Association between emphysema and other pulmonary computed tomography patterns in primary varicella pneumonia: A retrospective cohort study

This study aims to evaluate chest computed tomography (CT) findings in hospital patients with primary varicella pneumonia (PVP). We retrospectively analyzed CT images of 77 PVP patients using 3D Slicer, an open-source software, to model lesions and lungs. This retrospective cohort study was approved by the Institutional Review Board (Ethical Committee, Renmin Hospital, Hubei University of Medicine, Shiyan, China) and waived the requirement for written informed consent. The left lung was more frequently and severely affected in PVP, with significant differences between the 2 groups in CT involvement percentage of each lung region, except for total lung inflation. Group A showed higher median percentages of lung collapse compared to Group B. The extent of left lung involvement is a critical predictor of emphysema in PVP patients, highlighting the importance of also monitoring the right lung for more severe cases. Lower emphysema levels correspond to more collapsed and infiltrated lung segments, suggesting a more severe clinical presentation.

Chest Examination 3.0 With Wireless Technology in a Clinical Case Based on Literature Review

Physicians use auscultation as a standard method of thoracic examination: it is simple, reliable, non-invasive, and widely accepted. Artificial intelligence (AI) is the new frontier of thoracic examination as it makes it possible to integrate all available data (clinical, instrumental, laboratory, functional), allowing for objective assessments, precise diagnoses, and even the phenotypical characterization of lung diseases. Increasing the sensitivity and specificity of examinations helps provide tailored diagnostic and therapeutic indications, which also take into account the patient's clinical history and comorbidities. Several clinical studies, mainly conducted in children, have shown a good concordance between traditional and AI-assisted auscultation in detecting fibrotic diseases. On the other hand, the use of AI for the diagnosis of obstructive pulmonary disease is still debated as it gave inconsistent results when detecting certain types of lung noises, such as wet and dry crackles. Therefore, the application of AI in clinical practice needs further investigation. In particular, the pilot case report aims to address the use of this technology in restrictive lung disease, which in this specific case is pulmonary sarcoidosis. In the case we present, data integration allowed us to make the right diagnosis, avoid invasive procedures, and reduce the costs for the national health system; we show that integrating technologies can improve the diagnosis of restrictive lung disease. Randomized controlled trials will be needed to confirm the conclusions of this preliminary work.

Age Estimation Based on CT Chest Analysis of Ossification of the Xiphisternal Joint in a Living Population Aged 35-50 in a Tertiary Setup

Introduction Age estimation has been an area of special interest in the medicolegal context because of its necessity in various criminal and civil cases like assaults, murders, rapes, inheritance, insurance claims, etc. While legal documents are useful in daily activities that require age identity, they cannot be relied on for criminal and civil proceedings because of being falsifiable and inaccessible to some people. Scientific methods of age determination like physical, dental, and radiological examinations are used for reliable age estimation due to their universal and non-falsifiable nature. The skeletal examination is of great importance here because the human skeleton provides many sites for age estimation in different age groups. The xiphisternal joint between the xiphoid process and the body of the sternum provides one such opportunity in participants of 35-50 years of age. The ossification in this joint proceeds gradually in approximately the third to fifth decade of life; this natural variation in the morphology of the joint can be leveraged for age estimation. Previous studies showed that the mean age of fusion varied with the ethnicity of individuals and environmental factors. Thus, it is critical to have statistical information for the concerned population to avoid errors. Also, the relation of gender with the mean age of complete fusion remained ambiguous with the previous studies. The xiphisternal joint can be studied by radiological techniques like computed tomography (CT) and plain radiographs. Radiological methods have the benefit that they can be used on both living and dead participants and are non-invasive. The present study aims at gathering data relevant for use in India (Maharashtra) and to find out the reference age group in which there is complete ossification of the xiphisternal joint in males and females.  Methods and materials This was a cross-sectional observational study in a tertiary care setup over a period of one year. High-resolution computed tomography (HRCT) was used for assessing joint fusion due to its high spatial resolution. The participants were included in the study if they were referred for HRCT chest by a physician for some pathology, did not have any trauma or lesion of the sternum and consented to the use of their information for the purpose of this study. Results The study included a total of 384 participants, out of whom 195 (50.8%) were males and 189 (49.2%) were females. The mean age of participants was 42.87 years. The mean age of complete xiphisternal joint fusion was observed to be 46.31 years (95% CI: 45.61 to 47.00) in males and 45.57 years (95% CI: 44.73 to 46.42) in females. Similarly, the mean age of participants with an unfused xiphisternal joint was observed to be 38.42 years (95% CI: 37.47 to 39.39) in males and 37.85 years (95% CI: 37.14 to 38.57) in females. There was no statistically significant difference in the age above which males and females show complete ossification of the xiphisternal joint. Conclusion The xiphisternal joint fusion can be used to determine the chronological age of an individual. It can be estimated as lesser than or equal to 45 years if the xiphisternal joint is unossified and greater than or equal to 37 years if the joint is ossified, with a 95% level of confidence.

Diagnostic performance of corona virus disease 2019 chest computer tomography image recognition based on deep learning: Systematic review and meta-analysis

BACKGROUND

To analyze the diagnosis performance of deep learning model used in corona virus disease 2019 (COVID-19) computer tomography(CT) chest scans. The included sample contains healthy people, confirmed COVID-19 patients and unconfirmed suspected patients with corresponding symptoms.

METHODS

PubMed, Web of Science, Wiley, China National Knowledge Infrastructure, WAN FANG DATA, and Cochrane Library were searched for articles. Three researchers independently screened the literature, extracted the data. Any differences will be resolved by consulting the third author to ensure that a highly reliable and useful research paper is produced. Data were extracted from the final articles, including: authors, country of study, study type, sample size, participant demographics, type and name of AI software, results (accuracy, sensitivity, specificity, ROC, and predictive values), other outcome(s) if applicable.

RESULTS

Among the 3891 searched results, 32 articles describing 51,392 confirmed patients and 7686 non-infected individuals met the inclusion criteria. The pooled sensitivity, the pooled specificity, positive likelihood ratio, negative likelihood ratio and the pooled diagnostic odds ratio (OR) is 0.87(95%CI [confidence interval]: 0.85, 0.89), 0.85(95%CI: 0.82, 0.87), 6.7(95%CI: 5.7, 7.8), 0.14(95%CI: 0.12, 0.16), and 49(95%CI: 38, 65). Further, the AUROC (area under the receiver operating characteristic curve) is 0.94(95%CI: 0.91, 0.96). Secondary outcomes are specific sensitivity and specificity within subgroups defined by different models. Resnet has the best diagnostic performance, which has the highest sensitivity (0.91[95%CI: 0.87, 0.94]), specificity (0.90[95%CI: 0.86, 0.93]) and AUROC (0.96[95%CI: 0.94, 0.97]), according to the AUROC, we can get the rank Resnet > Densenet > VGG > Mobilenet > Inception > Effficient > Alexnet.

CONCLUSIONS

Our study findings show that deep learning models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of deep learning-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.

Personalized Chest Computed Tomography: Minimum Diagnostic Radiation Dose Levels for the Detection of Fibrosis, Nodules, and Pneumonia

OBJECTIVES

The purpose of this study was to evaluate the minimum diagnostic radiation dose level for the detection of high-resolution (HR) lung structures, pulmonary nodules (PNs), and infectious diseases (IDs).

MATERIALS AND METHODS

A preclinical chest computed tomography (CT) trial was performed with a human cadaver without known lung disease with incremental radiation dose using tin filter-based spectral shaping protocols. A subset of protocols for full diagnostic evaluation of HR, PN, and ID structures was translated to clinical routine. Also, a minimum diagnostic radiation dose protocol was defined (MIN). These protocols were prospectively applied over 5 months in the clinical routine under consideration of the individual clinical indication. We compared radiation dose parameters, objective and subjective image quality (IQ).

RESULTS

The HR protocol was performed in 38 patients (43%), PN in 21 patients (24%), ID in 20 patients (23%), and MIN in 9 patients (10%). Radiation dose differed significantly among HR, PN, and ID (5.4, 1.2, and 0.6 mGy, respectively; P < 0.001). Differences between ID and MIN (0.2 mGy) were not significant (P = 0.262). Dose-normalized contrast-to-noise ratio was comparable among all groups (P = 0.087). Overall IQ was perfect for the HR protocol (median, 5.0) and decreased for PN (4.5), ID-CT (4.3), and MIN-CT (2.5). The delineation of disease-specific findings was high in all dedicated protocols (HR, 5.0; PN, 5.0; ID, 4.5). The MIN protocol had borderline IQ for PN and ID lesions but was insufficient for HR structures. The dose reductions were 78% (PN), 89% (ID), and 97% (MIN) compared with the HR protocols.

CONCLUSIONS

Personalized chest CT tailored to the clinical indications leads to substantial dose reduction without reducing interpretability. More than 50% of patients can benefit from such individual adaptation in a clinical routine setting. Personalized radiation dose adjustments with validated diagnostic IQ are especially preferable for evaluating ID and PN lesions.

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

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

Role of cardiovascular computed tomography parameters and lungs findings in predicting severe COVID-19 patients: a single-centre retrospective study

Background

Results

Conclusions

COVID-19-Related Lung Involvement at Different Time Intervals: Evaluation of Computed Tomography Images With Semiquantitative Scoring System and COVID-19 Reporting and Data System Scoring

INTRODUCTION

This study aimed to evaluate the frequency of typical and atypical thoracic CT findings in patient groups diagnosed during different periods of the pandemic, examine disease severity using radiological scoring methods, and determine the relationship between atypical CT findings and disease severity.

MATERIALS AND METHODS

One hundred fifty-one patients with positive reverse transcription polymerase chain reaction (RT-PCR) test and thoracic CT scan were included in the study. The patients were divided into two groups as group 1 (March to August 2020) diagnosed in the first six months of the pandemic and group 2 (September 2020 to February 2021) diagnosed in the second six months. CT images of the patients were analyzed for the frequency of typical and atypical findings. Evaluation was made in terms of disease suspicion and severity by scoring methods, and the relationship between atypical findings and disease severity was examined.

RESULTS

There was no statistically significant difference between the frequency and distribution patterns of typical CT findings observed in both groups. The most common atypical finding in both groups was nodular lesions. Central distribution, one of the atypical findings, was not seen in group 1, whereas it was present in nine patients in group 2 (p=0.001). The mean CT severity score was higher in group 2, and there was a statistically significant difference between the mean CT scores of both groups (p<0.001). In addition, six (7.2%) patients in group 1 and 34 (50%) patients in group 2 had CT scores above the cut-off value (p<0.001). There was no statistically significant relationship between atypical findings and severity score.

CONCLUSION

Other diseases and atypical findings that may accompany COVID-19 pneumonia may increase the rate of misdiagnosis. In the diagnosis of the disease, clinical signs and symptoms and radiological findings should be evaluated together, and it should be kept in mind that lung findings in thorax CT change over time.

Using Statistical Measures and Density Maps Generated From Chest Computed Tomography Scans to Identify and Monitor COVID-19 Cases in Radiation Oncology Rapidly

Objectives This study aimed to evaluate quantitative and qualitative screening measures for anomalous computed tomography (CT) scans in cancer patients with potential coronavirus disease 2019 (COVID-19) as an automated detection tool in a radiation oncology treatment setting. Methods We identified a non-COVID-19 cohort and patients with suspected COVID-19 with chest CT scans from February 1, 2020 to June 30, 2020. Lungs were segmented, and a mean normal Hounsfield Unit (HU) histogram was generated for the non-COVID-19 CT scans; these were used to define thresholds for designating the COVID-19-suspected histograms as normal or abnormal. Statistical measures were computed and compared to the threshold levels, and density maps were generated to examine the difference between lungs with and without COVID-19 qualitatively. Results The non-COVID-19 cohort consisted of 70 patients with 70 CT scans, and the cohort of suspected COVID-19 patients consisted of 59 patients with 80 CT scans. Sixty-two patients were positive for COVID-19. The mean HUs and skewness of the intensity histogram discriminated between COVID-19 positive and negative cases, with an area under the curve of 0.948 for positive and 0.944 for negative cases. Skewness correctly identified 57 of 62 positive cases, whereas mean HUs correctly identified 17 of 18 negative cases. Density maps allowed for visualization of the temporal evolution of COVID-19 disease. Conclusions The statistical measures and density maps evaluated here could be employed in an automated screening algorithm for COVID-19 infection. The accuracy is high enough for a simple and rapid screening tool for early identification of suspected infection in patients treated with chemotherapy and radiation therapy already receiving CT scans as part of clinical care. This screening tool could also identify other infections that present critical risks for patients undergoing chemotherapy and radiation therapy, such as pneumonitis.

Comparison of chest computed tomography features between pulmonary tuberculosis patients with culture-positive and culture-negative sputum for non-mycobacteria: A retrospective observational study

Although complication with non-mycobacterial pneumonia among patients with pulmonary tuberculosis (TB) may lead to poor prognosis, discrimination between TB complicated with and without non-mycobacterial pneumonia using radiological imaging has not been fully elucidated. We aimed to clarify the differences in chest computed tomography (CT) features between pulmonary TB patients with culture-positive and culture-negative sputum for non-mycobacteria.We retrospectively included consecutive patients admitted to our hospital from January 2013 to December 2015 for bacteriologically-confirmed pulmonary TB, who were tested by sputum culture for non-mycobacteria, and who underwent chest CT within 2 weeks before or after admission. Chest CT features were compared between pulmonary TB patients who had positive non-mycobacterial cultures and in those who had not.Of 202 patients with pulmonary TB, 186 (92%) were tested by sputum culture for non-mycobacteria and underwent chest CT. Among these, non-mycobacteria were isolated in 118 patients (63%), while 68 patients (37%) had negative cultures. Patients with a positive culture for non-mycobacteria were significantly older and had lower levels of physical activity and albumin, higher levels of C-reactive protein, and a greater number of respiratory failures. By CT, emphysematous lesions, ground-glass opacities, airspace consolidation, air-bronchogram, interlobular septal thickening, bronchiectasis, pleural effusion, pleural thickening, and lymph node enlargement were more frequently in patients with a positive culture for non-mycobacteria. These chest CT features could be helpful for detecting complication with non-mycobacterial pneumonia in patients with pulmonary TB.

Diagnostic yield, safety, and advantages of ultra-low dose chest CT compared to chest radiography in early stage suspected SARS-CoV-2 pneumonia: A retrospective observational study

ABSTRACT

To determine the role of ultra-low dose chest computed tomography (uld CT) compared to chest radiographs in patients with laboratory-confirmed early stage SARS-CoV-2 pneumonia.Chest radiographs and uld CT of 12 consecutive suspected SARS-CoV-2 patients performed up to 48 hours from hospital admission were reviewed by 2 radiologists. Dosimetry and descriptive statistics of both modalities were analyzed.On uld CT, parenchymal abnormalities compatible with SARS-CoV-2 pneumonia were detected in 10/12 (83%) patients whereas on chest X-ray in, respectively, 8/12 (66%) and 5/12 (41%) patients for reader 1 and 2. The average increment of diagnostic performance of uld CT compared to chest X-ray was 29%. The average effective dose was, respectively, of 0.219 and 0.073 mSv.Uld CT detects substantially more lung injuries in symptomatic patients with suspected early stage SARS-CoV-2 pneumonia compared to chest radiographs, with a significantly better inter-reader agreement, at the cost of a slightly higher equivalent radiation dose.

COVID-19-Associated Bronchiectasis and Its Impact on Prognosis

Coronavirus disease 2019 (COVID-19), which initially emerged in Wuhan, China, has rapidly swept around the world, causing grave morbidity and mortality. It manifests with several symptoms, on a spectrum from asymptomatic to severe illness and death. Many typical imaging features of this disease are described, such as bilateral multi-lobar ground-glass opacities (GGO) or consolidations with a predominantly peripheral distribution. COVID-19-associated bronchiectasis is an atypical finding, and it is not a commonly described sequel of the disease. Here, we present a previously healthy middle-aged man who developed progressive bronchiectasis evident on serial chest CT scans with superimposed bacterial infection following COVID-19 pneumonia. The patient's complicated hospital course of superimposed bacterial infection in the setting of presumed bronchiectasis secondary to COVID-19 is alleged to have contributed to his prolonged hospital stay, with difficulty in weaning off mechanical ventilation. Clinicians should have high suspicion and awareness of such a debilitating complication, as further follow-up and management might be warranted.

The Role of Computed Tomography and Real-Time Polymerase Chain Reaction in the Diagnosis and Prognostication of COVID-19

With the rapid spread of coronavirus disease 2019 (COVID-19) starting in early 2020, there has been much interest in the applicability of radiologic imaging in managing affected patients. From the initial screening to addressing the extent of pulmonary involvement, CT scans provide great value to hospitals overwhelmed by an influx of patients, including those with suspected COVID-19. Because CTs come at a high financial cost, lower cost real-time polymerase chain reaction (RT-PCR) COVID-19 tests are critical due to their ability to identify asymptomatic carriers and properly handle patients during the ongoing pandemic. However, unlike RT-PCR, CT scans can also provide insight into the progression of the virus. The signs of acute COVID-19 infection include unique patterns of ground-glass opacities (GGO) with vascular thickening, enabling radiologists to diagnose COVID-19 with a high specificity. Additionally, there may be a significant value in the use of CT scans in predicting the outcomes.

COVID-19 pneumonia in a patient with sarcoidosis: A case report

Key prognostic elements to consider in sarcoidosis patients who contract COVID-19 pneumonia are pulmonary involvement, the underlying immune system dysfunction, immunosuppressive therapies' use, and the increased risk for hypercoagulability.

Endobronchial Tuberculosis and Bronchostenosis: A Rare Case of Bronchial Occlusion in a Patient With History of Tuberculosis

Pulmonary tuberculosis is common worldwide, and many of these patients develop endobronchial tuberculosis (EBTB). Bronchostenosis is a known complication of EBTB though most patients with endobronchial stenosis do not develop severe bronchostenosis or occlusion. We present a rare case of a patient with a right upper lobe bronchus occlusion and a history of tuberculosis.

Clinical and radiological imaging as prognostic predictors in COVID-19 patients

Background

Since the announcement of COVID-19 as a pandemic infection, several studies have been performed to discuss the clinical picture, laboratory finding, and imaging features of this disease. The aim of this study is to demarcate the imaging features of novel coronavirus infected pneumonia (NCIP) in different age groups and outline the relation between radiological aspect, including CT severity, and clinical aspect, including age, oxygen saturation, and fatal outcome. We implemented a prospective observational study enrolled 299 laboratory-confirmed COVID-19 patients (169 males and 130 females; age range = 2–91 years; mean age = 38.4 ± 17.2). All patients were submitted to chest CT with multi-planar reconstruction. The imaging features of NCIP in different age groups were described. The relations between CT severity and age, oxygen saturation, and fatal outcome were evaluated.

Results

The most predominant CT features were bilateral (75.4%), posterior (66.3%), pleural-based (93.5%), lower lobe involvement (89.8%), and ground-glass opacity (94.7%). ROC curve analysis revealed that the optimal cutoff age that was highly exposed to moderate and severe stages of NCIP was 38 years old (AUC = 0.77, p < 0.001). NCIP was noted in 42.6% below 40-year-old age group compared to 84% above 40-year-old age group. The CT severity was significantly related to age and fatal outcome (p < 0.001). Anterior, centrilobular, hilar, apical, and middle lobe involvements had a significant relation to below 90% oxygen saturation. A significant negative correlation was found between CT severity and oxygen saturation (r = − 0.49, p < 0.001). Crazy-paving pattern, anterior aspect, hilar, centrilobular involvement, and moderate and severe stages had a statistically significant relation to higher mortality.

Conclusion

The current study confirmed the value of CT as a prognostic predictor in NCIP through demonstration of the strong relation between CT severity and age, oxygen saturation, and the fatal outcome. In the era of COVID-19 pandemic, this study is considered to be an extension to other studies discussing chest CT features of COVID-19 in different age groups with demarcation of the relation of chest CT severity to different pattern and distribution of NCIP, age, oxygen saturation, and mortality rate.

Iatrogenic Bronchopleural Fistula

A bronchopleural fistula (BPF) is a communication between the pleural space and the bronchial tree or the lung parenchyma. Despite being a rare entity, a BPF may carry a high mortality rate. Symptoms of BPF are often nonspecific and subtle, so a high index of clinical suspicion is essential for its correct diagnosis, with imaging playing an extremely important role both in the diagnosis and in the selection of the most appropriate therapeutic approach for each patient. This paper reports a case of a 60-year-old male admitted to the hospital for an etiological investigation of a unilateral pleural effusion. The patient underwent several procedures, among them a video-assisted thoracic surgery, complicated by a peripheral BPF. Therapeutic approach for BPFs must be adapted to each particular case. In this patient, a conservative approach proved to be effective. Meanwhile, the patient was diagnosed with pleural tuberculosis, being discharged on antibacillary medication and while improving BPF’s manifestations.

Case report of tracheobronchial injuries after acid ingestion: CT findings with serial follow-up: Airway complication after acid ingestion

RATIONALE

Tracheobronchial injury from acid ingestion is a less reported clinical presentation than injury of the gastrointestinal tract, but it can occur due to direct exposure from acid aspiration and cause fatal complications.

PATIENT CONCERNS

A 43-year-old man presented to the emergency department after ingesting nitric acid complaining of chest pain and dyspnea.

DIAGNOSES

The initial chest computed tomography (CT) images revealed an acute lung injury related to acid aspiration. The follow-up chest CT showed acute and late tracheobronchial injures.

INTERVENTIONS

Bronchoscopy showed deep caustic airway injuries consisting of hemorrhage, sloughing of the mucosa, and ulceration of the trachea and left-side bronchial tree.

OUTCOMES

Progressive narrowing of the left main bronchus with total collapse of the left lung occurred as a late complication of acid ingestion.

LESSONS

Tracheobronchial injury should be considered in cases of aspiration pneumonia after acid ingestion; chest CT can be used to detect and assess acute and late complications of tracheobronchial injuries.

The Limited Sensitivity of Chest Computed Tomography Relative to Reverse Transcription Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus-2 Infection: A Systematic Review on COVID-19 Diagnostics

OBJECTIVES

Several studies suggest the sensitivity of chest computed tomography (CT) is far greater than that of reverse transcription polymerase chain reaction (RT-PCR) in diagnosing COVID-19 patients, and therefore, CT should be included as a primary diagnostic tool. This systematic review aims to stratify studies as high or low risk of bias to determine the true sensitivity of CT for severe acute respiratory syndrome coronavirus-2 infection according to the unbiased (low risk) studies, a topic of particular importance given the insufficient quantity of RT-PCR kits in many countries. We focus on sensitivity as that is the chief advantage perceived of CT.

MATERIALS AND METHODS

This systematic review involved searching the PubMed and Google Scholar databases for articles conducted and published between January 1 and April 15, 2020. The quality assessment tool QUADAS-2 was used to stratify studies according to their risk of bias, and exclusion criteria included not providing the information deemed relevant for such a stratification, such as not indicating if the patients were symptomatic or asymptomatic, or identifying the source of the specimen for the reference standard, RT-PCR (eg, nasal, oropharyngeal, etc). Sensitivity values were then extracted, and random effects meta-analyses were performed.

RESULTS

Of 641 search results, 37 studies (n = 9610 patients) were included in the analysis. The mean sensitivity of RT-PCR for COVID-19 reported by the biased studies was 70% (n = 5409/7 studies; 95% confidence interval [CI], 43-97; I = 99.1%), compared with 78% by unbiased studies (n = 534/4 studies; 95% CI, 69-87, I = 89.9%). For chest CT, the mean sensitivity reported by biased studies was 94% (n = 3371 patients/24 studies; 95% CI, 92-96; I = 93.1%), compared with 75% by unbiased studies (n = 957/10 studies; 95% CI, 67-83; I = 89.5%).

CONCLUSIONS

The difference between the sensitivities of CT and RT-PCR for severe acute respiratory syndrome coronavirus-2 infection is lower than previously thought, as after stratifying the studies, the true sensitivity for CT based on the unbiased studies is limited.

Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Characteristics of Chest CT Images in Patients With COVID-19 Pneumonia in London, UK

Background and objective Novel coronavirus 2019 (COVID-19) outbreak was first reported in Wuhan, Hubei Province in China in December 2019; it has then spread quickly and exponentially beyond the Chinese borders and is now regarded as a global pandemic. We aimed to evaluate the chest CT radiological characteristics and lesion distribution patterns in patients of COVID-19 pneumonia in London, UK. Methods We performed a retrospective study and reviewed data of patients with clinically suspected COVID-19 who underwent chest CT between February 1 and May 5, 2020. All patients underwent the reverse transcription-polymerase chain reaction (RT-PCR) test. Lung lesion characteristics and distribution patterns were evaluated by two radiologists. Fisher's exact test was used for statistical analysis, and a p-value of <0.05 was considered statistically significant. Results A total of 18 patients (nine men and nine women) were analyzed. All of them had bilateral patchy lesions in the chest CT images. There was no correlation between the severity score and mortality (p=0.790). The distinctive CT features included ground-glass opacity (GGO) and consolidative patchy amorphous lesions, bilateral posterior and peripheral multi-lobar lung involvement, pleural effusions, subpleural fibrotic lines, subpleural sparing, vascular engorgement, occasional crazy paving, occasional mediastinal lymphadenopathy, pleural thickening, lack of cavitation, and absence of reverse halo (atoll) signs. Conclusion CT can facilitate the diagnosis of COVID-19 pneumonia. Our UK cohort showed slight variations compared with previously reported Asian and continental European cases with respect to chest CT images.

Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence

Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.

Positive Chest CT Features in Patients With COVID-19 Pneumonia and Negative Real-Time Polymerase Chain Reaction Test

Objectives Clinically suspicious novel coronavirus (COVID-19) lung pneumonia can be observed typically on computed tomography (CT) chest scans even in patients with a negative real-time polymerase chain reaction (RT-PCR) test. The purpose of the study was to describe the CT imaging findings of five patients with negative RT-PCR results on initial and repeated testing but a high radiological suspicion of COVID-19 pneumonia. Methods Out of 19 clinically and/or radiologically diagnosed COVID-19 patients from our institution, five patients were selected for our study who had typical findings of COVID-19 on CT scan despite two negative RT-PCR results. Two district general hospital radiologists reviewed the chest CT images without prior knowledge of the RT-PCR test results. Scans were analyzed for the density of opacification and the distribution of disease. Results Out of 19 patients, five (26%) had initial negative RT-PCR test findings but positive CT chest features consistent with COVID-19. All patients had typical CT imaging findings of COVID-19. These included one patient with purely ground-glass opacities (GGO) and four patients with mixed GGO and consolidation. The typical distribution of parenchymal involvement was bilateral, posterior, and peripheral. Of the five patients with negative RT-PCR and positive CT findings, the range of CT severity score was 5 to 14. The median score, seen in three patients, was a score of 5, which corresponded to mild disease. One patient had a score of 8, corresponding to moderate disease, and one patient had severe disease with a score of 14. Conclusion Lung parenchymal changes related to COVID-19 can be seen on chest CT clearly despite repeated RT-PCR negative results.

Radiological diagnosis of Coronavirus Disease 2019 (COVID-19): a Practical Guide

Novel beta-coronavirus (2019-nCoV) is the cause of Coronavirus disease-19 (COVID-19), and on March 12th 2020, the World Health Organization defined COVID-19 as a controllable pandemic. Currently, the 2019 novel coronavirus (SARS-CoV-2) can be identified by virus isolation or viral nucleic acid detection; however, false negatives associated with the nucleic acid detection provide a clinical challenge. Imaging examination has become the indispensable means not only in the early detection and diagnosis but also in monitoring the clinical course, evaluating the disease severity, and may be presented as an important warning signal preceding the negative RT-PCR test results. Different radiological modalities can be used in different disease settings. Radiology Departments must be nimble in implementing operational changes to ensure continued radiology services and protect patients and staff health.

An uncommon manifestation of COVID-19 pneumonia on CT scan with small cavities in the lungs: A case report

RATIONALE

Chest computed tomography (CT) scans play a key role in diagnosing and managing of COVID-19 pneumonia. The typical manifestations of COVID-19 pneumonia on a chest CT scan are ground glass opacities, consolidation, nodules, and linear opacities. It can be accompanied by a "crazy-paving" pattern, air bronchograms, pleural hypertrophy, and pleural effusion. However, no literature has reported a case with cavities in the lungs.

PATIENT CONCERNS

A 34-year-old male patient complained of fever, cough, fatigue, myalgia, diarrhea, headache, and dizziness for 2 weeks. This patient is living in Xiaogan, a city around Wuhan, and he had contact with a patient with COVID-19 pneumonia from Wuhan <14 days before he had fever.

DIAGNOSIS

A nucleic acid test by rRT-PCR returned positive on a pharyngeal swab, confirming the diagnosis of COVID-19 pneumonia.

INTERVENTIONS

Isolation antiviral treatment.

OUTCOMES

After 19 days of isolation and antiviral treatment, his temperature returned to normal and the symptoms were relieved. The laboratory results also were returning to normal levels. The chest CT scan showed that the acute inflammation had subsided significantly. With 2 consecutive novel coronavirus nucleic acid tests had returned negative, the patient was discharged from the hospital and sent to a government designated hotel for quarantine observation. The unique chest CT manifestation in this case was the small cavities in both lungs during the absorption phase of this disease. These small cavities developed into consolidated nodules with clear edges and gradually shrank or disappeared.

LESSONS

Although 2 consecutive nucleic acid tests returned negative in this patient, the small cavity changes in the lungs were observed, so the patient was quarantined for 14 days. However, follow-up CT after the first 14 days' quarantine showed new small cavity changes on the lungs, a further 14 days of quarantine was recommended. Therefore, in some COVID-19 cases, even if the nucleic acid tests turns negative, the disappearance of lung lesions may take a long time. The repeated chest CT scan plays an important role in the diagnosis and evaluation of the recovery of COVID-19.

Common CT Findings of Novel Coronavirus Disease 2019 (COVID-19): A Case Series

Given the highly infectious nature of the coronavirus disease 2019 (COVID-19) virus and the lack of proven specific therapeutic drugs and licensed vaccines effective against it, early diagnosis of the disease is of paramount importance. The common chest CT imaging of confirmed COVID-19 cases is discussed here, which shows ground-glass opacity, crazy paving, and consolidation.

[Clinical features and chest CT findings of coronavirus disease 2019 in infants and young children]

OBJECTIVE

To study the clinical features and chest CT findings of coronavirus disease 2019 (COVID-19) in infants and young children.

METHODS

A retrospective analysis was performed for the clinical data and chest CT images of 9 children, aged 0 to 3 years, who were diagnosed with COVID-19 by nucleic acid detection between January 20 and February 10, 2020.

RESULTS

All 9 children had an epidemiological history, and family clustering was observed for all infected children. Among the 9 children with COVID-19, 5 had no symptoms, 4 had fever, 2 had cough, and 1 had rhinorrhea. There were only symptoms of the respiratory system. Laboratory examination showed no reductions in leukocyte or lymphocyte count. Among the 9 children, 6 had an increase in lymphocyte count and 2 had an increase in leukocyte count. CT examination showed that among the 9 children, 8 had pulmonary inflammation located below the pleura or near the interlobar fissure and 3 had lesions distributed along the bronchovascular bundles. As for the morphology of the lesions, 6 had nodular lesions and 7 had patchy lesions; ground glass opacity with consolidation was observed in 6 children, among whom 3 had halo sign, and there was no typical paving stone sign.

CONCLUSIONS

Infants and young children with COVID-19 tend to have mild clinical symptoms and imaging findings not as typical as those of adults, and therefore, the diagnosis of COVID-19 should be made based on imaging findings along with epidemiological history and nucleic acid detection. Chest CT has guiding significance for the early diagnosis of asymptomatic children.

Point-of-care Ultrasonography for Detecting the Etiology of Unexplained Acute Respiratory and Chest Complaints in the Emergency Department: A Prospective Analysis

Introduction  Point-of-care ultrasound (POCUS) is increasingly used as a diagnostic tool in emergency departments. As the number and type of POCUS protocols expand, there is a need to validate their efficacy in comparison with current diagnostic standards. This study compares POCUS to chest radiography in patients with undifferentiated respiratory or chest complaints. Methods A prospective convenience sample of 59 adult patients were enrolled from those presenting with unexplained acute respiratory or chest complaints (and having orders for chest radiography) to a single emergency department in an academic tertiary-care hospital. After a brief educational session, a medical student, blinded to chest radiograph results, performed and interpreted images from the modified Rapid Assessment of Dyspnea in Ultrasound (RADiUS) protocol. The images were reviewed by a blinded ultrasound fellowship-trained emergency physician and compared to chest radiography upon chart review. The primary "gold standard" endpoint diagnosis was the diagnosis at discharge. A secondary analysis was performed using the chest computed tomography (CT) diagnosis as the endpoint diagnosis in the subset of patients with chest CTs. Results When using diagnosis at discharge as the endpoint diagnosis, the modified RADiUS protocol had a higher sensitivity (79% vs. 67%) and lower specificity (71% vs. 83%) than chest radiography. When using chest CT diagnosis as the endpoint diagnosis (in the subset of patients with chest CTs), the modified RADiUS protocol had a higher sensitivity (76% vs. 65%) and lower specificity (71% vs. 100%) than chest radiography. The medical student performed and interpreted the 59 POCUS scans with 92% accuracy. Conclusion The sensitivity and specificity of POCUS using the modified RADiUS protocol was not significantly different than chest radiography. In addition, a medical student was able to perform the protocol and interpret scans with a high level of accuracy. POCUS has potential value for diagnosing the etiology of undifferentiated acute respiratory and chest complaints in adult patients presenting to the emergency department, but larger clinical validation studies are required.

Ultralow dose CT for follow-up of solid pulmonary nodules: A pilot single-center study using Bland-Altman analysis

Solid pulmonary nodules are a common finding requiring serial computed tomography (CT) imaging. We sought to explore the detection and measurement accuracy of an ultralow-dose CT (ULDCT) protocol compared with our standard low-dose CT (LDCT) nodule follow-up protocol.In this pragmatic single-center pilot prospective cohort study, patients scheduled for clinically indicated CT surveillance of 1 or more known solid pulmonary nodules >2 mm underwent ULDCT immediately after routine LDCT. The Bland-Altman 95% limits of agreement for diameter and volumetry were calculated.In all, 57 patients underwent 60 imaging episodes, with 170 evaluable nodules. ULDCT detected all known solid pulmonary nodules >2 mm. Bland-Altman analyses demonstrated clinically agreement for both nodule diameter and volume, both of which fell within prespecified limits.This single-center pilot study suggests that ULDCT may be of use in surveillance of known solid pulmonary nodules >2 mm.

Diagnostic value of chest CT combined with x-ray for premature infants with bronchopulmonary dysplasia

Bronchopulmonary dysplasia (BPD) is a common chronic lung disease in the newborns. Staging of BPD severity does not have a high predictive value for the outcomes. This study was aimed to assess the diagnostic value of chest computed tomography (CT) combined with x-ray for premature infants with BPD.Twenty-five premature infants with mild BPD and 20 premature infants with moderate to severe BPD treated at our hospital from January 2015 to December 2015 were randomly selected. The imaging features were compared between premature infants with different severity of BPD.In mild BPD group, the incidence of increased lung opacity (at 3-10 and 29 days) were significantly higher than those in infants with moderate to severe BPD (P = .034, P = .003, respectively). However, the incidences of stage III BPD (3-10 days) and stage IV BPD (11-27 days) were significantly lower in infants with mild BPD than those in infants with moderate to severe BPD (P = .013, P = .033, respectively). The chest x-ray score in the mild BPD group was significantly lower than that in moderate to severe BPD group [3.0 (1.0) vs 5.0 (1.0), P < .001]. Spearman rank correlation analysis indicated that chest x-ray score had significant correlation (r = 0.787, P < .001) with the clinical severity. In the mild BPD group, the chest CT scan score was 11.52 ± 3.49, which was considerably lower than that in the moderate to severe BPD group (24.70 ± 4.32) (P < .001). Moreover, the severity of BPD in the premature infants was significantly correlated to the chest CT scan score (r = 0.855, P < .001).Chest CT combined with x-ray is an effective method for predicting the severity of BPD in premature infants.

CT measurements of central pulmonary vasculature as predictors of severe exacerbation in COPD

To identify a predictive value for the exacerbation status of chronic obstructive pulmonary disease (COPD) subjects, we evaluated the relationship between pulmonary vascular measurements on chest CT and severe COPD exacerbation.Six hundred three subjects enrolled in the COPDGene population were included and divided into nonexacerbator (n = 313) and severe exacerbator (n = 290) groups, based on whether they had an emergency room visit and/or hospitalization for COPD exacerbation. We measured the diameter of the main pulmonary artery (MPA) and ascending aorta (AA) at 2 different sites of the MPA (the tubular midportion and bifurcation) on both axial images and multiplanar reconstructions. Using multiple logistic regression analyses, we evaluated the relationship between each CT-measured pulmonary vasculature and exacerbation status.Axial and multiplanar MPA to AA diameter ratios (PA:AA ratios) at the tubular midportion and the axial PA:AA ratios at the bifurcation indicated significant association with severe exacerbation. The strongest association was found with the axial PA:mean AA ratio at the bifurcation (adjusted odds ratio [OR] = 12.53, 95% confidence interval [CI] = 2.35-66.74, P = .003) and the axial PA:major AA ratio at the tubular midportion (adjusted OR = 10.72, 95% CI = 1.99-57.86, P = .006). No differences were observed in the MPA diameter. Receiver operating characteristic analysis of these variables indicates that they may serve as a good predictive value for severe exacerbation (area under the curve, 0.77-0.78). The range of cut-off value for PA:AA ratio was 0.8 to 0.87.CT-measured PA:AA ratios at either the bifurcation or the tubular site, measured either on axial or multiplanar images, are useful for identification of the risk of severe exacerbation, and consequently can be helpful in guiding the management of COPD. Although CT measurement was used at the level of pulmonary bifurcation in previous studies, we suggest that future studies should monitor the tubular site of the MPA for maximum diagnostic value of CT in pulmonary hypertension or severe COPD exacerbation, as the tubular site of the MPA remains relatively constant on CT images.

Organ doses to adult patients for chest CT

PURPOSE

The goal of this study was to estimate organ doses for chest CT examinations using volume computed tomography dose index (CTDIvol) data as well as accounting for patient weight.

METHODS

A CT dosimetry spreadsheet (ImPACT CT patient dosimetry calculator) was used to compute organ doses for a 70 kg patient undergoing chest CT examinations, as well as volume computed tomography dose index (CTDIvol) in a body CT dosimetry phantom at the same CT technique factors. Ratios of organ dose to CTDIvoI (f(organ)) were generated as a function of anatomical location in the chest for the breasts, lungs, stomach, red bone marrow, liver, thyroid, liver, and thymus. Values of f(organ) were obtained for x-ray tube voltages ranging from 80 to 140 kV for 1, 4, 16, and 64 slice CT scanners from two vendors. For constant CT techniques, we computed ratios of dose in water phantoms of differing diameter. By modeling patients of different weights as equivalent water cylinders of different diameters, we generated factors that permit the estimation of the organ doses in patients weighing between 50 and 100 kg who undergo chest CT examinations relative to the corresponding organ doses received by a 70 kg adult.

RESULTS

For a 32 cm long CT scan encompassing the complete lungs, values of f(organ) ranged from 1.7 (thymus) to 0.3 (stomach). Organs that are directly in the x-ray beam, and are completely irradiated, generally had f(organ), values well above 1 (i.e., breast, lung, heart, and thymus). Organs that are not completely irradiated in a total chest CT scan generally had f(organ) values that are less than 1 (e.g., red bone marrow, liver, and stomach). Increasing the x-ray tube voltage from 80 to 140 kV resulted in modest increases in f(organ) for the heart (9%) and thymus (8%), but resulted in larger increases for the breast (19%) and red bone marrow (21%). Adult patient chests have been modeled by water cylinders with diameters between approximately 20 cm for a 50 kg patient and approximately 28 cm for a 100 kg patient. At constant x-ray techniques, a 50 kg patient is expected to have doses that are approximately 18% higher than those in a 70 kg adult, whereas a 100 kg patient will have doses that are apparoximately 18% lower.

CONCLUSIONS

We describe a practical method to use CTDI data provided by commercial CT scanners to obtain patient and examination specific estimates of organ dose for chest CT examinations.

Imaging pulmonary disease in AIDS: state of the art

The spectrum of pulmonary diseases in AIDS including infections and neoplasms that affect the lungs are reviewed. Characteristic plain film and CT findings are illustrated.