Chest radiography and computed tomography imaging in cystic fibrosis: current challenges and new perspectives

Update on the Role of Chest Imaging in Cystic Fibrosis

Cystic fibrosis is a genetic disease with multisystem involvement and associated morbidity and mortality that are most directly related to progressive lung disease. The hallmark findings of cystic fibrosis in the lungs are chronic inflammation and infection, leading to progressive loss of pulmonary function and often requiring lung transplant. Predominant lung findings include mucous plugging, bronchiectasis, and air trapping, often with associated atelectasis, consolidation, and emphysema; these findings form the basis of several clinical scoring systems that are used for imaging assessment. Recently, there have been major breakthroughs in the pharmacologic management of cystic fibrosis, including highly effective modulator therapies that directly target the underlying cystic fibrosis transmembrane conductance regulator molecular defect, often leading to remarkable improvements in lung function and quality of life with corresponding significant improvements in imaging markers. The authors review current guidelines regarding cystic fibrosis with respect to disease monitoring, identifying complications, and managing advanced lung disease. In addition, they discuss the evolving role of imaging, including current trends, emerging technologies, and proposed updates to imaging guidelines endorsed by international expert committees on cystic fibrosis, which favor increased use of cross-sectional imaging to enable earlier detection of structural changes in early disease and more sensitive detection of acute changes in advanced disease. It is important for radiologists to be familiar with these trends and updates so that they can most effectively assist clinicians in guiding the management of patients with cystic fibrosis in all stages of disease. ©RSNA, 2024.

Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans

Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results.

A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection

The Coronavirus Disease 2019(COVID-19) has caused widespread and significant harm globally. In order to address the urgent demand for a rapid and reliable diagnostic approach to mitigate transmission, the application of deep learning stands as a viable solution. The impracticality of many existing models is attributed to excessively large parameters, significantly limiting their utility. Additionally, the classification accuracy of the model with few parameters falls short of desirable levels. Motivated by this observation, the present study employs the lightweight network MobileNetV3 as the underlying architecture. This paper incorporates the dense block to capture intricate spatial information in images, as well as the transition layer designed to reduce the size and channel number of the feature map. Furthermore, this paper employs label smoothing loss to address the inter-class similarity effects and uses class weighting to tackle the problem of data imbalance. Additionally, this study applies the pruning technique to eliminate unnecessary structures and further reduce the number of parameters. As a result, this improved model achieves an impressive 98.71% accuracy on an openly accessible database, while utilizing only 5.94 million parameters. Compared to the previous method, this maximum improvement reaches 5.41%. Moreover, this research successfully reduces the parameter count by up to 24 times, showcasing the efficacy of our approach. This demonstrates the significant benefits in regions with limited availability of medical resources.

Update on advances in cystic fibrosis towards a cure and implications for primary care clinicians

During the past quarter century, the diagnosis and treatment of cystic fibrosis (CF) have been transformed by molecular sciences that initiated a new era with discovery of the cystic fibrosis transmembrane conductance regulator (CFTR) gene. The knowledge gained from that breakthrough has had dramatic clinical impact. Although once a diagnostic dilemma with long delays, preventable deaths, and irreversible pathology, CF can now be routinely diagnosed shortly after birth through newborn screening programs. This strategy of pre-symptomatic identification has eliminated the common diagnostic "odyssey" that was a failure of the healthcare delivery system causing psychologically traumatic experiences for parents. Therapeutic advances of many kinds have culminated in CFTR modulator treatment that can reduce the effects of or even correct the molecular defect in the chloride channel -the basic cause of CF. This astonishing advance has transformed CF care as described fully herein. Despite this impressive progress, there are challenges and controversies in the delivery of care. Issues include how best to achieve high sensitivity newborn screening with acceptable specificity; what course of action is appropriate for children who are identified through the unavoidable incidental findings of screening tests (CFSPID/CRMS cases and heterozygote carriers); how best to ensure genetic counseling; when to initiate the very expensive but life-saving CFTR modulator drugs; how to identify new CFTR modulator drugs for patients with non-responsive CFTR variants; how to adjust other therapeutic modalities; and how to best partner with primary care clinicians. Progress always brings new challenges, and this has been evident worldwide for CF. Consequently, this article summarizes the major advances of recent years along with controversies and describes their implications with an international perspective.

Standards for the care of people with cystic fibrosis (CF); recognising and addressing CF health issues

This is the third in a series of four papers updating the European Cystic Fibrosis Society (ECFS) standards for the care of people with CF. This paper focuses on recognising and addressing CF health issues. The guidance was produced with wide stakeholder engagement, including people from the CF community, using an evidence-based framework. Authors contributed sections, and summary statements which were reviewed by a Delphi consultation. Monitoring and treating airway infection, inflammation and pulmonary exacerbations remains important, despite the widespread availability of CFTR modulators and their accompanying health improvements. Extrapulmonary CF-specific health issues persist, such as diabetes, liver disease, bone disease, stones and other renal issues, and intestinal obstruction. These health issues require multidisciplinary care with input from the relevant specialists. Cancer is more common in people with CF compared to the general population, and requires regular screening. The CF life journey requires mental and emotional adaptation to psychosocial and physical challenges, with support from the CF team and the CF psychologist. This is particularly important when life gets challenging, with disease progression requiring increased treatments, breathing support and potentially transplantation. Planning for end of life remains a necessary aspect of care and should be discussed openly, honestly, with sensitivity and compassion for the person with CF and their family. CF teams should proactively recognise and address CF-specific health issues, and support mental and emotional wellbeing while accompanying people with CF and their families on their life journey.

Understanding and addressing the needs of people with cystic fibrosis in the era of CFTR modulator therapy

Cystic fibrosis is a multiorgan disease caused by impaired function of the cystic fibrosis transmembrane conductance regulator (CFTR). Since the introduction of the CFTR modulator combination elexacaftor-tezacaftor-ivacaftor (ETI), which acts directly on mutant CFTR to enhance its activity, most people with cystic fibrosis (pwCF) have seen pronounced reductions in symptoms, and studies project marked increases in life expectancy for pwCF who are eligible for ETI. However, modulator therapy has not cured cystic fibrosis and the success of CFTR modulators has resulted in immediate questions about the new state of cystic fibrosis disease and clinical challenges in the care of pwCF. In this Series paper, we summarise key questions about cystic fibrosis disease in the era of modulator therapy, highlighting state-of-the-art research and clinical practices, knowledge gaps, new challenges faced by pwCF and the potential for future health-care challenges, and the pressing need for additional therapies to treat the underlying genetic or molecular causes of cystic fibrosis.

Management of respiratory tract exacerbations in people with cystic fibrosis: Focus on imaging

Respiratory tract exacerbations play a crucial role in progressive lung damage of people with cystic fibrosis, representing a major determinant in the loss of functional lung tissue, quality of life and patient survival. Detection and monitoring of respiratory tract exacerbations are challenging for clinicians, since under- and over-treatment convey several risks for the patient. Although various diagnostic and monitoring tools are available, their implementation is hampered by the current definition of respiratory tract exacerbation, which lacks objective "cut-offs" for clinical and lung function parameters. In particular, the latter shows a large variability, making the current 10% change in spirometry outcomes an unreliable threshold to detect exacerbation. Moreover, spirometry cannot be reliably performed in preschool children and new emerging tools, such as the forced oscillation technique, are still complementary and need more validation. Therefore, lung imaging is a key in providing respiratory tract exacerbation-related structural and functional information. However, imaging encompasses several diagnostic options, each with different advantages and limitations; for instance, conventional chest radiography, the most used radiological technique, may lack sensitivity and specificity in respiratory tract exacerbations diagnosis. Other methods, including computed tomography, positron emission tomography and magnetic resonance imaging, are limited by either radiation safety issues or the need for anesthesia in uncooperative patients. Finally, lung ultrasound has been proposed as a safe bedside option but it is highly operator-dependent and there is no strong evidence of its possible use during respiratory tract exacerbation. This review summarizes the clinical challenges of respiratory tract exacerbations in patients with cystic fibrosis with a special focus on imaging. Firstly, the definition of respiratory tract exacerbation is examined, while diagnostic and monitoring tools are briefly described to set the scene. This is followed by advantages and disadvantages of each imaging technique, concluding with a diagnostic imaging algorithm for disease monitoring during respiratory tract exacerbation in the cystic fibrosis patient.