Diagnosis Of COVID-19 Pneumonia Research Articles

Chest X-ray findings of COVID-19 pneumonia in children: Experiences in a multicenter study in Thailand.

Although chest X-ray is commonly used to diagnose COVID-19 pneumonia, few studies have explored findings in pediatric patients. This study aimed to reveal chest X-ray characteristics in children with COVID-19 pneumonia and compare between non-severe and severe cases. This multicenter, nationwide retrospective study included all children aged 0 to 15 years who were admitted to 13 medical facilities throughout Thailand with COVID-19 pneumonia between January 2020 and October 2021. We analyzed the demographics, clinical features, and chest X-ray results of these children, and compared differences between the non-severe and severe groups. During the study period, 1018 children (52% male, median age 5 years) were admitted with COVID-19 pneumonia. Most chest radiographic findings showed bilateral (51%) patchy/ground glass opacities (61%) in the central area (64%). Only 12% of the children exhibited typical classification for COVID-19 pneumonia, whereas 74% of chest radiographs were categorized as indeterminate. Comorbidities including chronic lung diseases [adjusted OR (95%CI): 14.56 (3.80-55.75), P-value <0.001], cardiovascular diseases [adjusted OR (95%CI): 7.54 (1.44-39.48), P-value 0.017], genetic diseases [adjusted OR (95%CI): 28.39 (4.55-177.23), P-value <0.001], clinical dyspnea [adjusted OR (95%CI): 12.13 (5.94-24.77), P-value <0.001], tachypnea [adjusted OR (95%CI): 3.92 (1.79-8.55), P-value 0.001], and bilateral chest X-ray infiltrations [adjusted OR (95%CI): 1.99 (1.05-3.78), P-value 0.036] were factors associated with severe COVID-19 pneumonia. Most children with COVID-19 pneumonia had indeterminate chest X-rays according to the previous classification. We suggest using chest X-rays in conjunction with clinical presentation to screen high-risk patients for early detection of COVID-19 pneumonia.

Challenges in Diagnosis of COVID-19 Pneumonia under Ocrelizumab and De-Risking Strategies in Multiple Sclerosis—The Elephant Is (Still) in the Room

Severe SARS-CoV-2 infections may still be observed in people bearing risk factors, such as the use of anti-CD20 monoclonal antibodies (mAbs), which are adopted in several autoimmune disorders including multiple sclerosis (MS). COVID-19 diagnosis is routinely based on nasopharyngeal swab testing, but suboptimal sensitivity for SARS-CoV-2 detection compared to bronchoalveolar lavage (BAL) may lead to misdiagnosis in some cases. Such diagnostic issues were described in a few MS patients receiving anti-CD20 mAbs, including middle-aged people and lacking information on subsequent MS therapeutic management, a debated topic as no evidence-based guidance on de-risking strategies is currently available. Here, we report the case of a young MS patient who developed severe COVID-19 pneumonia under treatment with the anti-CD20 mAb ocrelizumab, and who was finally diagnosed with SARS-CoV-2 by BAL despite repeatedly negative nasopharyngeal swabs. Ocrelizumab was then discontinued, and treatment with a sphingosine-1 phosphate receptor modulator was started, followed by maintenance of clinical and radiological MS stability. Challenges in diagnosing COVID-19 pneumonia in people without risk factors other than immunomodulatory treatment are hence discussed, as well as potential strategies for de-risking MS therapies. The latter topic is increasingly debated based on raising concerns for potential long-term safety issues of high-efficacy treatments, including anti-CD20 mAbs.

Exploring diaphragmatic response to high-flow nasal cannula in patients with COVID-19 pneumonia using ultrasound: a proof of concept study.

Recent studies have highlighted the recognition of diaphragmatic dysfunction as a significant factor contributing to respiratory disturbances in severely ill COVID-19 patients. In the field of noninvasive respiratory support, high-flow nasal cannula (HFNC) has shown effectiveness in relieving diaphragm dysfunction. This study aims to investigate the diaphragmatic response to HFNC in patients with COVID-19 pneumonia by utilizing ultrasound. This retrospective study was conducted in a medical-surgical intensive care unit (ICU) at a tertiary care center in Buenos Aires, Argentina (Sanatorio de Los Arcos) over a 16-month period (January 2021-June 2022). The study included patients admitted to the ICU with a diagnosis of COVID-19 pneumonia who were deemed suitable candidates for HFNC therapy by the attending physician. Diaphragm ultrasound was conducted, measuring diaphragmatic excursion (DE) both before and during the utilization of HFNC for these patients. A total of 10 patients were included in the study. A statistically significant decrease in respiratory rate was observed with the use of HFNC (p = 0.02), accompanied by a significant increase in DE (p = 0.04). HFNC leads to a reduction in respiratory rate and an increase in DE as observed by ultrasound in patients with COVID-19 pneumonia, indicating promising enhancements in respiratory mechanics. However, further research is required to validate these findings.

Effect of Model-Based Iterative Reconstruction on Image Quality of Chest Computed Tomography for COVID-19 Pneumonia.

This study aimed to compare the image quality of chest computed tomography (CT) scans for COVID-19 pneumonia using forward-projected model-based iterative reconstruction solution-LUNG (FIRST-LUNG) with filtered back projection (FBP) and hybrid iterative reconstruction (HIR). The CT images of 44 inpatients diagnosed with COVID-19 pneumonia between December 2022 and June 2023 were retrospectively analyzed. The CT images were reconstructed using FBP, HIR, and FIRST-LUNG-MILD/STANDARD/STRONG. The CT values and noise of the lumen of the main trachea and erector spine muscle were measured for each group. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Subjective evaluations included overall image quality, noise, streak artifact, visualization of normal lung structures, and abnormal CT features. One-way analysis of variance was used to compare the objective and subjective indicators among the five groups. The task-based transfer function was derived for three distinct contrasts representing anatomical structures, lower-contrast lesion, and higher-contrast lesion. The results of the study demonstrated significant differences in image noise, SNR, and CNR among the five groups (P < 0.001). The FBP images exhibited the highest levels of noise and the lowest SNR and CNR among the five groups (P < 0.001). When compared to the FBP and HIR groups, the noise was lower in the FIRST-LUNG-MILD/STANDARD/STRONG group, while the SNR and CNR were higher (P < 0.001). The subjective overall image quality score of FIRST-LUNG-MILD/STANDARD was significantly better than FBP and FIRST-LUNG-STRONG (P < 0.001). FIRST-LUNG-MILD was superior to FBP, HIR, FIRST-LUNG-STANDARD, and FIRST-LUNG-STRONG in visualizing proximal and peripheral bronchovascular and subpleural vessels (P < 0.05). Additionally, FIRST-LUNG-MILD achieved the best scores in evaluating abnormal lung structure (P < 0.001). The overall interobserver agreement was substantial (intraclass correlation coefficient = 0.891). The task-based transfer function 50% values of FIRST reconstructions are consistently higher compared to FBP and HIR. The FIRST-LUNG-MILD/STANDARD algorithm can enhance the image quality of chest CT in patients with COVID-19 pneumonia, while preserving important details of the lesions, better than the FBP and HIR algorithms. After evaluating various COVID-19 pneumonia lesions and considering the improvement in image quality, we recommend using the FIRST-LUNG-MILD reconstruction for diagnosing COVID-19 pneumonia.

Rhabdomyolysis – A Rare Presentation of COVID-19

Abstract COVID-19 pandemic presented a great global health-care challenge. The spectrum of illness comprises pulmonary and extrapulmonary manifestations including hepatic, renal, and neurological complications. Our patient is a young male who presented with fever, severe myalgia, and respiratory distress. After a thorough clinical examination and laboratory investigations, the diagnosis of COVID-19 pneumonia and associated rhabdomyolysis was made. He developed acute renal failure for which intravenous hydration was given. He also required renal replacement therapy. He was successfully treated and discharged with complete remission of symptoms. We did a literature search for similar cases and have discussed the potential mechanisms implied for these complications.

The Comparison of Ultrasound and Tomographic Images of Lung Involvement in Critically Ill Patients With COVID-19 Pneumonia: A Prospective Observational Study.

Introduction Computed tomography (CT) has a high sensitivity for diagnosing COVID-19 pneumonia in critically ill patients, but it has significant limitations. Lung ultrasonography (LUS) is an imaging method increasingly used in intensive care units. Our primary aim is to evaluate the relationship between LUS and CT images by scoring a critically ill patient who was previously diagnosed with COVID-19 pneumonia and underwent CT, as well as to determine their relationship with the patient's oxygenation. Methods This was a single-center, prospective observational study. The study included COVID-19 patients (positive reverse transcription polymerase chain reaction, RT-PCR) who were admitted to the intensive care unit between June 2020 and December 2020, whose oxygen saturation (SpO2) was below 92%, and who underwent a chest tomography scan within the last 12 hours. CT findings were scored by the radiologist using the COVID-19 Reporting and Data System (CO-RADS). The intensivist evaluated 12 regions to determine the LUS score. The ratio of the partial pressure of oxygen in the arterial blood to the inspiratory oxygen concentration (PaO2/FiO2) was used to assess the patient's oxygenation. Results The study included 30 patients and found a weak correlation (ICC = 0.45, 95% CI = 0.25-0.65, p < 0.05) between total scores obtained from LUS and CT scans. The correlation between the total LUS score and oxygenation (r = -0.514, p = 0.004) was stronger than that between the CT score and oxygenation (r = -0.400, p = 0.028). The most common sonographic findings were abnormalities in the pleural line, white lung, and subpleural consolidation. On the other hand, the CT images revealed dense ground-glass opacities and consolidation patterns classified as CO-RADS 5. Conclusion A weak correlation was found between LUS and CT scores in critically ill COVID-19 pneumonia patients. Also, as both scores increased, oxygenation was detected to be impaired, and such a correlation is more evident with the LUS score.

Usar una herramienta comercial de inteligencia artificial no entrenada para COVID-19 mejora ligeramente la interpretación de las radiografías con neumonía COVID-19, especialmente entre lectores inexpertos

IntroductionOur objective is to evaluate how useful an artificial intelligence (AI) tool is to chest radiograph readers with various levels of expertise for the diagnosis of COVID-19 pneumonia when the tool has been trained on a non-COVID-19 pneumonia pathology. MethodsData was collected for patients who had previously undergone a chest radiograph and digital tomosynthesis due to suspected COVID-19 pneumonia. The gold standard consisted of the readings of two expert radiologists who assessed the presence and distribution of COVID-19 pneumonia on the images. Six medical students, two radiology trainees, and two other expert thoracic radiologists participated as additional readers. Two radiograph readings and a third supported by the AI Thoracic Care Suite tool were performed. COVID-19 pneumonia distribution and probability were assessed along with the contribution made by AI. Agreement and diagnostic performance were analysed. ResultsThe sample consisted of 113 cases, of which 56 displayed lung opacities, 52.2% were female, and the mean age was 50.70±14.9. Agreement with the gold standard differed between students, trainees, and radiologists. There was a non-significant improvement for four of the six students when AI was used. The use of AI by students did not improve the COVID-19 pneumonia diagnostic performance but it did reduce the difference in diagnostic performance with the more expert radiologists. Furthermore, it had more influence on the interpretation of mild pneumonia than severe pneumonia and normal radiograph findings. AI resolved more doubts than it generated, especially among students (31.30 vs 8.32%), followed by trainees (14.45 vs 5.7%) and radiologists (10.05% vs 6.15%). ConclusionFor expert and lesser experienced radiologists, this commercial AI tool has shown no impact on chest radiograph readings of patients with suspected COVID-19 pneumonia. However, it aided the assessment of inexperienced readers and in cases of mild pneumonia.

Laboratory changes and parenchymal changes in computed tomography after COVID-19 pneumonia

Aims: Novel coronavirus-2019 (2019-nCoV) has caused a global pandemic. Fort his reason, our study is to determine the variables of thorax tomography findings and laboratory data after covid 19 pneumonia in cases with severe covid 19 pneumonia and to detect the findings of possible interstitial lung diseases. Methods: In this single-center study, 61 consecutive patients were examined. These patients were admitted to the COVID-19 Pandemic Clinic of Malatya Training and Research Hospital between July 15, 2020 and August 28, 2020 and were hospitalized with a diagnosis of COVID-19 pneumonia. Patients were discharged after the illness, and after 6 months, they applied to the outpatient clinic for follow-up. In this study, we compared the changes in laboratory variables and thorax CT scans at the time of diagnosis and 6 months later. Patients were divided into groups 1 and 2. Group 1: patients who were diagnosed with COVID 19 pneumonia at initial presentation and had thorax CT and laboratory parameters at the time of diagnosis and group 2: patients who presented to the outpatient clinic for 6-month follow-up during the postcovid pneumonia period and had control thorax CT and laboratory parameters. Results: When the laboratory parameters of group 1 and group 2 patients were statistically compared. In addition to the increase in glucose, creatinine, aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transferase, lactate dehydrogenase, aptt, INR, fibrinogen, neutrophil percentage, mean erythrocyte hemoglobin, albumin, the decrease in calcium, sodium, leukocyte, platelet, hemoglobin, lymphocyte count, erythrocyte distribution range variables were found to be statistically significant (p&lt;0.05). In our study, ground glass opacity was seen most frequently in Group 1 Thorax CT and was found in 57 patients. In 10 of these 57 patients, ground-glass opacity was positive on Group 1 and Group 2 Thorax CT, while ground-glass opacity was negative on Group 2 Thorax CT in 47 patients. In 50 patients, consolidation was found on Group 1 Thorax CT and consolidation was positive on Group 1 and Group 2 Thorax CT in 5 patients. The changes in fibrosis, parenchymal band, reticular opacity, traction bronchiectasis, irregular interfaces, ground glass opacity, consolidation, and pulmonary nodule variables in Group 2 Thorax CT were statistically significant (p&lt;0.05). Conclusion: Laboratory data is very important in terms of COVID-19 infection diagnosis, prognosis and guiding treatment. It may be due to the fact that patients with residual abnormalities on control thorax CT after COVID-19 pneumonia were older, had more comorbid diseases, and had severe clinical disease at the time of hospitalization.

Machine learning enabled detection of COVID-19 pneumonia using exhaled breath analysis: a proof-of-concept study

Detection of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) relies on real-time-reverse-transcriptase polymerase chain reaction (RT-PCR) on nasopharyngeal swabs. The false-negative rate of RT-PCR can be high when viral burden and infection is localized distally in the lower airways and lung parenchyma. An alternate safe, simple and accessible method for sampling the lower airways is needed to aid in the early and rapid diagnosis of COVID-19 pneumonia. In a prospective unblinded observational study, patients admitted with a positive RT-PCR and symptoms of SARS-CoV-2 infection were enrolled from three hospitals in Ontario, Canada. Healthy individuals or hospitalized patients with negative RT-PCR and without respiratory symptoms were enrolled into the control group. Breath samples were collected and analyzed by laser absorption spectroscopy (LAS) for volatile organic compounds (VOCs) and classified by machine learning (ML) approaches to identify unique LAS-spectra patterns (breathprints) for SARS-CoV-2. Of the 135 patients enrolled, 115 patients provided analyzable breath samples. Using LAS-breathprints to train ML classifier models resulted in an accuracy of 72.2%–81.7% in differentiating between SARS-CoV2 positive and negative groups. The performance was consistent across subgroups of different age, sex, body mass index, SARS-CoV-2 variants, time of disease onset and oxygen requirement. The overall performance was higher than compared to VOC-trained classifier model, which had an accuracy of 63%–74.7%. This study demonstrates that a ML-based breathprint model using LAS analysis of exhaled breath may be a valuable non-invasive method for studying the lower airways and detecting SARS-CoV-2 and other respiratory pathogens. The technology and the ML approach can be easily deployed in any setting with minimal training. This will greatly improve access and scalability to meet surge capacity; allow early and rapid detection to inform therapy; and offers great versatility in developing new classifier models quickly for future outbreaks.

A New Classification Method for Diagnosing COVID-19 Pneumonia via Joint Parallel Deformable MLP Modules and Bi-LSTM With Multi-Source Generated Data of CXR Images

A New Classification Method for Diagnosing COVID-19 Pneumonia via Joint Parallel Deformable MLP Modules and Bi-LSTM With Multi-Source Generated Data of CXR Images

An Advanced Deep Learning Framework for Multi-Class Diagnosis from Chest X-ray Images

Chest X-ray imaging plays a vital and indispensable role in the diagnosis of lungs, enabling healthcare professionals to swiftly and accurately identify lung abnormalities. Deep learning (DL) approaches have attained popularity in recent years and have shown promising results in automated medical image analysis, particularly in the field of chest radiology. This paper presents a novel DL framework specifically designed for the multi-class diagnosis of lung diseases, including fibrosis, opacity, tuberculosis, normal, viral pneumonia, and COVID-19 pneumonia, using chest X-ray images, aiming to address the need for efficient and accessible diagnostic tools. The framework employs a convolutional neural network (CNN) architecture with custom blocks to enhance the feature maps designed to learn discriminative features from chest X-ray images. The proposed DL framework is evaluated on a large-scale dataset, demonstrating superior performance in the multi-class diagnosis of the lung. In order to evaluate the effectiveness of the presented approach, thorough experiments are conducted against pre-existing state-of-the-art methods, revealing significant accuracy, sensitivity, and specificity improvements. The findings of the study showcased remarkable accuracy, achieving 98.88%. The performance metrics for precision, recall, F1-score, and Area Under the Curve (AUC) averaged 0.9870, 0.9904, 0.9887, and 0.9939 across the six-class categorization system. This research contributes to the field of medical imaging and provides a foundation for future advancements in DL-based diagnostic systems for lung diseases.

Texture analysis of chest X-ray images for the diagnosis of COVID-19 pneumonia

PurposeMedical imaging is one of the main methods of diagnosing COVID-19, along with real-time reverse trans­cription-polymerase chain reaction (RT-PCR) tests. The purpose of the study was to analyse the texture parameters of chest X-rays (CXR) of patients suspected of having COVID-19.Material and methodsTexture parameters of the CXRs of 70 patients with symptoms typical of COVID-19 infection were analysed using LIFEx software. The regions of interest (ROIs) included each lung separately, for which 57 para­meters were tested. The control group consisted of 30 healthy, age-matched patients with no pathological findings in CXRs.ResultsAccording to the ROC analysis, 13 of the tested parameters differentiate the radiological image of lungs with COVID-19 features from the image of healthy lungs: GLRLM_LRHGE (AUC 0.91); DISCRETIZED_Q3 (AUC 0.90); GLZLM_HGZE (AUC 0.90); GLRLM_HGRE (AUC 0.89); DISCRETIZED_mean (AUC 0.89); DISCRETIZED_Q2 (AUC 0.61); GLRLM_SRHGE (AUC 0.87); GLZLM_LZHGE (AUC 0.87); GLZLM_SZHGE (AUC 0.84); DISCRETIZED_Q1 (AUC 0.81); NGLDM_Coarseness (AUC 0.70); DISCRETIZED_std (AUC 0.64); CONVENTIONAL_Q2 (AUC 0.61).ConclusionsSelected texture parameters of radiological CXRs make it possible to distinguish COVID-19 features from healthy ones.

Can chest computed tomography be useful to emergency residents in the diagnosis of COVID-19 pneumonia?

&lt;b&gt;Aim:&lt;/b&gt; During the novel coronavirus disease (COVID-19) pandemic, the workload of emergency department residents (ER) has increased since emergency departments (EDs) are among the first health units to which patients apply. The aim of this study was to investigate the diagnostic value of chest computed tomography (CT) interpretations of emergency department residents in the diagnosis of COVID-19 pneumonia.&lt;br /&gt; &lt;b&gt;Material and methods: &lt;/b&gt;Patients who admitted to the tertiary ED between 30.03.2020 and 30.12.2020 with suspected COVID-19 pneumonia and underwent chest CT imaging were retrospectively analyzed. Chest CT images previously reported by the radiologist were reinterpreted by the ER and the diagnostic performance of the ER for COVID-19 pneumonia was calculated.&lt;br /&gt; &lt;b&gt;Results: &lt;/b&gt;The study included 2045 patients with suspected COVID-19 pneumonia. Chest CT images of all patients were interpreted and compared by a radiologist and the ER In this study, interpretation by a radiologist was considered as the gold standard. In all patients, the diagnostic performance capacity of ER for COVID-19 pneumonia included a low sensitivity of 66%, a high specificity of 92%, a positive predictive value (PPV) of 83% and a negative predictive value (NPV) of 82%. When patients with positive COVID-19 RT-PCR test results were analyzed, the highest sensitivity, specificity, PPV and NPV of ER were 96%, 82%, 95%, 95% and 86%, respectively.&lt;br /&gt; &lt;b&gt;Conclusion: &lt;/b&gt;ER detected COVID-19 pneumonia in COVID-19 (+) patients with high diagnostic value and excluded inconsistent findings. Chest CT interpretation in the ER has a high diagnostic value. It is as important as any other radiologist in outbreak management in the ED.

The application value of lung ultrasound scoring in assessing disease severity: Evaluation of small-scale outbreaks of COVID-19.

This study explored the use of transthoracic lung ultrasound for evaluating COVID-19 patients, compared it with computed tomography (CT), and examined its effectiveness using 8 and 12 lung regions. A total of 100 patients with COVID-19 and 40 healthy volunteers were assessed using 12 regions (bilateral upper/lower regions of the anterior/lateral/posterior chest) and simplified 8 zones (bilateral upper/lower regions of the anterior/lateral chest) transthoracic lung ultrasound. The relationships between ultrasound, CT, and clinical indicators were analyzed to evaluate the diagnostic value of ultrasound scores in COVID-19. Increased disease severity correlated with increased 8- and 12-zone ultrasound and CT scores (all p < 0.05). The modified 8-zone method strongly correlated with the 12-zone method (Pearson's r = 0.908, p < 0.05). The 8- and 12-zone methods correlated with CT scoring (correlation = 0.568 and 0.635, respectively; p < 0.05). The intragroup correlation coefficients of the 8-zone, 12-zone, and CT scoring methods were highly consistent (intragroup correlation coefficient = 0.718, p < 0.01). The 8-zone ultrasound score correlated negatively with oxygen saturation (rs = 0.306, p < 0.05) and Ca (rs = 0.224, p < 0.05) and positively with IL-6 (rs = 0.0.335, p < 0.05), erythrocyte sedimentation rate (rs = 0.327, p < 0.05), alanine aminotransferase (rs = 0.230, p < 0.05), and aspartate aminotransferase (rs = 0.251, p < 0.05). The 12-zone scoring method correlated negatively with oxygen saturation (rs = 0.338, p < 0.05) and Ca (rs = 0.245, p < 0.05) and positively with IL-6 (rs = 0.354, p < 0.05) and erythrocyte sedimentation rate (rs = 0.495, p < 0.05). Lung ultrasound scores represent the clinical severity and have high clinical value for diagnosing COVID-19 pneumonia. The 8-zone scoring method can improve examination efficiency and reduce secondary injuries caused by patient movement.

Retrospective review of the code status of individuals with Down syndrome during the COVID-19 era.

Code status is a label in the medical record indicating a patient's wishes for end-of-life (EOL) care in the event of a cardiopulmonary arrest. People with intellectual disabilities had a higher risk of both diagnosis and mortality from coronavirus infections (COVID-19)than the general population. Clinicians and disability advocates raised concerns that bias, diagnostic overshadowing, and ableism could impact the allocation of code status and treatment options, for patients with intellectual disabilities, including Down syndrome (DS). To study this, retrospective claims data from the Vizient® Clinical Data Base (used with permission of Vizient, all rights reserved.) of inpatient encounters with pneumonia (PNA) and/or COVID-19 at 825 hospitals from January 2019 to June 2022 were included. Claims data was analyzed for risk of mortality and risk of "Do Not Resuscitate" (DNR) status upon admission, considering patient age, admission source, Elixhauser comorbidities (excluding behavioral health), and DS. Logistic regression models with backward selection were created. In total, 1,739,549 inpatient encounters with diagnoses of COVID-19, PNA, or both were included. After controlling for other risk factors, a person with a diagnosis of DS and a diagnosis of COVID-19 PNA had 6.321 odds ratio of having a DNR status ordered at admission to the hospital compared with those with COVID-19 PNA without DS. The diagnosis of DS had the strongest association with DNR status after controlling for other risk factors. Open and honest discussions among healthcare professionals to foster equitable approaches to EOL care and code status are needed.

CoviX-Net: A Deep Learning-based System for Diagnosis and Differentiation of Covid-19 Infection and Pneumonia in Chest Radiography Images

CoviX-Net: A Deep Learning-based System for Diagnosis and Differentiation of Covid-19 Infection and Pneumonia in Chest Radiography Images

Systemic lymphangitic carcinomatosis from unknown primary cancer mimicking lung disease in a new coronavirus infection (COVID-19): a case report

lymphangitic carcinomatosis is characterized by the spread of cancer cells through pulmonary lymphatic vessels, which results in secondary interstitial inflammation. The diagnosis of lymphangitic carcinomatosis is challenging due to nonspecific clinical symptoms and radiological findings that similar to those of interstitial lung disease.The purpose of the study was to demonstrate difficulties in the diagnosis of lymphangitic carcinomatosis with a systemic dissemination from unknown primary site. Case presentation. We report on a case of lymphangitic carcinomatosis from cancer of unknown primary origin with systemic involvement of internal organs imitating pulmonary disease caused by coVid-19. patient K., 58 years old, was treated for 12 bed-days in therapy departments with the diagnosis of bilateral viral coVid-19 pneumonia. clinical, laboratory and instrumental data were nonspecific. despite treatment, heart failure and kidney disease progressed to terminal stages, leading to the death of the patient. Autopsy revealed systemic lymphangitic carcinomatosis from cancer of unknown primary origin with involvement of the lungs, heart, liver, pancreas, spleen, kidneys and adrenal glands. lymphangitic carcinomatosis mimicked interstitial lung disease in a novel coronavirus infection coVid-19. metastasis to the heart mimicked coronary disease, and metastases to the kidneys led to acute kidney failure, which along with acute respiratory and heart failure caused the death of the patient.Conclusion. lymphangitic carcinomatosis, which does not have specific clinical manifestations, is able to mimic interstitial lung diseases, including a new coronavirus infection. cancer of unknown primary and multiple visceral metastases in this case confirm the concept of the independence of different types of tumor progression.

Quantitative CT at Follow-Up of COVID-19 Pneumonia: Relationship with Pulmonary Function Tests.

The role of quantitative chest computed tomography (CT) is controversial in the follow-up of patients with COVID-19 pneumonia. The aim of this study was to test during the follow-up of COVID-19 pneumonia the association between pulmonary function tests (PFTs) and quantitative parameters extrapolated from follow-up (FU) CT scans performed at least 6 months after COVID-19 onset. The study included patients older than 18 years old, admitted to the emergency department of our institution between 29 February 2020 and 31 December 2020, with a diagnosis of COVID-19 pneumonia, who underwent chest CT at admission and FU CT at least 6 months later; PFTs were performed within 6 months of FU CT. At FU CT, quantitative parameters of well-aerated lung and pneumonia extent were identified both visually and by software using CT density thresholds. The association between PFTs and quantitative parameters was tested by the calculation of the Spearman's coefficient of rank correlation (rho). The study included 40 patients (38% females; median age 63 years old, IQR, 56-71 years old). A significant correlation was identified between low attenuation areas% (%LAAs) <950 Hounsfield units (HU) and both forced expiratory volume in 1s/forced vital capacity (FEV1/FVC) ratio (rho -0.410, 95% CIs -0.639--0.112, p = 0.008) and %DLCO (rho -0.426, 95% CIs -0.678--0.084, p = 0.017). The remaining quantitative parameters failed to demonstrate a significant association with PFTs (p > 0.05). At follow-up, CT scans performed at least 6 months after COVID-19 pneumonia onset showed %LAAs that were inversely associated with %DLCO and could be considered a marker of irreversible lung damage.

Machine learning-based computer-aided simple triage (CAST) for COVID-19 pneumonia as compared with triage by board-certified chest radiologists

PurposeSeveral reporting systems have been proposed for providing standardized language and diagnostic categories aiming for expressing the likelihood that lung abnormalities on CT images represent COVID-19. We developed a machine learning (ML)-based CT texture analysis software for simple triage based on the RSNA Expert Consensus Statement system. The purpose of this study was to conduct a multi-center and multi-reader study to determine the capability of ML-based computer-aided simple triage (CAST) software based on RSNA expert consensus statements for diagnosis of COVID-19 pneumonia.MethodsFor this multi-center study, 174 cases who had undergone CT and polymerase chain reaction (PCR) tests for COVID-19 were retrospectively included. Their CT data were then assessed by CAST and consensus from three board-certified chest radiologists, after which all cases were classified as either positive or negative. Diagnostic performance was then compared by McNemar’s test. To determine radiological finding evaluation capability of CAST, three other board-certified chest radiologists assessed CAST results for radiological findings into five criteria. Finally, accuracies of all radiological evaluations were compared by McNemar’s test.ResultsA comparison of diagnosis for COVID-19 pneumonia based on RT-PCR results for cases with COVID-19 pneumonia findings on CT showed no significant difference of diagnostic performance between ML-based CAST software and consensus evaluation (p > 0.05). Comparison of agreement on accuracy for all radiological finding evaluations showed that emphysema evaluation accuracy for investigator A (AC = 91.7%) was significantly lower than that for investigators B (100%, p = 0.0009) and C (100%, p = 0.0009).ConclusionThis multi-center study shows COVID-19 pneumonia triage by CAST can be considered at least as valid as that by chest expert radiologists and may be capable for playing as useful a complementary role for management of suspected COVID-19 pneumonia patients as well as the RT-PCR test in routine clinical practice.

Prevalence of Adrenal Incidentaloma by Chest Computed Tomography in Patients with a Preliminary Diagnosis of COVID-19 Pneumonia

Aim: Adrenal incidentalomas are typically discovered as an incidental finding during routine computed tomography (CT) or magnetic resonance imaging (MRI) scans conducted for unrelated purposes. Our objective was to examine the frequency of adrenal incidentaloma, in individuals who underwent thoracic CT scan as a result of Covid-19 infection.&#x0D; Material and Methods: In the retrospective descriptive study, 808 patients who applied to the emergency radiology department with the suspicion of covid 19 and underwent thoracic CT between March 2020 and July 2020 were included. The presence of incidentaloma in the CT images of the patients was evaluated.&#x0D; Results: Adrenal incidentaloma was detected in 78 (9.7%) of the 808 patients, and of those 78 adrenal incidentalomas, 70 (8.7%) were unilateral and 8 (1%) were bilateral. The mean diameter of the adrenal incidentaloma was 20 mm, and it was 18.5 mm in females and 21.5 mm in males. A total of 808 patients, 351 (43.4%) females and 457 (56.6%) males, were included in the study.&#x0D; Conclusion: Adrenal incidentaloma have become more common radiological findings with the recent COVID-19 pandemic due to the increased frequency of CT scans, and it is important to appropriately manage these patients.