparing the two models is lacking. Here, we show that inhalation and intranasal SARS-CoV-2 (106 PFU) infection models established in K18-hACE2 mice develop unique pathological features in both the respiratory and central nervous systems, which could be directly attributed to the infection method. While the inhalation-infection model exhibited relatively milder pathological parameters, it closely mimicked the prevalent chest CT pattern observed in COVID-19 patients with focal, peripheral lesions and fibrotic scarring in the recuperating lung. We also found the evidence of direct neuron-invasion from the olfactory receptor neurons to the olfactory bulb in the intranasal model and showed the trigeminal nerve as an alternative route of transmission to the brain in inhalation infected mice. Even after viral clearance confirmed at 14 days post-infection, mild lesions were still found in the brain of inhalation-infected mice. These findings suggest that the inhalation-infection model has advantages over the intranasal-infection model in closely mimicking the pathological features of non-fatal symptoms of COVID-19, demonstrating its potential to study the sequelae and possible interventions for long COVID.

Paraseptal Lucencies Mimicking Emphysema in a Non-smoker With Acute Lung Injury in COVID-19

Paraseptal emphysema can be smoking-related but has other causes, including surfactant deficiency, COVID-19, and age. The typical acute chest tomographic findings of COVID-19 include bilateral ground-glass opacities with or without consolidation and interstitial thickening in a peripheral and posterior predominant distribution. Evolution of these findings can occur and ultimately lead to fibrosis. The development of bullae, pneumomediastinum, and pneumothorax can occur as complications of non-invasive or mechanical ventilation. This case report describes incidental paraseptal lucencies that mimicked paraseptal emphysema in a patient with acute hypoxemic respiratory failure secondary to COVID-19 without a prior history of smoking only requiring a high-flow nasal cannula.

Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers

OBJECTIVE

To analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs).

METHODS

The case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs.

RESULTS

Of the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P<0.001), maximum nodule diameter (P<0.001), vascular convergence sign (P<0.001), vacuole sign (P = 0.0007), air bronchogram sign (P = 0.0005), and lobulation sign (P = 0.0005). Malignant SPNs were associated with significantly higher levels of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) compared to benign SPNs (P < 0.05), while no significant difference was found in carbohydrate antigen 125 (CA125) levels (P = 0.054 for non-COVID-19; P = 0.072 for COVID-19). The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the comprehensive diagnosis combining serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05). A visual nomogram predictive model for malignant pulmonary nodules was constructed.

CONCLUSION

Combining CT with testing for CEA, CA125, and NSE levels offers high diagnostic accuracy and sensitivity, enables precise differentiation between benign and malignant nodules, particularly in the context of COVID-19, thereby reducing the risk of unnecessary surgical interventions.

Corticosteroid Therapy Duration and Dosage According to the Timing of Treatment Initiation for Post-COVID-19 Organizing Pneumonia

COVID-19 can lead to pulmonary complications, including organizing pneumonia. Steroids are essential in treating post-COVID-19 organizing pneumonia. However, research on the clinical benefits of initiating steroid treatment early for this condition is limited. To investigate the steroid initiation time in its association with treatment duration and corticosteroid dose for treating post-COVID-19 organizing pneumonia, we analyzed the data of 91 patients with post-COVID-19 organizing pneumonia at Chonnam National University Hospital between October 2020 and December 2022. Patients were categorized into early and late groups based on time from COVID-19 diagnosis to steroid initiation time for organizing pneumonia. The mean time interval between COVID-19 infection and steroid initiation time for treating organizing pneumonia, was 18.4±8.6 days. Within the early treatment group (treatment initiated <18.4 days after COVID-19), which included 55 patients, the mean duration of steroid treatment was 43.1±18.3days. In contrast, the late treatment group (initiated ≥18.4 days after COVID-19), which consisted of 36 patients, had a longer mean duration of steroid treatment 59.1±22.6 days) (p<0.01). Regarding corticosteroid dosing, the early treatment group had an average dosage of 0.5±0.3 mg/kg/day, in contrast to the late group, which averaged 0.8±0.3 mg/kg/day (p<0.01). Regression analysis showed steroid initiation time significantly influenced treatment duration (β=0.80 , p<0.01) and dosage (β=0.03, p<0.01). The clinical benefits of early steroid treatment for post-COVID-19 organizing pneumonia may lie in its association with reduced steroid treatment duration and dosage.

Evaluating the Accuracy of Chest CT in Detecting COVID-19 Through Tracheobronchial Wall Thickness: Insights From Emergency Department Patients in Mid-2023

Background The post-pandemic phase of the coronavirus infectious disease that emerged in 2019 (COVID-19) has necessitated updates in radiology, with emerging evidence suggesting tracheobronchial wall thickness as a potential new diagnostic marker. Purpose To evaluate the accuracy of chest computed tomography (CT) scans in identifying COVID-19 by assessing tracheobronchial wall thickness in mid-2023. Material and methods A retrospective review was conducted on 60 patients who underwent thoracoabdominal CT and the severe acute respiratory syndrome coronavirus (SARS-CoV-2) antigen tests during emergency visits between June and August 2023. Tracheobronchial wall thickness was measured using a 4-point scale (1=no thickening, 2=mild, 3=moderate, 4=significant). Lung assessment employed the COVID-19 Reporting and Data System (CO-RADS). Patients were classified based on antigen test results. The Mann-Whitney U test and Fisher's exact test compared characteristics and CT findings. Diagnostic performance was evaluated using the area under the receiver operating characteristic curves (AUC). Results The SARS-CoV-2-positive group showed significantly thicker tracheobronchial walls (median 1.5 mm vs. 1.2 mm, P < 0.001), especially in the trachea's membranous wall (median 1.2 mm vs. 0.9 mm, P < 0.001) and higher scores (median 3 vs. 2, P < 0.001). CO-RADS scores showed no significant difference. Quantitative and qualitative wall thickness assessments demonstrated high diagnostic value, with AUCs of 0.90 and 0.94, and accuracies of 85% and 87%, respectively. Conclusion Tracheobronchial wall thickness on chest CT exhibited high diagnostic accuracy, establishing it as a reliable marker for COVID-19 detection in mid-2023.

Emergency Presentations of Coronavirus Disease (COVID-19): A Review of the Literature and Radiologic Perspective

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the debilitating global pandemic known as Coronavirus disease (COVID-19). In this paper, we highlight the widespread manifestations and complications across disease systems. In addition, we present their relevant imaging findings to inform appropriate investigations and management in patients presenting to the Emergency Department with COVID-19 and its respective sequalae. Of note, we outline considerations for diagnosis of long COVID, an important medium to long term sequalae in patients with previous COVID-19 infections.

Chest CT Findings at Six Months Following COVID-19 ARDS - Correlation With the mMRC Dyspnea Scale and Pulmonary Function Tests

Background: Many survivors of severe COVID-19 pneumonia experience lingering respiratory issues. There is limited research on follow-up chest imaging findings in patients with COVID-19 ARDS, particularly in relation to their mMRC dyspnea scores and pulmonary function tests (PFTs). This study addresses this gap by investigating the clinical characteristics, mMRC dyspnea scores, PFTs, and chest CT findings of COVID-19 ARDS patients at the 6 months post-recovery. By analyzing these variables together, we aim to gain a better understanding of the long-term health consequences of COVID-19 ARDS. Methods: This prospective observational study included 56 subjects with COVID-19 ARDS with dyspnea at the six-month follow-up visits. These patients were evaluated by chest CT, mMRC dyspnea scale, and PFT. The CT severity score was calculated individually for each of the four major imaging findings - ground glass opacities (GGOs), parenchymal/atelectatic bands, reticulations/septal thickening, and consolidation - using a modified CT severity scoring system. Statistics were carried out to find any association between individual CT chest findings and the mMRC dyspnea scale and forced vital capacity (FVC). p values < 0.05 were considered statistically significant. Results: Our study population had a mean age of 55.86 ± 9.60 years, with 44 (78.6%) being men. Grades 1, 2, 3, and 4 on the mMRC dyspnea scale were seen in 57.1%, 30.4%, 10.7%, and 1.8% of patients respectively. Common CT findings observed were GGOs (94.6%), reticulations/septal thickening (96.4%), parenchymal/atelectatic bands (92.8%), and consolidation (14.3%). The mean modified CT severity scores for GGOs, reticulations/septal thickening, parenchymal/atelectatic bands, and consolidation were 10.32 ± 5.51 (range: 0-21), 7.66 ± 4.33 (range: 0-19), 4.77 ± 3.03 (range: 0-14) and 0.29 ± 0.91 (range 0-5) respectively. Reticulations/septal thickening (p = 0.0129) and parenchymal/atelectatic bands (p = 0.0453) were associated with an increased mMRC dyspnea scale. Parenchymal/atelectatic bands were also associated with abnormal FVC (<80%) (p = 0.0233). Conclusion: Six-month follow-up chest CTs of COVID-19 ARDS survivors with persistent respiratory problems showed a statistically significant relationship between increased mMRC dyspnea score and imaging patterns of reticulations/septal thickening and parenchymal/atelectatic bands; while parenchymal/atelectatic bands also showed a statistically significant correlation with reduced FVC.

A Literature Review on the Relative Diagnostic Accuracy of Chest CT Scans versus RT-PCR Testing for COVID-19 Diagnosis

BACKGROUND

Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19.

METHOD

A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost.

FINDINGS

In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review.

CONCLUSION

RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19.

Evaluation of a Lung Ultrasound Score in Hospitalized Adult Patients with COVID-19 in Barcelona, Spain

Background/Objectives: During the COVID-19 pandemic and the burden on hospital resources, the rapid categorization of high-risk COVID-19 patients became essential, and lung ultrasound (LUS) emerged as an alternative to chest computed tomography, offering speed, non-ionizing, repeatable, and bedside assessments. Various LUS score systems have been used, yet there is no consensus on an optimal severity cut-off. We assessed the performance of a 12-zone LUS score to identify adult COVID-19 patients with severe lung involvement using oxygen saturation (SpO2)/fractional inspired oxygen (FiO2) ratio as a reference standard to define the best cut-off for predicting adverse outcomes. Methods: We conducted a single-centre prospective study (August 2020-April 2021) at Hospital del Mar, Barcelona, Spain. Upon admission to the general ward or intensive care unit (ICU), clinicians performed LUS in adult patients with confirmed COVID-19 pneumonia. Severe lung involvement was defined as a SpO2/FiO2 ratio <315. The LUS score ranged from 0 to 36 based on the aeration patterns. Results: 248 patients were included. The admission LUS score showed moderate performance in identifying a SpO2/FiO2 ratio <315 (area under the ROC curve: 0.71; 95%CI 0.64-0.77). After adjustment for COVID-19 risk factors, an admission LUS score ≥17 was associated with an increased risk of in-hospital death (OR 5.31; 95%CI: 1.38-20.4), ICU admission (OR 3.50; 95%CI: 1.37-8.94) and need for IMV (OR 3.31; 95%CI: 1.19-9.13). Conclusions: Although the admission LUS score had limited performance in identifying severe lung involvement, a cut-off ≥17 score was associated with an increased risk of adverse outcomes. and could play a role in the rapid categorization of COVID-19 pneumonia patients, anticipating the need for advanced care.

The Short and Long of COVID-19: A Review of Acute and Chronic Radiologic Pulmonary Manifestations of SARS-2-CoV and Their Clinical Significance

Coronavirus disease 2019 (COVID-19) pneumonia has had catastrophic effects worldwide. Radiology, in particular computed tomography (CT) imaging, has proven to be valuable in the diagnosis, prognostication, and longitudinal assessment of those diagnosed with COVID-19 pneumonia. This article will review acute and chronic pulmonary radiologic manifestations of COVID-19 pneumonia with an emphasis on CT and also highlighting histopathology, relevant clinical details, and some notable challenges when interpreting the literature.

Characteristics and outcomes of people living with HIV hospitalised at tertiary healthcare institutions during the COVID-19 pandemic in Mexico City

BACKGROUND

While existing research on people living with HIV (PWH) during the COVID-19 pandemic primarily focused on their clinical outcomes, a critical gap remains in understanding the implications of COVID-19 delivery of in-hospital care services to PWH. Our study aimed to describe the characteristics and outcomes of PWH hospitalised during 2020 in Mexico City, comparing patients admitted due to COVID-19 vs. patients admitted due to other causes.

METHODS

All PWH hospitalised for ≥ 24 h at four institutions in Mexico City from January 1st to December 31st, 2020 were included. Patients were classified into two groups according to the leading cause of their first hospitalisation: COVID-19 or non-COVID-19. Characteristics among groups were compared using chi-square and Kruskal tests. A Cox model was used to describe the risk of death after hospitalisation and the characteristics associated with this outcome. Mortality and hospitalisation events were compared to data from 2019.

RESULTS

Overall, we included 238 PWH hospitalised in 2020. Among them, 42 (18%) were hospitalised due to COVID-19 and 196 (82%) due to non-COVID-19 causes, mainly AIDS-defining events (ADE). PWH hospitalised due to COVID-19 had higher CD4 + cell counts (380 cells/mm3 [IQR: 184-580] vs. 97 cells/mm3 [IQR: 34-272], p < 0.01) and a higher proportion of virologic suppression (VS) compared to those hospitalised due to non-COVID-19 causes (92% vs. 55%, p < 0.01). The adjusted hazard ratio (aHR) for AIDS was 3.1 (95%CI: 1.3-7.2). COVID-19 was not associated with death (aHR 0.9 [95%CI: 0.3-2.9]). Compared to 2019, mortality was significantly higher in 2020 (19% vs. 9%, p < 0.01), while hospitalisations decreased by 57%.

CONCLUSIONS

PWH with COVID-19 had higher VS and CD4 + cell counts and lower mortality compared to those hospitalised due to non-COVID-19-related causes, who more often were recently diagnosed with HIV and had ADEs. Most hospitalisations and deaths in 2020 in PWH were related to advanced HIV disease. The increased mortality and decreased hospitalisations of PWH during 2020 evidence the impact of the interruption of health services delivery for PWH with advanced disease due to the pandemic. Our findings highlight the challenges faced by PWH during 2020 in a country where advanced HIV remains a concern.

Multimodal data fusion using sparse canonical correlation analysis and cooperative learning: a COVID-19 cohort study

Through technological innovations, patient cohorts can be examined from multiple views with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict outcomes. Here, we aim to present our approach for analyzing multimodal data using unsupervised and supervised sparse linear methods in a COVID-19 patient cohort. This prospective cohort study of 149 adult patients was conducted in a tertiary care academic center. First, we used sparse canonical correlation analysis (CCA) to identify and quantify relationships across different data modalities, including viral genome sequencing, imaging, clinical data, and laboratory results. Then, we used cooperative learning to predict the clinical outcome of COVID-19 patients: Intensive care unit admission. We show that serum biomarkers representing severe disease and acute phase response correlate with original and wavelet radiomics features in the LLL frequency channel (cor(Xu1, Zv1) = 0.596, p value < 0.001). Among radiomics features, histogram-based first-order features reporting the skewness, kurtosis, and uniformity have the lowest negative, whereas entropy-related features have the highest positive coefficients. Moreover, unsupervised analysis of clinical data and laboratory results gives insights into distinct clinical phenotypes. Leveraging the availability of global viral genome databases, we demonstrate that the Word2Vec natural language processing model can be used for viral genome encoding. It not only separates major SARS-CoV-2 variants but also allows the preservation of phylogenetic relationships among them. Our quadruple model using Word2Vec encoding achieves better prediction results in the supervised task. The model yields area under the curve (AUC) and accuracy values of 0.87 and 0.77, respectively. Our study illustrates that sparse CCA analysis and cooperative learning are powerful techniques for handling high-dimensional, multimodal data to investigate multivariate associations in unsupervised and supervised tasks.

Radiologic Abnormalities in Prolonged SARS-CoV-2 Infection: A Systematic Review

We systematically reviewed radiological abnormalities in patients with prolonged SARS-CoV-2 infection, defined as persistently positive polymerase chain reaction (PCR) results for SARS-CoV-2 for > 21 days, with either persistent or relapsed symptoms. We extracted data from 24 patients (median age, 54.5 [interquartile range, 44-64 years]) reported in the literature and analyzed their representative CT images based on the timing of the CT scan relative to the initial PCR positivity. Our analysis focused on the patterns and distribution of CT findings, severity scores of lung involvement on a scale of 0-4, and the presence of migration. All patients were immunocompromised, including 62.5% (15/24) with underlying lymphoma and 83.3% (20/24) who had received anti-CD20 therapy within one year. Median duration of infection was 90 days. Most patients exhibited typical CT appearance of coronavirus disease 19 (COVID-19), including ground-glass opacities with or without consolidation, throughout the follow-up period. Notably, CT severity scores were significantly lower during ≤ 21 days than during > 21 days (P < 0.001). Migration was observed on CT in 22.7% (5/22) of patients at ≤ 21 days and in 68.2% (15/22) to 87.5% (14/16) of patients at > 21 days, with rare instances of parenchymal bands in previously affected areas. Prolonged SARS-CoV-2 infection usually presents as migrating typical COVID-19 pneumonia in immunocompromised patients, especially those with impaired B-cell immunity.

Time-dependent CT score-based model for identifying severe/critical COVID-19 at a fever clinic after the emergence of Omicron variant

Rationale and objectives

The computed tomography (CT) score has been used to evaluate the severity of COVID-19 during the pandemic; however, most studies have overlooked the impact of infection duration on the CT score. This study aimed to determine the optimal cutoff CT score value for identifying severe/critical COVID-19 during different stages of infection and to construct corresponding predictive models using radiological characteristics and clinical factors.

Materials and methods

This retrospective study collected consecutive baseline chest CT images of confirmed COVID-19 patients from a fever clinic at a tertiary referral hospital from November 28, 2022, to January 8, 2023. Cohorts were divided into three subcohorts according to the time interval from symptom onset to CT examination at the hospital: early phase (0-3 days), intermediate phase (4-7 days), and late phase (8-14 days). The binary endpoints were mild/moderate and severe/critical infection. The CT scores and qualitative CT features were manually evaluated. A logistic regression analysis was performed on the CT score as determined by a visual assessment to predict severe/critical infection. Receiver operating characteristic analysis was performed and the area under the curve (AUC) was calculated. The optimal cutoff value was determined by maximizing the Youden index in each subcohort. A radiology score and integrated models were then constructed by combining the qualitative CT features and clinical features, respectively, using multivariate logistic regression with stepwise elimination.

Results

A total of 962 patients (aged, 61.7 ± 19.6 years; 490 men) were included; 179 (18.6%) were classified as severe/critical COVID-19, while 344 (35.8%) had a typical Radiological Society of North America (RSNA) COVID-19 appearance. The AUCs of the CT score models reached 0.91 (95% confidence interval (CI) 0.88-0.94), 0.82 (95% CI 0.76-0.87), and 0.83 (95% CI 0.77-0.89) during the early, intermediate, and late phases, respectively. The best cutoff values of the CT scores during each phase were 1.5, 4.5, and 5.5. The predictive accuracies associated with the time-dependent cutoff values reached 88% (vs.78%), 73% (vs. 63%), and 87% (vs. 57%), which were greater than those associated with universal cutoff value (all P < 0.001). The radiology score models reached AUCs of 0.96 (95% CI 0.94-0.98), 0.90 (95% CI 0.87-0.94), and 0.89 (95% CI 0.84-0.94) during the early, intermediate, and late phases, respectively. The integrated models including demographic and clinical risk factors greatly enhanced the AUC during the intermediate and late phases compared with the values obtained with the radiology score models; however, an improvement in accuracy was not observed.

Conclusion

The time interval between symptom onset and CT examination should be tracked to determine the cutoff value for the CT score for identifying severe/critical COVID-19. The radiology score combining qualitative CT features and the CT score complements clinical factors for identifying severe/critical COVID-19 patients and facilitates timely hierarchical diagnoses and treatment.

Myosteatosis predicts short-term mortality in patients with COVID-19: A multicenter analysis

OBJECTIVES

Body composition on computed tomography can predict prognosis in patients with COVID-19. The reported data are based on small retrospective studies. The aim of the present study was to analyze the prognostic relevance of skeletal muscle parameter derived from chest computed tomography for prediction of 30-d mortality in patients with COVID-19 in a multicenter setting.

METHODS

The clinical databases of three centers were screened for patients with COVID-19 between 2020 and 2022. Overall, 447 patients (142 female; 31.7%) were included into the study. The mean age at the time of computed tomography acquisition was 63.8 ± 14.7 y and median age was 65 y. Skeletal muscle area and skeletal muscle density were defined on level T12 of the chest.

RESULTS

Overall, 118 patients (26.3%) died within the 30-d observation period. Of the patient sample, 255 patients (57.0%) were admitted to an intensive care unit and 122 patients needed mechanical ventilation (27.3%). The mean skeletal muscle area of all patients was 96.1 ± 27.2 cm² (range = 23.2-200.7 cm²). For skeletal muscle density, the mean was 24.3 ± 11.1 Hounsfield units (range = -5.6 to 55.8 Hounsfield units). In survivors, the mean skeletal muscle density was higher compared with the lethal cases (mean 25.8 ± 11.2 versus 20.1 ± 9.6; P < 0.0001). Presence of myosteatosis was independently associated with 30-d mortality: odds ratio = 2.72 (95% CI, 1.71-4.32); P = 0.0001.

CONCLUSIONS

Myosteatosis is strongly associated with 30-d mortality in patients COVID-19. Patients with COVID-19 with myosteatosis should be considered a risk group.

Quantitative CT Texture Analysis of COVID-19 Hospitalized Patients during 3-24-Month Follow-Up and Correlation with Functional Parameters

BACKGROUND

To quantitatively evaluate CT lung abnormalities in COVID-19 survivors from the acute phase to 24-month follow-up. Quantitative CT features as predictors of abnormalities' persistence were investigated.

METHODS

Patients who survived COVID-19 were retrospectively enrolled and underwent a chest CT at baseline (T0) and 3 months (T3) after discharge, with pulmonary function tests (PFTs). Patients with residual CT abnormalities repeated the CT at 12 (T12) and 24 (T24) months after discharge. A machine-learning-based software, CALIPER, calculated the CT percentage of the whole lung of normal parenchyma, ground glass (GG), reticulation (Ret), and vascular-related structures (VRSs). Differences (Δ) were calculated between time points. Receiver operating characteristic (ROC) curve analyses were performed to test the baseline parameters as predictors of functional impairment at T3 and of the persistence of CT abnormalities at T12.

RESULTS

The cohort included 128 patients at T0, 133 at T3, 61 at T12, and 34 at T24. The GG medians were 8.44%, 0.14%, 0.13% and 0.12% at T0, T3, T12 and T24. The Ret medians were 2.79% at T0 and 0.14% at the following time points. All Δ significantly differed from 0, except between T12 and T24. The GG and VRSs at T0 achieved AUCs of 0.73 as predictors of functional impairment, and area under the curves (AUCs) of 0.71 and 0.72 for the persistence of CT abnormalities at T12.

CONCLUSIONS

CALIPER accurately quantified the CT changes up to the 24-month follow-up. Resolution mostly occurred at T3, and Ret persisting at T12 was almost unchanged at T24. The baseline parameters were good predictors of functional impairment at T3 and of abnormalities' persistence at T12.

Functional Impairment in Small Airways Associated With the Breathlessness Symptoms in Long-Coronavirus Disease

PURPOSE

This study aimed to determine the association between functional impairment in small airways and symptoms of dyspnea in patients with Long-coronavirus disease (COVID), using imaging and computational modeling analysis.

PATIENTS AND METHODS

Thirty-four patients with Long-COVID underwent thoracic computed tomography and hyperpolarized Xenon-129 magnetic resonance imaging (HP Xe MRI) scans. Twenty-two answered dyspnea-12 questionnaires. We used a computed tomography-based full-scale airway network (FAN) flow model to simulate pulmonary ventilation. The ventilation distribution projected on a coronal plane and the percentage lobar ventilation modeled in the FAN model were compared with the HP Xe MRI data. To assess the ventilation heterogeneity in small airways, we calculated the fractal dimensions of the impaired ventilation regions in the HP Xe MRI and FAN models.

RESULTS

The ventilation distribution projected on a coronal plane showed an excellent resemblance between HP Xe MRI scans and FAN models (structure similarity index: 0.87 ± 0.04). In both the image and the model, the existence of large clustered ventilation defects was not identifiable regardless of dyspnea severity. The percentage lobar ventilation of the HP Xe MRI and FAN model showed a strong correlation (ρ = 0.63, P < 0.001). The difference in the fractal dimension of impaired ventilation zones between the low and high dyspnea-12 score groups was significant (HP Xe MRI: 1.97 [1.89 to 2.04] and 2.08 [2.06 to 2.14], P = 0.005; FAN: 2.60 [2.59 to 2.64] and 2.64 [2.63 to 2.65], P = 0.056).

CONCLUSIONS

This study has identified a potential association of small airway functional impairment with breathlessness in Long-COVID, using fractal analysis of HP Xe MRI scans and FAN models.

CT-based Assessment at 6-Month Follow-up of COVID-19 Pneumonia patients in China

This study aimed to assess pulmonary changes at 6-month follow-up CT and predictors of pulmonary residual abnormalities and fibrotic-like changes in COVID-19 pneumonia patients in China following relaxation of COVID restrictions in 2022. A total of 271 hospitalized patients with COVID-19 pneumonia admitted between November 29, 2022 and February 10, 2023 were prospectively evaluated at 6 months. CT characteristics and Chest CT scores of pulmonary abnormalities were compared between the initial and the 6-month CT. The association of demographic and clinical factors with CT residual abnormalities or fibrotic-like changes were assessed using logistic regression. Follow-up CT scans were obtained at a median of 177 days (IQR, 170-185 days) after hospital admission. Pulmonary residual abnormalities and fibrotic-like changes were found in 98 (36.2%) and 39 (14.4%) participants. In multivariable analysis of pulmonary residual abnormalities and fibrotic-like changes, the top three predictive factors were invasive ventilation (OR 13.6; 95% CI 1.9, 45; P < .001), age > 60 years (OR 9.1; 95% CI 2.3, 39; P = .01), paxlovid (OR 0.11; 95% CI 0.04, 0.48; P = .01) and invasive ventilation (OR 10.3; 95% CI 2.9, 33; P = .002), paxlovid (OR 0.1; 95% CI 0.03, 0.48; P = .01), smoker (OR 9.9; 95% CI 2.4, 31; P = .01), respectively. The 6-month follow-up CT of recent COVID-19 pneumonia cases in China showed a considerable proportion of the patients with pulmonary residual abnormalities and fibrotic-like changes. Antivirals against SARS-CoV-2 like paxlovid may be beneficial for long-term regression of COVID-19 pneumonia.

Standardization of a CT Protocol for Imaging Patients with Suspected COVID-19-A RACOON Project

CT protocols that diagnose COVID-19 vary in regard to the associated radiation exposure and the desired image quality (IQ). This study aims to evaluate CT protocols of hospitals participating in the RACOON (Radiological Cooperative Network) project, consolidating CT protocols to provide recommendations and strategies for future pandemics. In this retrospective study, CT acquisitions of COVID-19 patients scanned between March 2020 and October 2020 (RACOON phase 1) were included, and all non-contrast protocols were evaluated. For this purpose, CT protocol parameters, IQ ratings, radiation exposure (CTDIvol), and central patient diameters were sampled. Eventually, the data from 14 sites and 534 CT acquisitions were analyzed. IQ was rated good for 81% of the evaluated examinations. Motion, beam-hardening artefacts, or image noise were reasons for a suboptimal IQ. The tube potential ranged between 80 and 140 kVp, with the majority between 100 and 120 kVp. CTDIvol was 3.7 ± 3.4 mGy. Most healthcare facilities included did not have a specific non-contrast CT protocol. Furthermore, CT protocols for chest imaging varied in their settings and radiation exposure. In future, it will be necessary to make recommendations regarding the required IQ and protocol parameters for the majority of CT scanners to enable comparable IQ as well as radiation exposure for different sites but identical diagnostic questions.

Lung, Pleura, and Diaphragm Point-of-Care Ultrasound

Thoracic Ultrasonography involves the ultrasonographic examination of the lungs, pleura, and diaphragm. This provides a plethora of clinical information during the point of care assessment of patients. The air filled lungs create consistent artifacts and careful examination and understanding of these artefactual signs can provide useful information on underlying clinicopathologic states. This review aims to provide a review of the ultrasound signs and features that can be seen in horacic ultrasonography and summarize the clinical evidence to support its use.

Derivation and validation of novel integrated inpatient mortality prediction score for COVID-19 (IMPACT) using clinical, laboratory, and AI-processed radiological parameter upon admission: a multicentre study

Limited studies explore the use of AI for COVID-19 prognostication. This study investigates the relationship between AI-aided radiographic parameters, clinical and laboratory data, and mortality in hospitalized COVID-19 patients. We conducted a multicentre retrospective study. The derivation and validation cohort comprised of 512 and 137 confirmed COVID-19 patients, respectively. Variable selection for constructing an in-hospital mortality scoring model was performed using the least absolute shrinkage and selection operator, followed by logistic regression. The accuracy of the scoring model was assessed using the area under the receiver operating characteristic curve. The final model included eight variables: anosmia (OR: 0.280; 95%CI 0.095-0.826), dyspnoea (OR: 1.684; 95%CI 1.049-2.705), loss of consciousness (OR: 4.593; 95%CI 1.702-12.396), mean arterial pressure (OR: 0.928; 95%CI 0.900-0.957), peripheral oxygen saturation (OR: 0.981; 95%CI 0.967-0.996), neutrophil % (OR: 1.034; 95%CI 1.013-1.055), serum urea (OR: 1.018; 95%CI 1.010-1.026), affected lung area score (OR: 1.026; 95%CI 1.014-1.038). The Integrated Inpatient Mortality Prediction Score for COVID-19 (IMPACT) demonstrated a predictive value of 0.815 (95% CI 0.774-0.856) in the derivation cohort. Internal validation resulted in an AUROC of 0.770 (95% CI 0.661-0.879). Our study provides valuable evidence of the real-world application of AI in clinical settings. However, it is imperative to conduct prospective validation of our findings, preferably utilizing a control group and extending the application to broader populations.

Epicardial Adipose Tissue as a Prognostic Marker in COVID-19

BACKGROUND/AIM

Epicardial adipose tissue (EAT) has been established as a quantitative imaging biomarker associated with the prognosis of several diseases, especially cardiovascular diseases. The cardiac injury by coronavirus disease 2019 (COVID-19) might be linked to the EAT. This study aimed to use this prognostic marker derived from computed tomography (CT) images to predict 30-day mortality in patients with COVID-19.

PATIENTS AND METHODS

Consecutive patients with COVID-19 were retrospectively screened between 2020 and 2022. Overall, 237 patients (78 female, 32.9%) were included in the present study. The study end-point was the 30-day mortality. EAT was measured using the diagnostic CT in a semiquantitative manner. EAT volume and density were measured for each patient.

RESULTS

Overall, 70 patients (29.5%) died within the 30-day observation period and 143 patients (60.3%) were admitted to the intensive care unit (ICU). The mean EAT volume was 140.9±89.1 cm3 in survivors and 132.9±77.7 cm3 in non-survivors, p=0.66. The mean EAT density was -71.9±8.1 Hounsfield units (HU) in survivors, and -67.3±8.4 HU in non-survivors, p=0.0001. EAT density was associated with 30-day mortality (p<0.0001) and ICU admission (p<0.0001). EAT volume was not associated with mortality and/or ICU admission.

CONCLUSION

EAT density was associated with 30-day mortality and ICU admission in patients with COVID-19.

Comparison of Computed Tomography Findings between Adult and Pediatric COVID-19 Patients

PURPOSE

This study aims to compare chest computed tomography (CT) findings between adult and pediatric patients with coronavirus disease-19 (COVID-19) pneumonia.

MATERIALS AND METHODS

This study included 30 pediatric patients aged 1 to 17 years and 30 adult patients over 18 years of age with COVID-19 pneumonia confirmed by reverse transcriptase-polymerase chain reaction (RT-PCR) who have findings related to COVID-19 on Chest Computed Tomography. The CT findings of adult and pediatric patients were compared with a z-test.

RESULTS

Bilateral involvement (p:0.00056), involvement in all five lobes (p<0.00001), and central and peripheral involvement (p:0.01928) were significantly higher in the adult group compared to the pediatric group. In the pediatric group, the frequency of unilateral involvement (p:0.00056), involvement of solitary lobe (p:0.00132), and peripheral involvement (p: 0.01928) were significantly higher than in the adult group. The most common parenchymal finding in adults and pediatric patients was ground-glass opacities (100% and 83%, respectively). Among the parenchymal findings in adults, ground-glass opacities with consolidation (63%) were the second most common finding, followed by air bronchogram (60%) in adults, while in pediatric patients, halo sign (27%) and nodule (27%) were the second most common, followed by the ground-glass opacities with consolidation (23%).

CONCLUSION

The CT findings of pediatric COVID-19 patients must be well-known as the course of the disease is usually less severe, and the radiological findings are uncertain when compared with adults.

Role of Imaging in Diagnosis and Management of COVID-19: Evidence-Based Approaches

Imaging has been demonstrated to play a crucial role in both the diagnosis and management of COVID-19. Depending on resources, pre-test probability, and risk factors for severe disease progression, real-time polymerase chain reaction (RT-PCR) testing may be followed by chest radiography (CXR) or chest computed tomography (CT) to further aid in diagnosis or excluding COVID-19 disease. SARS-CoV-2 has been shown not only to pathologically impact the pulmonary system, but also the cardiovascular, gastrointestinal, and neurological systems to name a few. Imaging has again proven useful in further investigating and managing extrapulmonary disease, with the use of echocardiogram, CT angiography of the cardiovascular and cerebrovascular structures, MRI of the brain, as well as ultrasound of the abdomen and CT of the abdomen and pelvis proving particularly useful. Research in artificial intelligence and its application in the diagnosis of COVID-19 and disease severity prediction is underway, and point-of-care ultrasound is an emerging bedside technique that may allow for more efficient and timely diagnosis of COVID-19.

ERS International Congress 2023: highlights from the Thoracic Oncology Assembly

Lung cancer is the leading cause of cancer mortality in the world. It greatly affects the patients' quality of life, and is thus a challenge for the daily practice in respiratory medicine. Advances in the genetic knowledge of thoracic tumours' mutational landscape, and the development of targeted therapies and immune checkpoint inhibitors, have led to a paradigm shift in the treatment of lung cancer and pleural mesothelioma. During the 2023 European Respiratory Society Congress in Milan, Italy, experts from all over the world presented their high-quality research and reviewed best clinical practices. Lung cancer screening, management of early stages of lung cancer, application of artificial intelligence and biomarkers were discussed and they will be summarised here.

Inter-observer Variability in the Analysis of CO-RADS Classification for COVID-19 Patients

During the early stages of the pandemic, computed tomography (CT) of the chest, along with serological and clinical data, was frequently utilized in diagnosing COVID-19, particularly in regions facing challenges such as shortages of PCR kits. In these circumstances, CT scans played a crucial role in diagnosing COVID-19 and guiding patient management. The COVID-19 Reporting and Data System (CO-RADS) was established as a standardized reporting system for cases of COVID-19 pneumonia. Its implementation necessitates a high level of agreement among observers to prevent any potential confusion. This study aimed to assess the inter-observer agreement between physicians from different specialties with variable levels of experience in their CO-RADS scoring of CT chests for confirmed COVID-19 patients, and to assess the feasibility of applying this reporting system to those having little experience with it. All chest CT images of patients with positive RT-PCR tests for COVID-19 were retrospectively reviewed by seven observers. The observers were divided into three groups according to their type of specialty (three radiologists, three house officers, and one pulmonologist). The observers assessed each image and categorized the patients into five CO-RADS groups. A total of 630 participants were included in this study. The inter-observer agreement was almost perfect among the radiologists, substantial among a pulmonologist and the house officers, and moderate-to-substantial among the radiologists, the pulmonologist, and the house officers. There was substantial to almost perfect inter-observer agreement when reporting using the CO-RADS among observers with different experience levels. Although the inter-observer variability among the radiologists was high, it decreased compared to the pulmonologist and house officers. Radiologists, house officers, and pulmonologists applying the CO-RADS can accurately and promptly identify typical CT imaging features of lung involvement in COVID-19.

Diagnosis and detection of pneumonia using weak-label based on X-ray images: a multi-center study

PURPOSE

Development and assessment the deep learning weakly supervised algorithm for the classification and detection pneumonia via X-ray.

METHODS

This retrospective study analyzed two publicly available dataset that contain X-ray images of pneumonia cases and normal cases. The first dataset from Guangzhou Women and Children's Medical Center. It contains a total of 5,856 X-ray images, which are divided into training, validation, and test sets with 8:1:1 ratio for algorithm training and testing. The deep learning algorithm ResNet34 was employed to build diagnostic model. And the second public dataset were collated by researchers from Qatar University and the University of Dhaka along with collaborators from Pakistan and Malaysia and some medical doctors. A total of 1,300 images of COVID-19 positive cases, 1,300 normal images and 1,300 images of viral pneumonia for external validation. Class activation map (CAM) were used to location the pneumonia lesions.

RESULTS

The ResNet34 model for pneumonia detection achieved an AUC of 0.9949 [0.9910-0.9981] (with an accuracy of 98.29% a sensitivity of 99.29% and a specificity of 95.57%) in the test dataset. And for external validation dataset, the model obtained an AUC of 0.9835[0.9806-0.9864] (with an accuracy of 94.62%, a sensitivity of 92.35% and a specificity of 99.15%). Moreover, the CAM can accurately locate the pneumonia area.

CONCLUSION

The deep learning algorithm can accurately detect pneumonia and locate the pneumonia area based on weak supervision information, which can provide potential value for helping radiologists to improve their accuracy of detection pneumonia patients through X-ray images.

CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations

Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 which enters the body via the angiotensin-converting enzyme 2 (ACE2) and altering its gene expression. Altered ACE2 plays a crucial role in the pathogenesis of COVID-19. Gene expression profiling, however, is invasive and costly, and is not routinely performed. In contrast, medical imaging such as computed tomography (CT) captures imaging features that depict abnormalities, and it is widely available. Computerized quantification of image features has enabled 'radiogenomics', a research discipline that identifies image features that are associated with molecular characteristics. Radiogenomics between ACE2 and COVID-19 has yet to be done primarily due to the lack of ACE2 expression data among COVID-19 patients. Similar to COVID-19, patients with lung adenocarcinoma (LUAD) exhibit altered ACE2 expression and, LUAD data are abundant. We present a radiogenomics framework to derive image features (ACE2-RGF) associated with ACE2 expression data from LUAD. The ACE2-RGF was then used as a surrogate biomarker for ACE2 expression. We adopted conventional feature selection techniques including ElasticNet and LASSO. Our results show that: i) the ACE2-RGF encoded a distinct collection of image features when compared to conventional techniques, ii) the ACE2-RGF can classify COVID-19 from normal subjects with a comparable performance to conventional feature selection techniques with an AUC of 0.92, iii) ACE2-RGF can effectively identify patients with critical illness with an AUC of 0.85. These findings provide unique insights for automated COVID-19 analysis and future research.

Multimodal Biomedical Data Fusion Using Sparse Canonical Correlation Analysis and Cooperative Learning: A Cohort Study on COVID-19

Through technological innovations, patient cohorts can be examined from multiple views with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict outcomes. Here, we aim to present our approach for analyzing multimodal data using unsupervised and supervised sparse linear methods in a COVID-19 patient cohort. This prospective cohort study of 149 adult patients was conducted in a tertiary care academic center. First, we used sparse canonical correlation analysis (CCA) to identify and quantify relationships across different data modalities, including viral genome sequencing, imaging, clinical data, and laboratory results. Then, we used cooperative learning to predict the clinical outcome of COVID-19 patients. We show that serum biomarkers representing severe disease and acute phase response correlate with original and wavelet radiomics features in the LLL frequency channel (corr(Xu1, Zv1) = 0.596, p-value < 0.001). Among radiomics features, histogram-based first-order features reporting the skewness, kurtosis, and uniformity have the lowest negative, whereas entropy-related features have the highest positive coefficients. Moreover, unsupervised analysis of clinical data and laboratory results gives insights into distinct clinical phenotypes. Leveraging the availability of global viral genome databases, we demonstrate that the Word2Vec natural language processing model can be used for viral genome encoding. It not only separates major SARS-CoV-2 variants but also allows the preservation of phylogenetic relationships among them. Our quadruple model using Word2Vec encoding achieves better prediction results in the supervised task. The model yields area under the curve (AUC) and accuracy values of 0.87 and 0.77, respectively. Our study illustrates that sparse CCA analysis and cooperative learning are powerful techniques for handling high-dimensional, multimodal data to investigate multivariate associations in unsupervised and supervised tasks.

Could the presence of adrenal incidentaloma negatively affect COVID 19 outcomes?

BACKGROUND AND OBJECTIVE

Adrenal incidentalomas (AIs) are lesions larger than 1 cm that are incidentally detected in the adrenal glands. Chest computed tomography (CCT) is widely used in the first evaluation of patients with suspected coronavirus disease (COVID-19) that resulted in many incidental findings in the thorax and upper abdomen. In this study, we aimed to investigate the frequency of AI and their effects on the course and outcome of COVID-19 regardless of functional status.

MATERIAL AND METHODS

We included 2493 patients aged between 18 and 99 years and whose adrenal glands were clearly visible in CCT images. They were divided into two groups: those with AI (AI group) and without AI (Control group).

RESULTS

AI was detected in 148 (5.93%) patients and 80 (54.1%) of them were male. There was no difference in sex distribution between the groups, but the median age of patients with AI was significantly higher than those without AI [54.5 (20-94 years) vs. 42 (18-99 years); p < 0.001)]. In addition, in the AI group, both hospitalizations due to COVID-19-related conditions (30.4 vs. 21.2%, p = 0.008) and the mortality rate experienced during this time was significantly higher (14.7 vs. 7%, p < 0.001) diseases. The AI group had a significantly higher comorbidity rate than the control group (61.5 vs. 41.9%, p < 0.001). The most common comorbid diseases were hypertension, cardiovascular diseases, diabetes mellitus, respiratory system diseases, and hyperlipidaemia. Advanced age and male gender in terms of mortality, advanced age and covid 19 positivity in terms of hospitalization were determined as significant risk factors.

CONCLUSIONS

The presence of AI may increase the morbidity and mortality rates associated with COVID-19, regardless of their functional status. Therefore, patients subjected to CCT imaging for COVID-19-related lung diseases should also be evaluated for AI. Careful follow-up of patients with COVID-19 and AI is necessary to monitor the progression of COVID-19.

EVALUATION OF PATIENTS WITH COVID-19 IN THE EARLY HYPOXEMIC STAGE AND PATIENTS WITH VIRAL RESPIRATORY TRACT INFECTION IN TERMS OF PULMONARY HYPERTENSION

Arterial hypoxemia occurs in many COVID-19 patients. Hypoxemia is one of the causes of pulmonary hypertension (PH). Main pulmonary artery dilatation and the main pulmonary artery diameter (mPAD) to ascending aorta diameter (AAD) ratio of ≥1 are significant findings regarding PH. In this study, COVID-19 patients and non-COVID-19 patients with viral respiratory tract infection were evaluated retrospectively in terms of PH. A total of 124 patients (71 male and 53 female), age range 18-85 years, were included in the study as case group and control group. Thoracic computed tomography (CT) images, blood and biochemical parameters, and demographic information were compared between the case group and control group. The normality of numerical variables was examined with Kolmogorov-Smirnov test and homogeneity of the variances with Levene's test. This is the first study researching the effect of early hypoxemic stage COVID-19 infection on development of PH. As a result, it was specified that COVID-19 infection had no effects on mPAD, whereas it had a positive effect on AAD and thus led to a decrease in the mPAD/AAD ratio. Through these values, which could be easily calculated from thoracic CT images, the changes caused by COVID-19 infection on vessel diameters were put forward.

CT coronary artery calcification score as a prognostic marker in COVID-19

Background

Coronary artery calcification (CA) score has been established as a quantitative imaging biomarker to reflect arteriosclerosis and general vessel status. It is established as an important prognostic factor for coronary heart disease but also for other disease entities. Our aim was to use this imaging marker derived from computed tomography (CT) images to elucidate the prognostic relevance in patients with coronavirus disease 2019 (COVID-19).

Methods

The clinical database was retrospectively screened for patients with COVID-19 between 2020 and 2022. A total of 241 patients (85 female patients, 35.3%) were included into the analysis. CA scoring was performed semiquantitatively on thoracic CT images with the established Weston score.

Results

Overall, 61 patients (25.3%) of the investigated patient sample died. In survivors, the mean CA score was 2.3±3.0 and in non-survivors, it was 4.2±4.1 (P=0.002). In univariable regression analysis, CA was associated with 30-day mortality [odds ratio (OR) =1.15; 95% confidence interval (CI): 1.06-1.25, P<0.001]. These results were confirmed by the multivariable regression analysis adjusted for age and sex, the CA score predicted 30-day mortality (OR =1.28; 95% CI: 1.08-1.4, P=0.002).

Conclusions

CA score is an independent risk factor in COVID-19. As CA scoring can easily be performed by the radiologist, it should be further investigated as an imaging marker in patients with COVID-19 and potentially be translated into clinical routine.

Obstructive jaundice treatment during the COVID-19 pandemic: retrospective cohort study at a single tertiary care center in Serbia

OBJECTIVE

We aimed to compare mortality and complication rates in patients treated for obstructive jaundice before and during the COVID-19 pandemic in a tertiary care center in Serbia.

METHODS

We conducted a retrospective cohort study among a first group of patients treated between 1 January 2017 and 1 January 2019. The second group was treated between 1 March 2020 and 1 March 2022.

RESULTS

The first group comprised 35 patients, and the second group (in which all patients were SARS-CoV-2 positive) included 18 patients; 37 and 16 patients were treated for malignant and benign diseases, respectively. The groups did not differ significantly regarding the diagnoses and treatment received. The second group showed significantly higher aspartate aminotransferase levels and lower white blood cell, C-reactive protein, and interleukin 6 levels. Mortality and complication rates did not differ significantly between groups. All deceased patients in the second group had significant radiologic findings associated with COVID-19 pneumonia.

CONCLUSIONS

COVID-19 infection is a risk factor in treating obstructive jaundice. This study illustrates the potential influence of COVID-19 on mortality after obstructive jaundice treatment. COVID-19 pneumonia may be a significant risk factor for mortality in patients treated for obstructive jaundice.

Quantitative Analysis for Lung Disease on Thin-Section CT

Thin-section computed tomography (CT) is widely employed not only for assessing morphology but also for evaluating respiratory function. Three-dimensional images obtained from thin-section CT provide precise measurements of lung, airway, and vessel volumes. These volumetric indices are correlated with traditional pulmonary function tests (PFT). CT also generates lung histograms. The volume ratio of areas with low and high attenuation correlates with PFT results. These quantitative image analyses have been utilized to investigate the early stages and disease progression of diffuse lung diseases, leading to the development of novel concepts such as pre-chronic obstructive pulmonary disease (pre-COPD) and interstitial lung abnormalities. Quantitative analysis proved particularly valuable during the COVID-19 pandemic when clinical evaluations were limited. In this review, we introduce CT analysis methods and explore their clinical applications in the context of various lung diseases. We also highlight technological advances, including images with matrices of 1024 × 1024 and slice thicknesses of 0.25 mm, which enhance the accuracy of these analyses.

Low-rank latent matrix-factor prediction modeling for generalized high-dimensional matrix-variate regression

Motivated by diagnosing the COVID-19 disease using two-dimensional (2D) image biomarkers from computed tomography (CT) scans, we propose a novel latent matrix-factor regression model to predict responses that may come from an exponential distribution family, where covariates include high-dimensional matrix-variate biomarkers. A latent generalized matrix regression (LaGMaR) is formulated, where the latent predictor is a low-dimensional matrix factor score extracted from the low-rank signal of the matrix variate through a cutting-edge matrix factor model. Unlike the general spirit of penalizing vectorization plus the necessity of tuning parameters in the literature, instead, our prediction modeling in LaGMaR conducts dimension reduction that respects the geometric characteristic of intrinsic 2D structure of the matrix covariate and thus avoids iteration. This greatly relieves the computation burden, and meanwhile maintains structural information so that the latent matrix factor feature can perfectly replace the intractable matrix-variate owing to high-dimensionality. The estimation procedure of LaGMaR is subtly derived by transforming the bilinear form matrix factor model onto a high-dimensional vector factor model, so that the method of principle components can be applied. We establish bilinear-form consistency of the estimated matrix coefficient of the latent predictor and consistency of prediction. The proposed approach can be implemented conveniently. Through simulation experiments, the prediction capability of LaGMaR is shown to outperform some existing penalized methods under diverse scenarios of generalized matrix regressions. Through the application to a real COVID-19 dataset, the proposed approach is shown to predict efficiently the COVID-19.

Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection

This work aimed to explore the diagnostic value of a deep convolutional neural network (CNN) combined with computed tomography (CT) images in patients with severe pneumonia complicated with pulmonary infection. A total of 120 patients with severe pneumonia complicated by pulmonary infection admitted to the hospital were selected as research subjects and underwent CT imaging scans. The empty convolution (EC) and U-net phase were combined to construct an EC-U-net, which was applied to process the CT images. The results showed that the learning rate of the EC-U-net model decreased substantially with increasing training times until it stabilized and reached zero after 40 training times. The segmentation result of the EC-U-net model for the CT image was very similar to that of the mask image, except for some deviations in edge segmentation. The EC-U-net model exhibited a significantly smaller cross-entropy loss function (CELF) and a higher Dice coefficient than the CNN algorithm. The diagnostic accuracy of CT images based on the EC-U-net model for severe pneumonia complicated with pulmonary infection was substantially higher than that of CT images alone, while the false negative rate (FNR) and false positive rate (FPR) were substantially lower (P < 0.05). Moreover, the true positive rates (TPRs) of CT images based on the EC-U-net model for patchy high-density shadows, diffuse ground glass density shadows, pleural effusion, and lung consolidation were obviously higher than those of the original CT images (P < 0.05). In short, the EC-U-net model was superior to the traditional algorithm regarding the overall performance of CT image segmentation, which can be clinically applied. CT images based on the EC-U-net model can clearly display pulmonary infection lesions, improve the clinical diagnosis of severe pneumonia complicated with pulmonary infection, and help to screen early pulmonary infection and carry out symptomatic treatment.

Digital tomosynthesis improves chest radiograph accuracy and reduces microbiological false negatives in COVID-19 diagnosis

PURPOSE

Diagnosing pneumonia by radiograph is improvable. We aimed (a) to compare radiograph and digital thoracic tomosynthesis (DTT) performances and agreement for COVID-19 pneumonia diagnosis, and (b) to assess the DTT ability for COVID-19 diagnosis when polymerase chain reaction (PCR) and radiograph are negative.

METHODS

Two emergency radiologists with 11 (ER1) and 14 experience-years (ER2) retrospectively evaluated radiograph and DTT images acquired simultaneously in consecutively clinically suspected COVID-19 pneumonia patients in March 2020-January 2021. Considering PCR and/or serology as reference standard, DTT and radiograph diagnostic performance and interobserver agreement, and DTT contributions in unequivocal, equivocal, and absent radiograph opacities were analysed by the area under the curve (AUC), Cohen's Kappa, Mc-Nemar's and Wilcoxon tests.

RESULTS

We recruited 480 patients (49 ± 15 years, 277 female). DTT increased ER1 (from 0.76, CI95% 0.7-0.8 to 0.79, CI95% 0.7-0.8; P=.04) and ER2 (from 0.77 CI95% 0.7-0.8 to 0.80 CI95% 0.8-0.8, P=.02) radiograph-AUCs, sensitivity, specificity, predictive values, and positive likelihood ratio. In false negative microbiological cases, DTT suggested COVID-19 pneumonia in 13% (4/30; P=.052, ER1) and 20% (6/30; P=.020, ER2) more than radiograph. DTT showed new or larger opacities in 33-47% of cases with unequivocal opacities in radiograph, new opacities in 2-6% of normal radiographs and reduced equivocal opacities by 13-16%. Kappa increased from 0.64 (CI95% 0.6-0.8) to 0.7 (CI95% 0.7-0.8) for COVID-19 pneumonia probability, and from 0.69 (CI95% 0.6-0.7) to 0.76 (CI95% 0.7-0.8) for pneumonic extension.

CONCLUSION

DTT improves radiograph performance and agreement for COVID-19 pneumonia diagnosis and reduces PCR false negatives.