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

Medical imaging of pulmonary disease in SARS-CoV-2-exposed non-human primates

Chest X-ray (CXR), computed tomography (CT), and positron emission tomography-computed tomography (PET-CT) are noninvasive imaging techniques widely used in human and veterinary pulmonary research and medicine. These techniques have recently been applied in studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-exposed non-human primates (NHPs) to complement virological assessments with meaningful translational readouts of lung disease. Our review of the literature indicates that medical imaging of SARS-CoV-2-exposed NHPs enables high-resolution qualitative and quantitative characterization of disease otherwise clinically invisible and potentially provides user-independent and unbiased evaluation of medical countermeasures (MCMs). However, we also found high variability in image acquisition and analysis protocols among studies. These findings uncover an urgent need to improve standardization and ensure direct comparability across studies.

Characteristics and Prognostic Factors of Pulmonary Fibrosis After COVID-19 Pneumonia

Background

Few is known about the long-term pulmonary sequelae after COVID-19 infection. Hence, the aim of this study is to characterize patients with persisting pulmonary sequelae at follow-up after hospitalization. We also aimed to explore clinical and radiological predictors of pulmonary fibrosis following COVID-19.

Methods

Two hundred and 20 consecutive patients were evaluated at 3–6 months after discharge with high-resolution computed tomography (HRCT) and categorized as recovered (REC) or not recovered (NOT-REC). Both HRCTs at hospitalization (HRCT₀), when available, and HRCT₁ during follow-up were analyzed semiquantitatively as follows: ground-glass opacities (alveolar score, AS), consolidations (CONS), and reticulations (interstitial score, IS).

Results

A total of 175/220 (80%) patients showed disease resolution at their initial radiological evaluation following discharge. NOT-REC patients (45/220; 20%) were mostly older men [66 (35–85) years vs. 56 (19–87); p = 0.03] with a longer in-hospital stay [16 (0–75) vs. 8 (1–52) days; p < 0.0001], and lower P/F at admission [233 (40–424) vs. 318 (33–543); p = 0.04]. Moreover, NOT-REC patients presented, at hospital admission, higher ALV [14 (0.0–62.0) vs. 4.4 (0.0–44.0); p = 0.0005], CONS [1.9 (0.0–26.0) vs. 0.4 (0.0–18.0); p = 0.0064], and IS [11.5 (0.0– 29.0) vs. 0.0 (0.0–22.0); p < 0.0001] compared to REC patients. On multivariate analysis, the presence of CONS and IS at HRCT₀ was independent predictors of radiological sequelae at follow-up [OR 14.87 (95% CI: 1.25–175.8; p = 0.03) and 28.9 (95% CI: 2.17–386.6; p = 0.01, respectively)].

Conclusions

In our population, only twenty percent of patients showed persistent lung abnormalities at 6 months after hospitalization for COVID-19 pneumonia. These patients are predominantly older men with longer hospital stay. The presence of reticulations and consolidation on HRCT at hospital admission predicts the persistence of radiological abnormalities during follow-up.

The Role of Ionizing Radiation for Diagnosis and Treatment against COVID-19: Evidence and Considerations

The Coronavirus disease 2019 (COVID-19) pandemic continues to spread worldwide with over 260 million people infected and more than 5 million deaths, numbers that are escalating on a daily basis. Frontline health workers and scientists diligently fight to alleviate life-threatening symptoms and control the spread of the disease. There is an urgent need for better triage of patients, especially in third world countries, in order to decrease the pressure induced on healthcare facilities. In the struggle to treat life-threatening COVID-19 pneumonia, scientists have debated the clinical use of ionizing radiation (IR). The historical literature dating back to the 1940s contains many reports of successful treatment of pneumonia with IR. In this work, we critically review the literature for the use of IR for both diagnostic and treatment purposes. We identify details including the computed tomography (CT) scanning considerations, the radiobiological basis of IR anti-inflammatory effects, the supportive evidence for low dose radiation therapy (LDRT), and the risks of radiation-induced cancer and cardiac disease associated with LDRT. In this paper, we address concerns regarding the effective management of COVID-19 patients and potential avenues that could provide empirical evidence for the fight against the disease.

Incidental radiological findings suggestive of COVID-19 in asymptomatic patients

Despite routine screening of patients for coronavirus disease 2019 (COVID-19) symptoms and signs at hospital entrances, patients may slip between the cracks and be incidentally discovered to have lung findings that could indicate COVID-19 infection on imaging obtained for other reasons. Multiple case reports and case series have been published to identify the pattern of this highly infectious disease. This article addresses the radiographic findings in different imaging modalities that may be incidentally seen in asymptomatic patients who carry COVID-19. In general, findings of COVID-19 infection may appear in computed tomography (CT), magnetic resonance imaging, positron emission tomography-CT, ultrasound, or plain X-rays that show lung or only apical or basal cuts. The identification of these characteristics by radiologists and clinicians is crucial because this would help in the early recognition of cases so that a rapid treatment protocol can be established, the immediate isolation to reduce community transmission, and the organization of close monitoring. Thus, it is important to both the patient and the physician that these findings are highlighted and reported.

Evaluating the Association Between Comorbidities and COVID-19 Severity Scoring on Chest CT Examinations Between the Two Waves of COVID-19: An Imaging Study Using Artificial Intelligence

Background

Coronavirus disease 2019 (COVID-19) has accounted for over 352 million cases and five million deaths globally. Although it affects populations across all nations, developing or transitional, of all genders and ages, the extent of the specific involvement is not very well known. This study aimed to analyze and determine how different were the first and second waves of the COVID-19 pandemic by assessing computed tomography severity scores (CT-SS).

Methodology

This was a retrospective, cross-sectional, observational study performed at a tertiary care Institution. We included 301 patients who underwent CT of the chest between June and October 2020 and 1,001 patients who underwent CT of the chest between February and April 2021. All included patients were symptomatic and were confirmed to be COVID-19 positive. We compared the CT-SS between the two datasets. In addition, we analyzed the distribution of CT-SS concerning age, comorbidities, and gender, as well as their differences between the two waves of COVID-19. Analysis was performed using the SPSS version 22 (IBM Corp., Armonk, NY, USA). The artificial intelligence platform U-net architecture with Xception encoder was used in the analysis.

Results

The study data revealed that while the mean CT-SS did not differ statistically between the two waves of COVID-19, the age group most affected in the second wave was almost a decade younger. While overall the disease had a predilection toward affecting males, our findings showed that females were more afflicted in the second wave of COVID-19 compared to the first wave. In particular, the disease had an increased severity in cases with comorbidities such as hypertension, diabetes mellitus, bronchial asthma, and tuberculosis.

Conclusions

This assessment demonstrated no significant difference in radiological severity score between the two waves of COVID-19. The secondary objective revealed that the two waves showed demographical differences. Hence, we iterate that no demographical subset of the population should be considered low risk as the disease manifestation was heterogeneous.

Inter-Observer Agreement between Low-Dose and Standard-Dose CT with Soft and Sharp Convolution Kernels in COVID-19 Pneumonia

Computed tomography (CT) has been an essential diagnostic tool during the COVID-19 pandemic. The study aimed to develop an optimal CT protocol in terms of safety and reliability. For this, we assessed the inter-observer agreement between CT and low-dose CT (LDCT) with soft and sharp kernels using a semi-quantitative severity scale in a prospective study (Moscow, Russia). Two consecutive scans with CT and LDCT were performed in a single visit. Reading was performed by ten radiologists with 3–25 years’ experience. The study included 230 patients, and statistical analysis showed LDCT with a sharp kernel as the most reliable protocol (percentage agreement 74.35 ± 43.77%), but its advantage was marginal. There was no significant correlation between radiologists’ experience and average percentage agreement for all four evaluated protocols. Regarding the radiation exposure, CTDI_vol was 3.6 ± 0.64 times lower for LDCT. In conclusion, CT and LDCT with soft and sharp reconstructions are equally reliable for COVID-19 reporting using the “CT 0-4” scale. The LDCT protocol allows for a significant decrease in radiation exposure but may be restricted by body mass index.

Impact of sarcopenia in SARS-CoV-2 patients during two different epidemic waves

Background

Sarcopenia was reported to be associated with poor clinical outcome, higher incidence of community-acquired pneumonia, increased risk of infections and reduced survival in different clinical settings. The aim of our work is to evaluate the prognostic role of sarcopenia in patients with the 2019 novel coronavirus disease (COVID-19).

Materials and methods

272 COVID-19 patients admitted to the University Hospital of Modena (Italy) from February 2020 to January 2021 were retrospectively studied. All included patients underwent a chest computed tomography (CT) scan to assess pneumonia during their hospitalization and showed a positive SARS-CoV-2 molecular test. Sarcopenia was defined by skeletal muscle area (SMA) evaluation at the 12th thoracic vertebra (T12). Clinical, laboratory data and adverse clinical outcome (admission to Intensive Care Unit and death) were collected for all patients.

Results

Prevalence of sarcopenia was high (41.5%) but significantly different in each pandemic wave (57.9% vs 21.6% p < 0.0000). At the multivariate analysis, sarcopenia during the first wave (Hazard Ratio 2.29, 95% confidence intervals 1.17 to 4.49 p = 0.0162) was the only independent prognostic factor for adverse clinical outcome. There were no significant differences in comorbidities and COVID19 severity in terms of pulmonary involvement at lung CT comparing during the first and second wave. Mixed pattern with peripheral and central involvement was found to be dominant in both groups.

Conclusion

We highlight the prognostic impact of sarcopenia in COVID-19 patients hospitalized during the first wave. T12 SMA could represent a potential tool to identify sarcopenic patients in particular settings. Further studies are needed to better understand the association between sarcopenia and COVID-19.

Intersections between pneumonia, lowered oxygen saturation percentage and immune activation mediate depression, anxiety, and chronic fatigue syndrome-like symptoms due to COVID-19: A nomothetic network approach

BACKGROUND

COVID-19 is associated with neuropsychiatric symptoms including increased depressive, anxiety and chronic fatigue-syndrome (CFS)-like and physiosomatic symptoms.

AIMS

To delineate the associations between affective and CFS-like symptoms in COVID-19 and chest computed tomography scan anomalies (CCTAs), oxygen saturation (SpO₂), interleukin (IL)-6, IL-10, C-Reactive Protein (CRP), albumin, calcium, magnesium, soluble angiotensin converting enzyme (ACE2) and soluble advanced glycation products (sRAGEs).

METHOD

The above biomarkers were assessed in 60 COVID-19 patients and 30 healthy controls who had measurements of the Hamilton Depression (HDRS) and Anxiety (HAM-A) and the Fibromyalgia and Chronic Fatigue (FF) Rating Scales.

RESULTS

Partial Least Squares-SEM analysis showed that reliable latent vectors could be extracted from a) key depressive and anxiety and physiosomatic symptoms (the physio-affective or PA-core), b) IL-6, IL-10, CRP, albumin, calcium, and sRAGEs (the immune response core); and c) different CCTAs (including ground glass opacities, consolidation, and crazy paving) and lowered SpO₂% (lung lesions). PLS showed that 70.0% of the variance in the PA-core was explained by the regression on the immune response and lung lesions latent vectors. One common "infection-immune-inflammatory (III) core" underpins pneumonia-associated CCTAs, lowered SpO₂ and immune activation, and this III core explains 70% of the variance in the PA core, and a relevant part of the variance in melancholia, insomnia, and neurocognitive symptoms.

DISCUSSION

Acute SARS-CoV-2 infection is accompanied by lung lesions and lowered SpO₂ which may cause activated immune-inflammatory pathways, which mediate the effects of the former on the PA-core and other neuropsychiatric symptoms due to SARS-CoV-2 infection.

Comparison of semiquantitative chest CT scoring systems to estimate severity in coronavirus disease 2019 (COVID-19) pneumonia

OBJECTIVES

To compare the clinical usefulness among three different semiquantitative computed tomography (CT) severity scoring systems for coronavirus disease 2019 (COVID-19) pneumonia.

METHODS

Two radiologists independently reviewed chest CT images in 108 patients to rate three CT scoring systems (total CT score [TSS], chest CT score [CCTS], and CT severity score [CTSS]). We made a minor modification to CTSS. Quantitative dense area ratio (QDAR: the ratio of lung involvement to lung parenchyma) was calculated using the U-net model. Clinical severity at admission was classified as severe (n = 14) or mild (n = 94). Interobserver agreement, interpretation time, and degree of correlation with clinical severity as well as QDAR were evaluated.

RESULTS

Interobserver agreement was excellent (intraclass correlation coefficient: 0.952-0.970, p < 0.001). Mean interpretation time was significantly longer in CTSS (48.9-80.0 s) than in TSS (25.7-41.7 s, p < 0.001) and CCTS (27.7-39.5 s, p < 0.001). Area under the curve for differentiating clinical severity at admission was 0.855-0.842 in TSS, 0.853-0.850 in CCTS, and 0.853-0.836 in CTSS. All scoring systems correlated with QDAR in the order of CCTS (ρ = 0.443-0.448), TSS (ρ = 0.435-0.437), and CTSS (ρ = 0.415-0.426).

CONCLUSIONS

All semiquantitative scoring systems demonstrated substantial diagnostic performance for clinical severity at admission with excellent interobserver agreement. Interpretation time was significantly shorter in TSS and CCTS than in CTSS. The correlation between the scoring system and QDAR was highest in CCTS, followed by TSS and CTSS. CCTS appeared to be the most appropriate CT scoring system for clinical practice.

KEY POINTS

• Three semiquantitative scoring systems demonstrate substantial accuracy (area under the curve: 0.836-0.855) for diagnosing clinical severity at admission and (area under the curve: 0.786-0.802) for risk of developing critical illness. • Total CT score (TSS) and chest CT score (CCTS) were considered to be more appropriate in terms of clinical usefulness as compared with CT severity score (CTSS), given the shorter interpretation time in TSS and CCTS, and the lowest correlation with quantitative dense area ratio in CTSS. • CCTS is assumed to distinguish subtle from mild lung involvement better than TSS by adopting a 5% threshold in scoring the degree of severity.

Liver Transplant Imaging prior to and during the COVID-19 Pandemic

BACKGROUND

The suspension of the surgical activity, the burden of the infection in immunosuppressed patients, and the comorbidities underlying end-stage organ disease have impacted transplant programs significantly, even life-saving procedures, such as liver transplantation.

METHODS

A review of the literature was conducted to explore the challenges faced by transplant programs and the adopted strategies to overcome them, with a focus on indications for imaging in liver transplant candidates.

RESULTS

Liver transplantation relies on an appropriate imaging method for its success. During the Coronavirus Disease 2019 (COVID-19) pandemic, chest CT showed an additional value to detect early signs of SARS-CoV-2 infection and other screening modalities are less accurate than radiology.

CONCLUSION

There is an emerging recognition of the chest CT value to recommend its use and help COVID-19 detection in patients. This examination appears highly sensitive for liver transplant candidates and recipients, who otherwise would have not undergone it, particularly when asymptomatic.

[Pulmonary nodules and pneumonia : A diagnostic guideline]

Hintergrund

Das konventionelle Röntgenbild zählt zu den am häufigsten durchgeführten radiologischen Untersuchungen. Seine Interpretation gehört zu den Grundkenntnissen jedes Radiologen.

Fragestellung

Ziel dieses Artikels ist es, häufige Zeichen und Muster der Pneumonie sowie Merkmale von Pseudoläsionen im konventionellen Röntgenbild zu erkennen und einen diagnostischen Leitfaden für junge Radiologen zu schaffen.

Methoden

Analyse aktueller Studien und Daten sowie eine Übersicht der häufigsten Zeichen und Muster im konventionellen Röntgenbild.

Ergebnisse

Die Kenntnis über häufige Zeichen und Muster im Röntgenbild bietet eine Hilfestellung in der Diagnostik und kann hinweisend für die Ursache einer Infektion sein. Häufig sind diese Zeichen jedoch unspezifisch und sollten daher immer in klinische Korrelation gesetzt werden. In der Detektion und Beurteilung von pulmonalen Rundherden gewinnt die Computertomographie (CT) durch ihre deutlich höhere Sensitivität in der Primärdiagnostik immer mehr an Bedeutung.

Schlussfolgerung

Das konventionelle Röntgenbild bildet weiterhin eine führende Rolle in der Primärdiagnostik; der Radiologe sollte jedoch die Limitationen des konventionellen Bildes kennen.

Visual classification of three computed tomography lung patterns to predict prognosis of COVID-19: a retrospective study

BACKGROUND

Quantitative evaluation of radiographic images has been developed and suggested for the diagnosis of coronavirus disease 2019 (COVID-19). However, there are limited opportunities to use these image-based diagnostic indices in clinical practice. Our aim in this study was to evaluate the utility of a novel visually-based classification of pulmonary findings from computed tomography (CT) images of COVID-19 patients with the following three patterns defined: peripheral, multifocal, and diffuse findings of pneumonia. We also evaluated the prognostic value of this classification to predict the severity of COVID-19.

METHODS

This was a single-center retrospective cohort study of patients hospitalized with COVID-19 between January 1st and September 30th, 2020, who presented with suspicious findings on CT lung images at admission (n = 69). We compared the association between the three predefined patterns (peripheral, multifocal, and diffuse), admission to the intensive care unit, tracheal intubation, and death. We tested quantitative CT analysis as an outcome predictor for COVID-19. Quantitative CT analysis was performed using a semi-automated method (Thoracic Volume Computer-Assisted Reading software, GE Health care, United States). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (- 500, 100 HU). We collected patient clinical data, including demographic and clinical variables at the time of admission.

RESULTS

Patients with a diffuse pattern were intubated more frequently and for a longer duration than patients with a peripheral or multifocal pattern. The following clinical variables were significantly different between the diffuse pattern and peripheral and multifocal groups: body temperature (p = 0.04), lymphocyte count (p = 0.01), neutrophil count (p = 0.02), c-reactive protein (p < 0.01), lactate dehydrogenase (p < 0.01), Krebs von den Lungen-6 antigen (p < 0.01), D-dimer (p < 0.01), and steroid (p = 0.01) and favipiravir (p = 0.03) administration.

CONCLUSIONS

Our simple visual assessment of CT images can predict the severity of illness, a resulting decrease in respiratory function, and the need for supplemental respiratory ventilation among patients with COVID-19.

Magnetic resonance imaging features of post-COVID-19 regional and invasive sino-nasal mucormycosis

Background

Sino-nasal mucormycosis is an opportunistic, invasive fungal disease which has shown a rising trend in the setting of COVID-19. The objective of this study is to document and analyze demographic data, clinical presentation and MR imaging spectra for early detection and management of post-COVID-19 sino-nasal mucormycosis.

Results

Sixty-two cases of sino-nasal mucormycosis were enrolled in this study; their mean age was 50.65 ± 8.25 years, with significant female predominance. Nine patients (14.5%) had active COVID-19 and 53 (85.5%) were recent COVID-19 cases. Sixty patients have not received COVID-19 vaccine. The mean duration from the initial COVID-19 laboratory confirmation to the detection of sino-nasal mucormycosis was 25.7 +/− 4.6 days. Thirty-five patients (56.5%) were kept in the hospital for COVID management and 4 of them received intensive care unit (ICU) treatment. Twenty-seven patients (43.5%) were treated in home isolation. Corticosteroids were administered in 48 cases (77.4%). Twenty-nine patients (46.8%) had been given oxygen for an average time of 11.2 ± 4.15 days. Diabetes was found in 56 cases (90.3%). The most common clinical symptoms were headache, seen in 52 patients (83.87%). The ethmoid sinus was the most common paranasal sinus involved in our study, seen in 47 cases (75.81%). In 36 cases (58%), multiple sinuses were involved. MRI staging according to the extent of regional involvement. Stage 1 seen in 2 cases (3.23%), stage 2 in 13 cases (20.97%), stage 3 in 35 cases (56.45%) and stage 4 in 12 cases (19.35%).

Conclusions

MRI shows a spectrum of findings in sino-nasal mucormycosis. Imaging plays a major role in staging and assessing the extent of involvement and complications. In light of this, mortality and morbidity can be dramatically decreased with adequate evaluation and therapy.

COVID-view: Diagnosis of COVID-19 using Chest CT

Significant work has been done towards deep learning (DL) models for automatic lung and lesion segmentation and classification of COVID-19 on chest CT data. However, comprehensive visualization systems focused on supporting the dual visual+DL diagnosis of COVID-19 are non-existent. We present COVID-view, a visualization application specially tailored for radiologists to diagnose COVID-19 from chest CT data. The system incorporates a complete pipeline of automatic lungs segmentation, localization/isolation of lung abnormalities, followed by visualization, visual and DL analysis, and measurement/quantification tools. Our system combines the traditional 2D workflow of radiologists with newer 2D and 3D visualization techniques with DL support for a more comprehensive diagnosis. COVID-view incorporates a novel DL model for classifying the patients into positive/negative COVID-19 cases, which acts as a reading aid for the radiologist using COVID-view and provides the attention heatmap as an explainable DL for the model output. We designed and evaluated COVID-view through suggestions, close feedback and conducting case studies of real-world patient data by expert radiologists who have substantial experience diagnosing chest CT scans for COVID-19, pulmonary embolism, and other forms of lung infections. We present requirements and task analysis for the diagnosis of COVID-19 that motivate our design choices and results in a practical system which is capable of handling real-world patient cases.

COVID-19 and MERS: Are their Chest X-ray and Computed Tomography Scanning Signs Related?

Background

COVID-19 is a respiratory infection brought about by SARS-COV-2. Most of the patients contaminated by this pathogen are afflicted by respiratory syndrome with multiple stages ranging from mild upper respiratory involvement to severe dyspnea and acute respiratory distress syndrome cases. Keeping in mind the high sensitivity of computed tomography (CT) scan in detecting abnormalities, it became the number one modality in COVID-19 diagnosis. A wide diversity of CT features can be found in COVID-19 cases, which can be observed before the onset of clinical signs. The review article is aimed to highlight recent discrepancies in CT-scan and chest X-ray (CXR) characteristics between COVID-19 and Middle East Respiratory Syndrome (MERS).

Method

This review study was performed in the literature from the beginning of COVID-19 until the middle of April 2021. For this reason, all relevant works through scientific citation websites such as Google Scholar, PubMed, and Web of Science have been investigated in the mentioned period.

Results

COVID-19 was more reproductive than MERS, while MERS was significantly higher in terms of mortality rate (COVID-19: 2.3% and MERS: 34.4%). Signs of ground-glass opacity (GGO), peripheral consolidation, and GGO accompanying with consolidation are the same signs CXR in both MERS and COVID-19. Indeed, fever, cough, headache, and sore throat are the most symptoms in all studied patients.

Conclusion

Both COVID-19 and MERS have the same imaging signs. The most similar chest CT findings are GGO, peripheral consolidation, and GGO superimposed by consolidation in both studied diseases, and no statistical differences were seen among the mean number of chest CT-scans in MERS and COVID-19 cases.

Extrapulmonary CT Findings Predict In-Hospital Mortality in COVID-19. A Systematic Review and Meta-Analysis

RATIONALE AND OBJECTIVES

Several prognostic factors have been identified for COVID-19 disease. Our aim was to elucidate the influence of non-pulmonary findings of thoracic computed tomography (CT) on unfavorable outcomes and in-hospital mortality in COVID-19 patients based on a large patient sample.

MATERIALS AND METHODS

MEDLINE library, Cochrane and SCOPUS databases were screened for the associations between CT-defined features and mortality in COVID-19 patients up to June 2021. In total, 22 studies were suitable for the analysis, and included into the present analysis. Overall, data regarding 4 extrapulmonary findings could be pooled: pleural effusion, pericardial effusion, mediastinal lymphadenopathy, and coronary calcification.

RESULTS

The included studies comprised 7859 patients. The pooled odds ratios for the effect of the identified extrapulmonary findings on in-hospital mortality are as follows: pleural effusion, 4.60 (95% CI 2.97-7.12); pericardial effusion, 1.29 (95% CI 0.83-1.98); coronary calcification, 2.68 (95% CI 1.78-4.04); mediastinal lymphadenopathy, 2.02 (95% CI 1.18-3.45).

CONCLUSION

Pleural effusion, mediastinal lymphadenopathy and coronary calcification have a relevant association with in-hospital mortality in COVID-19 patients and should be included as prognostic biomarker into clinical routine.

[Influence of therapeutic exercises and hardware massage in electrostatic field on lung damage in patients with novel coronavirus pneumonia]

OBJECTIVE

To analyze the efficacy and safety of therapeutic exercises and chest hardware massage in electrostatic field in patients with COVID-associated viral pneumonia.

MATERIAL AND METHODS

We retrospectively analyzed 1551 patients admitted to the Clinical Hospital No. 1 (MEDSI Group JSC) with COVID-associated pneumonia between April 01, 2020 and June 15, 2021 (ICD-10 U07.1 and U07.2). Considering inclusion and exclusion criteria, we enrolled 153 patients. All patients were divided into comparable groups and subgroups depending on the methods of rehabilitation treatment and CT stage of viral pneumonia. Lung damage was assessed semi-automatically using Philips Portal v11 COPD software. Rehabilitation measures included therapeutic exercises and chest hardware massage in electrostatic field. therapeutic exercises.

RESULTS

Therapeutic exercises significantly reduced severity of lung damage in patients with viral pneumonia CT-2 and no oxygen support (from 28.05% [28; 29.5] at admission to 15.3% [14.2; 19.3] at discharge). It was not observed in patients without rehabilitation treatment and in patients undergoing therapeutic exercises and massage in electrostatic field.

CONCLUSION

Therapeutic exercises in patients with COVID-19 and baseline lung damage > 25% and < 50% (CT-2 stage) significantly reduce severity of lung damage at discharge compared to the control group.

18F-FDG PET-CT incidental lung findings in asymptomatic COVID-19 patients: evidences from the Italian core of the first pandemic peak

OBJECTIVES

To illustrate incidental ¹⁸F-FDG PET-CT findings and related CT alterations of suspicious pulmonary interstitial involvement in asymptomatic oncologic patients during the first COVID-19 outbreak in the core of Italian peak.

METHODS

We retrospectively evaluated the ¹⁸F-FDG PET-CT follow-up examinations performed during the first Italian COVID-19 outbreak (March 3rd-April 15th, 2020) in 10 asymptomatic oncologic patients with a highly suspicious interstitial pulmonary involvement on CT. Six cases were confirmed SARS-CoV-2 by molecular tests. The following parameters were assessed: 1) lung involvement on co-registration CT as extension (laterality, number of lobes), pattern (ground-glass opacities/GGO, consolidations, mixed) and stage (early, progressive, peak, and absorption); 2) the maximum standardized uptake value (SUV_max) of lung lesions on ¹⁸F-FDG PET.

RESULTS

The involved lobes were 5 in 5 cases (3 confirmed SARS-CoV-2), 2-4 in 4 cases and 1 in 1 case. GGO were found in all patients; 3 cases also showed a combination of GGO and peripheral consolidations (mixed). Five cases were suggestive for an early stage of interstitial pneumonia, 4 for progressive and 1 for peak. All the lung lesions showed increased FDG uptake. In early stages SUV_max ranged from 1.5 to 11, in progressive from 3.3 to 6.8, in peak from 2.4 to 7.7. SUV_max ranged 1.5-11 in patients with only GGO and 2.8-7.7 in those with mixed pattern.

CONCLUSIONS

¹⁸F-FDG PET-CT findings in suspected COVID-19 pulmonary involvement of asymptomatic oncologic patients showed an increase in FDG uptake of GGO and consolidations, but with a wide and apparently nonspecific range of SUV_max values.

COVID-19 pneumonia: pathophysiology and management

Coronavirus disease 2019 (COVID-19) pneumonia is an evolving disease. We will focus on the development of its pathophysiologic characteristics over time, and how these time-related changes determine modifications in treatment. In the emergency department: the peculiar characteristic is the coexistence, in a significant fraction of patients, of severe hypoxaemia, near-normal lung computed tomography imaging, lung gas volume and respiratory mechanics. Despite high respiratory drive, dyspnoea and respiratory rate are often normal. The underlying mechanism is primarily altered lung perfusion. The anatomical prerequisites for PEEP (positive end-expiratory pressure) to work (lung oedema, atelectasis, and therefore recruitability) are lacking. In the high-dependency unit: the disease starts to worsen either because of its natural evolution or additional patient self-inflicted lung injury (P-SILI). Oedema and atelectasis may develop, increasing recruitability. Noninvasive supports are indicated if they result in a reversal of hypoxaemia and a decreased inspiratory effort. Otherwise, mechanical ventilation should be considered to avert P-SILI. In the intensive care unit: the primary characteristic of the advance of unresolved COVID-19 disease is a progressive shift from oedema or atelectasis to less reversible structural lung alterations to lung fibrosis. These later characteristics are associated with notable impairment of respiratory mechanics, increased arterial carbon dioxide tension (P_aCO2), decreased recruitability and lack of response to PEEP and prone positioning.

COVID-19 pneumonia cannot be correctly described, analysed and treated if the time-factor is not taken into accounthttps://bit.ly/3AOKxc4

Review and Prospect: Artificial Intelligence in Advanced Medical Imaging

Artificial intelligence (AI) as an emerging technology is gaining momentum in medical imaging. Recently, deep learning-based AI techniques have been actively investigated in medical imaging, and its potential applications range from data acquisition and image reconstruction to image analysis and understanding. In this review, we focus on the use of deep learning in image reconstruction for advanced medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). Particularly, recent deep learning-based methods for image reconstruction will be emphasized, in accordance with their methodology designs and performances in handling volumetric imaging data. It is expected that this review can help relevant researchers understand how to adapt AI for medical imaging and which advantages can be achieved with the assistance of AI.

Pulmonary tuberculosis and COVID-19 coinfection: A new medical challenge

Radiological findings in chest radiography and respiratory symptomatology represent a great challenge of interpretation during the COVID-19 (Coronavirus Disease 2019) pandemic, as their patterns can generate uncertainty at the time of diagnosis. This case highlights the importance in achieving an adequate correlation between diagnostic imaging and the clinical picture. We present a male adult who was admitted for 8 days of respiratory symptoms. Management with steroids was initiated according to the RECOVERY (Randomized Evaluation of COVID-19 Therapy) protocol and later confirmation of SARS-CoV-2 infection was received. In the following weeks, he deteriorated slowly and progressively clinically, without reaching respiratory failure. Imaging showed a thick-walled cavitation in the right lower lobe. Tuberculosis was suspected and confirmed. The uniqueness of this case of COVID-19 coinfection in a patient with undiagnosed tuberculosis, represents a diagnostic and clinical management challenge, where the proper interpretation of chest radiology is a fundamental tool.

Role of CT scan thorax in nCovid19—a case-based review

Background

Most of the morbidity and mortality in nCovid19 is due to pneumonia which can be reduced by early diagnosis and treatment. Chest CT scan plays an important role in the early diagnosis and management of respiratory complications due to nCovid19. Clinicians should be aware about the indications for the CT scan of the thorax, timing of investigation, and limitations of CT.

Main body of abstract

Chest CT scan is indicated in patients with moderate to severe respiratory symptoms and pretest probability of nCovid19 infection, when RT-PCR test results are negative, and in patients for whom an RT-PCR test is not performed or not readily available. When a rapid antigen test is negative and an RT-PCR test report takes time, CT can be used in seriously ill patients to decide whether it is COVID or not. For patients who are dependent on oxygen even after 2 weeks, CT may help to show the extent of lung involvement and predict long-term prognosis. CT may be done to exclude nCovid19 pneumonia. For patients with high risk for nCovid19 who require an immediate diagnosis to rule out lung involvement, CT can be done. A normal CT excludes nCovid19 pneumonia. CT scan is required in confirmed cases of nCovid19 pneumonia when complications are suspected clinically. These include pulmonary thromboembolism, pneumothorax, mediastinal/surgical emphysema, bacterial pneumonia, and unexplained deterioration with new shadows in chest X-ray. CT pulmonary angiogram is indicated when pulmonary embolism is suspected, and in other cases, plain CT should be done. In pre-operative cases where emergency surgery is required, nCovid19 disease is suspected clinically, and RT-PCR report awaited or not available, CT thorax can be done.

Conclusion

CT scan is useful for early diagnosis of lung involvement, detection complications, triaging of cases, risk stratification, and preoperative evaluation in select cases. CT scan should be done only when there is a definite indication so to reduce radiation hazards and to reduce health care expenditure. Normal CT excludes nCovid19 lung involvement, but the patient may have upper respiratory involvement which may progress later to involve lungs.

COVID-19-Related Lung Parenchymal Uptake on 18F-PSMA-1007 PET/CT

ABSTRACT

A 70-year-old man with newly diagnosed prostate cancer underwent 18F-PSMA-1007 PET/CT for staging. PSMA-avid primary prostatic malignancy was identified. Incidental intense patchy peripheral lung uptake was also noted. The patient tested positive for COVID-19 infection.

Diagnostic performance of lung ultrasound compared to CT scan in the diagnosis of pulmonary lesions of COVID-19 induced pneumonia: a preliminary study

Chest CT scan is currently used to assess the extent of lung involvement in patients with the coronavirus disease 2019 (COVID-19). The aim of this study was to evaluate the diagnostic performance of lung ultrasound in the diagnosis of COVID-19 pulmonary manifestations in comparison to CT scan. Thirty-three symptomatic patients with suspected COVID-19 pneumonia were evaluated by lung ultrasound and then, at a short interval, chest CT scan. In the anterior chest, each hemithorax was divided into four areas. In the posterior chest, eight zones similar to the anterior part were examined. The axillary areas were also divided into upper and lower zones (20 zones were determined per patient). Mean age of the patients was 58.66 years. The sensitivity (95% CI) and specificity (95% CI) of lung ultrasound for the diagnosis of parenchymal lesions were 90.5% (69.6-98.8%) and 50% (21.1-78.9%), respectively. In the evaluation of pleural lesions, the sensitivity (95% CI) and specificity (95% CI) of lung ultrasound were 100% (71.5-100%) and 22.7% (7.8-45.4%), respectively. Owing to the high sensitivity of ultrasound in identifying lung lesions in patients with COVID-19 pneumonia, it can be recommended to use lung ultrasound as a tool for initial screening of patients with high clinical suspicion for SARS-CoV-2 infection during the pandemic.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13337-021-00736-w.

ANCA vasculitis: A manifestation of Post-Covid-19 Syndrome

The SARS-CoV-2 infection has been found to present with different degrees of response and variable levels of inflammation. Patients who have recovered from the initial infection can develop long-term symptomatology. We present a unique case of a middle aged-healthy man who developed complications of ANCA-associated vasculitis after recovering from a mild COVID-19 infection.

A previously healthy 53-year-old male presented with hemoptysis and acute renal failure. One month prior, the patient tested positive for COVID-19; not requiring hospitalization. Physical exam findings included bilateral lower extremity petechiae. CT Chest showed bilateral diffuse patchy lung consolidations with cavitary lesions with urinalysis revealing erythrocytes, +1 protein. Hemodialysis and workup for pulmonary-renal syndromes were initiated.

Infectious workup results included: negative COVID-19, negative MTB-PCR, respiratory culture revealing yeast. Additional workup revealed; elevated CRP, D-Dimer, and Fibrinogen. Notably, the patient had; decreased C3 and C4 levels; negative Anti-GBM antibody; negative Anti-streptolysin-O; and positive ANCA assay, Proteinase antibody, and mildly positive Myeloperoxidase antibody.

Worsening coagulopathy and atrophic kidneys delayed renal biopsy for definitive diagnosis. The patient's respiratory status acutely worsened during hemodialysis with imaging showing markedly increased pulmonary infiltrates. Upon urgent intubation, active frank red bleeding was noted, and the patient sustained 2 cardiac arrests with eventual expiration.

Much is to be learned from the Novel SARS-CoV-2 virus and suspected complications. This case highlights a unique complication of COVID-19 leading to a possible AAV and the importance of keeping a broad differential when treating patients who have recovered from the initial infection.

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COVID-19 renal complications.

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ANCA-associated vasculitis after COVID-19.

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Acute renal failure after resolution of COVID-19.

The "Target Sign" in a 46-Year-Old Patient with COVID-19 Pneumonia

COVID-19 has various imaging manifestations, most commonly peripheral ground-glass opacities with a basilar posterior predominance. Less common imaging manifestations include consolidations, findings typical of organizing pneumonia, such as “halo” or a “reverse halo” sign, and vascular enlargement. Our case describes a “target sign” on CT, which is uncommon but is increasingly being recognized. The target sign consists of a central nodular opacity with surrounding ground-glass opacity, then a surrounding relatively lucent ring, and a more peripheral ring of consolidation or ground-glass opacification. This may be the sequela of focal vascular enlargement, endothelial injury, microangiopathy, and perivascular inflammation. The case described involves a 46-year-old male who presented with subjective fevers, nonproductive cough, and hypoxia, subsequently diagnosed with COVID-19. CT imaging performed as part of initial work-up revealed multifocal ground-glass opacities scattered throughout the lung parenchyma, as well as multiple target sign lesions. Although it is a rare finding, the target sign, when present, may suggest the diagnosis of COVID-19.

CT Examinations for COVID-19: A Systematic Review of Protocols, Radiation Dose, and Numbers Needed to Diagnose and Predict

Purpose

Although chest CT has been discussed as a first-line test for coronavirus disease 2019 (COVID-19), little research has explored the implications of CT exposure in the population. To review chest CT protocols and radiation doses in COVID-19 publications and explore the number needed to diagnose (NND) and the number needed to predict (NNP) if CT is used as a first-line test.

Materials and Methods

We searched nine highly cited radiology journals to identify studies discussing the CT-based diagnosis of COVID-19 pneumonia. Study-level information on the CT protocol and radiation dose was collected, and the doses were compared with each national diagnostic reference level (DRL). The NND and NNP, which depends on the test positive rate (TPR), were calculated, given a CT sensitivity of 94% (95% confidence interval [CI]: 91%-96%) and specificity of 37% (95% CI: 26%-50%), and applied to the early outbreak in Wuhan, New York, and Italy.

Results

From 86 studies, the CT protocol and radiation dose were reported in 81 (94.2%) and 17 studies (19.8%), respectively. Low-dose chest CT was used more than twice as often as standard-dose chest CT (39.5% vs.18.6%), while the remaining studies (44.2%) did not provide relevant information. The radiation doses were lower than the national DRLs in 15 of the 17 studies (88.2%) that reported doses. The NND was 3.2 scans (95% CI: 2.2-6.0). The NNPs at TPRs of 50%, 25%, 10%, and 5% were 2.2, 3.6, 8.0, 15.5 scans, respectively. In Wuhan, 35418 (TPR, 58%; 95% CI: 27710-56755) to 44840 (TPR, 38%; 95% CI: 35161-68164) individuals were estimated to have undergone CT examinations to diagnose 17365 patients. During the early surge in New York and Italy, daily NNDs changed up to 5.4 and 10.9 times, respectively, within 10 weeks.

Conclusion

Low-dose CT protocols were described in less than half of COVID-19 publications, and radiation doses were frequently lacking. The number of populations involved in a first-line diagnostic CT test could vary dynamically according to daily TPR; therefore, caution is required in future planning.

CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images

Early detection of COVID-19 is vital to control its spread. Deep learning methods have been presented to detect suggestive signs of COVID-19 from chest CT images. However, due to the novelty of the disease, annotated volumetric data are scarce. Here we propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN). For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones, and the decision layer of our CNN is a support vector machine. As we have no control over the CT image acquisition, we also propose an intensity standardization approach. Our method can achieve mean accuracy and kappa values of 0.97 and 0.93, respectively, on a dataset with 117 CT images extracted from different sites, surpassing its counterpart in all scenarios.

Pulmonary Cystic Disease Associated With COVID 19 Pneumonia: An Emerging Atypical Manifestation

Chest tomography has played an essential role during the coronavirus disease 2019 (COVID-19) pandemic since it has allowed to suspect and diagnose the disease early and to assess the severity of lung involvement, predict the disease's course, and detect the complications associated with it. Certain chest CT findings have been reported in more than 70% of reverse transcription polymerase chain reaction (RT-PCR) test-proven COVID-19 cases, including ground-glass opacities, vascular enlargement, bilateral abnormalities, lower lobe involvement, and posterior predilection. In COVID-19-endemic regions, observing these chest CT findings should raise the suspicion of a possible COVID-19 diagnosis. Rare reported CT findings in RT-PCR test-proven COVID-19 cases include pleural effusion, lymphadenopathy, tree-in-bud sign, central lesion distribution, pericardial effusion, and cavitating lung lesions. The observation of one or more of these findings suggests an alternative diagnosis, although COVID-19 cannot be excluded from the differential diagnosis. Here, we report an interesting case of a patient with no relevant history presenting a COVID-19 infection which, as a complication, presented cystic lesions; we discuss its etiology briefly.

A Comparative Study of Chest CT With Lung Ultrasound After SARS-CoV-2 Infection in the Assessment of Pulmonary Lesions in Rhesus Monkeys (Macaca Mulatta)

Lung ultrasound (LUS) is a fast and non-invasive modality for the diagnosis of several diseases. In humans, LUS is nowadays of additional value for bedside screening of hospitalized SARS-CoV-2 infected patients. However, the diagnostic value of LUS in SARS-CoV-2 infected rhesus monkeys, with mild-to-moderate disease, is unknown. The aim of this observational study was to explore correlations of the LUS appearance of abnormalities with COVID-19-related lesions detected on computed tomography (CT). There were 28 adult female rhesus monkeys infected with SARS-CoV-2 included in this study. Chest CT and LUS were obtained pre-infection and 2-, 7-, and 14-days post infection. Twenty-five animals were sub-genomic PCR positive in their nose/throat swab for at least 1 day. CT images were scored based on the degree of involvement for lung lobe. LUS was scored based on the aeration and abnormalities for each part of the lungs, blinded to CT findings. Most common lesions observed on CT were ground glass opacities (GGOs) and crazy paving patterns. With LUS, confluent or multiple B-lines with or without pleural abnormalities were observed which is corresponding with GGOs on CT. The agreement between the two modalities was similar over the examination days. Pleural line abnormalities were clearly observed with LUS, but could be easily missed on CT. Nevertheless, due to the air interface LUS was not able to examine the complete volume of the lung. The sensitivity of LUS was high though the diagnostic efficacy for mild-to-moderate disease, as seen in macaques, was relatively low. This leaves CT the imaging modality of choice for diagnosis, monitoring, and longitudinal assessment of a SARS-CoV-2 infection in macaques.

Experiences of diagnostic radiographers through the Covid-19 pandemic

Introduction

Diagnostic Radiography plays a major role in the diagnosis and management of patients with Covid-19. This has seen an increase in the demand for imaging services, putting pressure on the workforce. Diagnostic radiographers, as with many other healthcare professions, have been on the frontline, dealing with an unprecedented situation. This research aimed to explore the experience of diagnostic radiographers working clinically during the Covid-19 pandemic.

Methods

Influenced by interpretative phenomenology, this study explored the experiences of diagnostic radiographers using virtual focus group interviews as a method of data collection.

Results

Data were analysed independently by four researchers and five themes emerged from the data. Adapting to new ways of working, feelings and emotions, support mechanisms, self-protection and resilience, and professional recognition.

Conclusion

The adaptability of radiographers came across strongly in this study. Anxieties attributed to the provision of personal protective equipment (PPE), fear of contracting the virus and spreading it to family members were evident. The resilience of radiographers working throughout this pandemic came across strongly throughout this study. A significant factor for coping has been peer support from colleagues within the workplace. The study highlighted the lack of understanding of the role of the radiographer and how the profession is perceived by other health care professionals.

Implications for practice

This study highlights the importance of interprofessional working and that further work is required in the promotion of the profession.

COVID-19 pneumonia and its lookalikes: How radiologists perform in differentiating atypical pneumonias

Purpose

To examine the performance of radiologists in differentiating COVID-19 from non-COVID-19 atypical pneumonia and to perform an analysis of CT patterns in a study cohort including viral, fungal and atypical bacterial pathogens.

Methods

Patients with positive RT-PCR tests for COVID-19 pneumonia (n = 90) and non-COVID-19 atypical pneumonia (n = 294) were retrospectively included. Five radiologists, blinded to the pathogen test results, assessed the CT scans and classified them as COVID-19 or non-COVID-19 pneumonia. For both groups specific CT features were recorded and a multivariate logistic regression model was used to calculate their ability to predict COVID-19 pneumonia.

Results

The radiologists differentiated between COVID-19 and non-COVID-19 pneumonia with an overall accuracy, sensitivity, and specificity of 88% ± 4 (SD), 79% ± 6 (SD), and 90% ± 6 (SD), respectively. The percentage of correct ratings was lower in the early and late stage of COVID-19 pneumonia compared to the progressive and peak stage (68 and 71% vs 85 and 89%). The variables associated with the most increased risk of COVID-19 pneumonia were band like subpleural opacities (OR 5.55, p < 0.001), vascular enlargement (OR 2.63, p = 0.071), and subpleural curvilinear lines (OR 2.52, p = 0.021). Bronchial wall thickening and centrilobular nodules were associated with decreased risk of COVID-19 pneumonia with OR of 0.30 (p = 0.013) and 0.10 (p < 0.001), respectively.

Conclusions

Radiologists can differentiate between COVID-19 and non-COVID-19 atypical pneumonias at chest CT with high overall accuracy, although a lower performance was observed in the early and late stage of COVID 19 pneumonia. Specific CT features might help to make the correct diagnosis.

Factors determining generalization in deep learning models for scoring COVID-CT images

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position.

One year of COVID-19 pandemic: what we Radiologists have learned about imaging

BACKGROUND

 Since its outbreak in December 2019, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has infected more than 151 million people worldwide. More than 3.1 million have died from Coronavirus Disease 2019 (COVID-19), the illness caused by SARS-CoV-2. The virus affects mainly the upper respiratory tract and the lungs causing pneumonias of varying severity. Moreover, via direct and indirect pathogenetic mechanisms, SARS-CoV-2 may lead to a variety of extrapulmonary as well as vascular manifestations.

METHODS

 Based on a systematic literature search via PubMed, original research articles, meta-analyses, reviews, and case reports representing the current scientific knowledge regarding diagnostic imaging of COVID-19 were selected. Focusing on the imaging appearance of pulmonary and extrapulmonary manifestations as well as indications for imaging, these data were summarized in the present review article and correlated with basic pathophysiologic mechanisms.

RESULTS AND CONCLUSION

 Typical signs of COVID-19 pneumonia are multifocal, mostly bilateral, rounded, polycyclic or geographic ground-glass opacities and/or consolidations with mainly peripheral distribution. In severe cases, peribronchovascular lung zones are affected as well. Other typical signs are the "crazy paving" pattern and the halo and reversed halo (the latter two being less common). Venous thromboembolism (and pulmonary embolism in particular) is the most frequent vascular complication of COVID-19. However, arterial thromboembolic events like ischemic strokes, myocardial infarctions, and systemic arterial emboli also occur at higher rates. The most frequent extrapulmonary organ manifestations of COVID-19 affect the central nervous system, the heart, the hepatobiliary system, and the gastrointestinal tract. Usually, they can be visualized in imaging studies as well. The most important imaging modality for COVID-19 is chest CT. Its main purpose is not to make the primary diagnosis, but to differentiate COVID-19 from other (pulmonary) pathologies, to estimate disease severity, and to detect concomitant diseases and complications.

KEY POINTS

  · Typical signs of COVID-19 pneumonia are multifocal, mostly peripheral ground-glass opacities/consolidations.. · Imaging facilitates differential diagnosis, estimation of disease severity, and detection of complications.. · Venous thromboembolism (especially pulmonary embolism) is the predominant vascular complication of COVID-19.. · Arterial thromboembolism (e. g., ischemic strokes, myocardial infarctions) occurs more frequently as well.. · The most common extrapulmonary manifestations affect the brain, heart, hepatobiliary system, and gastrointestinal system..

CITATION FORMAT

· Gross A, Albrecht T. One year of COVID-19 pandemic: what we Radiologists have learned about imaging. Fortschr Röntgenstr 2021; DOI: 10.1055/a-1522-3155.

The Valuable Role of Imaging Modalities in the Diagnosis of the Uncommon Presentations of COVID-19: An Educative Case Series

The outbreak of coronavirus disease 2019 (COVID-19) in late 2019 rapidly turned into a global pandemic. Although the symptoms of COVID-19 are mainly respiratory ones, the infection is associated with a wide range of clinical signs and symptoms. The main imaging modality in COVID-19 is lung computed tomography (CT) scanning, but the diagnosis of the vast spectrum of complications needs the application of various imaging modalities. Owing to the novelty of the disease and its presentations, its complications-particularly uncommon ones-can be easily missed. In this study, we describe some uncommon presentations of COVID-19 diagnosed by various imaging modalities. The first case presented herein was a man with respiratory distress, who transpired to suffer from pneumothorax and pneumomediastinum in addition to the usual pneumonia of COVID-19. The second patient was a hospitalized COVID-19 case, whose clinical condition suddenly deteriorated with the development of abdominal symptoms diagnosed as mesenteric ischemia by abdominal CT angiography. The third patient was a case of cardiac involvement in the COVID-19 course, detected as myocarditis by cardiac magnetic resonance imaging (MRI). The fourth and fifth cases were COVID-19-associated encephalitis whose diagnoses were established by brain MRI. COVID-19 is a multisystem disorder with a wide range of complications such as pneumothorax, pneumomediastinum, mesenteric ischemia, myocarditis, and encephalitis. Prompt diagnosis with appropriate imaging modalities can lead to adequate treatment and better survival.

SARS-CoV-2 and approaches for a testing and diagnostic strategy

The COVID-19 pandemic has led to an unprecedented global health challenge, creating sudden, massive demands for diagnostic testing, treatment, therapies, and vaccines. In particular, the development of diagnostic assays for SARS-CoV-2 has been pursued as they are needed for quarantine, disease surveillance, and patient treatment. One of the major lessons the pandemic highlighted was the need for fast, cheap, scalable and reliable diagnostic methods, such as paper-based assays. Furthermore, it has previously been suggested that paper-based tests may be more suitable for settings with lower resource availability and may help alleviate some supply chain challenges which arose during the COVID-19 pandemic. Therefore, we explore how such devices may fit in a comprehensive diagnostic strategy and how some of the challenges to the technology, e.g. low sensitivity, may be addressed. We discuss the properties of the SARS-CoV-2 virus itself, the COVID-19 disease pathway, and the immune response. We then describe the different diagnostic strategies that have been pursued, focusing on molecular strategies for viral genetic material, antigen tests, and serological assays, and innovations for improving the diagnostic sensitivity and capabilities. Finally, we discuss pressing issues for the future, and what needs to be addressed for the ongoing pandemic and future outbreaks.

Role of chest imaging in the diagnosis and treatment of COVID-19

Background: Thousands of new patients are diagnosed with coronavirus disease 2019 (COVID-19) daily worldwide. We reviewed the role of chest imaging in the diagnosis and treatment of patients with COVID-19.Current Concepts: Chest imaging is not recommended as a primary diagnostic tool for COVID-19. However, when real-time polymerase chain reaction is difficult to perform or when COVID-19 is strongly suspected, chest imaging can assist in the diagnosis. Thus, chest imaging is recommended for high-risk patients and patients with worsening respiratory symptoms, but not for asymptomatic patients. Bilateral peripheral pneumonia is a typical imaging finding in patients with COVID-19. However, there are cases where chest imaging shows atypical findings or appears normal. The extent of COVID-19 pneumonia on chest imaging is related to the severity of the disease. The presence and extent of pneumonia on chest imaging can help monitor patients, select appropriate treatment agents, determine whether the patient should be hospitalized, and predict the prognosis.Discussion and Conclusion: Appropriate use of chest imaging is needed for clinicians to help triage patients with COVID-19 and decide on the treatment plan.

Use of post-mortem computed tomography during the COVID-19 pandemic

Post mortem computed tomography (PMCT) is widely used in England and Wales to supplement or replace traditional invasive Coroner's autopsy. Using PMCT and coronary angiography, the cause of death can be determined without invasive examination in approximately 70% of cases, assuming a typical Coroner's autopsy case mix. Coroner's autopsy services continued during the COVID-19 pandemic and have identified deaths resulting from COVID-19 undiagnosed in life. In some areas of England, PMCT was used to replace traditional autopsy due to concerns over infection risk to mortuary staff associated with invasive autopsy. Health and safety concerns also resulted in changes to post mortem scanning protocols. PMCT has been used to identify potential COVID-19 deaths and assist in the selection of cases for viral studies. There is typically bilateral ground-glass opacities and consolidation within the lungs on CT; although these changes are not specific for COVID-19, the diagnosis can be confirmed with post mortem nose and throat swabs.

How did radiologists' diagnostic performance has changed in COVID-19 pneumonia: A single-centre retrospective study

AIMS

Delay and false positivity in PCR test results have necessitated accurate chest CT reporting for the management of patients with COVID-19-suspected symptoms. Pandemic related workload and level of experience on covid-dedicated chest CT scans might have affected the diagnostic performance of on-call radiologists. The aim of this study was to reveal the interpretation errors (IEs) in chest CT reports of COVID-19-suspected patients admitted to the Emergency Room (ER).

METHODS

Chest CT scans between March and June 2020 were re-evaluated and compared with the former reports and PCR test results. CT scan results were classified into four groups. Parenchymal involvement ratios, radiology departments' workload, COVID-19-related educational activities have been examined.

RESULTS

Out of 5721 Chest CT scans, 783 CTs belonging to 664 patients (340 female, 324 male) were included in this study. PCR test was positive in 398; negative in 385 cases. PCR positivity was found to be highest in "normal" and "typical for covid" groups whereas lowest in "atypical for covid" and "not covid" groups. 5%-25% parenchymal involvement ratio was found in 84.2% of the cases. Regarding the number of chest CT scans performed, radiologists' workload has found to be increased six-folds. With the re-evaluation, a total of 145 IEs (18.5%) have been found. IEs were mostly precipitated in the first two months (88.3%) and mostly in the "not covid" class (60%) regardless of PCR positivity. COVID-19 and radiology entitled educational activities along with the ER admission rates within the first two months of the pandemic have seemed to be related to the decline of IEs within time.

CONCLUSION

COVID-19 pandemic made a great impact on radiology departments with an inevitable burden of daily chest CT reporting. This workload and concomitant factors have effects on diagnostic challenges in COVID-19 pneumonia.

Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the COVID-19 pandemic

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, over 103214008 cases have been reported, with more than 2231158 deaths as of January 31, 2021. Although the gold standard for diagnosis of this disease remains the reverse-transcription polymerase chain reaction of nasopharyngeal and oropharyngeal swabs, its false-negative rates have ignited the use of medical imaging as an important adjunct or alternative. Medical imaging assists in identifying the pathogenesis, the degree of pulmonary damage, and the characteristic features in each imaging modality. This literature review collates the characteristic radiographic findings of COVID-19 in various imaging modalities while keeping the preliminary focus on chest radiography, computed tomography (CT), and ultrasound scans. Given the higher sensitivity and greater proficiency in detecting characteristic findings during the early stages, CT scans are more reliable in diagnosis and serve as a practical method in following up the disease time course. As research rapidly expands, we have emphasized the CO-RADS classification system as a tool to aid in communicating the likelihood of COVID-19 suspicion among healthcare workers. Additionally, the utilization of other scoring systems such as MuLBSTA, Radiological Assessment of Lung Edema, and Brixia in this pandemic are reviewed as they integrate the radiographic findings into an objective scoring system to risk stratify the patients and predict the severity of disease. Furthermore, current progress in the utilization of artificial intelligence via radiomics is evaluated. Lastly, the lesson from the first wave and preparation for the second wave from the point of view of radiology are summarized.

Rapid Screening of COVID-19 Directly from Clinical Nasopharyngeal Swabs Using the MasSpec Pen

The outbreak of COVID-19 has created an unprecedent global crisis. While the polymerase chain reaction (PCR) is the gold standard method for detecting active SARS-CoV-2 infection, alternative high-throughput diagnostic tests are of a significant value to meet universal testing demands. Here, we describe a new design of the MasSpec Pen technology integrated to electrospray ionization (ESI) for direct analysis of clinical swabs and investigate its use for COVID-19 screening. The redesigned MasSpec Pen system incorporates a disposable sampling device refined for uniform and efficient analysis of swab tips via liquid extraction directly coupled to an ESI source. Using this system, we analyzed nasopharyngeal swabs from 244 individuals including symptomatic COVID-19 positive, symptomatic negative, and asymptomatic negative individuals, enabling rapid detection of rich lipid profiles. Two statistical classifiers were generated based on the lipid information acquired. Classifier 1 was built to distinguish symptomatic PCR-positive from asymptomatic PCR-negative individuals, yielding a cross-validation accuracy of 83.5%, sensitivity of 76.6%, and specificity of 86.6%, and validation set accuracy of 89.6%, sensitivity of 100%, and specificity of 85.3%. Classifier 2 was built to distinguish symptomatic PCR-positive patients from negative individuals including symptomatic PCR-negative patients with moderate to severe symptoms and asymptomatic individuals, yielding a cross-validation accuracy of 78.4%, specificity of 77.21%, and sensitivity of 81.8%. Collectively, this study suggests that the lipid profiles detected directly from nasopharyngeal swabs using MasSpec Pen-ESI mass spectrometry (MS) allow fast (under a minute) screening of the COVID-19 disease using minimal operating steps and no specialized reagents, thus representing a promising alternative high-throughput method for screening of COVID-19.

COVID-19: Impact on radiology departments and implications for future service design, service delivery, and radiology education

The pandemic caused by SARS-CoV-2 (severe adult respiratory distress syndrome Coronavirus-2) and its most severe clinical syndrome, COVID-19, has dramatically impacted service delivery in many radiology departments. Radiology (primarily chest radiography and CT) has played a pivotal role in managing the pandemic in countries with well-developed healthcare systems, enabling early diagnosis, triage of patients likely to require intensive care and detection of arterial and venous thrombosis complicating the disease. We review the lessons learned during the early response to the pandemic, placing these in the wider context of the responsibility radiology departments have to mitigate the impact of hospital-acquired infection on clinical care and staff wellbeing. The potential long-term implications for design and delivery of radiology services are considered. The need to achieve effective social distancing and ensure continuity of service during the pandemic has brought about a step change in the implementation of virtual clinical team working, off-site radiology reporting and postgraduate education in radiology. The potential consequences of these developments for the nature of radiological practice and the education of current and future radiologists are discussed.

Predicting gene regulatory networks from multi-omics to link genetic risk variants and neuroimmunology to Alzheimer's disease phenotypes

BACKGROUND

Genome-wide association studies have found many genetic risk variants associated with Alzheimer's disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. To address these problems, we performed an integrative multi-omics analysis of genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes.

METHOD

First, given the population gene expression data of a cohort, we construct and cluster its gene co-expression network to identify gene co-expression modules for various AD phenotypes. Next, we predict transcription factors (TFs) regulating co-expressed genes and AD risk SNPs that interrupt TF binding sites on regulatory elements. Finally, we construct a gene regulatory network (GRN) linking SNPs, interrupted TFs, and regulatory elements to target genes and gene modules for each phenotype in the cohort. This network thus provides systematic insights into gene regulatory mechanisms from risk variants to AD phenotypes.

RESULTS

Our analysis predicted GRNs in three major AD-relevant regions: Hippocampus, Dorsolateral Prefrontal Cortex (DLPFC), Lateral Temporal Lobe (LTL). Comparative analyses revealed cross-region-conserved and region-specific GRNs, in which many immunological genes are present. For instance, SNPs rs13404184 and rs61068452 disrupt SPI1 binding and regulation of AD gene INPP5D in the Hippocampus and LTL. However, SNP rs117863556 interrupts bindings of REST to regulate GAB2 in DLPFC only. Driven by emerging neuroinflammation in AD, we used Covid-19 as a proxy to identify possible regulatory mechanisms for neuroimmunology in AD. To this end, we looked at the GRN subnetworks relating to genes from shared AD-Covid pathways. From those subnetworks, our machine learning analysis prioritized the AD-Covid genes for predicting Covid-19 severity. Decision Curve Analysis also validated our AD-Covid genes outperform known Covid-19 genes for classifying severe Covid-19 patients. This suggests AD-Covid genes along with linked SNPs can be potential novel biomarkers for neuroimmunology in AD. Finally, our results are open-source available as a comprehensive functional genomic map for AD, providing a deeper mechanistic understanding of the interplay among multi-omics, brain regions, gene functions like neuroimmunology, and phenotypes.

Antibiotic Prescriptions Targeting Bacterial Respiratory Infections in Admitted Patients with COVID-19: A Prospective Observational Study

Introduction

Although bacterial co- and superinfections are rarely present in patients with COVID-19, overall antibiotic prescribing in admitted patients is high. In order to counter antibiotic overprescribing, antibiotic stewardship teams need reliable data concerning antibiotic prescribing in admitted patients with COVID-19.

Methods

In this prospective observational cohort study, we performed a quantitative and qualitative evaluation of antibiotic prescriptions in patients admitted to the COVID-19 ward of a 721-bed Belgian university hospital between 1 May and 2 November 2020. Data on demographics, clinical and microbiological parameters and antibiotic consumption were collected. Defined daily doses (DDD) were calculated for antibiotics prescribed in the context of a (presumed) bacterial respiratory tract infection and converted into two indicators: DDD/admission and DDD/100 hospital bed days. A team of infectious disease specialists performed an appropriateness evaluation for every prescription. A driver analysis was performed to identify factors increasing the odds of an antibiotic prescription in patients with a confirmed COVID-19 diagnosis.

Results

Of 403 eligible participants with a suspected COVID-19 infection, 281 were included. In 13.8% of the 203 admissions with a COVID-19 confirmed diagnosis, antibiotics were initiated for a (presumed) bacterial respiratory tract co-/superinfection (0.86 DDD/admission; 8.92 DDD/100 bed days; 39.4% were scored as ‘appropriate’). Five drivers of antibiotic prescribing were identified: history of cerebrovascular disease, high neutrophil/lymphocyte ratio in male patients, age, elevated ferritin levels and the collection of respiratory samples for bacteriological analysis.

Conclusion

In the studied population, the antibiotic consumption for a (presumed) bacterial respiratory tract co-/superinfection was low. In particular, the small total number of DDDs in patients with confirmed COVID-19 diagnosis suggests thoughtful antibiotic use. However, antibiotic stewardship programmes remain crucial to counter unnecessary and inappropriate antibiotic use in hospitalized patients with COVID-19.

Trial Registration

The study is registered at ClinicalTrials.gov (NCT04544072).

Supplementary Information

The online version contains supplementary material available at 10.1007/s40121-021-00535-2.

COVID-19 infection in the cancer population: a study of emergency department imaging utilization and findings

Purpose

To analyze emergency department (ED) computerized tomography (CT) utilization in cancer patients with coronavirus disease 2019 (COVID-19).

Methods

A retrospective chart review was performed to identify cancer patients who received COVID-19 diagnosis within the single healthcare system and presented to the ED within 30 days of COVID-19 positive date between May 1 and December 31, 2020.

Results

In our 61 patients, the mean age was 72.5 years old, with 34% of patients (n = 21) on active cancer therapy and 66% (n = 40) on surveillance only. Most patients (n = 53) received their COVID-19 diagnosis within the ED, with 8 patients diagnosed prior to initial ED visit. The most common CT studies ordered within the ED were CT chest (n = 25), CT abdomen/pelvis (A/P) (n = 20), CT head (n = 8), and CT chest/abdomen/pelvis (C/A/P) (n = 7). COVID-19 findings were present on 33 scans, findings of worsening malignancy on 12 scans, and non-COVID non-cancer findings on 9 scans. Significant differences in CT severity score (p = 0.0001), indication for hospitalization (p = 0.026), length of hospitalization (p = 0.004), interventions (remdesivir, mechanical ventilation, and vasopressor support) while hospitalized (p < 0.05), and mortality (p = 0.042) were found between the prior diagnosis and ED diagnosis groups. No such differences were found between the active treatment and surveillance groups.

Conclusion

ED CT imaging findings in patients with cancer and COVID-19 are predominantly related to COVID-19 infection, rather than cancer history or anti-cancer therapy status.

Detection of COVID-19 findings by the local interpretable model-agnostic explanations method of types-based activations extracted from CNNs

Covid-19 is a disease that affects the upper and lower respiratory tract and has fatal consequences in individuals. Early diagnosis of COVID-19 disease is important. Datasets used in this study were collected from hospitals in Istanbul. The first dataset consists of COVID-19, viral pneumonia, and bacterial pneumonia types. The second dataset consists of the following findings of COVID-19: ground glass opacity, ground glass opacity, and nodule, crazy paving pattern, consolidation, consolidation, and ground glass. The approach suggested in this paper is based on artificial intelligence. The proposed approach consists of three steps. As a first step, preprocessing was applied and, in this step, the Fourier Transform and Gradient-weighted Class Activation Mapping methods were applied to the input images together. In the second step, type-based activation sets were created with three different ResNet models before the Softmax method. In the third step, the best type-based activations were selected among the CNN models using the local interpretable model-agnostic explanations method and re-classified with the Softmax method. An overall accuracy success of 99.15% was achieved with the proposed approach in the dataset containing three types of image sets. In the dataset consisting of COVID-19 findings, an overall accuracy success of 99.62% was achieved with the recommended approach.

CT-Based Risk Stratification for Intensive Care Need and Survival in COVID-19 Patients-A Simple Solution

We evaluated a simple semi-quantitative (SSQ) method for determining pulmonary involvement in computed tomography (CT) scans of COVID-19 patients. The extent of lung involvement in the first available CT was assessed with the SSQ method and subjectively. We identified risk factors for the need of invasive ventilation, intensive care unit (ICU) admission and for time to death after infection. Additionally, the diagnostic performance of both methods was evaluated. With the SSQ method, a 10% increase in the affected lung area was found to significantly increase the risk for need of ICU treatment with an odds ratio (OR) of 1.68 and for invasive ventilation with an OR of 1.35. Male sex, age, and pre-existing chronic lung disease were also associated with higher risks. A larger affected lung area was associated with a higher instantaneous risk of dying (hazard ratio (HR) of 1.11) independently of other risk factors. SSQ measurement was slightly superior to the subjective approach with an AUC of 73.5% for need of ICU treatment and 72.7% for invasive ventilation. SSQ assessment of the affected lung in the first available CT scans of COVID-19 patients may support early identification of those with higher risks for need of ICU treatment, invasive ventilation, or death.