Lung Nodules and Masses in Patients Who Are Not HIV Immunocompromised: A Clinical Imaging Algorithmic Approach

TOPIC IMPORTANCE

The incidence of pulmonary nodules and masses in immunocompromised patients without HIV has significantly increased due to advancements in hematopoietic stem cell transplantation and solid organ transplantation and the widespread use of chemotherapy and immunosuppressive therapies. Differentiating between infectious and noninfectious causes is critical for appropriate diagnosis and management, especially because radiologic and clinical presentations can be nonspecific.

REVIEW FINDINGS

This review provides a practical framework for evaluating pulmonary nodules and masses in immunocompromised patients without HIV, incorporating clinical, immunologic, and radiologic features. It emphasizes the importance of differentiating between infectious and noninfectious etiologies based on imaging and clinical context. The review highlights the importance of correlating imaging features with the patient's immune status and underlying clinical conditions to narrow down the differential diagnosis.

SUMMARY

Pulmonary nodules and masses in immunocompromised patients represent a diagnostic challenge due to overlapping radiologic and clinical presentations. By integrating clinical context, immune status, and imaging findings, clinicians can more accurately diagnose and manage these lesions, improving patient outcomes. This review presents an algorithmic approach for differentiating between various causes of pulmonary nodules and masses in immunocompromised individuals without HIV, providing a valuable tool for clinical practice.

Can clinical findings at admission allow withholding of antibiotics in patients hospitalized for community acquired pneumonia when a test for a respiratory virus is positive?

BACKGROUND

Current guidelines recommend empiric antibiotic therapy for patients who require hospitalization for community-acquired pneumonia (CAP). We sought to determine whether clinical, imaging or laboratory features in patients hospitalized for CAP in whom PCR is positive for a respiratory virus enable exclusion of bacterial coinfection so that antibiotics can be withheld.

METHODS

For this prospective study, we selected patients in whom an etiologic diagnosis was likely to be reached, namely those who provided a high-quality sputum sample at or shortly after admission, and in whom PCR was done to test for a respiratory virus. We performed quantitative bacteriologic studies on sputum to determine the presence of bacterial infection or coinfection and reviewed all clinical, imaging and laboratory studies.

RESULTS

Of 122 CAP patients studied, 77 (63.1%) had bacterial infection, 16 (13.1%) viral infection, and 29 (23.8%) bacterial/viral coinfection. Underlying pulmonary disease and a history of smoking were more common in bacterial pneumonia. Upper respiratory symptoms were more common, and mean white blood cell (WBC) counts were lower viral pneumonia. Nevertheless, no clinical, laboratory or imaging findings allowed exclusion of bacterial coinfection in patients who tested positive for a respiratory virus. In fact, patients with bacterial/viral coinfection were sicker than those with bacterial or viral pneumonia; 30% were admitted required transfer to the ICU during their hospital course, compared to 17% and 19% of patients with bacterial or viral infection, respectively (p < .05). In this study, 64.4% of patients who tested positive for a respiratory virus had a bacterial coinfection.

CONCLUSIONS

If a test for a respiratory virus test is positive in a patient hospitalized for CAP, no sufficiently differentiating features exclude bacterial coinfection, thereby supporting the recommendation that empiric antibiotics be administered to all patients who are sufficiently ill to require hospitalization for CAP.

Quantitative Chest CT Analysis: Three Different Approaches to Quantify the Burden of Viral Interstitial Pneumonia Using COVID-19 as a Paradigm

Objectives: To investigate the relationship between COVID-19 pneumonia outcomes and three chest CT analysis approaches. Methods: Patients with COVID-19 pneumonia who underwent chest CT were included and divided into survivors/non-survivors and intubated/not-intubated. Chest CTs were analyzed through a (1) Total Severity Score visually quantified by an emergency (TSS1) and a thoracic radiologist (TSS2); (2) density mask technique quantifying normal parenchyma (DM_Norm 1) and ground glass opacities (DM_GGO1) repeated after the manual delineation of consolidations (DM_Norm2, DM_GGO2, DM_Consolidation); (3) texture analysis quantifying normal parenchyma (TA_Norm) and interstitial lung disease (TA_ILD). Association with outcomes was assessed through Chi-square and the Mann-Whitney test. The TSS inter-reader variability was assessed through intraclass correlation coefficient (ICC) and Bland-Altman analysis. The relationship between quantitative variables and outcomes was investigated through multivariate logistic regression analysis. Variables correlation was investigated using Spearman analysis. Results: Overall, 192 patients (mean age, 66.8 ± 15.4 years) were included. TSS was significantly higher in intubated patients but only TSS1 in survivors. TSS presented an ICC of 0.83 (0.76; 0.88) and a bias (LOA) of 1.55 (-4.69, 7.78). DM_Consolidation showed the greatest median difference between survivors/not survivors (p = 0.002). The strongest independent predictor for mortality was DM_Consolidation (AUC 0.688), while the strongest independent predictor for the intensity of care was TSS2 (0.7498). DM_Norm 2 was the singular feature independently associated with both the outcomes. DM_GGO1 strongly correlated with TA_ILD (ρ = 0.977). Conclusions: The DM technique and TA achieved consistent measurements and a better correlation with patient outcomes.

Diseases Involving the Lung Peribronchovascular Region: A CT Imaging Pathologic Classification

TOPIC IMPORTANCE

Chest CT imaging holds a major role in the diagnosis of lung diseases, many of which affect the peribronchovascular region. Identification and categorization of peribronchovascular abnormalities on CT imaging can assist in formulating a differential diagnosis and directing further diagnostic evaluation.

REVIEW FINDINGS

The peribronchovascular region of the lung encompasses the pulmonary arteries, airways, and lung interstitium. Understanding disease processes associated with structures of the peribronchovascular region and their appearances on CT imaging aids in prompt diagnosis. This article reviews current knowledge in anatomic and pathologic features of the lung interstitium composed of intercommunicating prelymphatic spaces, lymphatics, collagen bundles, lymph nodes, and bronchial arteries; diffuse lung diseases that present in a peribronchovascular distribution; and an approach to classifying diseases according to patterns of imaging presentations. Lung peribronchovascular diseases can appear on CT imaging as diffuse thickening, fibrosis, masses or masslike consolidation, ground-glass or air space consolidation, and cysts, acknowledging that some diseases may have multiple presentations.

SUMMARY

A category approach to peribronchovascular diseases on CT imaging can be integrated with clinical features as part of a multidisciplinary approach for disease diagnosis.

Pulmonary Infection

Pneumonia is a significant cause of morbidity and mortality in the community and hospital settings. Bacterial, viral, mycobacterial, and fungal pathogens are all potential causative agents of pulmonary infection. Chest radiographs and computed tomography are frequently utilized in the assessment of pneumonia. Learning the imaging patterns of different potential organisms allows the radiologist to formulate an appropriate differential diagnosis. An organism-based approach is used to discuss the imaging findings of different etiologies of pulmonary infection.

Immune Checkpoints Receptors Expression of Macrophage/Monocytes in Response to Acute Viral Respiratory Infection

BACKGROUND

We aimed to monitor the phenotypic changes in macrophages and their polarization in patients with acute viral respiratory diseases, including coronavirus disease diagnosis, focusing on the variations in the percentages of macrophages and monocytes and their sub-populations in those patients compared to healthy control. Moreover, we defined the correlation between macrophage subtypes and some inflammatory indices.

METHODS

Twenty-seven patients with clinical and radiologic diagnosis of acute viral respiratory infection admitted in Al-Azhar and Assiut University hospitals were recruited. Fresh peripheral blood samples were collected from all patients and healthy controls for flow cytometric analysis using BD FACSCanto II analyzer equipped with three lasers.

RESULTS

Compared to healthy controls, accumulation of cluster of differentiation (CD)11B+CD68+ macrophages (M) (P = 0.018), CD274+ M1 (P = 0.01), CD274+ M2 (P < 0.001), and CD80-CD206+ M2 (P = 0.001) was more evident in patients. Moreover, CD273+ M2 (P = 0.03), CD80+CD206- M1 (P = 0.002), and CD80+CD86+ M1 (P = 0.002) were highly expressed in controls compared with patients.

CONCLUSION

The examination of clinical specimens obtained from patients with signs of acute respiratory viral infection showed the role of the macrophage in the immune response. Dysfunction in macrophages results in heightened immune activity and inflammation, which plays a role in the progression of viral diseases and the emergence of accompanying health issues. This malfunction in macrophages is a common characteristic seen in various viruses, making it a promising focus for antiviral therapies with broad applicability. The immune checkpoint could be a target for immune modulation in patients with severe symptoms.

Comparison between high-flow nasal oxygen (HFNO) alternated with non-invasive ventilation (NIV) and HFNO and NIV alone in patients with COVID-19: a retrospective cohort study

BACKGROUND

Non-invasive respiratory support (conventional oxygen therapy [COT], non-invasive ventilation [NIV], high-flow nasal oxygen [HFNO], and NIV alternated with HFNO [NIV + HFNO] may reduce the need for invasive mechanical ventilation (IMV) in patients with COVID-19. The outcome of patients treated non-invasively depends on clinical severity at admission. We assessed the need for IMV according to NIV, HFNO, and NIV + HFNO in patients with COVID-19 according to disease severity and evaluated in-hospital survival rates and hospital and intensive care unit (ICU) lengths of stay.

METHODS

This cohort study was conducted using data collected between March 2020 and July 2021. Patients ≥ 18 years admitted to the ICU with a diagnosis of COVID-19 were included. Patients hospitalized for < 3 days, receiving therapy (COT, NIV, HFNO, or NIV + HFNO) for < 48 h, pregnant, and with no primary outcome data were excluded. The COT group was used as reference for multivariate Cox regression model adjustment.

RESULTS

Of 1371 patients screened, 958 were eligible: 692 (72.2%) on COT, 92 (9.6%) on NIV, 31 (3.2%) on HFNO, and 143 (14.9%) on NIV + HFNO. The results for the patients in each group were as follows: median age (interquartile range): NIV (64 [49-79] years), HFNO (62 [55-70] years), NIV + HFNO (62 [48-72] years) (p = 0.615); heart failure: NIV (54.5%), HFNO (36.3%), NIV + HFNO (9%) (p = 0.003); diabetes mellitus: HFNO (17.6%), NIV + HFNO (44.7%) (p = 0.048). > 50% lung damage on chest computed tomography (CT): NIV (13.3%), HFNO (15%), NIV + HFNO (71.6%) (p = 0.038); SpO2/FiO2: NIV (271 [118-365] mmHg), HFNO (317 [254-420] mmHg), NIV + HFNO (229 [102-317] mmHg) (p = 0.001); rate of IMV: NIV (26.1%, p = 0.002), HFNO (22.6%, p = 0.023), NIV + HFNO (46.8%); survival rate: HFNO (83.9%), NIV + HFNO (63.6%) (p = 0.027); ICU length of stay: NIV (8.5 [5-14] days), NIV + HFNO (15 [10-25] days (p < 0.001); hospital length of stay: NIV (13 [10-21] days), NIV + HFNO (20 [15-30] days) (p < 0.001). After adjusting for comorbidities, chest CT score and SpO2/FiO2, the risk of IMV in patients on NIV + HFNO remained high (hazard ratio, 1.88; 95% confidence interval, 1.17-3.04).

CONCLUSIONS

In patients with COVID-19, NIV alternating with HFNO was associated with a higher rate of IMV independent of the presence of comorbidities, chest CT score and SpO2/FiO2. Trial registration ClinicalTrials.gov identifier: NCT05579080.

A Challenging Case of Viral Pneumonia in the COVID-19 Pandemic Era

Cytomegalovirus (CMV) is a DNA virus that can cause widespread, severe infection in immunocompromised patients. While CMV usually leads to a subclinical infection in immunocompetent individuals, it can rarely cause severe disease in this population. The SARS-CoV-2 virus is an RNA virus and part of the Coronaviridae family. SARS-CoV-2 led to the COVID-19 (coronavirus disease 2019) pandemic. Even though COVID-19 usually presents with signs and symptoms of upper respiratory tract infection in younger adults, viral pneumonia, cytopenia, and neurological symptoms become more apparent with increasing age. Herein, we describe an immunocompetent 73-year-old female patient in whom oxygen demand and pancytopenia developed during hospitalization for post-ablation inguinal access site infection. The thorax CT revealed viral pneumonia, but two subsequent SARS-CoV-2 polymerase chain reaction (PCR) tests and a viral respiratory multiplex PCR panel were negative. The CMV viral load was high in the blood sample, and the patient responded to valganciclovir treatment. Although SARS-CoV-2 should be evaluated in patients with viral pneumonia and cytopenia, other viral etiologies mimicking SARS-CoV-2 infection, such as CMV, should not be overlooked in the era of the COVID-19 pandemic.

CT findings of 144 in-hospital patients with influenza pneumonia: A retrospective analysis

BACKGROUND/PURPOSE

Patients with influenza infection during their period of admission may have worse computed tomography (CT) manifestation according to the clinical status. This study aimed to evaluate the CT findings of in-hospital patients due to clinically significant influenza pneumonia with correlation of clinical presentations.

METHODS

In this retrospective, single center case series, 144 patients were included. All in-hospital patients were confirmed influenza infection and underwent CT scan. These patients were divided into three groups according to the clinical status of the most significant management: (1) without endotracheal tube and mechanical ventilator (ETTMV) or extracorporeal membrane oxygenation (ECMO); (2) with ETTMV; (3) with ETTMV and ECMO. Pulmonary opacities were scored according to extent. Spearman rank correlation analysis was used to evaluate the correlation between clinical parameters and CT scores.

RESULTS

The predominant CT manifestation of influenza infection was mixed ground-glass opacity (GGO) and consolidation with both lung involvement. The CT scores were all reach significant difference among all three groups (8.73 ± 6.29 vs 12.49 ± 6.69 vs 18.94 ± 4.57, p < 0.05). The chest CT score was correlated with age, mortality, and intensive care unit (ICU) days (all p values were less than 0.05). In addition, the CT score was correlated with peak lactate dehydrogenase (LDH) level and peak C-reactive protein (CRP) level (all p values were less than 0.05). Concomitant bacterial infection had higher CT score than primary influenza pneumonia (13.02 ± 7.27 vs 8.95 ± 5.99, p < 0.05).

CONCLUSION

Thin-section chest CT scores correlated with clinical and laboratory parameters in in-hospital patients with influenza pneumonia.

Opportunistic Infections Post-Lung Transplantation: Viral, Fungal, and Mycobacterial

Opportunistic infections are a leading cause of lung transplant recipient morbidity and mortality. Risk factors for infection include continuous exposure of the lung allograft to the external environment, high levels of immunosuppression, impaired mucociliary clearance and decreased cough reflex, and impact of the native lung microbiome in single lung transplant recipients. Infection risk is mitigated through careful pretransplant screening of recipients and donors, implementation of antimicrobial prophylaxis strategies, and routine surveillance posttransplant. This review describes common viral, fungal, and mycobacterial infectious after lung transplant and provides recommendations on prevention and treatment.

Radiological pulmonary sequelae after COVID-19 and correlation with clinical and functional pulmonary evaluation: results of a prospective cohort

OBJECTIVES

Whether COVID-19 leads to long-term pulmonary sequelae or not remains unknown. The aim of this study was to assess the prevalence of persisting radiological pulmonary fibrotic lesions in patients hospitalized for COVID-19.

MATERIALS AND METHODS

We conducted a prospective single-center study among patients hospitalized for COVID-19 between March and May 2020. Patients with residual symptoms or admitted into intensive care units were investigated 4 months after discharge by a chest CT (CCT) and pulmonary function tests (PFTs). The primary endpoint was the rate of persistent radiological fibrotic lesions after 4 months. Secondary endpoints included further CCT evaluation at 9 and 16 months, correlation of fibrotic lesions with clinical and PFT evaluation, and assessment of predictive factors.

RESULTS

Among the 1151 patients hospitalized for COVID-19, 169 patients performed a CCT at 4 months. CCTs showed pulmonary fibrotic lesions in 19% of the patients (32/169). These lesions were persistent at 9 months and 16 months in 97% (29/30) and 95% of patients (18/19) respectively. There was no significant clinical difference based on dyspnea scale in patients with pulmonary fibrosis. However, PFT evaluation showed significantly decreased diffusing lung capacity for carbon monoxide (p < 0.001) and total lung capacity (p < 0.001) in patients with radiological lesions. In multivariate analysis, the predictive factors of radiological pulmonary fibrotic lesions were pulmonary embolism (OR = 9.0), high-flow oxygen (OR = 6.37), and mechanical ventilation (OR = 3.49).

CONCLUSION

At 4 months, 19% of patients investigated after hospitalization for COVID-19 had radiological pulmonary fibrotic lesions; they persisted up to 16 months.

CLINICAL RELEVANCE STATEMENT

Whether COVID-19 leads to long-term pulmonary sequelae or not remains unknown. The aim of this study was to assess the prevalence of persisting radiological pulmonary fibrotic lesions in patients hospitalized for COVID-19. The prevalence of persisting lesions after COVID-19 remains unclear. We assessed this prevalence and predictive factors leading to fibrotic lesions in a large cohort. The respiratory clinical impact of these lesions was also assessed.

KEY POINTS

• Nineteen percent of patients hospitalized for COVID-19 had radiological fibrotic lesions at 4 months, remaining stable at 16 months. • COVID-19 fibrotic lesions did not match any infiltrative lung disease pattern. • COVID-19 fibrotic lesions were associated with pulmonary function test abnormalities but did not lead to clinical respiratory manifestation.

Prediction of viral pneumonia based on machine learning models analyzing pulmonary inflammation index scores

OBJECT

To obtain Pulmonary Inflammation Index scores from imaging chest CT and combine it with clinical correlates of viral pneumonia to predict the risk and severity of viral pneumonia using a computer learning model.

METHODS

All patients with suspected viral pneumonia on CT examination admitted to The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University from December 2022 to March 2023 were retrospectively selected. The respiratory viruses were monitored by RT-PCR and categorized into patients with viral pneumonia and those with non-viral pneumonia. The extent of lung inflammation was quantified according to the Pulmonary Inflammation Index score (PII). Information on patient demographics, comorbidities, laboratory tests, pathogenetic testing, and radiological data were collected. Five machine learning models containing Random Forest(RF), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), K Nearest Neighbour Algorithm (KNN), and Kernel Ridge Regression (KRR) were used to predict the risk of onset and severity of viral pneumonia based on the clinically relevant factors or PII.

RESULTS

Among the five models, the SVM model performed best in ACC (76.75 %), SN (73.99 %), and F1 (72.42 %) and achieved a better area under the receiver operating characteristic curve (ROC) (0.8409) when predicting the risk of developing viral pneumonia. RF had the best overall classification accuracy in predicting the severity of viral pneumonia, especially in predicting pneumonia with a PII classification of grade I, the RF model achieved an accuracy of 98.89%.

CONCLUSION

Machine learning models are valuable in assessing the risk of viral pneumonia. Meanwhile, machine learning models confirm the importance in predicting the severity of viral pneumonia through PII. The establishment of machine learning models for predicting the risk and severity of viral pneumonia promotes the further development of machine learning in the medical field.

Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm

COVID-19-related pneumonia is typically diagnosed using chest x-ray or computed tomography images. However, these techniques can only be used in hospitals. In contrast, thermal cameras are portable, inexpensive devices that can be connected to smartphones. Thus, they can be used to detect and monitor medical conditions outside hospitals. Herein, a smartphone-based application using thermal images of a human back was developed for COVID-19 detection. Image analysis using a deep learning algorithm revealed a sensitivity and specificity of 88.7% and 92.3%, respectively. The findings support the future use of noninvasive thermal imaging in primary screening for COVID-19 and associated pneumonia.

Comparison of Ultrasound and CT Imaging for the Diagnosis of Coronavirus Disease and Influenza A Pneumonia

OBJECTIVE

The outbreak of coronavirus disease (COVID-19) coincided with the season of influenza A pneumonia, a common respiratory infectious disease. Therefore, this study compared ultrasonography and computed tomography (CT) for the diagnosis of the two diseases.

METHODS

Patients with COVID-19 or influenza A infection hospitalized at our hospital were included. The patients were examined by ultrasonography every day. The CT examination results within 1 day before and after the day of the highest ultrasonography score were selected as the controls. The similarities and differences between the ultrasonography and CT results in the two groups were compared.

RESULTS

There was no difference between the ultrasonography and CT scores (P = .307) for COVID-19, while there was a difference between ultrasonography and CT scores for influenza A pneumonia (P = .024). The ultrasonography score for COVID-19 was higher than that for influenza A pneumonia (P = .000), but there was no difference between the CT scores (P = .830). For both diseases, there was no difference in ultrasonography and CT scores between the left and right lungs; there were differences between the CT scores of the upper and middle lobes, as well as between the upper and lower lobes of the lungs; however, there was no difference between the lower and middle lobes of the lungs.

CONCLUSION

Ultrasonography is equivalent to the gold standard CT for diagnosing and monitoring the progression of COVID-19. Because of its convenience, ultrasonography has important application value. Furthermore, the diagnostic value of ultrasonography for COVID-19 is higher than that for influenza A pneumonia.

Sono-Elastography: An Ultrasound Quantitative Non-Invasive Measurement to Guide Bacterial Pneumonia Diagnosis in Children

Lung ultrasound (LUS) is, at present, a standard technique for the diagnosis of acute lower respiratory tract infections (ALRTI) and other lung pathologies. Its protocolised use has replaced chest radiography and has led to a drastic reduction in radiation exposure in children. Despite its undeniable usefulness, there are situations in which certain quantitative measurements could provide additional data to differentiate the etiology of some pulmonary processes and thus adapt the treatment. Our research group hypothesises that several lung processes such pneumonia may lead to altered lung tissue stiffness, which could be quantified with new diagnostic tests such as lung sono-elastography (SE). An exhaustive review of the literature has been carried out, concluding that the role of SE for the study of pulmonary processes is currently scarce and poorly studied, particularly in pediatrics. The aim of this review is to provide an overview of the technical aspects of SE and to explore its potential usefulness as a non-invasive diagnostic technique for ALRTI in children by implementing an institutional image acquisition protocol.

A specific combination of laboratory data is associated with overweight lungs in patients with COVID-19 pneumonia at hospital admission: secondary cross-sectional analysis of a randomized clinical trial

Background

Lung weight may be measured with quantitative chest computed tomography (CT) in patients with COVID-19 to characterize the severity of pulmonary edema and assess prognosis. However, this quantitative analysis is often not accessible, which led to the hypothesis that specific laboratory data may help identify overweight lungs.

Methods

This cross-sectional study was a secondary analysis of data from SARITA2, a randomized clinical trial comparing nitazoxanide and placebo in patients with COVID-19 pneumonia. Adult patients (≥18 years) requiring supplemental oxygen due to COVID-19 pneumonia were enrolled between April 20 and October 15, 2020, in 19 hospitals in Brazil. The weight of the lungs as well as laboratory data [hemoglobin, leukocytes, neutrophils, lymphocytes, C-reactive protein, D-dimer, lactate dehydrogenase (LDH), and ferritin] and 47 additional specific blood biomarkers were assessed.

Results

Ninety-three patients were included in the study: 46 patients presented with underweight lungs (defined by ≤0% of excess lung weight) and 47 patients presented with overweight lungs (>0% of excess lung weight). Leukocytes, neutrophils, D-dimer, and LDH were higher in patients with overweight lungs. Among the 47 blood biomarkers investigated, interferon alpha 2 protein was higher and leukocyte inhibitory factor was lower in patients with overweight lungs. According to CombiROC analysis, the combinations of D-dimer/LDH/leukocytes, D-dimer/LDH/neutrophils, and D-dimer/LDH/leukocytes/neutrophils achieved the highest area under the curve with the best accuracy to detect overweight lungs.

Conclusion

The combinations of these specific laboratory data: D-dimer/LDH/leukocytes or D-dimer/LDH/neutrophils or D-dimer/LDH/leukocytes/neutrophils were the best predictors of overweight lungs in patients with COVID-19 pneumonia at hospital admission.

Clinical trial registration

Brazilian Registry of Clinical Trials (REBEC) number RBR-88bs9x and ClinicalTrials.gov number NCT04561219.

Long-term effects of COVID-19 on lungs and the clinical relevance: a 6-month prospective cohort study

Background: We aimed to explore the prevalence of prolonged symptoms, pulmonary impairments and residual disease on chest tomography (CT) in COVID-19 patients at 6 months after acute illness. Methods: In this prospective, single-center study, hospitalized patients with radiologically and laboratory-confirmed COVID-19 were included. Results: A high proportion of the 116 patients reported persistent symptoms (n = 54; 46.6%). On follow-up CT, 33 patients (28.4%) demonstrated residual disease. Multivariate analyses revealed that only neutrophil-to-lymphocyte ratio was an independent predictor for residual disease. Conclusion: Hospitalized patients with mild/moderate COVID-19 still had persistent symptoms and were prone to develop long-term pulmonary sequelae on chest CT. However, it did not have a significant effect on long-term pulmonary functions.

Opportunistic Infections Post-Lung Transplantation: Viral, Fungal, and Mycobacterial

Opportunistic infections are a leading cause of lung transplant recipient morbidity and mortality. Risk factors for infection include continuous exposure of the lung allograft to the external environment, high levels of immunosuppression, impaired mucociliary clearance and decreased cough reflex, and impact of the native lung microbiome in single lung transplant recipients. Infection risk is mitigated through careful pretransplant screening of recipients and donors, implementation of antimicrobial prophylaxis strategies, and routine surveillance posttransplant. This review describes common viral, fungal, and mycobacterial infectious after lung transplant and provides recommendations on prevention and treatment.

DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images

COVID-19 is a severe respiratory contagious disease that has now spread all over the world. COVID-19 has terribly impacted public health, daily lives and the global economy. Although some developed countries have advanced well in detecting and bearing this coronavirus, most developing countries are having difficulty in detecting COVID-19 cases for the mass population. In many countries, there is a scarcity of COVID-19 testing kits and other resources due to the increasing rate of COVID-19 infections. Therefore, this deficit of testing resources and the increasing figure of daily cases encouraged us to improve a deep learning model to aid clinicians, radiologists and provide timely assistance to patients. In this article, an efficient deep learning-based model to detect COVID-19 cases that utilizes a chest X-ray images dataset has been proposed and investigated. The proposed model is developed based on ResNet50V2 architecture. The base architecture of ResNet50V2 is concatenated with six extra layers to make the model more robust and efficient. Finally, a Grad-CAM-based discriminative localization is used to readily interpret the detection of radiological images. Two datasets were gathered from different sources that are publicly available with class labels: normal, confirmed COVID-19, bacterial pneumonia and viral pneumonia cases. Our proposed model obtained a comprehensive accuracy of 99.51% for four-class cases (COVID-19/normal/bacterial pneumonia/viral pneumonia) on Dataset-2, 96.52% for the cases with three classes (normal/ COVID-19/bacterial pneumonia) and 99.13% for the cases with two classes (COVID-19/normal) on Dataset-1. The accuracy level of the proposed model might motivate radiologists to rapidly detect and diagnose COVID-19 cases.

Parenchymal lung abnormalities following hospitalisation for COVID-19 and viral pneumonitis: a systematic review and meta-analysis

INTRODUCTION

Persisting respiratory symptoms in COVID-19 survivors may be related to development of pulmonary fibrosis. We assessed the proportion of chest CT scans and pulmonary function tests consistent with parenchymal lung disease in the follow-up of people hospitalised with COVID-19 and viral pneumonitis.

METHODS

Systematic review and random effects meta-analysis of proportions using studies of adults hospitalised with SARS-CoV-2, SARS-CoV, MERS-CoV or influenza pneumonia and followed up within 12 months. Searches performed in MEDLINE and Embase. Primary outcomes were proportion of radiological sequelae on CT scans; restrictive impairment; impaired gas transfer. Heterogeneity was explored in meta-regression.

RESULTS

Ninety-five studies (98.9% observational) were included in qualitative synthesis, 70 were suitable for meta-analysis including 60 SARS-CoV-2 studies with a median follow-up of 3 months. In SARS-CoV-2, the overall estimated proportion of inflammatory sequelae was 50% during follow-up (0.50; 95% CI 0.41 to 0.58; I2=95%), fibrotic sequelae were estimated in 29% (0.29; 95% CI 0.22 to 0.37; I2=94.1%). Follow-up time was significantly associated with estimates of inflammatory sequelae (-0.036; 95% CI -0.068 to -0.004; p=0.029), associations with fibrotic sequelae did not reach significance (-0.021; 95% CI -0.051 to 0.009; p=0.176). Impaired gas transfer was estimated at 38% of lung function tests (0.38 95% CI 0.32 to 0.44; I2=92.1%), which was greater than restrictive impairment (0.17; 95% CI 0.13 to 0.23; I2=92.5%), neither were associated with follow-up time (p=0.207; p=0.864).

DISCUSSION

Sequelae consistent with parenchymal lung disease were observed following COVID-19 and other viral pneumonitis. Estimates should be interpreted with caution due to high heterogeneity, differences in study casemix and initial severity.

PROSPERO REGISTRATION NUMBER

CRD42020183139.

The value of chest X-ray and CT severity scoring systems in the diagnosis of COVID-19: A review

Coronavirus disease 2019 (COVID-19) is caused by a coronavirus family member known as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The main laboratory test to confirm the quick diagnosis of COVID-19 infection is reverse transcription-polymerase chain reaction (RT-PCR) based on nasal or throat swab sampling. A small percentage of false-negative RT-PCR results have been reported. The RT-PCR test has a sensitivity of 50-72%, which could be attributed to a low viral load in test specimens or laboratory errors. In contrast, chest CT has shown 56-98% of sensitivity in diagnosing COVID-19 at initial presentation and has been suggested to be useful in correcting false negatives from RT-PCR. Chest X-rays and CT scans have been proposed to predict COVID-19 disease severity by displaying the score of lung involvement and thus providing information about the diagnosis and prognosis of COVID-19 infection. As a result, the current study provides a comprehensive overview of the utility of the severity score index using X-rays and CT scans in diagnosing patients with COVID-19 when compared to RT-PCR.

Predicting the COVID-19 Patients Status Using Chest CT Scan Findings: A Risk Assessment Model Based on Decision Tree Analysis

BACKGROUND

The role of chest computed tomography (CT) to diagnose coronavirus disease 2019 (COVID-19) is still an open field to be explored. The aim of this study was to apply the decision tree (DT) model to predict critical or non-critical status of patients infected with COVID-19 based on available information on non-contrast CT scans.

METHODS

This retrospective study was performed on patients with COVID-19 who underwent chest CT scans. Medical records of 1078 patients with COVID-19 were evaluated. The classification and regression tree (CART) of decision tree model and k-fold cross-validation were used to predict the status of patients using sensitivity, specificity, and area under the curve (AUC) assessments.

RESULTS

The subjects comprised of 169 critical cases and 909 non-critical cases. The bilateral distribution and multifocal lung involvement were 165 (97.6%) and 766 (84.3%) in critical patients, respectively. According to the DT model, total opacity score, age, lesion types, and gender were statistically significant predictors for critical outcomes. Moreover, the results showed that the accuracy, sensitivity and specificity of the DT model were 93.3%, 72.8%, and 97.1%, respectively.

CONCLUSIONS

The presented algorithm demonstrates the factors affecting health conditions in COVID-19 disease patients. This model has the potential characteristics for clinical applications and can identify high-risk subpopulations that need specific prevention. Further developments including integration of blood biomarkers are underway to increase the performance of the model.

Differential diagnosis of infectious diseases, drug-induced lung injury, and pulmonary infiltration due to underlying malignancy in patients with hematological malignancy using HRCT

PURPOSE

To differentiate among infectious diseases, drug-induced lung injury (DILI) and pulmonary infiltration due to underlying malignancy (PIUM) based on high-resolution computed tomographic (HRCT) findings from patients with hematological malignancies who underwent chemotherapy or hematopoietic stem cell transplantation.

MATERIALS AND METHODS

A total of 221 immunocompromised patients with hematological malignancies who had proven chest complications (141 patients with infectious diseases, 24 with DILI and 56 with PIUM) were included. Two chest radiologists evaluated the HRCT findings, including ground-glass opacity, consolidation, nodules, and thickening of bronchovascular bundles (BVBs) and interlobular septa (ILS). After comparing these CT findings among the three groups using the χ2test, multiple logistic regression analyses (infectious vs noninfectious diseases, DILI vs non-DILI, and PIUM vs non-PIUM) were performed to detect useful indicators for differentiation.

RESULTS

Significant differences were detected in many HRCT findings by the χ2 test. The results from the multiple logistic regression analyses identified several indicators: nodules without a perilymphatic distribution [p = 0.012, odds ratio (95% confidence interval): 4.464 (1.355-11.904)], nodules with a tree-in-bud pattern [p = 0.011, 8.364 (1.637-42.741)], and the absence of ILS thickening[p = 0.003, 3.621 (1.565-8.381)] for infectious diseases, the presence of ILS thickening [p = 0.001, 7.166 (2.343-21.915)] for DILI, and nodules with a perilymphatic distribution [p = 0.011, 4.256 (1.397-12.961)] and lymph node enlargement (p = 0.008, 3.420 (1.385-8.441)] for PIUM.

CONCLUSION

ILS thickening, nodules with a perilymphatic distribution, tree-in-bud pattern, and lymph node enlargement could be useful indicators for differentiating among infectious diseases, DILI, and PIUM in patients with hematological malignancies.

Undiagnosed Long COVID-19 in China Among Non-vaccinated Individuals: Identifying Persistent Symptoms and Impacts on Patients' Health-Related Quality of Life

Is Long COVID-19 under-diagnosed? The definition of this new condition has received many contributions, and it is still under development as a great variety of symptoms have been associated to it. This study explores the possibility that there are non-diagnosed cases among individuals who have been infected by SARS-CoV-2 and have not been vaccinated. The long-term symptoms identified among a sample 255 individuals have been associated to Long COVID-19 by recent literature. The study relates these symptoms to risk factors and health-related quality of life (HRQoL) negative impacts. The individuals were screened 1 year after discharge to explore its potential relation to Long COVID-19. Patients diagnosed with COVID-19 and discharged from designated hospitals in a Chinese province between January and April 2020 were included in this study. They received computed tomography (CT) scans one month after discharge. One year after discharge, patients were invited to physical examination and interviewed with questionnaire on health-related quality of life (HRQoL) and post-COVID-19 symptoms. Tobit regression and Logistic regression were applied to evaluate the risk factors for health utility value and pain/discomfort and anxiety/depression. One year after discharge, 39.61% patients complained of several of the symptoms associated to Long COVID-19. More than half had abnormal chest CT. Previous studies focused on the post-COVID-19 symptoms and chest CT findings of patients, but few studies have assessed the COVID-19-associated risk factors for health-related quality of life.

Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics

OBJECTIVE

To develop a two-step machine learning (ML) based model to diagnose and predict involvement of lungs in COVID-19 and non COVID-19 pneumonia patients using CT chest radiomic features.

METHODS

Three hundred CT scans (3-classes: 100 COVID-19, 100 pneumonia, and 100 healthy subjects) were enrolled in this study. Diagnostic task included 3-class classification. Severity prediction score for COVID-19 and pneumonia was considered as mild (0-25%), moderate (26-50%), and severe (>50%). Whole lungs were segmented utilizing deep learning-based segmentation. Altogether, 107 features including shape, first-order histogram, second and high order texture features were extracted. Pearson correlation coefficient (PCC≥90%) followed by different features selection algorithms were employed. ML-based supervised algorithms (Naïve Bays, Support Vector Machine, Bagging, Random Forest, K-nearest neighbors, Decision Tree and Ensemble Meta voting) were utilized. The optimal model was selected based on precision, recall and area-under-curve (AUC) by randomizing the training/validation, followed by testing using the test set.

RESULTS

Nine pertinent features (2 shape, 1 first-order, and 6 second-order) were obtained after features selection for both phases. In diagnostic task, the performance of 3-class classification using Random Forest was 0.909±0.026, 0.907±0.056, 0.902±0.044, 0.939±0.031, and 0.982±0.010 for precision, recall, F1-score, accuracy, and AUC, respectively. The severity prediction task using Random Forest achieved 0.868±0.123 precision, 0.865±0.121 recall, 0.853±0.139 F1-score, 0.934±0.024 accuracy, and 0.969±0.022 AUC.

CONCLUSION

The two-phase ML-based model accurately classified COVID-19 and pneumonia patients using CT radiomics, and adequately predicted severity of lungs involvement. This 2-steps model showed great potential in assessing COVID-19 CT images towards improved management of patients.

A novel chest CT severity score in COVID-19 and its correlation with severity and prognosis of the lung disease: A retrospective cohort study

Background

HRCT chest has a high sensitivity in the diagnosis of patients with COVID-19 infection. Through our study, we intend to evaluate the diagnostic accuracy and inter-reader variability of a semi-quantitative CT severity score, a novel parameter designed for risk stratification and prognostication of COVID-19 pneumonia with clinical staging of disease.

Methods

It was a single-center retrospective analysis performed on an original cohort of 4180 symptomatic patients with the suspicion of SARS-CoV-2 interstitial pneumonia. Out of 4180, a total of 4004 patients with COVID-19 were confirmed by an RT-PCR. We used an HRCT chest severity score (CT-SS) to evaluate the COVID-19 disease burden on the initial scan obtained at admission. The data were analyzed with IBM SPSS Statistics Version 22.0 Release 2013.

Results

Our study subjects demonstrated the most common clinical features fever, cough, dyspnea, and body aches. Raised CRP levels (CRP >0.5 mg/dL) were found in 81.86% and increased D-dimer levels (>500 ng/mL) were found in 92.3% of patients. The most common radiological findings of the disease included ground-glass opacities, observed in 98.8%. Our study has a sensitivity of 89.2%, a specificity of 94.8%, a positive predictive value (PPV) of 90.6%, and a negative predictive value (NPV) of 94%.

Conclusion

As per our findings, this novel CT scoring system might aid in the risk stratification and the short-term prognostication of patients suffering from COVID-19 pneumonia. This will eventually help in curtailing the extensive burden on the healthcare system amid the current pandemic.

•

There is a correlation between the severity of lung disease in COVID-19 pneumonia and HRCT severity score.

•

The most common radiological findings of the disease included ground-glass opacities, followed by septal thickening (crazy paving), and bronchial wall thickening.

•

This novel HRCT scoring system can help us in classifying COVID-19 pneumonia and ultimately triaging the patients. .

The role of exhaled nitric oxide (FeNO) in the evaluation of lung parenchymal involvement in COVID-19 patients

The inflammatory balance is an important factor in the clinical course of COVID-19 (SARS-CoV-2) infection, which has affected over 300 million people globally since its appearance in December 2019. This study aimed to evaluate the correlation between exhaled nitric oxide (FeNO) level and parenchymal involvement in COVID-19. The study included 106 patients with the delta variant of COVID-19 identified by real-time PCR as well as 40 healthy control groups between October 2021 and March 2022. The patients were analyzed in three groups: moderate COVID-19 (group 1), severe COVID-19 without macrophage activation syndrome (MAS) (group 2), and severe COVID-19 with MAS (group 3). FeNO and CT scores were significantly higher in groups 2 and 3 at admission and discharge compared to group 1 (p = 0.001 for all). In addition, CT score at admission and CT score and FeNO level at discharge were higher in group 3 than in group 2 (p = 0.001 for all). It was found that the FeNO levels were higher in Groups 2 and 3 than in the control group (p = 0.001) during the admission. FeNO and CT scores showed strong positive correlation at admission and discharge (r = 0.917, p = 0.001; r = 0.790, p = 0.001). In receiver operating characteristic curve analysis for prediction of MAS, FeNO at a cut-off of 10.5 ppb had 66% sensitivity and 71% specificity. COVID-19 causes more severe lung involvement than other viral lower respiratory tract infections, leading to the frequent use of chest CT in these patients. FeNO assessment is a practical and noninvasive method that may be useful in evaluating for parenchymal infiltration in the diagnosis and follow-up of COVID-19 patients.

Radiological Findings in SARS-CoV-2 Viral Pneumonia Compared to Other Viral Pneumonias: A Single-Centre Study

BACKGROUND

Thorax computed tomography (CT) imaging is widely used as a diagnostic method in the diagnosis of coronavirus disease 2019 (COVID-19)-related pneumonia. Radiological differential diagnosis and isolation of other viral agents causing pneumonia in patients have gained importance, particularly during the pandemic.

AIMS

We aimed to investigate whether there is a difference between CT images from patients with COVID-19-associated pneumonia compared to CT images of patients with pneumonia due to other viral agents and which finding may be more effective in diagnosis. Study Design. The study included 249 adult patients with pneumonia identified by thorax CT examination and with a positive COVID-19 RT-PCR test compared to 94 patients diagnosed with non-COVID-19 pneumonia (viral PCR positive but no bacterial or fungal agents detected in other cultures) between 2015 and 2019. CT images were retrospectively analyzed using the PACS system. CT findings were evaluated by two radiologists with 5 and 20 years of experience, in a blinded fashion, and the outcome was decided by consensus.

METHODS

Demographic data (age, gender, and known chronic disease) and CT imaging findings (percentage of involvement, number of lesions, distribution preference, dominant pattern, ground-glass opacity distribution pattern, nodule, tree in bud sign, interstitial changes, crazy paving sign, reversed halo sign, vacuolar sign, halo sign, vascular enlargement, linear opacities, traction bronchiectasis, peribronchial wall thickness, air trapping, pleural retraction, pleural effusion, pericardial effusion, cavitation, mediastinal/hilar lymphadenopathy, dominant lesion size, consolidation, subpleural curvilinear opacities, air bronchogram, and pleural thickening) of the patients were evaluated. CT findings were also evaluated with the RSNA consensus guideline and the CORADS scoring system. Data were divided into two main groups-non-COVID-19 and COVID-19 pneumonia-and compared statistically with chi-squared tests and multiple regression analysis of independent variables.

RESULTS

RSNA and CORADS classifications of CT scan images were able to successfully differentiate between positive and negative COVID-19 pneumonia patients. Statistically significant differences were found between the two patient groups in various categories including the percentage of involvement, number of lesions, distribution preference, dominant pattern, nodule, tree in bud, interstitial changes, crazy paving, reverse halo vascular enlargement, peribronchial wall thickness, air trapping, pleural retraction, pleural/pericardial effusion, cavitation, and mediastinal/hilar lymphadenopathy (p < 0.01). Multiple linear regression analysis of independent variables found a significant effect in reverse halo sign (β = 0.097, p < 0.05) and pleural effusion (β = 10.631, p < 0.05) on COVID-19 pneumonia patients.

CONCLUSION

The presence of reverse halo and absence of pleural effusion was found to be characteristic of COVID-19 pneumonia and therefore a reliable diagnostic tool to differentiate it from non-COVID-19 pneumonia.

Medical decision support system using weakly-labeled lung CT scans

Purpose

Determination and development of an effective set of models leveraging Artificial Intelligence techniques to generate a system able to support clinical practitioners working with COVID-19 patients. It involves a pipeline including classification, lung and lesion segmentation, as well as lesion quantification of axial lung CT studies.

Approach

A deep neural network architecture based on DenseNet is introduced for the classification of weakly-labeled, variable-sized (and possibly sparse) axial lung CT scans. The models are trained and tested on aggregated, publicly available data sets with over 10 categories. To further assess the models, a data set was collected from multiple medical institutions in Colombia, which includes healthy, COVID-19 and patients with other diseases. It is composed of 1,322 CT studies from a diverse set of CT machines and institutions that make over 550,000 slices. Each CT study was labeled based on a clinical test, and no per-slice annotation took place. This enabled a classification into Normal vs. Abnormal patients, and for those that were considered abnormal, an extra classification step into Abnormal (other diseases) vs. COVID-19. Additionally, the pipeline features a methodology to segment and quantify lesions of COVID-19 patients on the complete CT study, enabling easier localization and progress tracking. Moreover, multiple ablation studies were performed to appropriately assess the elements composing the classification pipeline.

Results

The best performing lung CT study classification models achieved 0.83 accuracy, 0.79 sensitivity, 0.87 specificity, 0.82 F1 score and 0.85 precision for the Normal vs. Abnormal task. For the Abnormal vs COVID-19 task, the model obtained 0.86 accuracy, 0.81 sensitivity, 0.91 specificity, 0.84 F1 score and 0.88 precision. The ablation studies showed that using the complete CT study in the pipeline resulted in greater classification performance, restating that relevant COVID-19 patterns cannot be ignored towards the top and bottom of the lung volume.

Discussion

The lung CT classification architecture introduced has shown that it can handle weakly-labeled, variable-sized and possibly sparse axial lung studies, reducing the need for expert annotations at a per-slice level.

Conclusions

This work presents a working methodology that can guide the development of decision support systems for clinical reasoning in future interventionist or prospective studies.

Automatic diagnosis of severity of COVID-19 patients using an ensemble of transfer learning models with convolutional neural networks in CT images

Introduction: Quantification of lung involvement in COVID-19 using chest Computed tomography (CT) scan can help physicians to evaluate the progression of the disease or treatment response. This paper presents an automatic deep transfer learning ensemble based on pre-trained convolutional neural networks (CNNs) to determine the severity of COVID -19 as normal, mild, moderate, and severe based on the images of the lungs CT.

Material and methods: In this study, two different deep transfer learning strategies were used. In the first procedure, features were extracted from fifteen pre-trained CNNs architectures and then fed into a support vector machine (SVM) classifier. In the second procedure, the pre-trained CNNs were fine-tuned using the chest CT images, and then features were extracted for the purpose of classification by the softmax layer. Finally, an ensemble method was developed based on majority voting of the deep learning outputs to increase the performance of the recognition on each of the two strategies. A dataset of CT scans was collected and then labeled as normal (314), mild (262), moderate (72), and severe (35) for COVID-19 by the consensus of two highly qualified radiologists.

Results: The ensemble of five deep transfer learning outputs named EfficientNetB3, EfficientNetB4, InceptionV3, NasNetMobile, and ResNext50 in the second strategy has better results than the first strategy and also the individual deep transfer learning models in diagnosing the severity of COVID-19 with 85% accuracy.

Conclusions: Our proposed study is well suited for quantifying lung involvement of COVID-19 and can help physicians to monitor the progression of the disease.

Atlas-Based Lung Segmentation Combined With Automatic Densitometry Characterization In COVID-19 Patients: Training, Validation And First Application In A Longitudinal Study

Purpose

To develop and validate an automated segmentation tool for COVID-19 lung CTs. To combine it with densitometry information in identifying Aerated, Intermediate and Consolidated Volumes in admission (CT1) and follow up CT (CT3).

Materials and Methods

An Atlas was trained on manually segmented CT1 of 250 patients and validated on 10 CT1 of the training group, 10 new CT1 and 10 CT3, by comparing DICE index between automatic (AUTO), automatic-corrected (AUTOMAN) and manual (MAN) contours. A previously developed automatic method was applied on HU lung density histograms to quantify Aerated, Intermediate and Consolidated Volumes. Volumes of subregions in validation CT1 and CT3 were quantified for each method.

Results

In validation CT1/CT3, manual correction of automatic contours was not necessary in 40% of cases. Mean DICE values for both lungs were 0.94 for AUTOVsMAN and 0.96 for AUTOMANVsMAN. Differences between Aerated and Intermediate Volumes quantified with AUTOVsMAN contours were always < 6%. Consolidated Volumes showed larger differences (mean: −95 ± 72 cc). If considering AUTOMANVsMAN volumes, differences got further smaller for Aerated and Intermediate, and were drastically reduced for consolidated Volumes (mean: −36 ± 25 cc). The average time for manual correction of automatic lungs contours on CT1 was 5 ± 2 min.

Conclusions

An Atlas for automatic segmentation of lungs in COVID-19 patients was developed and validated. Combined with a previously developed method for lung densitometry characterization, it provides a fast, operator-independent way to extract relevant quantitative parameters with minimal manual intervention.

Association of serum Krebs von den Lungen-6 and chest CT as potential prognostic factors in severe acute respiratory syndrome SARS-CoV-2: a preliminary experience

PURPOSE

To correlate in COVID-19 pneumonia CT-based semi-quantitative score of pulmonary involvement with high serum levels of KL-6, a biomarker of disease severity.

METHODS

Between March 28 to May 21, 2020, 196 patients with strong suspicion of SARS-CoV-2 were evaluated with RT-PCR for SARS-CoV-2, chest CT scan and blood test, including KL-6 serum protein, in our Emergency Unit. The final population included only patients who underwent blood sampling for KL-6 within 5 days from CT scan (n = 63), including n = 37 COVID-19-positive patients and n = 26 with negative RT-PCR testing for SARS-CoV-2 (control group). A semi-quantitative CT score was calculated based on the extent of lobar involvement (0:0%; 1, < 5%; 2:5-25%; 3:26-50%; 4:51-75%; 5, > 75%; range 0-5; global score 0-25).

RESULTS

CT score was significantly correlated with serum value of KL-6 (r = 27, p = 0.035). This correlation was also present in COVID-19 positive patients (r = 0.423, p = 0.009) and CT score median value was significantly higher in patients with high KL-6 value (> 400 U/mL; 12.00, IQR 5.00-18.00, p-value 0.027). In control group, no statistically significant correlation was found between CT score and KL-6 value and CT score was higher in patients with high KL-6, although this difference was not statistically significant (5.00, IQR:1.75-8.00 versus 3.50, IQR:2.00-6.50). "Crazy paving" at the right upper (n = 8; 61.5%) and middle lobe (n = 4; 30.8%) and "consolidation" at the middle lobe (n=5; 38.5%) were observed in COVID-19 group with a significant difference between patients with high KL-6 value.

CONCLUSION

CT score is highly correlated with KL-6 value in COVID-19 patients and might be beneficial to speed-up diagnostic workflow in symptomatic cases.

Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images

(1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially declared on 26 February 2020. (2) Methods: This study proposes a federated learning approach with pre-trained deep learning models for COVID-19 detection. Three clients were locally deployed with their own dataset. The goal of the clients was to collaborate in order to obtain a global model without sharing samples from the dataset. The algorithm we developed was connected to our internal picture archiving and communication system and, after running backwards, it encountered chest CT changes suggestive for COVID-19 in a patient investigated in our medical imaging department on the 28 January 2020. (4) Conclusions: Based on our results, we recommend using an automated AI-assisted software in order to detect COVID-19 based on the lung imaging changes as an adjuvant diagnostic method to the current gold standard (RT-PCR) in order to greatly enhance the management of these patients and also limit the spread of the disease, not only to the general population but also to healthcare professionals.

Comparison between Suspected and Confirmed COVID-19 Respiratory Patients: What Is beyond the PCR Test

COVID-19 modified the healthcare system. Nasal-pharyngeal swab (NPS), with real-time reverse transcriptase-polymerase (PCR), is the gold standard for the diagnosis; however, there are difficulties related to the procedure that may postpone it. The study aims to evaluate whether other elements than the PCR-NPS are reliable and confirm the diagnosis of COVID-19. This is a cross-sectional study on data from the Lung Unit of Pavia (confirmed) and at the Emergency Unit of Palermo (suspected). COVID-19 was confirmed by positive NPS, suspected tested negative. We compared clinical, laboratory and radiological variables and performed Logistic regression to estimate which variables increased the risk of COVID-19. The derived ROC-AUCcurve, assessed the accuracy of the model to distinguish between COVID-19 suspected and confirmed. We selected 50 confirmed and 103 suspected cases. High Reactive C-Protein (OR: 1.02; CI95%: 0.11–1.02), suggestive CT-images (OR: 11.43; CI95%: 3.01–43.3), dyspnea (OR: 10.48; CI95%: 2.08–52.7) and respiratory failure (OR: 5.84; CI95%: 1.73–19.75) increased the risk of COVID-19, whereas pleural effusion decreased the risk (OR: 0.15; CI95%: 0.04–0.63). ROC confirmed the discriminative role of these variables between suspected and confirmed COVID-19 (AUC 0.91). Clinical, laboratory and imaging features predict the diagnosis of COVID-19, independently from the NPS result.

Multiorgan Involvement in SARS-CoV-2 Infection: The Role of the Radiologist from Head to Toe

Radiology plays a crucial role for the diagnosis and management of COVID-19 patients during the different stages of the disease, allowing for early detection of manifestations and complications of COVID-19 in the different organs. Lungs are the most common organs involved by SARS-CoV-2 and chest computed tomography (CT) represents a reliable imaging-based tool in acute, subacute, and chronic settings for diagnosis, prognosis, and management of lung disease and the evaluation of acute and chronic complications. Cardiac involvement can be evaluated by using cardiac computed tomography angiography (CCTA), considered as the best choice to solve the differential diagnosis between the most common cardiac conditions: acute coronary syndrome, myocarditis, and cardiac dysrhythmia. By using compressive ultrasound it's possible to study the peripheral arteries and veins and to exclude the deep vein thrombosis, directly linked to the onset of pulmonary embolism. Moreover, CT and especially MRI can help to evaluate the gastrointestinal involvement and assess hepatic function, pancreas involvement, and exclude causes of lymphocytopenia, thrombocytopenia, and leukopenia, typical of COVID-19 patients. Finally, radiology plays a crucial role in the early identification of renal damage in COVID-19 patients, by using both CT and US. This narrative review aims to provide a comprehensive radiological analysis of commonly involved organs in patients with COVID-19 disease.

Increased ACE2, sRAGE, and Immune Activation, but Lowered Calcium and Magnesium in COVID-19

BACKGROUND

The characterization of new biomarkers that could help externally validate the diagnosis of COVID-19 and optimize treatments is extremely important. Many studies have established changes in immune-inflammatory and antibody levels, but few studies measured the soluble receptor for the advanced glycation end product (sRAGE), angiotensin-converting enzyme 2 (ACE2), calcium, and magnesium in COVID-19.

OBJECTIVE

To evaluate serum advanced glycation end-product receptor (sRAGE) and angiotensin converting enzyme (ACE)2 and peripheral oxygen saturation (SpO2) and chest CT scan abnormalities (CCTA) in COVID-19.

METHODS

sRAGE, ACE2, interleukin (IL)-6, IL-10, C-reactive protein (CRP), calcium, magnesium, and albumin were measured in 60 COVID-19 patients and 30 healthy controls.

RESULTS

COVID-19 is characterized by significantly increased IL-6, CRP, IL-10, sRAGE, ACE2, and lowered SpO2, albumin, magnesium, and calcium. COVID-19 with CCTAs showed lower SpO₂ and albumin. SpO2 was significantly inversely correlated with IL-6, IL-10, CRP, sRAGE, and ACE2, and positively with albumin, magnesium, and calcium. Neural networks showed that a combination of calcium, IL-6, CRP, and sRAGE yielded an accuracy of 100% in detecting COVID-19 patients, with calcium being the most important predictor followed by IL-6 and CRP. Patients with positive IgG results showed a significant elevation in the serum level of IL-6, sRAGE, and ACE2 compared to the negatively IgG patient subgroup.

CONCLUSION

The results show that immune-inflammatory and RAGE pathways biomarkers may be used as an external validating criterion for the diagnosis of COVID-19. Those pathways coupled with lowered SpO2, calcium, and magnesium are drug targets that may help reduce the consequences of COVID-19.

Dose-response relationship between risk factors and incidence of COVID-19 in 325 hospitalized patients: A multicenter retrospective cohort study

BACKGROUND

The epidemiological and clinical characteristics of coronavirus disease 2019 (COVID-19) patients have been widely reported, but the assessment of dose-response relationships and risk factors for mortality and severe cases and clinical outcomes remain unclear.

AIM

To determine the dose-response relationship between risk factors and incidence of COVID-19.

METHODS

In this retrospective, multicenter cohort study, we included patients with confirmed COVID-19 infection who had been discharged or had died by February 6, 2020. We used multivariable logistic regression and Cox proportional hazard models to determine the dose-response relationship between risk factors and incidence of COVID-19.

RESULTS

It clarified that increasing risk of in-hospital death were associated with older age (HR: 1.04, 95%CI: 1.01-1.09), higher lactate dehydrogenase [HR: 1.04, 95% confidence interval (CI): 1.01-1.10], C-reactive protein (HR: 1.10, 95%CI: 1.01-1.23), and procalcitonin (natural log-transformed HR: 1.88, 95%CI: 1.22-2.88), and D-dimer greater than 1 μg/mL at admission (natural log transformed HR: 1.63, 95%CI: 1.03-2.58) by multivariable regression. D-dimer and procalcitonin were logarithmically correlated with COVID-19 mortality risk, while there was a linear dose-response correlation between age, lactate dehydrogenase, D-dimer and procalcitonin, independent of established risk factors.

CONCLUSION

Higher lactate dehydrogenase, D-dimer, and procalcitonin levels were independently associated with a dose-response increased risk of COVID-19 mortality.

Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19)

BACKGROUND

In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world.

PURPOSE

To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia.

MATERIAL AND METHODS

The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed.

RESULTS

Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867-0.957) in the training dataset and 0.907 (95% CI 0.849-0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts.

CONCLUSION

The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses.

Deep Residual Neural Network for COVID-19 Detection from Chest X-ray Images

The COVID-19 diffused quickly throughout the world and converted as a pandemic. It has caused a destructive effect on both regular lives, common health and global business. It is crucial to identify positive patients as shortly as desirable to limit this epidemic’s further diffusion and to manage immediately affected cases. The demand for quick assistant distinguishing devices has developed. Recent findings achieved utilizing radiology imaging systems propose that such images include salient data about the COVID-19. The utilization of progressive artificial intelligence (AI) methods linked by radiological imaging can help the reliable diagnosis of COVID-19. As radiography images can recognize pneumonia infections, this research brings an accurate and automatic technique based on a deep residual network to analyze chest X-ray images to monitor COVID-19 and diagnose verified patients. The physician states that it is significantly challenging to separate COVID-19 from common viral and bacterial pneumonia, while COVID-19 is additionally a variety of viruses. The proposed network is expanded to perform detailed diagnostics for two multi-class classification (COVID-19, Normal, Viral Pneumonia) and (COVID-19, Normal, Viral Pneumonia, Bacterial Pneumonia) and binary classification. By comparing the proposed network with the popular methods on public databases, the results show that the proposed algorithm can provide an accuracy of 92.1% in classifying multi-classes of COVID-19, normal, viral pneumonia, and bacterial pneumonia cases. It can be applied to support radiologists in verifying their first viewpoint.

Non-COVID-19 respiratory viral infection

Implemented control measures brought about by the coronavirus disease 2019 (COVID-19) pandemic have changed the prevalence of other respiratory viruses, often relegating them to a secondary plan. However, it must not be forgotten that a diverse group of viruses, including other human coronaviruses, rhinoviruses, respiratory syncytial virus, human metapneumoviruses, parainfluenza and influenza, continue to be responsible for a large burden of disease. In fact, they are among the most common causes of acute upper and lower respiratory tract infections globally. Viral respiratory infections can be categorised in several ways, including by clinical syndrome or aetiological agent. We describe their clinical spectrum. Distinctive imaging features, advances in microbiological diagnosis and treatment of severe forms are also discussed.

Educational aims

To summarise the knowledge on the spectrum of disease that respiratory viral infections can cause and recognise how often they overlap.To learn the most common causes of respiratory viral infections and acknowledge other less frequent agents that may target certain key populations (e.g. immunocompromised patients).To improve awareness of the recent advances in diagnostic methods, including molecular assays and helpful features in imaging techniques.To identify supportive care strategies pivotal in the management of severe respiratory viral infections.

Imaging Approach to Pulmonary Infections in the Immunocompromised Patient

Pulmonary infections are the major cause of morbidity and mortality in immunocompromised patients and almost one-third of intensive care unit patients with pulmonary infections belong to the immunocompromised category. Multiple organisms may simultaneously infect an immunocompromised patient and the overwhelming burden of mixed infections further predisposes critically ill patients to acute hypoxemic respiratory failure. Notwithstanding that lung ultrasound is coming into vogue, the primary imaging investigation is a chest radiograph, followed by thoracic CT scan. This review based on our experience at tertiary care teaching hospitals provides insights into the spectrum of imaging features of various pulmonary infections occurring in immunocompromised patients. This review is unique as, firstly, the imaging spectrum described by us is categorized on basis of the etiological infective agent, comprehensively and emphatically correlated with the clinical setting of the patient. Secondly, a characteristic imaging pattern is emphasized in the clinical setting-imaging-pattern conglomerate, to highlight the most likely diagnosis possible in such a combination. Thirdly, the simulating conditions for a relevant differential diagnosis are discussed in each section. Fourthly, not only are the specific diagnostic and tissue sampling techniques for confirmation of the suspected etiological agent described, but the recommended pharmaco-therapeutic agents are also enumerated, so as to provide a more robust insight to the radiologist. Last but not the least, we summarize and conclude with a diagnostic algorithm, derived by us from the characteristic illustrative cases. The proposed algorithm, illustrated as a flowchart, emphasizes a diagnostic imaging approach comprising: correlation of the imaging pattern with clinical setting and with associated abnormalities in the thorax and in other organs/systems, which is comprehensively analyzed in arriving at the most likely diagnosis. Since a rapid evaluation and emergent management of such patients is of pressing concern not only to the radiologist, but also for the general physicians, pulmonologists, critical care specialists, oncologists and transplant surgery teams, we believe our review is very informative to a wide spectrum reader audience.

A nomogram predicting the severity of COVID-19 based on initial clinical and radiologic characteristics

Aim: This study aimed to build an easy-to-use nomogram to predict the severity of COVID-19. Patients & methods: From December 2019 to January 2020, patients confirmed with COVID-19 in our hospital were enrolled. The initial clinical and radiological characteristics were extracted. Univariate and multivariate logistic regression were used to identify variables for the nomogram. Results: In total, 104 patients were included. Based on statistical analysis, age, levels of neutrophil count, creatinine, procalcitonin and numbers of involved lung segments were identified for nomogram. The area under the curve was 0.939 (95% CI: 0.893–0.984). The calibration curve showed good agreement between prediction of nomogram and observation in the primary cohort. Conclusion: An easy-to-use nomogram with great discrimination was built to predict the severity of COVID-19.

Herpes Simplex Virus Pneumonia Mimicking Legionella Pneumonia in an Elderly Patient With Heart and Liver Failure

The diagnosis of viral pneumonia is often difficult because of its varied presentations. Regarding the serological diagnosis of viral infections, it is difficult to perform a viral DNA test in general medical facilities, especially in rural settings. Among viral pneumonia cases, herpes simplex virus (HSV) pneumonia can occur in immunocompromised hosts. However, when the clinical course of HSV pneumonia is acute, and the features of pneumonia are not distinct, the diagnosis can be challenging. We report the case of a 69-year-old man who visited the hospital with complaints of dyspnea and cough for two days. Although the patient had no fever and the urine was negative for Legionella antigen, we suspected Legionella pneumonia based on the clinical course, Gram stain of sputum, and CT findings. After undergoing treatment with antibiotics, his condition worsened, with dyspnea and an increase in the demand for oxygen at 5 L. The patient also had complications related to the heart and liver. The sputum culture was negative, and the HSV serum test revealed that HSV IgM level was elevated to 1.16 (reference value: ≤0.80) and IgG level at admission and at follow-up 21 days later was elevated to 28.1 and 60.0 respectively (reference value: ≤2.0); accordingly, the patient was diagnosed with HSV pneumonia. In cases of pneumonia with atypical courses, the coexistence of multiple diseases, and immunosuppression, HSV pneumonia should be included in the differential diagnosis. In rural settings, viral pneumonia should be investigated using antibodies against viruses due to the limited availability of other medical resources. When a patient is judged to be immunosuppressed in the treatment of pneumonia of unknown cause, it is important to consider the possibility of HSV infection and take prompt action. Furthermore, it is essential to consider the possibility of multi-organ failure secondary to HSV infection, which requires systemic management.

Imaging of pulmonary infections after lung transplantation: a pictorial essay of early and late computed tomography findings

Pulmonary infections are among the most common complications after lung transplants and a major cause of morbidity and mortality in these patients. Computed tomography is one of the main non-invasive diagnostic tools for detecting lung infections but characterizing the correct etiology may be very challenging. Indeed, although several pathogens show typical patterns at imaging, others, such as bacteria, may demonstrate quite unspecific features. Therefore, additional parameters, like the timing of the infection, should be evaluated to support the radiologists in narrowing the differential diagnoses. In fact, it has been demonstrated that several pathogens, like Candida albicans, usually occurring within the first month after the transplant, frequently occur at specific time points. Thus, aim of this review is to make radiologists and clinicians familiar with the computed tomography patterns of pulmonary infections occurring after lung transplant, considering the etiology and the time of onset, according to the extensive experience gained in our tertiary center.

Clinical efficacy of iodine complex in SARS-CoV-2-infected patients with mild to moderate symptoms: study protocol for a randomized controlled trial

Background

Coronavirus disease 2019 (COVID-19) caused by the novel coronavirus-infected millions globally. Despite a wide range of advised options for the treatment of COVID-19, a single strategy to tackle this pandemic remains elusive, thus far. That is why we are conducting a clinical trial to find out the efficacy of iodine complex to clear a viral load of severe respiratory syndrome coronavirus-2 (SARS-CoV-2) along with a reduction in time taken to alleviate symptoms.

Method

The proposed study is a placebo-controlled, add-on, randomized trial using parallel group designs. This is a closed-label and adaptive with sample size reassessment, multi-centered design with a 1:1:1:1 allocation ratio and superiority framework. It will be conducted in Shaikh Zayed Post-Graduate Medical Complex, Ali Clinic, and Doctors Lounge, Lahore, Pakistan. This study will have three arms of mild to moderately symptomatic COVID-19 patients (50 patients in each) which will receive ionic-iodine polymer complex with 200 mg of elemental iodine: interventional arm A will have encapsulated, arm B will receive suspension syrup form, arm C will get throat spray, while arm X will be standard care with placebo. Data will be collected on self-constructed, close-ended questionnaires after obtaining written consent. Data will be analyzed using SAS version 9.4. COVID-19 patients will be monitored by RT-PCR and HRCT (high-resolution computed tomography) chest. In addition to these, the duration of the symptomatic phase and mortality benefits will be analyzed in both groups.

Discussion

The study is designed to measure the superior efficacy of the iodine complex as an add-on in treating COVID-19-positive patients with mild to moderate symptoms. This combination is hypothesized to improve various parameters like rapid viral load reduction, clinical and radiological improvement, lower mortality, and reduction in hospitalization. The trial will aid in devising a better strategy to cope with COVID-19 in a relatively inexpensive and accessible way. The implications are global, and this could prove itself to be the most manageable intervention against COVID-19 especially for patients from limited-resource countries with deprived socioeconomic status.

Trial registration

ClinicalTrials.govNCT04473261. Registered on July 16, 2020.