Mediastinal Hemangioma Masquerading as a Simple Cyst

ABSTRACT

This report presents imaging from a mediastinal mass in a patient with colon cancer. At baseline and surveillance chest computed tomography examinations, it was characterized as a pericardial cyst. However, during chemotherapy, complications arose and this mass was further characterized with a chest MRI. It was then decided to be removed, and histopathology confirmed the diagnosis of a hemangioma.

Predictors of persistent symptoms after mRNA SARS-CoV-2 vaccine-related myocarditis (myovacc registry)

Aims

Epidemiological surveillance has raised safety concerns for mRNA SARS-CoV-2-vaccination-related myocarditis. We aimed to analyze epidemiological, clinical and imaging findings associated with clinical outcomes in these patients in an international multi-center registry (NCT05268458).

Methods and results

Patients with clinical and CMR diagnosis of acute myocarditis within 30 days after mRNA SARS-CoV-2-vaccination were included from five centers in Canada and Germany between 05/21 and 01/22. Clinical follow-up on persistent symptoms was collected. We enrolled 59 patients (80% males, mean age 29 years) with CMR-derived mild myocarditis (hs-Troponin-T 552 [249-1,193] ng/L, CRP 28 [13-51] mg/L; LVEF 57 ± 7%, LGE 3 [2-5] segments). Most common symptoms at baseline were chest pain (92%) and dyspnea (37%). Follow-up data from 50 patients showed overall symptomatic burden improvement. However, 12/50 patients (24%, 75% females, mean age 37 years) reported persisting symptoms (median interval 228 days) of chest pain (n = 8/12, 67%), dyspnea (n = 7/12, 58%), with increasing occurrence of fatigue (n = 5/12, 42%) and palpitations (n = 2/12, 17%). These patients had initial lower CRP, lower cardiac involvement in CMR, and fewer ECG changes. Significant predictors of persisting symptoms were female sex and dyspnea at initial presentation. Initial severity of myocarditis was not associated with persisting complaints.

Conclusion

A relevant proportion of patients with mRNA SARS-CoV-2-vaccination-related myocarditis report persisting complaints. While young males are usually affected, patients with persisting symptoms were predominantly females and older. The severity of the initial cardiac involvement not predicting these symptoms may suggest an extracardiac origin.

Severe non-cardiovascular thoracic trauma: diagnostic clues on computed tomography

OBJECTIVE

About 60% of multiple trauma patients have thoracic trauma, and thoracic trauma results in the death of 10% of these patients. Computed tomography (CT) is the most sensitive and specific imaging modality for the diagnosis of acute disease, and it helps in the management and prognostic evaluation of patients with high-impact trauma. This paper aims to show the practical points that are key for diagnosing severe non-cardiovascular thoracic trauma by CT.

CONCLUSION

Knowing the key features of severe acute thoracic trauma on CT is crucial to avoid diagnostic errors. Radiologists play a fundamental role in the accurate early diagnosis of severe non-cardiovascular thoracic trauma, because the patient's management and outcome will depend largely on the imaging findings.

Host Expression Profiling From Diagnostic Coronavirus Disease 2019 Swabs Associates Upper Respiratory Tract Immune Responses With Radiologic Lung Pathology and Clinical Severity

Background

COVID-19 presents with a breadth of symptomatology including a spectrum of clinical severity requiring intensive care unit (ICU) admission. We investigated the mucosal host gene response at the time of gold standard COVID-19 diagnosis using clinical surplus RNA from upper respiratory tract swabs.

Methods

Host response was evaluated by RNA-sequencing, and transcriptomic profiles of 44 unvaccinated patients including outpatients and in-patients with varying levels of oxygen supplementation were included. Additionally, chest X-rays were reviewed and scored for patients in each group.

Results

Host transcriptomics revealed significant changes in the immune and inflammatory response. Patients destined for the ICU were distinguished by the significant upregulation of immune response pathways and inflammatory chemokines, including cxcl2 which has been linked to monocyte subsets associated with COVID-19 related lung damage. In order to temporally associate gene expression profiles in the upper respiratory tract at diagnosis of COVID-19 with lower respiratory tract sequalae, we correlated our findings with chest radiography scoring, showing nasopharygeal or mid-turbinate sampling can be a relevant surrogate for downstream COVID-19 pneumonia/ICU severity.

Conclusions

This study demonstrates the potential and relevance for ongoing study of the mucosal site of infection of SARS-CoV-2 using a single sampling that remains standard of care in hospital settings. We highlight also the archival value of high quality clinical surplus specimens, especially with rapidly evolving COVID-19 variants and changing public health/vaccination measures.

Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans

The main objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on volumetric chest CT scans, which are acquired in different imaging centers using different scanners and technical settings. We demonstrated that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, it performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. More specifically, we extracted the subset of the test images for which the model generated a confident prediction and used the extracted subset along with the training set to retrain and update the benchmark model (the model trained on the initial train set). Finally, we adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a single scanning protocol and standard radiation dose. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low-dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. This dataset is referred to as the "SPGC-COVID" dataset. The entire test dataset used in this study contains 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate that our proposed framework performs well on all test sets achieving total accuracy of 96.15% (95%CI: [91.25-98.74]), COVID-19 sensitivity of 96.08% (95%CI: [86.54-99.5]), CAP sensitivity of 92.86% (95%CI: [76.50-99.19]), Normal sensitivity of 98.04% (95%CI: [89.55-99.95]) while the confidence intervals are obtained using the significance level of 0.05. The obtained AUC values (One class vs Others) are 0.993 (95%CI: [0.977-1]), 0.989 (95%CI: [0.962-1]), and 0.990 (95%CI: [0.971-1]) for COVID-19, CAP, and Normal classes, respectively. The experimental results also demonstrate the capability of the proposed unsupervised enhancement approach in improving the performance and robustness of the model when being evaluated on varied external test sets.

Detection of solid and subsolid pulmonary nodules with lung MRI: performance of UTE, T1 gradient-echo, and single-shot T2 fast spin echo

BACKGROUND

Although MRI is a radiation-free imaging modality, it has historically been limited in lung imaging due to inherent technical restrictions. The aim of this study is to explore the performance of lung MRI in detecting solid and subsolid pulmonary nodules using T1 gradient-echo (GRE) (VIBE, Volumetric interpolated breath-hold examination), ultrashort time echo (UTE) and T2 Fast Spin Echo (HASTE, Half fourier Single-shot Turbo spin-Echo).

METHODS

Patients underwent a lung MRI in a 3 T scanner as part of a prospective research project. A baseline Chest CT was obtained as part of their standard of care. Nodules were identified and measured on the baseline CT and categorized according to their density (solid and subsolid) and size (> 4 mm/ ≤ 4 mm). Nodules seen on the baseline CT were classified as present or absent on the different MRI sequences by two thoracic radiologists independently. Interobserver agreement was determined using the simple Kappa coefficient. Paired differences were compared using nonparametric Mann-Whitney U tests. The McNemar test was used to evaluate paired differences in nodule detection between MRI sequences.

RESULTS

Thirty-six patients were prospectively enrolled. One hundred forty-nine nodules (100 solid/49 subsolid) with mean size 10.8 mm (SD = 9.4) were included in the analysis. There was substantial interobserver agreement (k = 0.7, p = 0.05). Detection for all nodules, solid and subsolid nodules was respectively; UTE: 71.8%/71.0%/73.5%; VIBE: 61.6%/65%/55.1%; HASTE 72.4%/72.2%/72.7%. Detection rate was higher for nodules > 4 mm in all groups: UTE 90.2%/93.4%/85.4%, VIBE 78.4%/88.5%/63.4%, HASTE 89.4%/93.8%/83.8%. Detection of lesions ≤4 mm was low for all sequences. UTE and HASTE performed significantly better than VIBE for detection of all nodules and subsolid nodules (diff = 18.4 and 17.6%, p = < 0.01 and p = 0.03, respectively). There was no significant difference between UTE and HASTE. There were no significant differences amongst MRI sequences for solid nodules.

CONCLUSIONS

Lung MRI shows adequate performance for the detection of solid and subsolid pulmonary nodules larger than 4 mm and can serve as a promising radiation-free alternative to CT.

TAVR in a Patient With Anomalous Origin and Course of the Left Main Coronary Artery

In patients with anomalous coronary arteries with high-risk features, corrective cardiac surgery should be considered. We report the first case of transcatheter aortic valve replacement using a self-expanding Evolut valve, in a patient with a single coronary artery arising from the right coronary cusp and an intramural course of the left main. (Level of Difficulty: Intermediate.).

Quantization-aware training for low precision photonic neural networks

Recent advances in Deep Learning (DL) fueled the interest in developing neuromorphic hardware accelerators that can improve the computational speed and energy efficiency of existing accelerators. Among the most promising research directions towards this is photonic neuromorphic architectures, which can achieve femtojoule per MAC efficiencies. Despite the benefits that arise from the use of neuromorphic architectures, a significant bottleneck is the use of expensive high-speed and precision analog-to-digital (ADCs) and digital-to-analog conversion modules (DACs) required to transfer the electrical signals, originating from the various Artificial Neural Networks (ANNs) operations (inputs, weights, etc.) in the photonic optical engines. The main contribution of this paper is to study quantization phenomena in photonic models, induced by DACs/ADCs, as an additional noise/uncertainty source and to provide a photonics-compliant framework for training photonic DL models with limited precision, allowing for reducing the need for expensive high precision DACs/ADCs. The effectiveness of the proposed method is demonstrated using different architectures, ranging from fully connected and convolutional networks to recurrent architectures, following recent advances in photonic DL.

CT patterns and serial CT Changes in lung Cancer patients post stereotactic body radiotherapy (SBRT)

Background

To evaluate computed tomography (CT) patterns of post-SBRT lung injury in lung cancer and identify time points of serial CT changes.

Materials and methods

One hundred eighty-three tumors in 170 patients were evaluated on sequential CTs within 29 months (median). Frequencies of post-SBRT CT patterns and time points of initiation and duration were assessed. Duration of increase of primary lesion or surrounding injury without evidence of local recurrence and time to stabilization or local recurrence were evaluated.

Results

Post-SBRT CT patterns could overlap in the same patient and were nodule-like pattern (69%), consolidation with ground glass opacity (GGO) (41%), modified conventional pattern (39%), peribronchial/patchy consolidation (42%), patchy GGO (24%), diffuse consolidation (16%), “orbit sign” (21%), mass-like pattern (19%), scar-like pattern (15%) and diffuse GGO (3%). Patchy GGO started at 4 months post-SBRT. Peribronchial/patchy consolidation and consolidation with GGO started at 4 and 5 months respectively. Diffuse consolidation, diffuse GGO and orbit sign started at 5, 6 and 8 months respectively. Mass-like, modified conventional and scar-like pattern started at 8, 12 and 12 months respectively. Primary lesion (n = 11) or surrounding injury (n = 85) increased up to 13 months. Primary lesion (n = 119) or surrounding injury (n = 115) started to decrease at 4 and 9 months respectively. Time to stabilization was 20 months. The most common CT pattern at stabilization was modified conventional pattern (49%), scar-like pattern (23%) and mass-like pattern (12%). Local recurrence (n = 15) occurred at a median time of 18 months.

Conclusion

Different CT patterns of lung injury post-SBRT appear in predictable time points and have variable but predictable duration. Familiarity with these patterns and timeframes of appearance helps differentiate them from local recurrence.

Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network

Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. Based on a cross validation, the AI model achieves COVID-19 sensitivity of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$89.5\%\pm 0.11$$\end{document}89.5%±0.11, CAP sensitivity of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$95\%\pm 0.11$$\end{document}95%±0.11, normal cases sensitivity (specificity) of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$85.7\%\pm 0.16$$\end{document}85.7%±0.16, and accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$90\%\pm 0.06$$\end{document}90%±0.06. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$94.3\%\pm 0.05$$\end{document}94.3%±0.05, CAP sensitivity of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$96.7\%\pm 0.07$$\end{document}96.7%±0.07, normal cases sensitivity (specificity) of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$91\%\pm 0.09$$\end{document}91%±0.09 , and accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$94.1\%\pm 0.03$$\end{document}94.1%±0.03. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.

COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images

Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) images can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensive and prone to error, which raises an urgent quest to develop practical autonomous solutions. In this context, first, the paper introduces an open-access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. Second, a Deep Neural Network (DNN)-based framework is proposed, referred to as the [Formula: see text], that autonomously segments lung abnormalities associated with COVID-19 from chest CT images. Performance of the proposed [Formula: see text] framework is evaluated through several experiments based on the introduced and external datasets. Third, an unsupervised enhancement approach is introduced that can reduce the gap between the training set and test set and improve the model generalization. The enhanced results show a dice score of 0.8069 and specificity and sensitivity of 0.9969 and 0.8354, respectively. Furthermore, the results indicate that the [Formula: see text] model can efficiently segment COVID-19 lesions in both 2D CT images and whole lung volumes. Results on the external dataset illustrate generalization capabilities of the [Formula: see text] model to CT images obtained from a different scanner.

Survival analysis in lung cancer patients with interstitial lung disease

OBJECTIVE

Lung cancer patients with interstitial lung disease (ILD) are prone for higher morbidity and mortality and their treatment is challenging. The purpose of this study is to investigate whether the survival of lung cancer patients is affected by the presence of ILD documented on CT.

MATERIALS AND METHODS

146 patients with ILD at initial chest CT were retrospectively included in the study. 146 lung cancer controls without ILD were selected. Chest CTs were evaluated for the presence of pulmonary fibrosis which was classified in 4 categories. Presence and type of emphysema, extent of ILD and emphysema, location and histologic type of cancer, clinical staging and treatment were evaluated. Kaplan-Meier estimates and Cox regression models were used to assess survival probability and hazard of death of different groups. P value < 0.05 was considered significant.

RESULTS

5-year survival for the study group was 41% versus 48% for the control group (log-rank test p = 0.0092). No significant difference in survival rate was found between the four different categories of ILD (log-rank test, p = 0.195) and the different histologic types (log-rank test, p = 0.4005). A cox proportional hazard model was used including presence of ILD, clinical stage and age. The hazard of death among patients with ILD was 1.522 times that among patients without ILD (95%CI, p = 0.029).

CONCLUSION

Patients with lung cancer and CT evidence of ILD have a significantly shorter survival compared to patients with lung cancer only. Documenting the type and grading the severity of ILD in lung cancer patients will significantly contribute to their challenging management.

Impact of COVID-19 Pandemic on Cardiovascular Testing in Asia

Background

The coronavirus disease-2019 (COVID-19) pandemic significantly affected management of cardiovascular disease around the world. The effect of the pandemic on volume of cardiovascular diagnostic procedures is not known.

Objectives

This study sought to evaluate the effects of the early phase of the COVID-19 pandemic on cardiovascular diagnostic procedures and safety practices in Asia.

Methods

The International Atomic Energy Agency conducted a worldwide survey to assess changes in cardiovascular procedure volume and safety practices caused by COVID-19. Testing volumes were reported for March 2020 and April 2020 and were compared to those from March 2019. Data from 180 centers across 33 Asian countries were grouped into 4 subregions for comparison.

Results

Procedure volumes decreased by 47% from March 2019 to March 2020, showing recovery from March 2020 to April 2020 in Eastern Asia, particularly in China. The majority of centers cancelled outpatient activities and increased time per study. Practice changes included implementing physical distancing and restricting visitors. Although COVID testing was not commonly performed, it was conducted in one-third of facilities in Eastern Asia. The most severe reductions in procedure volumes were observed in lower-income countries, where volumes decreased 81% from March 2019 to April 2020.

Conclusions

The COVID-19 pandemic in Asia caused significant reductions in cardiovascular diagnostic procedures, particularly in low-income countries. Further studies on effects of COVID-19 on cardiovascular outcomes and changes in care delivery are warranted.

Right Ventricular Function at Cardiac MRI Predicts Cardiovascular Events in Patients with an Implantable Cardioverter-Defibrillator

Background Right ventricular ejection fraction (RVEF) is an independent predictor of death and adverse cardiovascular outcomes in patients with various cardiac conditions. Purpose To investigate whether RVEF, measured with cardiac MRI, is a predictor of appropriate shock or death in implantable cardioverter-defibrillator (ICD) recipients for primary and secondary prevention of sudden cardiac death. Materials and Methods This retrospective, multicenter, observational study included patients who underwent cardiac MRI before ICD implantation between January 2007 and May 2017. Right ventricular end-diastolic and end-systolic volumes and RVEF were measured with cardiac MRI. The primary end point was a composite of all-cause mortality or appropriate ICD shock. The secondary end point was all-cause mortality. The association between RVEF and primary and secondary outcomes was evaluated by using multivariable Cox regression analysis. Potential interactions were tested between primary prevention, ischemic cause, left ventricular ejection fraction (LVEF), and RVEF. Results Among 411 patients (mean age ± standard deviation, 60 years; 315 men) during a median follow-up of 63 months, 143 (35%) patients experienced an appropriate ICD shock or died. In univariable analysis, lower RVEF was associated with greater risks for appropriate ICD shock or death and for death alone (log-rank trend test, P = .003 and .005 respectively). In multivariable Cox regression analysis adjusting for age at ICD implantation, LVEF, ICD indication (primary vs secondary), ischemic heart disease, and late gadolinium enhancement, RVEF was an independent predictor of the primary outcome (hazard ratio [HR], 1.21 per 10% lower RVEF; 95% CI: 1.04, 1.41; P = .01) and all-cause mortality (HR, 1.25 per 10% lower RVEF; 95% CI: 1.01, 1.55; P = .04). No evidence of significant interactions was found between RVEF and primary or secondary prevention (HR, 1.11 ± 0.17 [standard deviation]; P = .49), ischemic heart disease (HR, 1.02 ± 0.15; P = .78), and LVEF (HR, 0.91 ± 0.8; P = .29). Conclusion Right ventricular ejection fraction measured with cardiac MRI was a predictor of appropriate implantable cardioverter-defibrillator shock or death. © RSNA, 2021 See also the editorial by Nazarian and Zghaib in this issue.

COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans

The newly discovered Coronavirus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. The rapid outbreak of this disease has overwhelmed health care infrastructures and arises the need to allocate medical equipment and resources more efficiently. The early diagnosis of this disease will lead to the rapid separation of COVID-19 and non-COVID cases, which will be helpful for health care authorities to optimize resource allocation plans and early prevention of the disease. In this regard, a growing number of studies are investigating the capability of deep learning for early diagnosis of COVID-19. Computed tomography (CT) scans have shown distinctive features and higher sensitivity compared to other diagnostic tests, in particular the current gold standard, i.e., the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Current deep learning-based algorithms are mainly developed based on Convolutional Neural Networks (CNNs) to identify COVID-19 pneumonia cases. CNNs, however, require extensive data augmentation and large datasets to identify detailed spatial relations between image instances. Furthermore, existing algorithms utilizing CT scans, either extend slice-level predictions to patient-level ones using a simple thresholding mechanism or rely on a sophisticated infection segmentation to identify the disease. In this paper, we propose a two-stage fully automated CT-based framework for identification of COVID-19 positive cases referred to as the “COVID-FACT”. COVID-FACT utilizes Capsule Networks, as its main building blocks and is, therefore, capable of capturing spatial information. In particular, to make the proposed COVID-FACT independent from sophisticated segmentations of the area of infection, slices demonstrating infection are detected at the first stage and the second stage is responsible for classifying patients into COVID and non-COVID cases. COVID-FACT detects slices with infection, and identifies positive COVID-19 cases using an in-house CT scan dataset, containing COVID-19, community acquired pneumonia, and normal cases. Based on our experiments, COVID-FACT achieves an accuracy of 90.82%, a sensitivity of 94.55%, a specificity of 86.04%, and an Area Under the Curve (AUC) of 0.98, while depending on far less supervision and annotation, in comparison to its counterparts.

COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning

Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 2 million lives, since its emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare systems. The current standard diagnosis method, the Reverse Transcription Polymerase Chain Reaction (RT- PCR), is time consuming, and subject to low sensitivity. Chest Radiograph (CXR), the first imaging modality to be used, is readily available and gives immediate results. However, it has notoriously lower sensitivity than Computed Tomography (CT), which can be used efficiently to complement other diagnostic methods. This paper introduces a new COVID-19 CT scan dataset, referred to as COVID-CT-MD, consisting of not only COVID-19 cases, but also healthy and participants infected by Community Acquired Pneumonia (CAP). COVID-CT-MD dataset, which is accompanied with lobe-level, slice-level and patient-level labels, has the potential to facilitate the COVID-19 research, in particular COVID-CT-MD can assist in development of advanced Machine Learning (ML) and Deep Neural Network (DNN) based solutions.

Canadian Society of Thoracic Radiology/Canadian Association of Radiologists Clinical Practice Guidance for Non-Vascular Thoracic MRI

Historically thoracic MRI has been limited by the lower proton density of lung parenchyma, cardiac and respiratory motion artifacts and long acquisition times. Recent technological advancements in MR hardware systems and improvement in MR pulse sequences have helped overcome these limitations and expand clinical opportunities for non-vascular thoracic MRI. Non-vascular thoracic MRI has been established as a problem-solving imaging modality for characterization of thymic, mediastinal, pleural chest wall and superior sulcus tumors and for detection of endometriosis. It is increasingly recognized as a powerful imaging tool for detection and characterization of lung nodules and for assessment of lung cancer staging. The lack of ionizing radiation makes thoracic MRI an invaluable imaging modality for young patients, pregnancy and for frequent serial follow-up imaging. Lack of familiarity and exposure to non-vascular thoracic MRI and lack of consistency in existing MRI protocols have called for clinical practice guidance. The purpose of this guide, which was developed by the Canadian Society of Thoracic Radiology and endorsed by the Canadian Association of Radiologists, is to familiarize radiologists, other interested clinicians and MR technologists with common and less common clinical indications for non-vascular thoracic MRI, discuss the fundamental imaging findings and focus on basic and more advanced MRI sequences tailored to specific clinical questions.

Radiological, epidemiological and clinical patterns of pulmonary viral infections

Respiratory viruses are the most common causes of acute respiratory infections. However, identification of the underlying viral pathogen may not always be easy. Clinical presentations of respiratory viral infections usually overlap and may mimic those of diseases caused by bacteria. However, certain imaging morphologic patterns may suggest a particular viral pathogen as the cause of the infection. Although definitive diagnosis cannot be made on the basis of clinical or imaging features alone, the use of a combination of clinical and radiographic findings can substantially improve the accuracy of diagnosis. The purpose of this review is to present the clinical, epidemiological and radiological patterns of lower respiratory tract viral pathogens providing a comprehensive approach for their diagnosis and identification in hospitals and community outbreaks.

Pathological entities that may affect the lungs and the myocardium. Evaluation with chest CT and cardiac MR

Certain entities may simultaneously involve the lungs and the myocardium. Knowing their cardiac and thoracic manifestations enhances the understanding of those conditions and increases awareness and suspicion for possible concurrent cardiothoracic involvement. Entities that can present with pulmonary and myocardial involvement include infiltrative diseases like sarcoidosis and amyloidosis, eosinophil-associated conditions including eosinophilic granulomatosis with polyangiitis (EGPA) and hypereosinophilic syndrome (HES), connective tissue diseases such as systemic sclerosis (SSc) and lupus erythematosus and genetic disorders like Fabry disease (FD). Lung involvement in sarcoidosis is almost universal. While cardiac involvement is less common, concurrent cardiothoracic involvement can often be seen. Pulmonary amyloidosis is more often a localized process and generally occurs separately from cardiac involvement, except for diffuse alveolar-septal amyloidosis. EGPA and HES can present with consolidative or ground glass opacities, cardiac inflammation and endomyocardial fibrosis. Manifestations of SSc include interstitial lung disease, pulmonary hypertension and cardiomyopathy. Lupus can present with serositis, pneumonitis and cardiac inflammation. FD causes left ventricular thickening and fibrosis, and small airways disease. This article aims to review the clinicopathological features of chest and cardiac involvement of these entities and describe their main findings on chest CT and cardiac MR.

COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images

Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID-19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images, on the other hand, reveal specific manifestations associated with this disease. Overlap with other lung infections makes human-centered diagnosis of COVID-19 challenging. Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets. The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID-19. Our results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPS achieved an Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, and Area Under the Curve (AUC) of 0.97, while having far less number of trainable parameters in comparison to its counterparts. To potentially and further improve diagnosis capabilities of the COVID-CAPS, pre-training and transfer learning are utilized based on a new dataset constructed from an external dataset of X-ray images. This is in contrary to existing works on COVID-19 detection where pre-training is performed based on natural images. Pre-training with a dataset of similar nature further improved accuracy to 98.3% and specificity to 98.6%.

Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models

The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). Nodules were segmented, and geometric and CT attenuation features including functional principal component analysis features (FPC1 and FPC2) were extracted. After a feature selection step, two predictive models were built with ordinal regression: Model 1 based on volume (log) (logarithm of the nodule volume) and FPC1, and Model 2 based on volume (log) and Q.875 (CT attenuation value at the 87.5% percentile). Using the 200-repeats Monte-Carlo cross-validation method, these models provided a multiclass classification of invasiveness with discriminative power AUCs of 0.83 to 0.87 and predicted the class probabilities with less than a 10% average error. The predictive modelling approach adopted in this paper provides a detailed insight on how the value of the main predictors contribute to the probability of nodule invasiveness and underlines the role of nodule CT attenuation features in the nodule invasiveness classification.

[Formula: see text]: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer

Hand-crafted radiomics has been used for developing models in order to predict time-to-event clinical outcomes in patients with lung cancer. Hand-crafted features, however, are pre-defined and extracted without taking the desired target into account. Furthermore, accurate segmentation of the tumor is required for development of a reliable predictive model, which may be objective and a time-consuming task. To address these drawbacks, we propose a deep learning-based radiomics model for the time-to-event outcome prediction, referred to as DRTOP that takes raw images as inputs, and calculates the image-based risk of death or recurrence, for each patient. Our experiments on an in-house dataset of 132 lung cancer patients show that the obtained image-based risks are significant predictors of the time-to-event outcomes. Computed Tomography (CT)-based features are predictors of the overall survival (OS), with the hazard ratio (HR) of 1.35, distant control (DC), with HR of 1.06, and local control (LC), with HR of 2.66. The Positron Emission Tomography (PET)-based features are predictors of OS and recurrence free survival (RFS), with hazard ratios of 1.67 and 1.18, respectively. The concordance indices of [Formula: see text], [Formula: see text], and [Formula: see text] for predicting the OS, DC, and RFS show that the deep learning-based radiomics model is as accurate or better in predicting predefined clinical outcomes compared to hand-crafted radiomics, with concordance indices of [Formula: see text], [Formula: see text], and [Formula: see text], for predicting the OS, DC, and RFS, respectively. Deep learning-based radiomics has the potential to offer complimentary predictive information in the personalized management of lung cancer patients.

3D-MCN: A 3D Multi-scale Capsule Network for Lung Nodule Malignancy Prediction

Despite the advances in automatic lung cancer malignancy prediction, achieving high accuracy remains challenging. Existing solutions are mostly based on Convolutional Neural Networks (CNNs), which require a large amount of training data. Most of the developed CNN models are based only on the main nodule region, without considering the surrounding tissues. Obtaining high sensitivity is challenging with lung nodule malignancy prediction. Moreover, the interpretability of the proposed techniques should be a consideration when the end goal is to utilize the model in a clinical setting. Capsule networks (CapsNets) are new and revolutionary machine learning architectures proposed to overcome shortcomings of CNNs. Capitalizing on the success of CapsNet in biomedical domains, we propose a novel model for lung tumor malignancy prediction. The proposed framework, referred to as the 3D Multi-scale Capsule Network (3D-MCN), is uniquely designed to benefit from: (i) 3D inputs, providing information about the nodule in 3D; (ii) Multi-scale input, capturing the nodule's local features, as well as the characteristics of the surrounding tissues, and; (iii) CapsNet-based design, being capable of dealing with a small number of training samples. The proposed 3D-MCN architecture predicted lung nodule malignancy with a high accuracy of 93.12%, sensitivity of 94.94%, area under the curve (AUC) of 0.9641, and specificity of 90% when tested on the LIDC-IDRI dataset. When classifying patients as having a malignant condition (i.e., at least one malignant nodule is detected) or not, the proposed model achieved an accuracy of 83%, and a sensitivity and specificity of 84% and 81% respectively.

Combined pulmonary fibrosis and emphysema characteristics in a Greek cohort

Background

Combined pulmonary fibrosis and emphysema (CPFE) has recently received great attention, with studies suggesting that it presents a distinct clinical entity while others have challenged this hypothesis. This nationwide study aimed to describe a large cohort of Greek CPFE patients and to examine potential prognostic factors for survival.

Methods

This retrospective study included 97 patients with CPFE. Demographic and clinical data, pulmonary function tests, echocardiography results and bronchoalveolar lavage analysis were recorded.

Results

Most patients were male (94.8%) and 92% were current or ex-smokers. Spirometry results were abnormal (forced vital capacity (FVC) 72.9±19.9% pred and forced expiratory volume in 1 s/FVC 82.9±9.7%) with reduced diffusing capacity of the lung for carbon monoxide (D_LCO) (42.3±17.4% pred). Mean systolic pulmonary arterial pressure was 41.9±19.7 mmHg and pulmonary hypertension was present in 58.8% of patients. Mean 6-min walk distance was 335.4±159.4 m. Mean emphysema score was 14.23±8.69% and mean interstitial lung disease (ILD) extent was 39.58±19.82%. Mean survival was 84 months (95% CI 72–96 months). Patients with D_LCO ≥39% pred had better survival than patients with D_LCO <39% pred (p=0.031). Patients with ILD extent ≥30% had worse survival than patients with ILD extent <30% (p=0.037).

Conclusions

Our results indicate that CPFE patients have preserved lung volumes associated with disproportionately reduced D_LCO, while reduced D_LCO and increased ILD extent was associated with worse prognosis.

Prognosis of CPFE is associated with pulmonary function status and ILD extenthttp://ow.ly/izvd30nHFgh

The clinical significance of indeterminate pulmonary nodules in melanoma patients at baseline and during follow-up chest CT

Objective

This study aims to determine an appropriate timeline to monitor indeterminate pulmonary nodules (IPN) in melanoma patients to confirm metastatic origin.

Materials and Methods

588 clinically non-metastatic melanoma patients underwent curative intent surgery during 3 years. Patients with baseline chest CT and at least one follow-up (FU) CT were retrospectively analyzed to assess for IPN. Patients with definitely benign nodules, metastases and non-melanoma malignancies were excluded. Change in volume from first to FU CT, initial diameter (D1) and volume (V1), distance from pleura, peripheral and perifissural location, density and clinical stage were evaluated. Nodules were volumetrically measured on CTs and were considered metastases if they increased in size between two CTs or if increase was accompanied by multiple new nodules or extrapulmonary metastases.

Results

148 patients were included. Two out of 243 baseline IPN detected in 70 patients, increased significantly in volume in 3 and 5 months and were proven metastases. During FU, 86% of 40 interval IPN detected in 28 patients, were proven metastases. Interval nodule (p < 0.0001, HR:243,CI:[57.32,1033.74]), 3-month volume change (OR:1.023,CI:[1.014,1.033]), V1 (OR:1.006,CI:[1.003,1.009]), D1 (OR:1.424,CI:[1.23,1.648]), distance from pleura (OR:1.03,CI:[1.003,1.059]), and combined stage IIC + III (OR:11.29,CI:[1.514,84.174]), were associated with increased risk for metastasis. 43%, 72% and 94% of patients with IPN were confirmed with metastases in the first FU CT at 3, 6 and 12 months respectively.

Conclusion

Baseline IPN are most likely benign, while interval IPN are high risk for metastasis. Absence of volume increase of IPN within 6 months excluded metastasis in most patients.

Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy

We sought to quantify contribution of radiomics and SUVmax at PET/CT to predict clinical outcome in lung cancer patients treated with stereotactic body radiotherapy (SBRT). 150 patients with 172 lung cancers, who underwent SBRT were retrospectively included. Radiomics were applied on PET/CT. Principal components (PC) for 42 CT and PET-derived features were examined to determine which ones accounted for most of variability. Survival analysis quantified ability of radiomics and SUVmax to predict outcome. PCs including homogeneity, size, maximum intensity, mean and median gray level, standard deviation, entropy, kurtosis, skewness, morphology and asymmetry were included in prediction models for regional control (RC) [PC4-HR:0.38, p = 0.02], distant control (DC) [PC4-HR:0.51, p = 0.02 and PC1-HR:1.12, p = 0.01], recurrence free probability (RFP) [PC1-HR:1.08, p = 0.04], disease specific survival (DSS) [PC2-HR:1.34, p = 0.03 and PC3-HR:0.64, p = 0.02] and overall survival (OS) [PC4-HR:0.45, p = 0.004 and PC3-HR:0.74, p = 0.02]. In combined analysis with SUVmax, PC1 lost predictive ability over SUVmax for RFP [HR:1.1, p = 0.04] and DC [HR:1.13, p = 0.002], while PC4 remained predictive of DC independent of SUVmax [HR:0.5, p = 0.02]. Radiomics remained the only predictors of OS, DSS and RC. Neither SUVmax nor radiomics predicted recurrence free survival. Radiomics on PET/CT provided complementary information for prediction of control and survival in SBRT-treated lung cancer patients.

Aortic Elongation and Stanford B Dissection: The Tübingen Aortic Pathoanatomy (TAIPAN) Project

OBJECTIVE/BACKGROUND

Aortic elongation has not yet been considered as a potential risk factor for Stanford type B dissection (TBD). The role of both aortic elongation and dilatation in patients with TBD was evaluated.

METHODS

The aortic morphology of a healthy control group (n = 236) and patients with TBD (n = 96) was retrospectively examined using three dimensional computed tomography imaging. Curved multiplanar reformats were used to examine aortic diameters at defined landmarks and aortic segment lengths.

RESULTS

Diameters at all landmarks were significantly larger in the TBD group. The greatest diameter difference (56%) was measured in dissected descending aortas (p < .001). The segment with the most considerable difference between the study groups with regard to elongation was the non-dissected aortic arch of patients with TBD (36%; p < .001). Elongation in the aortic arch was accompanied by a diameter increase of 21% (p < .001). In receiver-operating curve analysis, the area under the curve was .85 for the diameter and .86 for the length of the aortic arch.

CONCLUSIONS

In addition to dilatation, aortic arch elongation is associated with the development of TBD. The diameter and length of the non-dissected aortic arch may be predictive for TBD and may possibly be used for risk assessment in the future. This study provides the basis for further prospective evaluation of these parameters.

Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we explore different strategies for overcoming these challenges and improving predictive performance of radiomics-based prognosis for non-small cell lung cancer (NSCLC). CT images of 112 patients (mean age 75 years) with NSCLC who underwent stereotactic body radiotherapy were used to predict recurrence, death, and recurrence-free survival using a comprehensive radiomics analysis. Different feature selection and predictive modeling techniques were used to determine the optimal configuration of prognosis analysis. To address feature redundancy, comprehensive analysis indicated that Random Forest models and Principal Component Analysis were optimum predictive modeling and feature selection methods, respectively, for achieving high prognosis performance. To address unbalanced data, Synthetic Minority Over-sampling technique was found to significantly increase predictive accuracy. A full analysis of variance showed that data endpoints, feature selection techniques, and classifiers were significant factors in affecting predictive accuracy, suggesting that these factors must be investigated when building radiomics-based predictive models for cancer prognosis.

Arterial input function placement effect on computed tomography lung perfusion maps

BACKGROUND

A critical source of variability in dynamic perfusion computed tomography (DPCT) is the arterial input function (AIF). However, the impact of the AIF location in lung DPCT has not been investigated yet. The purpose of this study is to determine whether the location of the AIF within the central pulmonary arteries influences the accuracy of lung DPCT maps.

METHODS

A total of 54 lung DPCT scans were performed in three pigs using different rates and volumes of iodinated contrast media. Pulmonary blood flow (PBF) perfusion maps were generated using first-pass kinetics in three different AIF locations: the main pulmonary trunk (PT), the right main (RM) and the left main (LM) pulmonary arteries. A total of 162 time density curves (TDCs) and corresponding PBF perfusion maps were generated. Linear regression and Spearman's rank correlation coefficient were used to compare the TDCs. PBF perfusion maps were compared quantitatively by taking twenty six regions of interest throughout the lung parenchyma. Analysis of variance (ANOVA) was used to compare the mean PBF values among the three AIF locations. Two chest radiologists performed qualitative assessment of the perfusion maps using a 3-point scale to determine regions of perfusion mismatch.

RESULTS

The linear regression of the TDCs from the RM and LM compared to the PT had a median (range) of 1.01 (0.98-1.03). The Spearman rank correlation between the TDCs was 0.88 (P<0.05). ANOVA analysis of the perfusion maps demonstrated no statistical difference (P>0.05). Qualitative comparison of the perfusion maps resulted in scores of 1 and 2, demonstrating either identical or comparable maps with no significant difference in perfusion defects between the different AIF locations.

CONCLUSIONS

Accurate PBF perfusion maps can be generated with the AIF located either at the PT, RM or LM pulmonary arteries.

Assessment of the Impact of Skeletonization on Pleuropulmonary Changes after Bilateral Internal Thoracic Artery Harvest for Coronary Artery Bypass Grafting

BACKGROUND AND AIMS

Skeletonization has been proposed as a technique to minimize the risk of sternal devascularization during bilateral internal thoracic artery harvest for coronary artery bypass grafting. The impact of this strategy on late radiologic pleuropulmonary changes has not been addressed.

MATERIAL AND METHODS

Post-operative chest radiographs from patients (n = 253 per group) undergoing bilateral internal thoracic artery harvest using skeletonized and non-skeletonized techniques were reviewed by blinded radiologists. The primary outcome was the incidence of atelectasis and pleural effusion. Multivariable linear regression models were derived to assess the relationship of radiologic pleuropulmonary outcomes to patients and operative variables.

RESULTS AND CONCLUSION

Patients in the skeletonized group were older (p < 0.0001), had a lower preoperative hematocrit (p = 0.014), had higher prevalence of peripheral vascular disease (p = 0.001), were of female gender (p = 0.015), underwent off-pump surgery (p < 0.001), had urgent/emergent status (p = 0.024), and had chronic obstructive pulmonary disease (p = 0.019). There was no difference in the incidence of post-operative complications, ventilation time, or intensive care unit stay. There was no difference in the severity of post-operative atelectasis in both groups. More patients in the non-skeletonized group had a grade 2/3 left pleural effusion on the late post-operative chest X-ray (p = 0.007). The independent effect of skeletonization on the development of a late left pleural effusion was significant (odds ratio = 0.558, 95% confidence interval = 0.359-0.866, p = 0.009). Skeletonization results in a decreased incidence of late post-operative left pleural effusion with no difference in early or late atelectasis. Further studies are warranted to assess the mechanism of these pleuropulmonary changes and the impact of other factors such as pleural violation during surgery.

Pulmonary fibrosis and emphysema: Is the emphysema type associated with the pattern of fibrosis?

AIM: To investigate whether the predominant emphysema type is associated with the high resolution computed tomography (HRCT) pattern of fibrosis in combined pulmonary fibrosis and emphysema (CPFE).

METHODS: Fifty-three smokers with upper lobe emphysema and lower lobe pulmonary fibrosis on - HRCT - were retrospectively evaluated. Patients were stratified into 3 groups according to the predominant type of emphysema: Centrilobular (CLE), paraseptal (PSE), CLE = PSE. Patients were also stratified into 3 other groups according to the predominant type of fibrosis on HRCT: Typical usual interstitial pneumonia (UIP), probable UIP and nonspecific interstitial pneumonia (NSIP). HRCTs were scored at 5 predetermined levels for the coarseness of fibrosis (Coarseness), extent of emphysema (emphysema), extent of interstitial lung disease (TotExtILD), extent of reticular pattern not otherwise specified (RetNOS), extent of ground glass opacity with traction bronchiectasis (extGGOBx), extent of pure ground glass opacity and extent of honeycombing. HRCT mean scores, pulmonary function tests, diffusion capacity (DLCO) and systolic pulmonary arterial pressure were compared among the groups.

RESULTS: The predominant type of emphysema was strongly correlated with the predominant type of fibrosis. The centrilobular emphysema group exhibited a significantly higher extent of emphysema (P < 0.001) and a lower extent of interstitial lung disease (P < 0.002), reticular pattern not otherwise specified (P < 0.023), extent of ground glass opacity with traction bronchiectasis (P < 0.002), extent of honeycombing (P < 0.001) and coarseness of fibrosis (P < 0.001) than the paraseptal group. The NSIP group exhibited a significantly higher extent of emphysema (P < 0.05), total lung capacity (P < 0.01) and diffusion capacity (DLCO) (P < 0.05) than the typical UIP group. The typical UIP group exhibited a significantly higher extent of interstitial lung disease, extent of reticular pattern not otherwise specified, extent of ground glass opacity with traction bronchiectasis, extent of honeycombing and coarseness of fibrosis (0.039 > P > 0.000). Although the pulmonary arterial pressure was higher in typical UIP group relative to the NSIP group, the difference was not statistically significant.

CONCLUSION: In CPFE patients, paraseptal emphysema is associated more with UIP-HRCT pattern and higher extent of fibrosis than centrilobular emphysema.

Increased incidence of autoimmune markers in patients with combined pulmonary fibrosis and emphysema

Background

Combined pulmonary fibrosis and emphysema (CPFE) is an umbrella term encompassing upper lobe emphysema and lower lobe pulmonary fibrosis with pathogenesis elusive. The aim of our study was to investigate the incidence of autoimmune markers in patients with CPFE.

Methods

In this multicenter study we retrospectively evaluated records from patients with CPFE (n=40) and IPF (n=60) without emphysema. Baseline demographic characteristics, high-resolution computed tomography (HRCT), spirometry, histopathological, treatment, serum immunologic and survival data were investigated. B cell presence was estimated with CD20 immunostaining in representative lung biopsy samples from CPFE patients and control subjects.

Results

A statistically significant increased number of CPFE patients with elevated serum ANA with or without positive p-ANCA titers compared to patients with IPF without emphysema was observed. Patients with CPFE and positive autoimmune markers exhibited improved survival compared to patients with a negative autoimmune profile. A massive infiltration of clusters of CD20+ B cells forming lymphoid follicles within the fibrotic lung in CPFE patients with positive serum immunologic profile compared to patients with negative profile, was noted and positively correlated with improved survival.

Conclusions

A significant proportion of patients with CPFE may present with underlying auto-immune disorders that may reside insidiously and be associated with favorable prognosis. Early identification of these patients using a panel of auto-antibodies may lead to more targeted and effective therapeutic applications.

Juxtacortical clavicular chondrosarcoma: diagnostic dilemmas: case report and review of literature

Juxtacortical chondrosarcoma is a rare primary malignant cartilaginous tumor accounting for 0.2% of all bone tumors. Wide surgical resection is the treatment of choice for juxtacortical chondrosarcomas. Accurate preoperative diagnosis is important in ensuring appropriate management, staging, and treatment of the patient. A combination of radiographs, three-dimensional imaging with computerized tomography (CT) scan and magnetic resonance imaging (MRI) can typically allow accurate diagnosis of juxtacortical chondrosarcomas. Bone scan and chest x-ray or CT chest scans are indicated for appropriate staging of the patient. Pet scan, ultrasound, bone scan, etc. are not typically needed for the diagnosis. Certainly, pulmonary imaging and bone scan are required for staging and could be commented upon.

Basal ganglia hyperintensity on T1-weighted MRI in Rendu-Osler-Weber disease

The purpose of this study was to evaluate possible central nervous system (CNS) involvement in Rendu-Osler-Weber (ROW) disease in magnetic resonance imaging (MRI). Three patients with symptomatic ROW disease underwent brain MRI. Brain MRI depicted in all three of them increased signal intensity on T1-weighted images involving the globus pallidus and cerebral crura bilaterally. Laboratory studies of the two men showed iron deficiency anemia, while all three of them had normal liver function tests and increased manganese blood concentration. Gastroscopy and colonoscopy revealed a gastric and a cecal arteriovenous malformation (AVM) in the first one, while pulmonary and hepatic computed tomography (CT) angiography did not detect any intrahepatic shunts. Liver ultrasound in the second one revealed dilatation of intrahepatic artery branches consistent with intrahepatic shunts, while it was normal in the third patient. Chest radiographs were normal in all three patients. Pallidal T1 hyperintensity on T1-weighted imaging may be a biomarker of manganese overload in ROW disease.

Stem cell therapy for idiopathic pulmonary fibrosis: a protocol proposal

Background

Idiopathic pulmonary fibrosis represents a lethal form of progressive fibrotic lung disorder with gradually increasing incidence worldwide. Despite intense research efforts its pathogenesis is still elusive and controversial reflecting in the current disappointing status regarding its treatment. Patients and Methods: We report the first protocol proposal of a prospective, unicentric, non-randomized, phase Ib clinical trial to study the safety and tolerability of the adipose-derived stem cells (ADSCs) stromal vascular fraction (SVF) as a therapeutic agent in IPF. After careful patient selection based on functional criteria (forced vital capacity-FVC > 50%, diffuse lung capacity for carbon monoxide-DL_CO > 35% of the predicted values) all eligible subjects will be subjected to lipoaspiration resulting in the isolation of approximately 100- 500 gr of adipose tissue. After preparation, isolation and labelling ADSCs-SVF will be endobronchially infused to both lower lobes of the fibrotic lungs. Procedure will be repeated thrice at monthly intervals. Primary end-point represent safety and tolerability data, while exploratory secondary end-points include assessment of clinical functional and radiological status. Results: Preliminary results recently presented in the form of an abstract seem promising and tantalizing since there were no cases of clinically significant allergic reactions, infections, disease acute exacerbations or ectopic tissue formation. In addition 6 months follow-up data revealed a marginal improvement at 6-minute walking distance and forced vital capacity.

Conclusions

Adipose tissue represents an abundant, safe, ethically uncontested and potentially beneficial source of stem cells for patients with IPF. Larger multicenter phase II and III placebo-controlled clinical trials are sorely needed in order to prove efficacy. However, pilot safety studies are of major importance and represent the first hamper that should be overcome to establish a rigid basis for larger clinical trials.