A modified Tseng algorithm approach to restoring thoracic diseases' computerized tomography images.

It is well-known that the Tseng algorithm and its modifications have been successfully employed in approximating zeros of the sum of monotone operators. In this study, we restored various thoracic diseases' computerized tomography (CT) images, which were degraded with a known blur function and additive noise, using a modified Tseng algorithm. The test images used in the study depict calcification of the Aorta, Subcutaneous Emphysema, Tortuous Aorta, Pneumomediastinum, and Pneumoperitoneum. Additionally, we employed well-known image restoration tools to enhance image quality and compared the quality of restored images with the originals. Finally, the study demonstrates the potential to advance monotone inclusion problem-solving, particularly in the field of medical image recovery.

Concordancia diagnóstica de las imágenes torácicas para enfermedad por Covid-19 / Diagnostic concordance of thoracic images for Covid-19 disease

La reacción en cadena de la polimerasa de transcripción inversa (RT-PCR) es el estándard de oro para el diagnóstico de enfermedad por SARS-CoV-2. En el contexto de la pandemia con accesibilidad limitada a esta prueba, las imágenes diagnósticas aportaron hallazgos que sustentan la sospecha diagnóstica, evitando retrasos en atención médica. Objetivo. Determinar la sensibilidad, especificidad, valor predictivo positivo y negativo de las imágenes diagnósticas y su concordancia respecto al resultado de RT-PCR. Metodología. Estudio transversal analítico. Se comparó el resultado del reporte por imágenes con los resultados de RT-PCR en 138 pacientes. Se calculó la sensibilidad, especificidad, valor predictivo positivo y valor predictivo negativo para los rayos X de tórax y tomografía computarizada para el diagnóstico de infección por SARS-CoV-2. Se utilizó el índice Kappa de Cohen y el factor de Bayes para medir la concordancia y fuerza de asociación entre las variables. Resultados. La tomografía computarizada presentó una sensibilidad de 92,9 %, una especificidad del 64 %, un valor predictivo positivo de 92,1 % y un valor predictivo negativo de 66,7 %; mientras que, los rayos X presentaron una sensibilidad del 86 %, una especificidad del 52,9 %, un valor predictivo positivo de 92,9 % y un valor predictivo negativo del 34,6 %. Conclusión. La tomografía mostró concordancia diagnóstica moderada; su utilidad es mayor en casos de sospecha clínica moderada-alta, discrepancia diagnóstica o confirmación de complicaciones. Los rayos X mostraron concordancia diagnóstica baja; este método es de utilidad en casos de alta sospecha clínica, pero necesita comprobación con RT-PCR para un diagnóstico definitivo.

Reverse transcription polymerase chain reaction (RT-PCR) is the gold standard method for diagnosing SARS-CoV-2 disease. However, due to limited accessibility to this test during the pandemic, diagnostic imaging was used to support diagnostic suspicion and avoid delays in medical care. Objective. Determine the accuracy of diagnostic imaging (chest X-ray and computed tomography) in diagnosing SARS-CoV-2 infection, compared to RT-PCR result. Methodology.An analytical cross-sectional study was conducted. The imaging reports of 138 patients were compared with their RT-PCR results to calculate sensitivity, specificity, positive predictive value, and negative predictive value for both chest X-ray and computed tomography. Concordance between the imaging results and RT-PCR was measured using Cohen's Kappa index and Bayes factor. Results. Computed tomography showed a sensitivity of 92.9 %, a specificity of 64 %, a positive predictive value of 92.1 %, and a negative predictive value of 66.7 %. On the other hand, X-rays showed a sensitivity of 86 %, a specificity of 52.9 %, a positive predictive value of 92.9 %, and a negative predictive value of 34.6 %. Conclusion. Computed tomography showed moderate diagnostic concordance and is particularly useful in cases of moderate to high clinical suspicion, diagnostic discrepancy, or the need to confirm complications. On the other hand, X-rays showed low diagnostic concordance and should be used in combination with RT-PCR for a definitive diagnosis, especially in cases of high clinical suspicion

Disorders with Ophthalmic and Thoracic Involvement.

A variety of systemic conditions involve the thorax and the eyes. While subtle or nonspecific eye symptoms can be the initial clinical manifestation of some disorders, there can be additional manifestations in the thorax that lead to a specific diagnosis and affect patient outcomes. For instance, the initial clinical manifestation of Sjögren syndrome is dry eye or xerophthalmia; however, the presence of Sjögren lung disease represents a fourfold increase in mortality. Likewise, patients with acute sarcoidosis can initially present with pain and redness of the eye from uveitis in addition to fever and parotitis. Nearly 90% of patients with sarcoidosis have thoracic involvement, and the ophthalmologic symptoms can precede the thoracic symptoms by several years in some cases. Furthermore, a diagnosis made in one system can result in the screening of other organs as well as prompt genetic evaluation and examination of family members, such as in the setting of Marfan syndrome or Ehlers-Danlos syndrome. Multimodality imaging, particularly CT and MRI, plays a vital role in identification and characterization of these conditions. While it is helpful for ophthalmologists to be knowledgeable about these conditions and their associations so that they can order the pertinent radiologic studies, it is also important for radiologists to use the clues from ophthalmologic examination in addition to imaging findings to suggest a specific diagnosis. Systemic conditions with thoracic and ophthalmologic manifestations can be categorized as infectious, inflammatory, autoimmune, neoplastic, or hereditary in origin. The authors describe a spectrum of these conditions based on their underlying cause. ©RSNA, 2024.

Congenital Chest Lesions and Interventions.

Anomalies of the fetal chest require advanced imaging with ultrasound and MR imaging as well as expertise on the part of the interpreting pediatric radiologist. Congenital diaphragmatic hernia and congenital lung malformation are the most frequently seen, and in both conditions, the radiologist should provide both detailed anatomic description and measurement data for prognostication. This article provides a detailed approach to imaging the anatomy, in-depth explanation of available measurements and prognostic value, and keys to identifying candidates for fetal intervention. Less common congenital lung tumors and mediastinal and chest wall masses are also reviewed.

Automated thorax disease diagnosis using multi-branch residual attention network.

Chest X-ray (CXR) is an extensively utilized radiological modality for supporting the diagnosis of chest diseases. However, existing research approaches suffer from limitations in effectively integrating multi-scale CXR image features and are also hindered by imbalanced datasets. Therefore, there is a pressing need for further advancement in computer-aided diagnosis (CAD) of thoracic diseases. To tackle these challenges, we propose a multi-branch residual attention network (MBRANet) for thoracic disease diagnosis. MBRANet comprises three components. Firstly, to address the issue of inadequate extraction of spatial and positional information by the convolutional layer, a novel residual structure incorporating a coordinate attention (CA) module is proposed to extract features at multiple scales. Next, based on the concept of a Feature Pyramid Network (FPN), we perform multi-scale feature fusion in the following manner. Thirdly, we propose a novel Multi-Branch Feature Classifier (MFC) approach, which leverages the class-specific residual attention (CSRA) module for classification instead of relying solely on the fully connected layer. In addition, the designed BCEWithLabelSmoothing loss function improves the generalization ability and mitigates the problem of class imbalance by introducing a smoothing factor. We evaluated MBRANet on the ChestX-Ray14, CheXpert, MIMIC-CXR, and IU X-Ray datasets and achieved average AUCs of 0.841, 0.895, 0.805, and 0.745, respectively. Our method outperformed state-of-the-art baselines on these benchmark datasets.

Which is a real valuable screening tool for lung cancer and measure thoracic diseases, chest radiography or low-dose computed tomography?: A review on the current status of Japan and other countries.

Chest radiography (CR) has been used as a screening tool for lung cancer and the use of low-dose computed tomography (LDCT) is not recommended in Japan. We need to reconsider whether CR really contributes to the early detection of lung cancer. In addition, we have not well discussed about other major thoracic disease detection by CR and LDCT compared with lung cancer despite of its high frequency. We review the usefulness of CR and LDCT as veridical screening tools for lung cancer and other thoracic diseases. In the case of lung cancer, many studies showed that LDCT has capability of early detection and improving outcomes compared with CR. Recent large randomized trial also supports former results. In the case of chronic obstructive pulmonary disease (COPD), LDCT contributes to early detection and leads to the implementation of smoking cessation treatments. In the case of pulmonary infections, LDCT can reveal tiny inflammatory changes that are not observed on CR, though many of these cases improve spontaneously. Therefore, LDCT screening for pulmonary infections may be less useful. CR screening is more suitable for the detection of pulmonary infections. In the case of cardiovascular disease (CVD), CR may be a better screening tool for detecting cardiomegaly, whereas LDCT may be a more useful tool for detecting vascular changes. Therefore, the current status of thoracic disease screening is that LDCT may be a better screening tool for detecting lung cancer, COPD, and vascular changes. CR may be a suitable screening tool for pulmonary infections and cardiomegaly.

Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times.

Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that enhances the efficiency of thoracic physicians in comparing and diagnosing X-ray images, ultimately reducing misjudgments. The proposed system encompasses four key components: segmentation, alignment, comparison, and classification of lung X-ray images. Utilizing a public NIH Chest X-ray14 dataset and a local dataset gathered by the Chiayi Christian Hospital in Taiwan, the efficacy of both the traditional methods and deep-learning methods were compared. Experimental results indicate that, in both the segmentation and alignment stages, the deep-learning method outperforms the traditional method, achieving higher average IoU, detection rates, and significantly reduced processing time. In the comparison stage, we designed nonlinear transfer functions to highlight the differences between pre- and post-images through heat maps. In the classification stage, single-input and dual-input network architectures were proposed. The inclusion of difference information in single-input networks enhances AUC by approximately 1%, and dual-input networks achieve a 1.2-1.4% AUC increase, underscoring the importance of difference images in lung disease identification and classification based on chest X-ray images. While the proposed system is still in its early stages and far from clinical application, the results demonstrate potential steps forward in the development of a comprehensive computer-aided diagnostic system for comparative analysis of chest X-ray images.

Robust Stochastic Neural Ensemble Learning With Noisy Labels for Thoracic Disease Classification.

Chest radiography is the most common radiology examination for thoracic disease diagnosis, such as pneumonia. A tremendous number of chest X-rays prompt data-driven deep learning models in constructing computer-aided diagnosis systems for thoracic diseases. However, in realistic radiology practice, a deep learning-based model often suffers from performance degradation when trained on data with noisy labels possibly caused by different types of annotation biases. To this end, we present a novel stochastic neural ensemble learning (SNEL) framework for robust thoracic disease diagnosis using chest X-rays. The core idea of our method is to learn from noisy labels by constructing model ensembles and designing noise-robust loss functions. Specifically, we propose a fast neural ensemble method that collects parameters simultaneously across model instances and along optimization trajectories. Moreover, we propose a loss function that both optimizes a robust measure and characterizes a diversity measure of ensembles. We evaluated our proposed SNEL method on three publicly available hospital-scale chest X-ray datasets. The experimental results indicate that our method outperforms competing methods and demonstrate the effectiveness and robustness of our method in learning from noisy labels. Our code is available at https://github.com/hywang01/SNEL.

Spectrum of Thoracic Imaging Findings in the Setting of Substance Abuse.

ABSTRACT: Substance abuse continues to be prevalent nationwide and can lead to a myriad of chest pathologies. Imaging findings are vast and can include nodules, masses, ground-glass opacities, airspace disease, and cysts. Radiologists with awareness of these manifestations can assist in early identification of disease in situations where information is unable to be obtained from the patient. This review focuses on thoracic imaging findings associated with various forms of substance abuse, which are organized by portal of entry into the thorax: inhalation, ingestion, and injection.

Interventional Pulmonology: Extending the Breadth of Thoracic Care.

Interventional pulmonary medicine has developed as a subspecialty focused on the management of patients with complex thoracic disease. Leveraging minimally invasive techniques, interventional pulmonologists diagnose and treat pathologies that previously required more invasive options such as surgery. By mitigating procedural risk, interventional pulmonologists have extended the reach of care to a wider pool of vulnerable patients who require therapy. Endoscopic innovations, including endobronchial ultrasound and robotic and electromagnetic bronchoscopy, have enhanced the ability to perform diagnostic procedures on an ambulatory basis. Therapeutic procedures for patients with symptomatic airway disease, pleural disease, and severe emphysema have provided the ability to palliate symptoms. The combination of medical and procedural expertise has made interventional pulmonologists an integral part of comprehensive care teams for patients with oncologic, airway, and pleural needs. This review surveys key areas in which interventional pulmonologists have impacted the care of thoracic disease through bronchoscopic intervention.

Ultrasound in the Study of Thoracic Diseases: Innovative Aspects.

Thoracic ultrasound (TU) has rapidly gained popularity over the past 10 years. This is in part because ultrasound equipment is available in many settings, more training programmes are educating trainees in this technique, and ultrasound can be done rapidly without exposure to radiation. The aim of this review is to present the most interesting and innovative aspects of the use of TU in the study of thoracic diseases. In pleural diseases, TU has been a real revolution. It helps to differentiate between different types of pleural effusions, guides the performance of pleural biopsies when necessary and is more cost-effective under these conditions, and assists in the decision to remove thoracic drainage after talc pleurodesis. With the advent of COVID19, the use of TU has increased for the study of lung involvement. Nowadays it helps in the diagnosis of pneumonias, tumours and interstitial diseases, and its use is becoming more and more widespread in the Pneumology ward. In recent years, TU guided biopsies have been shown to be highly cost-effective, with other advantages such as the absence of radiation and the possibility of being performed at bedside. The use of contrast in ultrasound to increase the cost-effectiveness of these biopsies is very promising. In the study of the mediastinum and peripheral pulmonary nodules, the introduction of echobronchoscopy has brought about a radical change. It is a fully established technique in the study of lung cancer patients. The introduction of elastography may help to further improve its cost-effectiveness. In critically-ill patients, diaphragmatic ultrasound helps in the assessment of withdrawal of mechanical ventilation, and is now an indispensable tool in the management of these patients. In neuromuscular patients, ultrasound is a good predictor of impaired lung function. Currently, in Neuromuscular Disease Units, TU is an indispensable tool. Ultrasound study of the intercostal musculature is also effective in the study of respiratory function, and is widely used in Respiratory Rehabilitation. In Intermediate Care Units, thoracic ultrasound is indispensable for patient management. In these units there are ultrasound protocols for the management of patients with acute dyspnoea that have proven to be very effective.

Thoracic Applications of Spectral CT Scan.

TOPIC IMPORTANCE: Thoracic imaging with CT scan has become an essential component in the evaluation of respiratory and thoracic diseases. Providers have historically used conventional single-energy CT; however, prevalence of dual-energy CT (DECT) is increasing, and as such, it is important for thoracic physicians to recognize the utility and limitations of this technology. REVIEW FINDINGS: The technical aspects of DECT are presented, and practical approaches to using DECT are provided. Imaging at multiple energy spectra allows for postprocessing of the data and the possibility of creating multiple distinct image reconstructions based on the clinical question being asked. The data regarding utility of DECT in pulmonary vascular disorders, ventilatory defects, and thoracic oncology are presented. A pictorial essay is provided to give examples of the strengths associated with DECT. SUMMARY: DECT has been most heavily studied in chronic thromboembolic pulmonary hypertension; however, it is increasingly being used across a wide spectrum of thoracic diseases. DECT combines morphologic and functional assessments in a single imaging acquisition, providing clinicians with a powerful diagnostic tool. Its role in the evaluation and treatment of thoracic diseases will likely continue to expand in the coming years as clinicians become more experienced with the technology.

Quantitative imaging workshop XIX: Utilizing quantitative thoracic imaging to optimize population health final summary.

Lung cancer screening involves the use of thoracic CT for both detection and measurements of suspicious lung nodules to guide the screening management. Since lung cancer screening eligibility typically requires age over 50 years along with >20 pack-year tobacco exposure, thoracic CT scans also frequently reveal evidence for pulmonary emphysema as well as coronary artery calcification. These three thoracic diseases are collectively three of the leading causes of premature death across the world. Screening for the major thoracic diseases in this heavily tobacco-exposed cohort is broadening the focus of lung cancer screening to a more comprehensive health evaluation including discussing the relevance of screen-detected findings of the heart and lung parenchyma. The status and implications of these emerging issues were reviewed in a multidisciplinary workshop focused on the process of quantitative imaging in the lung cancer screening setting to guide the evolution of this important new area of public health.

CheXNet and feature pyramid network: a fusion deep learning architecture for multilabel chest X-Ray clinical diagnoses classification.

The existing multilabel X-Ray image learning tasks generally contain much information on pathology co-occurrence and interdependency, which is very important for clinical diagnosis. However, the challenging part of this subject is to accurately diagnose multiple diseases that occurred in a single X-Ray image since multiple levels of features are generated in the images, and create different features as in single label detection. Various works were developed to address this challenge with proposed deep learning architectures to improve classification performance and enrich diagnosis results with multi-probability disease detection. The objective is to create an accurate result and a faster inference system to support a quick diagnosis in the medical system. To contribute to this state-of-the-art, we designed a fusion architecture, CheXNet and Feature Pyramid Network (FPN), to classify and discriminate multiple thoracic diseases from chest X-Rays. This concept enables the model to extract while creating a pyramid of feature maps with different spatial resolutions that capture low-level and high-level semantic information to encounter multiple features. The model's effectiveness is evaluated using the NIH ChestXray14 dataset, with the Area Under Curve (AUC) and accuracy metrics used to compare the results against other cutting-edge approaches. The overall results demonstrate that our method outperforms other approaches and has become promising for multilabel disease classification in chest X-Rays, with potential applications in clinical practice. The result demonstrated that we achieved an average AUC of 0.846 and an accuracy of 0.914. Further, our proposed architecture diagnoses images in 0.013 s, faster than the latest approaches.

Canadian Association of Radiologists Thoracic Imaging Referral Guideline.

The Canadian Association of Radiologists (CAR) Thoracic Expert Panel consists of radiologists, respirologists, emergency and family physicians, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 24 clinical/diagnostic scenarios, a rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of these clinical/diagnostic scenarios. Recommendations from 30 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) for guidelines framework were used to develop 48 recommendation statements across the 24 scenarios. This guideline presents the methods of development and the referral recommendations for screening/asymptomatic individuals, non-specific chest pain, hospital admission for non-thoracic conditions, long-term care admission, routine pre-operative imaging, post-interventional chest procedure, upper respiratory tract infection, acute exacerbation of asthma, acute exacerbation of chronic obstructive pulmonary disease, suspect pneumonia, pneumonia follow-up, immunosuppressed patient with respiratory symptoms/febrile neutropenia, chronic cough, suspected pneumothorax (non-traumatic), clinically suspected pleural effusion, hemoptysis, chronic dyspnea of non-cardiovascular origin, suspected interstitial lung disease, incidental lung nodule, suspected mediastinal lesion, suspected mediastinal lymphadenopathy, and elevated diaphragm on chest radiograph.