Lung Ultrasound in COVID-19 Pneumonia: Correlations with Chest CT on Hospital admission

Background: Lung ultrasound (LUS) is an accurate, safe, and cheap tool assisting in the diagnosis of several acute respiratory diseases. The diagnostic value of LUS in the workup of coronavirus disease-19 (COVID-19) in the hospital setting is still uncertain. Objectives: The aim of this observational study was to explore correlations of the LUS appearance of COVID-19-related pneumonia with CT findings. Methods: Twenty-six patients (14 males, age 64 ± 16 years) urgently hospitalized for COVID-19 pneumonia, who underwent chest CT and bedside LUS on the day of admission, were enrolled in this observational study. CT images were reviewed by expert chest radiologists, who calculated a visual CT score based on extension and distribution of ground-glass opacities and consolidations. LUS was performed by clinicians with certified competency in thoracic ultrasonography, blind to CT findings, following a systematic approach recommended by ultrasound guidelines. LUS score was calculated according to presence, distribution, and severity of abnormalities. Results: All participants had CT findings suggestive of bilateral COVID-19 pneumonia, with an average visual scoring of 43 ± 24%. LUS identified 4 different possible -abnormalities, with bilateral distribution (average LUS score 15 ± 5): focal areas of nonconfluent B lines, diffuse confluent B lines, small subpleural microconsolidations with pleural line irregularities, and large parenchymal consolidations with air bronchograms. LUS score was significantly correlated with CT visual scoring (r = 0.65, p < 0.001) and oxygen saturation in room air (r = -0.66, p < 0.001). Conclusion: When integrated with clinical data, LUS could represent a valid diagnostic aid in patients with suspect COVID-19 pneumonia, which reflects CT findings

European Respiratory Society Statement on Thoracic Ultrasound

Thoracic ultrasound is increasingly considered to be an essential tool for the pulmonologist. It is used in diverse clinical scenarios, including as an adjunct to clinical decision making for diagnosis, a real-time guide to procedures, and a predictor or measurement of treatment response. The aim of this European Respiratory Society task force was to produce a statement on thoracic ultrasound for pulmonologists using thoracic ultrasound within the field of respiratory medicine. The multidisciplinary panel performed a review of the literature, addressing major areas of thoracic ultrasound practice and application. The selected major areas include equipment and technique, assessment of the chest wall, parietal pleura, pleural effusion, pneumothorax, interstitial syndrome, lung consolidation, diaphragm assessment, intervention guidance, training, and the patient perspective. Despite the growing evidence supporting the use of thoracic ultrasound, the published literature still contains a paucity of data in some important fields. Key research questions for each of the major areas were identified, which serve to facilitate future multi-centre collaborations and research to further consolidate an evidence-based use of thoracic ultrasound, for the benefit of the many patients being exposed to clinicians using thoracic ultrasound

Transbronchial cryobiopsy and Neutrophil Lymphocyte Ratio - new precision medicine tools and markers in Interstitial Lung Disease

The interstitial lung diseases (ILDs) are a group of over 200 disease that may lead to progressive fibrosis and respiratory failure. ILDs are heterogenous, with varying amounts of inflammation and fibrosis, and differ in response to therapy and outcome. The most severe fibrotic (f) ILD, idiopathic pulmonary fibrosis (IPF), has a median survival of just three years. Progressive fILD may respond to antifibrotic treatments which slow down, but do not reverse, fibrosis albeit often with significant side effects. Better treatments or delivery of treatments are needed. Diagnosis of ILD relies on clinical history, imaging and, in some cases lung biopsy, with associated risks. Better diagnostic and prognostic biomarkers in ILD are urgently needed. This thesis examines the approach to diagnosis, prognostication, and treatment in fILDs, and, in particular IPF. It begins with the finding that Neutrophil Lymphocyte Ratio (NLR), derived from a simple, widely available blood test, is a prognostic biomarker in IPF. The role of lung biopsy in the diagnostic pathway is considered and the use of a relatively new minimally invasive technique of transbronchial cryo lung biopsy (TBCB) as an alternative to surgical lung biopsy (SLB) is described. The value of TBCB to obtain lung tissue for research is demonstrated with evaluation of the distribution of inhaled ipratropium in fILD. Using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) on samples of lung taken using TBCB, it was demonstrated that inhaled medication was able to reach the fibrotic lung, presenting a new approach to drug delivery in fILD. Further discussion focusses on the current role of SLB in the diagnostic pathway in ILD, the presentation of a systematic literature review, and a discussion of future trials to assess the potential benefits of a wider use of TBCB

Comparative Performance Analysis of State-of-the-Art Classification Algorithms Applied to Lung Tissue Categorization

In this paper, we compare five common classifier families in their ability to categorize six lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) and with healthy tissue. The evaluated classifiers are naive Bayes, k-nearest neighbor, J48 decision trees, multilayer perceptron, and support vector machines (SVM). The dataset used contains 843 regions of interest (ROI) of healthy and five pathologic lung tissue patterns identified by two radiologists at the University Hospitals of Geneva. Correlation of the feature space composed of 39 texture attributes is studied. A grid search for optimal parameters is carried out for each classifier family. Two complementary metrics are used to characterize the performances of classification. These are based on McNemar's statistical tests and global accuracy. SVM reached best values for each metric and allowed a mean correct prediction rate of 88.3% with high class-specific precision on testing sets of 423 ROI

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results\ud which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

Evaluating disease severity in idiopathic pulmonary fibrosis.

Accurate assessment of idiopathic pulmonary fibrosis (IPF) disease severity is integral to the care provided to patients with IPF. However, to date, there are no generally accepted or validated staging systems. There is an abundance of data on using information acquired from physiological, radiological and pathological parameters, in isolation or in combination, to assess disease severity in IPF. Recently, there has been interest in using serum biomarkers and computed tomography-derived quantitative lung fibrosis measures to stage disease severity in IPF. This review will focus on the suggested methods for staging IPF, at baseline and on serial assessment, their strengths and limitations, as well as future developments