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The identification and characterization of lung diseases is one of the most interesting research topics in recent years. They require accurate and rapid diagnosis. Although lung imaging techniques have many advantages for disease diagnosis, the interpretation of medial lung images has always been a major problem for physicians and radiologists due to diagnostic errors. This has encouraged the use of modern artificial intelligence techniques such as deep learning. In this paper, a deep learning architecture based on EfficientNetB7, known as the most advanced architecture among convolutional networks, has been constructed for classification of medical X-ray and CT images of lungs into three classes namely: common pneumonia, coronavirus pneumonia and normal cases. In terms of accuracy, the proposed model is compared with recent pneumonia detection techniques. The results provided robust and consistent features to this system for pneumonia detection with predictive accuracy according to the three classes mentioned above for both imaging modalities: radiography at 99.81% and CT at 99.88%. This work implements an accurate computer-aided system for the analysis of radiographic and CT medical images. The results of the classification are promising and will certainly improve the diagnosis and decision making of lung diseases that keep appearing over time.

BACKGROUND. CT-guided percutaneous transthoracic needle biopsy (PTNB) is widely used for evaluation of indeterminate pulmonary lesions, although guidelines are lacking regarding the experience need...

Background The necessity to identify and isolate COVID-19 patients to avoid intrahospital cross infections is particularly felt as a challenge. Clinically occult SARS-CoV-2 infection among patients admitted to the hospital is always considered a risk during the pandemic. The aim of our study is to describe the application of CT scan to reveal unexpected COVID-19 in patients needing hospital admission. Method In our emergency department, we prospectively enrolled adult patients needing hospital admission, without symptoms suspected of COVID-19, and showing negative reverse transcriptase-polymerase chain reaction (RT-PCR) swab test. CT scan was performed to diagnose clinically occult COVID-19 pneumonia. All the exams were read and discussed retrospectively by two expert radiologists and assigned to one of 4 exclusive diagnoses: typical (typCT), indeterminate (indCT), atypical (atyCT), negative (negCT). The clinical characteristics and final diagnoses were described and compared with the results of CT scans. Results From May 25 to August 18, 2020, we prospectively enrolled 197 patients. They showed 122 negCT, 52 atyCT, 22 indCT, and 1 typCT. Based on the CT imaging, the prevalence of suspected clinically occult COVID-19 pneumonia was 11.6% (23 patients). None had confirmation of SARS-CoV-2 infection after the hospital stay. Nineteen patients had negative serial RT-PCR while in 4 cases, the infection was excluded by clinical follow-up or appearance of positivity of RT-PCR after months. Conclusion Our descriptive analysis confirms that CT scan cannot be considered a valid tool to screen clinically occult COVID-19, when the asymptomatic patients need hospitalization for other conditions. Application of personnel protections and distancing among patients remains the best strategies to limit the possibility of intrahospital cross-infections.

Specific findings at thoracic imaging are associated with cytokine release syndrome grade following chimeric antigen receptor (CAR) T-cell infusion; CAR T-cell treatment yielded high rates of imagi...

Females have unique physiological considerations that must be considered during image acquisition and interpretation. Familiarity with imaging of pregnant and lactating patients requires modifications from standard protocols to mitigate radiation-induced risk to both the mother and the fetus. Manifestations in the chest related to conception, pregnancy, and gynecologic pathologies are female-specific. Furthermore, there are a number of conditions that exclusively or disproportionately affect females, including diffuse lung diseases. Here, we review the expected normal findings and variant appearances of diseases in female patients on chest imaging to help the radiologist arrive at the correct diagnosis and avoid diagnostic pitfalls. In addition, the general principles of thoracic imaging in women of reproductive age are discussed.

Objective To quantitatively evaluate computed tomography (CT) parameters of coronavirus disease 2019 (COVID-19) pneumonia an artificial intelligence (AI)-based software in different clinical severity groups during the disease course. Methods From March 11 to April 15, 2020, 51 patients (age, 18-84 years; 28 men) diagnosed and hospitalized with COVID-19 pneumonia with a total of 116 CT scans were enrolled in the study. Patients were divided into mild (n = 12), moderate (n = 31), and severe (n = 8) groups based on clinical severity. An AI-based quantitative CT analysis, including lung volume, opacity score, opacity volume, percentage of opacity, and mean lung density, was performed in initial and follow-up CTs obtained at different time points. Receiver operating characteristic analysis was performed to find the diagnostic ability of quantitative CT parameters for discriminating severe from nonsevere pneumonia. Results In baseline assessment, the severe group had significantly higher opacity score, opacity volume, higher percentage of opacity, and higher mean lung density than the moderate group (all P ≤ 0.001). Through consecutive time points, the severe group had a significant decrease in lung volume (P = 0.006), a significant increase in total opacity score (P = 0.003), and percentage of opacity (P = 0.007). A significant increase in total opacity score was also observed for the mild group (P = 0.011). Residual opacities were observed in all groups. The involvement of more than 4 lobes (sensitivity, 100%; specificity, 65.26%), total opacity score greater than 4 (sensitivity, 100%; specificity, 64.21), total opacity volume greater than 337.4 mL (sensitivity, 80.95%; specificity, 84.21%), percentage of opacity greater than 11% (sensitivity, 80.95%; specificity, 88.42%), total high opacity volume greater than 10.5 mL (sensitivity, 95.24%; specificity, 66.32%), percentage of high opacity greater than 0.8% (sensitivity, 85.71%; specificity, 80.00%) and mean lung density HU greater than -705 HU (sensitivity, 57.14%; specificity, 90.53%) were related to severe pneumonia. Conclusions An AI-based quantitative CT analysis is an objective tool in demonstrating disease severity and can also assist the clinician in follow-up by providing information about the disease course and prognosis according to different clinical severity groups.

Lung cancer (LC) diagnostic pathways are often initiated following suspicious clinical and radiologic findings. We identified delays from first thoracic imaging suspicious for LC to specialist eval...

OBJECTIVE With the recent development of various MRI-conditional cardiac implantable electronic devices (CIEDs), the accurate identification and characterization of CIEDs have become critical when performing MRI in patients with CIEDs. We aimed to develop and evaluate a deep learning-based algorithm (DLA) that performs the detection and characterization of parameters, including MRI safety, of CIEDs on chest radiograph (CR) in a single step and compare its performance with other related algorithms that were recently developed. MATERIALS AND METHODS We developed a DLA (X-ray CIED identification [XCID]) using 9912 CRs of 958 patients with 968 CIEDs comprising 26 model groups from 4 manufacturers obtained between 2014 and 2019 from one hospital. The performance of XCID was tested with an external dataset consisting of 2122 CRs obtained from a different hospital and compared with the performance of two other related algorithms recently reported, including PacemakerID (PID) and Pacemaker identification with neural networks (PPMnn). RESULTS The overall accuracies of XCID for the manufacturer classification, model group identification, and MRI safety characterization using the internal test dataset were 99.7% (992/995), 97.2% (967/995), and 98.9% (984/995), respectively. These were 95.8% (2033/2122), 85.4% (1813/2122), and 92.2% (1956/2122), respectively, with the external test dataset. In the comparative study, the accuracy for the manufacturer classification was 95.0% (152/160) for XCID and 91.3% for PPMnn (146/160), which was significantly higher than that for PID (80.0%,128/160; p < 0.001 for both). XCID demonstrated a higher accuracy (88.1%; 141/160) than PPMnn (80.0%; 128/160) in identifying model groups (p < 0.001). CONCLUSION The remarkable and consistent performance of XCID suggests its applicability for detection, manufacturer and model identification, as well as MRI safety characterization of CIED on CRs. Further studies are warranted to guarantee the safe use of XCID in clinical practice.

Background: Non-small-cell lung cancer (NSCLC) is a common, steady growing lung tumour that is often discovered when a surgical approach is forbidden. For locally advanced inoperable NSCLC, the clinical approach consists of a combination of chemotherapy and radiotherapy, eventually followed by adjuvant immunotherapy, a treatment that is useful but may cause several mild and severe adverse effect. Chest radiotherapy, specifically, may affect the heart and coronary artery, impairing heart function and causing pathologic changes in myocardial tissues. The aim of this study is to evaluate the damage coming from these therapies with the aid of cardiac imaging. Methods: This is a single-centre, prospective clinical trial. Patients with NSCLC who are enrolled will undergo computed tomography (CT) and magnetic resonance imaging (MRI) before chemotherapy 3 months, 6 months, and 9–12 months after the treatment. We expect to enrol 30 patients in 2 years. Conclusions: Our clinical trial will be an opportunity not only to highlight the timing and the radiation dose needed for pathological cardiac tissue changes to happen but will also provide useful data to set new follow-up schedules and strategies, keeping in mind that, more often than not, patients affected by NSCLC may present other heart- and lung-related pathological conditions.

The coronavirus disease 2019 (COVID-19) pandemic presents a significant global public health challenge. One in five individuals with COVID-19 presents with symptoms that last for weeks after hospital discharge, a condition termed "long COVID". Thus, efficient follow-up of patients is needed to assess the resolution of lung pathologies and systemic involvement. Thoracic imaging is multimodal and involves using different forms of waves to produce images of the organs within the thorax. In general, it includes chest X-ray, computed tomography, lung ultrasound and magnetic resonance imaging techniques. Such modalities have been useful in the diagnosis and prognosis of COVID-19. These tools have also allowed for the follow-up and assessment of long COVID. This review provides insights on the effectiveness of thoracic imaging techniques in the follow-up of COVID-19 survivors who had long COVID.

Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 +/- 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean +/- standard deviation [SD], 19.5 +/- 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 +/- 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 +/- 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

The World Health Organization (WHO) declared the transmission of SARS-CoV-2 a Public Health Emergency of International Concern. The novel coronavirus has diverse manifestations, usually similar to a common cold or influenza. The majority of patients with coronavirus disease have typical imaging features. The typical CT characteristics of patients with COVID-19 pneumonia are ground-glass opacities and consolidative lesions with a peripheral and posterior distribution. Noninvasive imaging methods are precise and rapid means of diagnosing pneumonia and cardiovascular complications caused by COVID-19 infection. Therefore, it is important for clinicians to understand the implications of this pandemic and to be familiar with the different imaging aspects of the novel coronavirus disease. This review focuses on the most commonly reported imaging findings of COVID-19 infection in different patients from different countries, the expert recommendations, and the cardiac manifestations of SARS-CoV-2 infection.

Objective To describe the clinical course of a dog with a pheochromocytoma multisystem crisis that exhibited autoresuscitation after cardiac arrest. Case summary An approximately 10-year-old male neutered terrier mix dog presented for collapse. Abdominal imaging was suggestive of a pheochromocytoma, and clinical pathology data and thoracic imaging suggested the presence of a multisystem crisis. The dog developed cardiopulmonary arrest that was followed by autoresuscitation. New or unique information provided To the authors' knowledge, this is the first report of autoresuscitation in a dog. The phenomenon may have been associated with a pheochromocytoma multisystem crisis.

Background There are characteristic findings of coronavirus disease 2019 (COVID-19) on chest images. An artificial intelligence (AI) algorithm to detect COVID-19 on chest radiographs might be useful for triage or infection control within a hospital setting, but prior reports have been limited by small data sets, poor data quality, or both. Purpose To present DeepCOVID-XR, a deep learning AI algorithm to detect COVID-19 on chest radiographs, that was trained and tested on a large clinical data set. Materials and Methods DeepCOVID-XR is an ensemble of convolutional neural networks developed to detect COVID-19 on frontal chest radiographs, with reverse-transcription polymerase chain reaction test results as the reference standard. The algorithm was trained and validated on 14 788 images (4253 positive for COVID-19) from sites across the Northwestern Memorial Health Care System from February 2020 to April 2020 and was then tested on 2214 images (1192 positive for COVID-19) from a single hold-out institution. Performance of the algorithm was compared with interpretations from five experienced thoracic radiologists on 300 random test images using the McNemar test for sensitivity and specificity and the DeLong test for the area under the receiver operating characteristic curve (AUC). Results A total of 5853 patients (mean age, 58 years ± 19 [standard deviation]; 3101 women) were evaluated across data sets. For the entire test set, accuracy of DeepCOVID-XR was 83%, with an AUC of 0.90. For 300 random test images (134 positive for COVID-19), accuracy of DeepCOVID-XR was 82%, compared with that of individual radiologists (range, 76%–81%) and the consensus of all five radiologists (81%). DeepCOVID-XR had a significantly higher sensitivity (71%) than one radiologist (60%, P < .001) and significantly higher specificity (92%) than two radiologists (75%, P < .001; 84%, P = .009). AUC of DeepCOVID-XR was 0.88 compared with the consensus AUC of 0.85 (P = .13 for comparison). With consensus interpretation as the reference standard, the AUC of DeepCOVID-XR was 0.95 (95% CI: 0.92, 0.98). Conclusion DeepCOVID-XR, an artificial intelligence algorithm, detected coronavirus disease 2019 on chest radiographs with a performance similar to that of experienced thoracic radiologists in consensus. © RSNA, 2020 Supplemental material is available for this article. See also the editorial by van Ginneken in this issue.

International audience; The role and performance of chest CT in the diagnosis of the coronavirus disease 2019 (COVID-19) pandemic remains under active investigation. Purpose To evaluate the French national experience using chest CT for COVID-19, results of chest CT and reverse transcription polymerase chain reaction (RT-PCR) assays were compared together and with the final discharge diagnosis used as the reference standard. Materials and Methods A structured CT scan survey (NCT04339686) was sent to 26 hospital radiology departments in France between March 2, 2020, and April 24, 2020. These dates correspond to the peak of the national COVID-19 epidemic. Radiology departments were selected to reflect the estimated geographic prevalence heterogeneities of the epidemic. All symptomatic patients suspected of having COVID-19 pneumonia who underwent both initial chest CT and at least one RT-PCR test within 48 hours were included. The final discharge diagnosis, based on multiparametric items, was recorded. Data for each center were prospectively collected and gathered each week. Test efficacy was determined by using the Mann-Whitney test, Student t test, χ2 test, and Pearson correlation coefficient. P < .05 indicated a significant difference. Results Twenty-six of 26 hospital radiology departments responded to the survey, with 7500 patients entered; 2652 did not have RT-PCR test results or had unknown or excess delay between the RT-PCR test and CT. After exclusions, 4824 patients (mean age, 64 years ± 19 [standard deviation], 2669 male) were included. With final diagnosis as the reference, 2564 of the 4824 patients had COVID-19 (53%). Sensitivity, specificity, negative predictive value, and positive predictive value of chest CT in the diagnosis of COVID-19 were 2319 of 2564 (90%; 95% CI: 89, 91), 2056 of 2260 (91%; 95% CI: 91, 92), 2056 of 2300 (89%; 95% CI: 87, 90), and 2319 of 2524 (92%; 95% CI: 91, 93), respectively. There was no significant difference for chest CT efficacy among the 26 geographically separate sites, each with varying amounts of disease prevalence. Conclusion Use of chest CT for the initial diagnosis and triage of patients suspected of having coronavirus disease 2019 was successful.

Objective To compare the diagnostic performance of contrast-enhanced radial T1-weighted gradient-echo 3-tesla (3T) magnetic resonance imaging (MRI) and computed tomography (CT) for the detection of visceral pleural surface invasion (VPSI). Visceral pleural invasion by non-small-cell lung cancer (NSCLC) can be classified into two types: PL1 (without VPSI), invasion of the elastic layer of the visceral pleura without reaching the visceral pleural surface, and PL2 (with VPSI), full invasion of the visceral pleura. Materials and methods Thirty-three patients with pathologically confirmed VPSI by NSCLC were retrospectively reviewed. Multidetector CT and contrast-enhanced 3T MRI with a free-breathing radial three-dimensional fat-suppressed volumetric interpolated breath-hold examination (VIBE) pulse sequence were compared in terms of the length of contact, angle of mass margin, and arch distance-to-maximum tumor diameter ratio. Supplemental evaluation of the tumor-pleura interface (smooth versus irregular) could only be performed with MRI (not discernible on CT). Results At the tumor-pleura interface, radial VIBE MRI revealed a smooth margin in 20 of 21 patients without VPSI and an irregular margin in 10 of 12 patients with VPSI, yielding an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F-score for VPSI detection of 91%, 83%, 95%, 91%, 91%, and 87%, respectively. The McNemar test and receiver operating characteristics curve analysis revealed no significant differences between the diagnostic accuracies of CT and MRI for evaluating the contact length, angle of mass margin, or arch distance-to-maximum tumor diameter ratio as predictors of VPSI. Conclusion The diagnostic performance of contrast-enhanced radial T1-weighted gradient-echo 3T MRI and CT were equal in terms of the contact length, angle of mass margin, and arch distance-to-maximum tumor diameter ratio. The advantage of MRI is its clear depiction of the tumor-pleura interface margin, facilitating VPSI detection.

Background Radiologists are proficient in differentiating between chest radiographs with and without symptoms of pneumonia but have found it more challenging to differentiate coronavirus disease 2019 (COVID-19) pneumonia from non–COVID-19 pneumonia on chest radiographs. Purpose To develop an artificial intelligence algorithm to differentiate COVID-19 pneumonia from other causes of abnormalities at chest radiography. Materials and Methods In this retrospective study, a deep neural network, CV19-Net, was trained, validated, and tested on chest radiographs in patients with and without COVID-19 pneumonia. For the chest radiographs positive for COVID-19, patients with reverse transcription polymerase chain reaction results positive for severe acute respiratory syndrome coronavirus 2 with findings positive for pneumonia between February 1, 2020, and May 30, 2020, were included. For the non–COVID-19 chest radiographs, patients with pneumonia who underwent chest radiography between October 1, 2019, and December 31, 2019, were included. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated to characterize diagnostic performance. To benchmark the performance of CV19-Net, a randomly sampled test data set composed of 500 chest radiographs in 500 patients was evaluated by the CV19-Net and three experienced thoracic radiologists. Results A total of 2060 patients (5806 chest radiographs; mean age, 62 years ± 16 [standard deviation]; 1059 men) with COVID-19 pneumonia and 3148 patients (5300 chest radiographs; mean age, 64 years ± 18; 1578 men) with non–COVID-19 pneumonia were included and split into training and validation and test data sets. For the test set, CV19-Net achieved an AUC of 0.92 (95% CI: 0.91, 0.93). This corresponded to a sensitivity of 88% (95% CI: 87, 89) and a specificity of 79% (95% CI: 77, 80) by using a high-sensitivity operating threshold, or a sensitivity of 78% (95% CI: 77, 79) and a specificity of 89% (95% CI: 88, 90) by using a high-specificity operating threshold. For the 500 sampled chest radiographs, CV19-Net achieved an AUC of 0.94 (95% CI: 0.93, 0.96) compared with an AUC of 0.85 (95% CI: 0.81, 0.88) achieved by radiologists. Conclusion CV19-Net was able to differentiate coronavirus disease 2019–related pneumonia from other types of pneumonia, with performance exceeding that of experienced thoracic radiologists. © RSNA, 2021 Online supplemental material is available for this article.

Introduction The United Kingdom (UK) has experienced one of the worst initial waves of the COVID-19 pandemic. Clinical signs help guide initial diagnosis, though definitive diagnosis is made using the laboratory technique reverse transcription polymerase chain reaction (RT-PCR). The chest x-ray (CXR) is used as the primary imaging investigation in the United Kingdom (UK) for patients with suspected COVID-19. In some hospitals these CXRs may be reported by a radiographer. Methods Retrospective review of CXR reports by radiographers for suspected COVID-19 patients attending the Emergency Department (ED) of a hospital in the UK. Interpretation and use of the British Society of Thoracic Imaging (BSTI) coding system was assessed. Report description and code use were cross-checked. Report and code usage were checked against the RT-PCR result to determine accuracy. Report availability was checked against the availability of the RT-PCR result. A confusion matrix was utilised to determine performance. The data were analysed manually using Excel. Results Sample size was 320 patients; 54.1% male patients (n = 173), 45.9% female patients (n = 147). The correct code matched report descriptions in 316 of the 320 cases (98.8%). In 299 of the 320 cases (93.4%), the reports were available before the RT-PCR swab result. CXR sensitivity for detecting COVID-19 was 85% compared to 93% for the initial RT-PCR. Conclusion Reporting radiographers can adequately utilise and apply the BSTI classification system when reporting COVID-19 CXRs. They can recognise the classic CXR appearances of COVID-19 and those with normal appearances. Future best practice includes checking laboratory results when reporting CXRs with ambiguous appearances. Implications for practice Utilisation of reporting radiographers to report CXRs in any future respiratory pandemic should be considered a service-enabling development.

An approximately 1‐year‐old male intact Shih Tzu dog was referred to a tertiary facility with a history of progressive tachypnea, increased respiratory effort, and weight loss over a 3‐month period that failed to improve with empirical antimicrobial treatment. Upon completion of a comprehensive respiratory evaluation, the dog was diagnosed with severe Pneumocystis pneumonia and secondary pulmonary hypertension. Clinical signs resolved and disease resolution was confirmed after completion of an 8‐week course of trimethoprim‐sulfonamide, 4‐week tapering dose of prednisone to decrease an inflammatory response secondary to acute die‐off of organisms, a 2‐week course of clopidogrel to prevent clot formation, and a 2‐week course of a phosphodiesterase‐5 inhibitor to treat pulmonary hypertension. Immunodiagnostic testing and genetic sequencing were performed to evaluate for potential immunodeficiency as an underlying cause for the development Pneumocystis pneumonia, and identified an X‐linked CD40 ligand deficiency.

Dynamic X-ray (DXR) is a functional imaging technique that uses sequential images obtained by a flat-panel detector (FPD). This article aims to describe the mechanism of DXR and the analysis methods used as well as review the clinical evidence for its use. DXR analyzes dynamic changes on the basis of X-ray translucency and can be used for analysis of diaphragmatic kinetics, ventilation, and lung perfusion. It offers many advantages such as a high temporal resolution and flexibility in body positioning. Many clinical studies have reported the feasibility of DXR and its characteristic findings in pulmonary diseases. DXR may serve as an alternative to pulmonary function tests in patients requiring contact inhibition, including patients with suspected or confirmed coronavirus disease 2019 or other infectious diseases. Thus, DXR has a great potential to play an important role in the clinical setting. Further investigations are needed to utilize DXR more effectively and to establish it as a valuable diagnostic tool.

The current SARS-CoV-2 outbreak leads to a growing need of point-of-care thoracic imaging that is compatible with isolation settings and infection prevention precautions. We retrospectively reviewed 17 COVID-19 patients who received point-of-care lung ultrasound imaging in our isolation unit. Lung ultrasound was able to detect interstitial lung disease effectively; severe cases showed bilaterally distributed B-Lines with or without consolidations; one case showed bilateral pleural plaques. Corresponding to CT scans, interstitial involvement is accurately depicted as B-Lines on lung ultrasound. Lung ultrasound might be suitable for detecting interstitial involvement in a bedside setting under high security isolation precautions.

Background: To develop a deep-learning (DL) pipeline that allowed an automated segmentation of epicardial adipose tissue (EAT) from low-dose computed tomography (LDCT) and investigate the link between EAT and COVID-19 clinical outcomes. Methods: This monocentric retrospective study included 353 patients: 95 for training, 20 for testing, and 238 for prognosis evaluation. EAT segmentation was obtained after thresholding on a manually segmented pericardial volume. The model was evaluated with Dice coefficient (DSC), inter-and intraobserver reproducibility, and clinical measures. Uni-and multi-variate analyzes were conducted to assess the prognosis value of the EAT volume, EAT extent, and lung lesion extent on clinical outcomes, including hospitalization, oxygen therapy, intensive care unit admission and death. Results: The mean DSC for EAT volumes was 0.85 ± 0.05. For EAT volume, the mean absolute error was 11.7 ± 8.1 cm3 with a non-significant bias of −4.0 ± 13.9 cm3 and a correlation of 0.963 with the manual measures (p < 0.01). The multivariate model providing the higher AUC to predict adverse outcome include both EAT extent and lung lesion extent (AUC = 0.805). Conclusions: A DL algorithm was developed and evaluated to obtain reproducible and precise EAT segmentation on LDCT. EAT extent in association with lung lesion extent was associated with adverse clinical outcomes with an AUC = 0.805.

Purpose COVID-19-related acute respiratory distress syndrome (ARDS) is characterized by the presence of signs of microvascular involvement at the CT scan, such as the vascular tree in bud (TIB) and the vascular enlargement pattern (VEP). Recent evidence suggests that TIB could be associated with an increased duration of invasive mechanical ventilation (IMV) and intensive care unit (ICU) stay. The primary objective of this study was to evaluate whether microvascular involvement signs could have a prognostic significance concerning liberation from IMV. Material and methods All the COVID-19 patients requiring IMV admitted to 16 Italian ICUs and having a lung CT scan recorded within 3 days from intubation were enrolled in this secondary analysis. Radiologic, clinical and biochemical data were collected. Results A total of 139 patients affected by COVID-19 related ARDS were enrolled. After grouping based on TIB or VEP detection, we found no differences in terms of duration of IMV and mortality. Extension of VEP and TIB was significantly correlated with ground-glass opacities (GGOs) and crazy paving pattern extension. A parenchymal extent over 50% of GGO and crazy paving pattern was more frequently observed among non-survivors, while a VEP and TIB extent involving 3 or more lobes was significantly more frequent in non-responders to prone positioning. Conclusions The presence of early CT scan signs of microvascular involvement in COVID-19 patients does not appear to be associated with differences in duration of IMV and mortality. However, patients with a high extension of VEP and TIB may have a reduced oxygenation response to prone positioning. Trial Registration: NCT04411459 Supplementary Information The online version contains supplementary material available at 10.1007/s11547-021-01444-7.

Teaching Point: Epipericardial fat torsion is a little-known and uncommon condition with sometimes subtle findings making general awareness essential for detection and for confident diagnosis.

Rationale: Clinical and epidemiologic data in coronavirus disease (COVID-19) have accrued rapidly since the outbreak, but few address the underlying pathophysiology. Objectives: To ascertain the physiologic, hematologic, and imaging basis of lung injury in severe COVID-19 pneumonia. Methods: Clinical, physiologic, and laboratory data were collated. Radiologic (computed tomography (CT) pulmonary angiography [n = 39] and dual-energy CT [DECT, n = 20]) studies were evaluated: observers quantified CT patterns (including the extent of abnormal lung and the presence and extent of dilated peripheral vessels) and perfusion defects on DECT. Coagulation status was assessed using thromboelastography. Measurements and Results: In 39 consecutive patients (male:female, 32:7; mean age, 53 ± 10 yr [range, 29–79 yr]; Black and minority ethnic, n = 25 [64%]), there was a significant vascular perfusion abnormality and increased physiologic dead space (dynamic compliance, 33.7 ± 14.7 ml/cm H2O; Murray lung injury score, 3.14 ± 0.53; mean ventilatory ratios, 2.6 ± 0.8) with evidence of hypercoagulability and fibrinolytic “shutdown”. The mean CT extent (±SD) of normally aerated lung, ground-glass opacification, and dense parenchymal opacification were 23.5 ± 16.7%, 36.3 ± 24.7%, and 42.7 ± 27.1%, respectively. Dilated peripheral vessels were present in 21/33 (63.6%) patients with at least two assessable lobes (including 10/21 [47.6%] with no evidence of acute pulmonary emboli). Perfusion defects on DECT (assessable in 18/20 [90%]) were present in all patients (wedge-shaped, n = 3; mottled, n = 9; mixed pattern, n = 6). Conclusions: Physiologic, hematologic, and imaging data show not only the presence of a hypercoagulable phenotype in severe COVID-19 pneumonia but also markedly impaired pulmonary perfusion likely caused by pulmonary angiopathy and thrombosis.

Background The standard for diagnosis of severe acute respiratory syndrome coronavirus 2 is a reverse transcription polymerase chain reaction (RT-PCR) test, but chest CT may play a complimentary role in the early detection of Coronavirus Disease 2019 (COVID-19) pneumonia. Purpose To investigate CT features of patients with COVID-19 in Rome, Italy, and to compare the accuracy of CT with that of RT-PCR. Materials and Methods In this prospective study from March 4, 2020, until March 19, 2020, consecutive patients suspected of having COVID-19 infection and respiratory symptoms were enrolled. Exclusion criteria were contrast material-enhanced chest CT performed for vascular indications, patients who refused chest CT or hospitalization, and severe CT motion artifact. All patients underwent RT-PCR and chest CT. Diagnostic performance of CT was calculated using RT-PCR as the reference standard. Chest CT features were calculated in a subgroup of patients with positive RT-PCR and CT findings. CT features of hospitalized patients and patients in home isolation were compared using the Pearson χ2 test. Results The study population included 158 consecutive participants (83 male, 75 female; mean age, 57 years ± 17 [standard deviation]). Of the 158 participants, fever was observed in 97 (61%), cough was observed in 88 (56%), dyspnea was observed in 52 (33%), lymphocytopenia was observed in 95 (60%), increased C-reactive protein level was observed in 139 (88%), and elevated lactate dehydrogenase level was observed in 128 (81%). Sensitivity, specificity, and accuracy of CT were 97% (95% confidence interval [CI]: 88%, 99%) (60 of 62), 56% (95% CI: 45%, 66%) (54 of 96), and 72% (95% CI: 64%, 78%) (114 of 158), respectively. In the subgroup of 58 participants with positive RT-PCR and CT findings, ground-glass opacities were present in all 58 (100%), both multilobe and posterior involvement were present in 54 (93%), bilateral pneumonia was present in 53 (91%), and subsegmental vessel enlargement (>3 mm) was present in 52 (89%). Conclusion The typical pattern of COVID-19 pneumonia in Rome, Italy, was peripheral ground-glass opacities with multilobe and posterior involvement, bilateral distribution, and subsegmental vessel enlargement (>3 mm). Chest CT had high sensitivity (97%) but lower specificity (56%). © RSNA, 2020.

Magnetic resonance imaging (MRI) has become a crucial tool for evaluating mediastinal masses considering that several lesions that appear indeterminate on computed tomography and radiography can be differentiated on MRI. Using a three-compartment model to localize the mass and employing a basic knowledge of MRI, radiologists can easily diagnose mediastinal masses. Here, we review the use of MRI in evaluating mediastinal masses and present the images of various mediastinal masses categorized using the International Thymic Malignancy Interest Group's three-compartment classification system. These masses include thymic hyperplasia, thymic cyst, pericardial cyst, thymoma, mediastinal hemangioma, lymphoma, mature teratoma, bronchogenic cyst, esophageal duplication cyst, mediastinal thyroid carcinoma originating from ectopic thyroid tissue, mediastinal liposarcoma, mediastinal pancreatic pseudocyst, neurogenic tumor, meningocele, and plasmacytoma.

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity. Results The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (±standard deviation) was 49 years ± 15, and there were slightly more men than women (1838 vs 1484, respectively; P = .29). The per-scan sensitivity and specificity for detecting COVID-19 in the independent test set was 90% (95% confidence interval [CI]: 83%, 94%; 114 of 127 scans) and 96% (95% CI: 93%, 98%; 294 of 307 scans), respectively, with an area under the receiver operating characteristic curve of 0.96 (P < .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020 Online supplemental material is available for this article.

Background CT may play a central role in the diagnosis and management of coronavirus disease 2019 (COVID-19) pneumonia. Purpose To perform a longitudinal study to analyze the serial CT findings over time in patients with COVID-19 pneumonia. Materials and Methods During January 16 to February 17, 2020, 90 patients (33 men, 57 women; mean age, 45 years) with COVID-19 pneumonia were prospectively enrolled and followed up until being discharged, death, or the end of the study. A total of 366 CT scans were acquired and reviewed by two groups of radiologists for the patterns and distribution of lung abnormalities, total CT scores, and number of zones involved. Those features were analyzed for temporal change. Results CT scores and number of zones involved progressed rapidly, peaked during illness days 6-11 (median CT score, 5; median number of zones involved, five), and were followed by persistence of high levels. The predominant pattern of abnormalities after symptom onset was ground-glass opacity (35 of 78 scans [45%] to 49 of 79 scans [62%] in different periods). The percentage of mixed pattern peaked on illness days 12-17 (30 of 78 scans [38%]) and became the second most predominant pattern thereafter. Pure ground-glass opacity was the most prevalent subtype of ground-glass opacity after symptom onset (20 of 50 scans [40%] to 20 of 28 scans [71%]). The percentage of ground-glass opacity with irregular linear opacity peaked on illness days 6-11 (14 of 50 scans [28%]) and became the second most prevalent subtype thereafter. The distribution of lesions was predominantly bilateral and subpleural. Sixty-six of the 70 patients discharged (94%) had residual disease on final CT scans (median CT score, 4; median number of zones involved, four), with ground-glass opacity (42 of 70 patients [60%]) and pure ground-glass opacity (31 of 42 patients [74%]) the most common pattern and subtype. Conclusion The extent of lung abnormalities at CT peaked during illness days 6-11. The temporal changes of the diverse CT manifestations followed a specific pattern, which might indicate the progression and recovery of the illness. © RSNA, 2020 Online supplemental material is available for this article.

OBJECTIVE: To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance. MATERIALS AND METHODS: In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated. RESULTS: Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; p < 0.001). CONCLUSION: Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.

The epidemic of 2019 novel coronavirus, later named as coronavirus disease (COVID-19), began in Wuhan, China in December 2019 and has spread rapidly worldwide. Early diagnosis is crucial for the management of the patients with COVID-19, but the gold standard diagnostic test for this infection, the reverse transcriptase polymerase chain reaction, has a low sensitivity and an increased turnaround time. In this scenario, chest computed tomography (CT) could play a key role for an early diagnosis of COVID-19 pneumonia. Here, we have reported a confirmed case of COVID-19 with an atypical CT presentation showing a "double halo sign," which we believe represents the pathological spectrum of this viral pneumonia.

The thoracic inlet is located at the crossroads between imaging of the neck and the chest. Its location is an important anatomic landmark, serving as the central conducting pathway for many vital structures extending from the neck into the chest and vice versa. Many critical body systems, including the respiratory, lymphatic, neurologic, enteric, musculoskeletal, endocrine, and vascular systems, are located within this region. Neoplasms, both benign and malignant, can arise in any of the body systems located in this area. Due to the small size of this anatomic location, pathology is easily overlooked and imagers should be aware of the imaging appearance of these neoplasms, as well as which imaging modality is the most appropriate for neoplasm evaluation. This article will present an image rich, system-based discussion of the neoplastic pathology that can occur in this region. The anatomy of the thoracic inlet and the non-neoplastic pathology of the thoracic inlet have been covered in our companion article.

Context: Few studies have been done for correlating asthma severity, IgE level, and spirometry results with high-resolution computed tomographic (HRCT) findings in allergic bronchopulmonary aspergillosis (ABPA). Aims: This prospective observational study was conducted to correlate asthma severity, IgE level, and spirometry results with HRCT findings in ABPA. Settings and Design: Prospective observational. Subjects and Methods: Fifty consecutive adult patients with asthma and positive specific IgE (>0.35 kUA/L) to Aspergillus fumigatus were recruited from October 2015 to July 2017. Asthma severity, IgE levels, and spirometry results were correlated with HRCT score, bronchiectasis score, air trapping segments, and low-attenuation lung volume on inspiratory CT and expiratory CT. Statistical Analysis Used: One way ANOVA, Spearman’s correlation coefficients. Results: Asthma severity showed a significant positive correlation with HRCT score and bronchiectasis score. MEF pre and postbronchodilator values showed a significant negative correlation with HRCT score, bronchiectasis score, and percentage expiratory volumes -851 to -950 HU voxels. FEV1 prebronchodilator value showed a significant negative correlation with percentage expiratory volume -851 to -950 HU voxels and percentage expiratory volume -851 to -1024 HU voxels. Specific IgE antibody level showed a significant positive correlation with bronchiectasis score. Conclusions: Asthma severity, specific IgE level, and MEF values showed a good correlation with HRCT findings. The restrictive pattern is common on spirometry in patients of ABPA. In addition to central bronchiectasis, peripheral bronchial and small airway involvement was an important finding in ABPA. Expiratory HRCT may reveal air trapping in patients having no abnormality on inspiratory CT.

OBJECTIVE: To compare the accuracies of quantitative computed tomography (CT) parameters and semiquantitative visual score in evaluating clinical classification of severity of coronavirus disease (COVID-19). MATERIALS AND METHODS: We retrospectively enrolled 187 patients with COVID-19 treated at Tongji Hospital of Tongji Medical College from February 15, 2020, to February 29, 2020. Demographic data, imaging characteristics, and clinical data were collected, and based on the clinical classification of severity, patients were divided into groups 1 (mild) and 2 (severe/critical). A semiquantitative visual score was used to estimate the lesion extent. A three-dimensional slicer was used to precisely quantify the volume and CT value of the lung and lesions. Correlation coefficients of the quantitative CT parameters, semiquantitative visual score, and clinical classification were calculated using Spearman's correlation. A receiver operating characteristic curve was used to compare the accuracies of quantitative and semi-quantitative methods. RESULTS: There were 59 patients in group 1 and 128 patients in group 2. The mean age and sex distribution of the two groups were not significantly different. The lesions were primarily located in the subpleural area. Compared to group 1, group 2 had larger values for all volume-dependent parameters (p < 0.001). The percentage of lesions had the strongest correlation with disease severity with a correlation coefficient of 0.495. In comparison, the correlation coefficient of semiquantitative score was 0.349. To classify the severity of COVID-19, area under the curve of the percentage of lesions was the highest (0.807; 95% confidence interval, 0.744-0.861: p < 0.001) and that of the quantitative CT parameters was significantly higher than that of the semiquantitative visual score (p = 0.001). CONCLUSION: The classification accuracy of quantitative CT parameters was significantly superior to that of semiquantitative visual score in terms of evaluating the severity of COVID-19.

OBJECTIVE: The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT) features for outcome prediction in patients with coronavirus disease (COVID-19). MATERIALS AND METHODS: The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitals were retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in the training cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in the validation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, or death. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. A nomogram was constructed based on the combination of clinical and CT features, and its prognostic performance was externally tested in the validation group. The predictive value of the combined model was compared with models built on the clinical and radiological attributes alone. RESULTS: Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohort experienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67-6.71; p < 0.001), lymphocyte count (HR, 0.12; 95% CI, 0.04-0.38; p < 0.001) and crazy-paving sign (HR, 2.15; 95% CI, 1.03-4.48; p = 0.042) were the independent factors. The nomogram displayed a concordance index (C-index) of 0.82 (95% CI, 0.76-0.88), and its prognostic value was confirmed in the validation cohort with a C-index of 0.89 (95% CI, 0.82-0.96). The combined model provided the best performance over the clinical or radiological model (p < 0.050). CONCLUSION: Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverse outcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predicting adverse outcomes of patients with COVID-19.

The past 5 years have seen an explosion of interest in the use of artificial intelligence (AI) and machine learning techniques in medicine. This has been driven by the development of deep neural networks (DNNs)—complex networks residing in silico but loosely modelled on the human brain—that can process complex input data such as a chest radiograph image and output a classification such as ‘normal’ or ‘abnormal’. DNNs are ‘trained’ using large banks of images or other input data that have been assigned the correct labels. DNNs have shown the potential to equal or even surpass the accuracy of human experts in pattern recognition tasks such as interpreting medical images or biosignals. Within respiratory medicine, the main applications of AI and machine learning thus far have been the interpretation of thoracic imaging, lung pathology slides and physiological data such as pulmonary function tests. This article surveys progress in this area over the past 5 years, as well as highlighting the current limitations of AI and machine learning and the potential for future developments.

The field of artificial intelligence (AI) is currently experiencing a period of extensive growth in a wide variety of fields, medicine not being the exception. The base of AI is mathematics and computer science, and the current fame of AI in industry and research stands on 3 pillars: big data, high performance computing infrastructure, and algorithms. In the current digital era, increased storage capabilities and data collection systems, lead to a massive influx of data for AI algorithm. The size and quality of data are 2 major factors influencing performance of AI applications. However, it is highly dependent on the type of task at hand and algorithm chosen to perform this task. AI may potentially automate several tedious tasks in radiology, particularly in cardiothoracic imaging, by pre-readings for the detection of abnormalities, accurate quantifications, for example, oncologic volume lesion tracking and cardiac volume and image optimization. Although AI-based applications offer great opportunity to improve radiology workflow, several challenges need to be addressed starting from image standardization, sophisticated algorithm development, and large-scale evaluation. Integration of AI into the clinical workflow also needs to address legal barriers related to security and protection of patient-sensitive data and liability before AI will reach its full potential in cardiothoracic imaging.

Background Angiotensin-converting enzyme 2, a target of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), demonstrates its highest surface expression in the lung, small bowel, and vasculature, suggesting abdominal viscera may be susceptible to injury. Purpose To report abdominal imaging findings in patients with coronavirus disease 2019. Materials and Methods In this retrospective cross-sectional study, patients consecutively admitted to a single quaternary care center from March 27 to April 10, 2020, who tested positive for SARS-CoV-2 were included. Abdominal imaging studies performed in these patients were reviewed, and salient findings were recorded. Medical records were reviewed for clinical data. Univariable analysis and logistic regression were performed. Results A total of 412 patients (average age, 57 years; range, 18 to >90 years; 241 men, 171 women) were evaluated. A total of 224 abdominal imaging studies were performed (radiography, n = 137; US, n = 44; CT, n = 42; MRI, n = 1) in 134 patients (33%). Abdominal imaging was associated with age (odds ratio [OR], 1.03 per year of increase; P = .001) and intensive care unit (ICU) admission (OR, 17.3; P < .001). Bowel-wall abnormalities were seen on 31% of CT images (13 of 42) and were associated with ICU admission (OR, 15.5; P = .01). Bowel findings included pneumatosis or portal venous gas, seen on 20% of CT images obtained in patients in the ICU (four of 20). Surgical correlation (n = 4) revealed unusual yellow discoloration of the bowel (n = 3) and bowel infarction (n = 2). Pathologic findings revealed ischemic enteritis with patchy necrosis and fibrin thrombi in arterioles (n = 2). Right upper quadrant US examinations were mostly performed because of liver laboratory findings (87%, 32 of 37), and 54% (20 of 37) revealed a dilated sludge-filled gallbladder, suggestive of bile stasis. Patients with a cholecystostomy tube placed (n = 4) had negative bacterial cultures. Conclusion Bowel abnormalities and gallbladder bile stasis were common findings on abdominal images of patients with coronavirus disease 2019. Patients who underwent laparotomy often had ischemia, possibly due to small-vessel thrombosis. © RSNA, 2020.

OBJECTIVE Patients with chronic obstructive pulmonary disease (COPD) are known to be at risk of osteoporosis. The purpose of this study was to evaluate the association between thoracic vertebral bone density measured on chest CT (DThorax) and clinical variables, including survival, in patients with COPD. MATERIALS AND METHODS A total of 322 patients with COPD were selected from the Korean Obstructive Lung Disease (KOLD) cohort. DThorax was measured by averaging the CT values of three consecutive vertebral bodies at the level of the left main coronary artery with a round region of interest as large as possible within the anterior column of each vertebral body using an in-house software. Associations between DThorax and clinical variables, including survival, pulmonary function test (PFT) results, and CT densitometry, were evaluated. RESULTS The median follow-up time was 7.3 years (range: 0.1-12.4 years). Fifty-six patients (17.4%) died. DThorax differed significantly between the different Global Initiative for Chronic Obstructive Lung Disease stages. DThorax correlated positively with body mass index (BMI), some PFT results, and the six-minute walk distance, and correlated negatively with the emphysema index (EI) (all p < 0.05). In the univariate Cox analysis, older age (hazard ratio [HR], 3.617; 95% confidence interval [CI], 2.119-6.173, p < 0.001), lower BMI (HR, 3.589; 95% CI, 2.122-6.071, p < 0.001), lower forced expiratory volume in one second (FEV₁) (HR, 2.975; 95% CI, 1.682-5.262, p < 0.001), lower diffusing capacity of the lung for carbon monoxide corrected with hemoglobin (DLCO) (HR, 4.595; 95% CI, 2.665-7.924, p < 0.001), higher EI (HR, 3.722; 95% CI, 2.192-6.319, p < 0.001), presence of vertebral fractures (HR, 2.062; 95% CI, 1.154-3.683, p = 0.015), and lower DThorax (HR, 2.773; 95% CI, 1.620-4.746, p < 0.001) were significantly associated with all-cause mortality and lung-related mortality. In the multivariate Cox analysis, lower DThorax (HR, 1.957; 95% CI, 1.075-3.563, p = 0.028) along with older age, lower BMI, lower FEV₁, and lower DLCO were independent predictors of all-cause mortality. CONCLUSION The thoracic vertebral bone density measured on chest CT demonstrated significant associations with the patients' mortality and clinical variables of disease severity in the COPD patients included in KOLD cohort.

Background Chest radiography has not been validated for its prognostic utility in evaluating patients with coronavirus disease 2019 (COVID-19). Purpose To analyze the prognostic value of a chest radiograph severity scoring system for younger (nonelderly) patients with COVID-19 at initial presentation to the emergency department (ED); outcomes of interest included hospitalization, intubation, prolonged stay, sepsis, and death. Materials and Methods In this retrospective study, patients between the ages of 21 and 50 years who presented to the ED of an urban multicenter health system from March 10 to March 26, 2020, with COVID-19 confirmation on real-time reverse transcriptase polymerase chain reaction were identified. Each patient's ED chest radiograph was divided into six zones and examined for opacities by two cardiothoracic radiologists, and scores were collated into a total concordant lung zone severity score. Clinical and laboratory variables were collected. Multivariable logistic regression was used to evaluate the relationship between clinical parameters, chest radiograph scores, and patient outcomes. Results The study included 338 patients: 210 men (62%), with median age of 39 years (interquartile range, 31-45 years). After adjustment for demographics and comorbidities, independent predictors of hospital admission (n = 145, 43%) were chest radiograph severity score of 2 or more (odds ratio, 6.2; 95% confidence interval [CI]: 3.5, 11; P < .001) and obesity (odds ratio, 2.4 [95% CI: 1.1, 5.4] or morbid obesity). Among patients who were admitted, a chest radiograph score of 3 or more was an independent predictor of intubation (n = 28) (odds ratio, 4.7; 95% CI: 1.8, 13; P = .002) as was hospital site. No significant difference was found in primary outcomes across race and ethnicity or those with a history of tobacco use, asthma, or diabetes mellitus type II. Conclusion For patients aged 21-50 years with coronavirus disease 2019 presenting to the emergency department, a chest radiograph severity score was predictive of risk for hospital admission and intubation. © RSNA, 2020 Online supplemental material is available for this article.

The epidemic of 2019 novel coronavirus, later named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still gradually spreading worldwide. The nucleic acid test or genetic sequencing serves as the gold standard method for confirmation of infection, yet several recent studies have reported false-negative results of real-time reverse-transcriptase polymerase chain reaction (rRT-PCR). Here, we report two representative false-negative cases and discuss the supplementary role of clinical data with rRT-PCR, including laboratory examination results and computed tomography features. Coinfection with SARS-COV-2 and other viruses has been discussed as well.

The field of artificial intelligence (AI) is currently experiencing a period of extensive growth in a wide variety of fields, medicine not being the exception. The base of AI is mathematics and computer science, and the current fame of AI in industry and research stands on 3 pillars: big data, high performance computing infrastructure, and algorithms. In the current digital era, increased storage capabilities and data collection systems, lead to a massive influx of data for AI algorithm. The size and quality of data are 2 major factors influencing performance of AI applications. However, it is highly dependent on the type of task at hand and algorithm chosen to perform this task. AI may potentially automate several tedious tasks in radiology, particularly in cardiothoracic imaging, by pre-readings for the detection of abnormalities, accurate quantifications, for example, oncologic volume lesion tracking and cardiac volume and image optimization. Although AI-based applications offer great opportunity to improve radiology workflow, several challenges need to be addressed starting from image standardization, sophisticated algorithm development, and large-scale evaluation. Integration of AI into the clinical workflow also needs to address legal barriers related to security and protection of patient-sensitive data and liability before AI will reach its full potential in cardiothoracic imaging.

From December 2019, Coronavirus disease 2019 (COVID-19) pneumonia (formerly known as the 2019 novel Coronavirus [2019-nCoV]) broke out in Wuhan, China. In this study, we present serial CT findings in a 40-year-old female patient with COVID-19 pneumonia who presented with the symptoms of fever, chest tightness, and fatigue. She was diagnosed with COVID-19 infection confirmed by real-time reverse-transcriptase-polymerase chain reaction. CT showed rapidly progressing peripheral consolidations and ground-glass opacities in both lungs. After treatment, the lesions were shown to be almost absorbed leaving the fibrous lesions.

Summary Dirofilaria immitis (Nematoda: Filarioidea) is the causative agent of heartworm disease (HWD), a severe and potentially fatal condition in dogs. Although cats are considered more resistant to infection than dogs, they are also susceptible to heartworm infection. Moreover, the clinical importance of feline dirofilariosis has increased in recent years, especially in heartworm endemic areas. In contrast to dog, definitive antemortem diagnosis of heartworm infection in cat is difficult to achieve and a combination of testing methods must be used for clinical confirmation. Here we describe a clinical case of heartworm infection in a 12-year-old male mixed breed cat, originated from Southern Romania, which was referred to a veterinary clinic with a history of vomiting and clinical signs of respiratory distress. The thoracic radiograph showed a diffuse bronchointerstitial pattern of the pulmonary parenchyma. The serological test for D. immitis circulating antibody was positive and heartworms were visualized by echocardiography in the main pulmonary artery. In conclusion, the present study clearly shows that cats are at risk for D. immitis infection in heartworm-endemic areas in Romania. Additionally, the findings highlight the urgent need for increased awareness among veterinary practitioners of the existence of feline heartworm diseases and for adequate prophylactic measures to be applied. To our knowledge, this is the first report on clinical evolution and radiographic and echocardiographic features of a naturally heartworm-infected cat in Romania.

Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main application of these techniques has been the detection and classification of pulmonary nodules. In addition, there have been other less intensely researched applications, such as the diagnosis of interstitial lung disease, chronic obstructive pulmonary disease, and the detection of pulmonary emboli. Despite extensive literature on the use of convolutional neural networks in thoracic imaging over the last few decades, we are yet to see these systems in use in clinical practice. The article reviews current state-of-the-art applications of AI and in detection, classification, and follow-up of pulmonary nodules and how deep-learning techniques might influence these going forward. Finally, we postulate the impact of these advancements on the role of radiologists and the importance of radiologists in the development and evaluation of these techniques.

Purpose To investigate the prospects of a minimal-TR half-radial dual-echo balanced steady-state free precession (bSSFP) acquisition for high-resolution and artifact-free thoracic imaging at 1.5T. Methods Feasibility of bSSFP imaging using isotropic half-radial dual-echo (hr-de) projections with TE1 /TE2 /TR of 0.12/1.18/1.39 ms acquired along Archimedean spiral trajectories was demonstrated for phantoms and in vivo thorax scans at 1.5T. The centered-out projection offers an ultra-short echo (UTE) comparable to contemporary spoiled gradient echo (SPGR) UTE radial acquisitions used for the assessment of chest morphology. Signal intensities of hr-de-bSSFP were measured and compared to UTE-SPGR in a phantom and for parenchyma and blood in vivo and compared to theory. Results For the lung parenchyma and the blood, hr-de-bSSFP provided more than 4 times higher signal intensity than contemporary UTE-SPGR imaging. The measured hr-de-bSSFP and UTE-SPGR signal ratios were in the agreement with theoretically simulated values. Overall, the very short TR of hr-de-bSSFP successfully mitigated off-resonance artifacts offering high-quality breath-hold thoracic imaging at isotropic resolution of 1.7 mm. The application of a smooth interleaved spiral trajectory for half-radial readouts improved the robustness of hr-de-bSSFP to cardiac motion. Conclusion Thoracic hr-de-bSSFP offers artifact-free chest images with considerably improved signal intensity as compared to contemporary UTE-SPGR imaging at 1.5T.

Background: The interpretation of thoracic imaging is often regarded as being challenging due to the intricacies of the included anatomy, the wide overlap in radiographic findings between pathological processes and the great potential for imaging artefacts in such a dynamic body region. Aim of the article: This article describes the use of a structured approach to help overcome these challenges for patients suspected of having a thoracic mass.

Background The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and the development of machine learning technologies that will improve care for patients hospitalized with a severe COVID-19 infection. This article introduces the training dataset, including a snapshot analysis covering the completeness of clinical data, and availability of image data for the various use-cases (diagnosis, prognosis, longitudinal risk). An additional cohort analysis measures how well the NCCID represents the wider COVID-19–affected UK population in terms of geographic, demographic, and temporal coverage. Findings The NCCID offers high-quality DICOM images acquired across a variety of imaging machinery; multiple time points including historical images are available for a subset of patients. This volume and variety make the database well suited to development of diagnostic/prognostic models for COVID-associated respiratory conditions. Historical images and clinical data may aid long-term risk stratification, particularly as availability of comorbidity data increases through linkage to other resources. The cohort analysis revealed good alignment to general UK COVID-19 statistics for some categories, e.g., sex, whilst identifying areas for improvements to data collection methods, particularly geographic coverage. Conclusion The NCCID is a growing resource that provides researchers with a large, high-quality database that can be leveraged both to support the response to the COVID-19 pandemic and as a test bed for building clinically viable medical imaging models.

Introduction: Computed tomography (CT) has approximately 98% sensitivity for Coronavirus disease-2019 (COVID-19). Various algorithms were designed using CT images. However, the interobserver agreement of different radiological classifications of COVID-19 is not yet known. Thus, this study aimed to investigate the interobserver agreement of different radiological classifications of COVID-19. Materials and Methods: This study included 212 patients who were positive on the polymerase chain reaction test and eligible for CT. Four radiologists examined all CT images simultaneously. They reached a consensus that CT images can provide definite findings of COVID-19. The Radiological Society of North America (RSNA) consensus statement, the British Society of Thoracic Imaging (BSTI) structured reporting statement, and COVID-19 Reporting and Data System (CO-RADS) were used. Fleiss’ Kappa was used to detect interobserver agreement. Kappa values of 0.00-0.20 were considered as slight, 0.21-0.40 as fair, 0.41-0.60 as moderate, 0.61-0.80 as substantial, and 0.81-1.00 as near-perfect agreement, and p