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With the advent of Artificial Intelligence (AI) and even more so recently in the field of Machine Learning (ML), there has been rapid progress across the field. One of the prominent examples is image recognition in the medical category, such as X-ray imaging, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). It has the potential to alleviate a doctor's heavy workload of sifting through large quantities of images. Due to the rising attention to lung-related diseases, such as pneumothorax and nodules, ML is being incorporated into the field in the hope of alleviating the already strained medical resources. In this study, we proposed a system that can detect pneumothorax diseases reliably. By comparing multiple models and hyperparameter configurations, we recommend a model for hospitals, as its focus on minimizing false positives aligns with the precision required by medical professionals. Through our cooperation with Poh-Ai Hospital, we acquired a total of over 8000 X-ray images, with more than 1000 of them from pneumothorax patients. We hope that by integrating AI systems into the automated process of scanning chest X-ray images with various diseases, more resources will be available in the already strained medical systems. Our proposed system showed that the best model that is used for transfer learning from our dataset performed with an AP of 51.57 and an AP75 of 61.40, with accuracy at 93.89%, a false positive of 1.12%, and a false negative of 4.99%. Based on the feedback from practicing doctors, they are more wary of false positives. For their use case, we recommend another model due to the lower false positive rate and higher accuracy compared with other models, which in our test shows a rate of only 0.88% and 95.68%, demonstrating the feasibility of the research. This promising result showed that it could be utilized in other types of diseases and expand to more hospitals and medical organizations, potentially benefitting more people. [ABSTRACT FROM AUTHOR]

This work proposes a novel technique called Enhanced JAYA (EJAYA) assisted Q-Learning for the classification of pulmonary diseases, such as pneumonia and tuberculosis (TB) sub-classes using chest x-ray images. The work introduces Fuzzy lattices formation to handle real time (non-linear and non-stationary) data based feature extraction using Schrödinger equation. Features based adaptive classification is made possible through the Q-learning algorithm wherein optimal Q-values selection is done via EJAYA optimization algorithm. Fuzzy lattice is formed using x-ray image pixels and lattice Kinetic Energy (K.E.) is calculated using the Schrödinger equation. Feature vector lattices having highest K.E. have been used as an input features for the classifier. The classifier has been employed for pneumonia classification (normal, mild and severe) and Tuberculosis detection (presence or absence). A total of 3000 images have been used for pneumonia classification yielding an accuracy, sensitivity, specificity, precision and F-scores of 97.90%, 98.43%, 97.25%, 97.78% and 98.10%, respectively. For Tuberculosis 600 samples have been used. The achived accuracy, sensitivity, specificity, precision and F-score are 95.50%, 96.39%, 94.40% 95.52% and 95.95%, respectively. Computational time are 40.96 and 39.98 s for pneumonia and TB classification. Classifier learning rate (training accuracy) for pneumonia classes (normal, mild and severe) are 97.907%, 95.375% and 96.391%, respectively and for tuberculosis (present and absent) are 96.928% and 95.905%, respectively. The results have been compared with contemporary classification techniques which shows superiority of the proposed approach in terms of accuracy and speed of classification. The technique could serve as a fast and accurate tool for automated pneumonia and tuberculosis classification., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

With the advent of Artificial Intelligence (AI) and even more so recently in the field of Machine Learning (ML), there has been rapid progress across the field. One of the prominent examples is image recognition in the medical category, such as X-ray imaging, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). It has the potential to alleviate a doctor’s heavy workload of sifting through large quantities of images. Due to the rising attention to lung-related diseases, such as pneumothorax and nodules, ML is being incorporated into the field in the hope of alleviating the already strained medical resources. In this study, we proposed a system that can detect pneumothorax diseases reliably. By comparing multiple models and hyperparameter configurations, we recommend a model for hospitals, as its focus on minimizing false positives aligns with the precision required by medical professionals. Through our cooperation with Poh-Ai Hospital, we acquired a total of over 8000 X-ray images, with more than 1000 of them from pneumothorax patients. We hope that by integrating AI systems into the automated process of scanning chest X-ray images with various diseases, more resources will be available in the already strained medical systems. Our proposed system showed that the best model that is used for transfer learning from our dataset performed with an AP of 51.57 and an AP75 of 61.40, with accuracy at 93.89%, a false positive of 1.12%, and a false negative of 4.99%. Based on the feedback from practicing doctors, they are more wary of false positives. For their use case, we recommend another model due to the lower false positive rate and higher accuracy compared with other models, which in our test shows a rate of only 0.88% and 95.68%, demonstrating the feasibility of the research. This promising result showed that it could be utilized in other types of diseases and expand to more hospitals and medical organizations, potentially benefitting more people.

The auscultation is a non-invasive and cost-effective method used for the diagnosis of lung diseases, which are one of the leading causes of death worldwide. However, the efficacy of the auscultation suffers from the limitations of the analog stethoscopes and the subjective nature of human interpretation. To overcome these limitations, the accurate diagnosis of these diseases by employing the computer based automated algorithms applied to the digitized lung sounds has been studied for the last decades. This study proposes a novel approach that uses a Tunable Q-factor Wavelet Transform (TQWT) based statistical feature extraction followed by individual and ensemble learning model training with the aim of lung disease classification. During the learning stage various machine learning algorithms are utilized as the individual learners as well as the hard and soft voting fusion approaches are employed for performance enhancement with the aid of the predictions of individual models. For an objective evaluation of the proposed approach, the study was structured into two main tasks that were investigated in detail by using several sub-tasks to comparison with state-of-the-art studies. Among the sub-tasks which investigates patient-based classification, the highest accuracy obtained for the binary classification was achieved as 97.63% (healthy vs. non-healthy), while accuracy values up to 66.32% for three-class classification (obstructive-related, restrictive-related, and healthy), and 53.42% for five-class classification (asthma, chronic obstructive pulmonary disease, interstitial lung disease, pulmonary infection, and healthy) were obtained. Regarding the other sub-task, which investigates sample-based classification, the proposed approach was superior to almost all previous findings. The proposed method underscores the potential of TQWT based signal decomposition that leverages the power of its adaptive time-frequency resolution property satisfied by Q-factor adjustability. The obtained results are very promising and the proposed approach paves the way for more accurate and automated digital auscultation techniques in clinical settings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Diagnosing lung diseases accurately and promptly is essential for effectively managing this significant public health challenge on a global scale. This paper introduces a new framework called Modified Segnet-based Lung Disease Segmentation and Severity Classification (MSLDSSC). The MSLDSSC model comprises four phases: "preprocessing, segmentation, feature extraction, and classification." Initially, the input image undergoes preprocessing using an improved Wiener filter technique. This technique estimates the power spectral density of the noisy and original images and computes the SNR assisted by PSNR to evaluate image quality. Next, the preprocessed image undergoes Segmentation to identify and separate the RoI from the background objects in the lung image. We employ a Modified Segnet mechanism that utilizes a proposed hard tanh-Softplus activation function for effective Segmentation. Following Segmentation, features such as MLDN, entropy with MRELBP, shape features, and deep features are extracted. Following the feature extraction phase, the retrieved feature set is input into a hybrid severity classification model. This hybrid model comprises two classifiers: SDPA-Squeezenet and DCNN. These classifiers train on the retrieved feature set and effectively classify the severity level of lung diseases., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Shafi, Chinnappan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Computed tomography (CT) scans are widely used to diagnose lung conditions due to their ability to provide a detailed overview of the body's respiratory system. Despite its popularity, visual examination of CT scan images can lead to misinterpretations that impede a timely diagnosis. Utilizing technology to evaluate images for disease detection is also a challenge. As a result, there is a significant demand for more advanced systems that can accurately classify lung diseases from CT scan images. In this work, we provide an extensive analysis of different approaches and their performances that can help young researchers to build more advanced systems. First, we briefly introduce diagnosis and treatment procedures for various lung diseases. Then, a brief description of existing methods used for the classification of lung diseases is presented. Later, an overview of the general procedures for lung disease classification using machine learning (ML) is provided. Furthermore, an overview of recent progress in ML-based classification of lung diseases is provided. Finally, existing challenges in ML techniques are presented. It is concluded that deep learning techniques have revolutionized the early identification of lung disorders. We expect that this work will equip medical professionals with the awareness they require in order to recognize and classify certain medical disorders., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Objective: On November 24, 2017, Organ Procurement and Transplantation Network implemented a change to lung allocation replacing donor service area with a 250 nautical mile radius around donor hospitals. We sought to evaluate the experience of a small to medium size center following implementation., Methods: Patients (47 pre and 54 post) undergoing lung transplantation were identified from institutional database from January 2016 to October 2019. Detailed chart review and analysis of institutional cost data was performed. Univariate analysis was performed to compare eras., Results: Similar short-term mortality and primary graft dysfunction were observed between groups. Decreased local donation (68% vs 6%; P < .001), increased travel distance (145 vs 235 miles; P = .004), travel cost ($8626 vs $14,482; P < .001), and total procurement cost ($60,852 vs $69,052; P = .001) were observed postimplementation. We also document an increase in waitlist mortality postimplementation (6.9 vs 31.6 per 100 patient-years; P < .001)., Conclusions: Following implementation of the new allocation policy in a small to medium size center, several changes were in accordance with policy intention. However, concerning shifts emerged, including increased waitlist mortality and resource utilization. Continued close monitoring of transplant centers stratified by size and location are paramount to maintaining global availability of lung transplantation to all Americans regardless of geographic residence or socioeconomic status., (Copyright © 2020 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.)

Background: Pivotal trials of chimeric antigen receptor T-cell (CAR-T) have identified common toxicities but may have been underpowered to detect cardiovascular and pulmonary adverse events (CPAEs)., Objectives: This study sought to investigate CPAEs associated with commercial CD19-directed CAR-T therapy., Methods: In this retrospective, pharmacovigilance study, the authors used the Food and Drug Administration adverse event reporting system to identify CPAEs associated with axicabtagene-ciloleucel and tisagenlecleucel. The authors evaluated disproportionate reporting by the reporting odds ratio (ROR) and the lower bound of the information component 95% credibility interval (IC 025 >0 is deemed significant). Significant associations were further adjusted to age and sex (adj.ROR)., Results: The authors identified CAR-T reports of 2,657 patients, including 546 CPAEs (20.5%). CPAEs overlapped with cytokine release syndrome in 68.3% (373 of 546) of the reports. Compared with the full database, CAR-T was associated with overreporting of tachyarrhythmias (n = 74 [2.8%], adj.ROR = 2.78 [95% CI: 2.21-3.51]), cardiomyopathy (n = 69 [2.6%], adj.ROR = 3.51 [2.42-5.09]), pleural disorders (n = 46 [1.7%], adj.ROR = 3.91 [2.92-5.23]), and pericardial diseases (n = 11 [0.4%], adj.ROR = 2.26 [1.25-4.09], all IC 025 >0). Venous thromboembolic events (VTEs) were associated only with axicabtagene-ciloleucel therapy (n = 28 [1.6%], adj.ROR = 1.80 [1.24-2.62], IC 025 >0). Atrial fibrillation (n = 55) was the leading tachyarrhythmia, followed by ventricular arrhythmias (n = 14). Tachyarrhythmias and VTEs were reported more often following axicabtagene-ciloleucel than tisagenlecleucel in an age- and sex-adjusted model (adj.ROR = 1.82 [1.04-3.18] and adj.ROR = 2.86 [1.18-6.93], respectively). Finally, the fatality rate of CPAEs was 30.9%., Conclusions: In this largest post-marketing study to date, the authors identified an association between CAR-T and various CPAEs, including tachyarrhythmias, cardiomyopathy, pericardial and pleural disorders, and VTEs. These findings should be considered in the multidisciplinary assessment for and monitoring of CAR-T therapy recipients., Competing Interests: Funding Support and Author Disclosures This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748. This study was supported in part by NIH/NCI grant RO1-CA 233610 (Dr Herrmann). Dr Perales has received personal fees from Abbvie, Bellicum, Bristol Myers Squibb, Celgene, Cidara Theraputics, Incyte, Kite/Gilead, Medigene, Miltenyi, MolMed, Nektar Therapeutics, NexImmune, Novartis, Omeros, Merck, Servier, and Tekeda; has served on data safety and monitoring boards for Cidara Therapeutics, Medigene, and Servier; and has received clinical trial support from Incyte, Kite/Gilead, and Miltenyi. Data presented in this study do not represent the opinion of the United States Food and Drug Administration (FDA). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

The National Heart, Lung, and Blood Institute and the Cardiovascular Medical Research and Education Fund held a workshop on the application of pulmonary vascular disease omics data to the understanding, prevention, and treatment of pulmonary vascular disease. Experts in pulmonary vascular disease, omics, and data analytics met to identify knowledge gaps and formulate ideas for future research priorities in pulmonary vascular disease in line with National Heart, Lung, and Blood Institute Strategic Vision goals. The group identified opportunities to develop analytic approaches to multiomic datasets, to identify molecular pathways in pulmonary vascular disease pathobiology, and to link novel phenotypes to meaningful clinical outcomes. The committee suggested support for interdisciplinary research teams to develop and validate analytic methods, a national effort to coordinate biosamples and data, a consortium of preclinical investigators to expedite target evaluation and drug development, longitudinal assessment of molecular biomarkers in clinical trials, and a task force to develop a master clinical trials protocol for pulmonary vascular disease., Competing Interests: Funding Support and Author Disclosures Several authors report funding support from the National Institutes of Health/National Heart, Lung, and Blood Institute (K08HL128802 to Dr. Oldham; R35HL140019 to Dr. Aldred; R01HL146588 to Dr. Brittain; R01HL135142, R01HL137927, R01HL089856, and R01HL147148 to Dr. Cho; R01HL136603 to Dr. Desai; K01HL146980 to Dr. Kelly; U01HL125215 to Dr. Leopold; and K23HL144418 to Dr. Modena). Dr. Oldham has received a research grant from the Cardiovascular Medical Research and Education Fund. Dr. Leopold was supported by grants from the American Heart Association. Dr. Wilkins was supported by the British Heart Foundation (RE/18/4/34215 and SP/18/10/33975). Dr. Hemnes has served as a consultant for Actelion, Bayer, Complexa, and United Therapeutics; and owns shares in PHPrecisionMed, all outside of the submitted work. Dr. Chan has served as a consultant for Zogenix, Aerpio, and United Therapeutics; is a director, officer, and shareholder in Synhale Therapeutics; has held research grants from Actelion and Pfizer; and has filed patent applications regarding the targeting of BMP signaling and metabolic pathways in pulmonary hypertension, all outside of the submitted work. Dr. Cho has received grant support from Bayer and GlaxoSmithKline; and served as a consultant for Genentech, AstraZeneca, and Illumina, all outside of the submitted work. Dr. Desai has served as a consultant for Novartis, outside of the submitted work. Dr. Modena has received honoraria from AstraZeneca, Genentech, GlaxoSmithKline, Regeneron, Sanofi, and Teva for consulting or lectures in the past; and has current research funding from Genentech and GlaxoSmithKline, all outside of the submitted work. Dr. Rhodes has received personal advisory board fees from Janssen and United Therapeutics, outside of the submitted work. Dr. Wilkins has consulted with Actelion, Novartis, GlaxoSmithKline, and MorphogenIX in the development of biomarkers for pulmonary hypertension, all outside of the submitted work. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021 American College of Cardiology Foundation. All rights reserved.)

The subpleural sparing pattern is a common finding on computed tomography (CT) of the lungs. It comprises of pulmonary opacities sparing the lung peripheries, typically 1cm and less from the pleural surface. This finding has a variety of causes, including idiopathic, inflammatory, infectious, inhalational, cardiac, traumatic, and bleeding disorders. Specific disorders that can cause subpleural sparing patterns include nonspecific interstitial pneumonia (NSIP), organizing pneumonia (OP), pulmonary alveolar proteinosis (PAP), diffuse alveolar hemorrhage (DAH), vaping-associated lung injury (VALI), cracked lung, pulmonary edema, pneumocystis jirovecii pneumonia (PJP), pulmonary contusion, and more recently, Coronavirus disease 2019 (COVID-19) pneumonia. Knowledge of the many etiologies of this pattern can be useful in preventing diagnostic errors. In addition, although the etiology of subpleural sparing pattern is frequently indistinguishable during an initial radiologic evaluation, the differences in location of opacities in the lungs, as well as the presence of additional radiologic findings, patient history, and clinical presentation, can often be useful to suggest the appropriate diagnosis. We did a comprehensive search on Pubmed and Google Scholar database using keywords of "subpleural sparing," "peripheral sparing," "sparing of peripheries," "CT chest," "chest imaging," and "pulmonary disease." This review aims to describe the primary differential diagnosis of subpleural sparing pattern seen on chest imaging with a strong emphasis on clinical and radiographic findings. We also discuss the pathogenesis and essential clues that are crucial to narrow the differential diagnosis., Competing Interests: Declaration of Conflicting Interests The author(s) declare(s) no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (Copyright © 2021 Southern Society for Clinical Investigation. Published by Elsevier Inc. All rights reserved.)

Advances in high-throughput biotechnologies have facilitated omics profiling, a key component of precision phenotyping, in patients with pulmonary vascular disease. Omics provides comprehensive information pertaining to genes, transcripts, proteins, and metabolites. The resulting omics big datasets may be integrated for more robust results and are amenable to analysis using machine learning or newer analytical methodologies, such as network analysis. Results from fully integrated multi-omics datasets combined with clinical data are poised to provide novel insight into pulmonary vascular disease as well as diagnose the presence of disease and prognosticate outcomes., Competing Interests: Disclosure J.A. Leopold has no disclosures relevant to this publication; A.R. Hemnes has no disclosures relevant to this publication., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The lung is one of several organs that can be affected by HTLV-1 mediated inflammation. Pulmonary inflammation associated with HTLV-1 infection involves the interstitium, airways and alveoli, resulting in several clinical entities including interstitial pneumonias, bronchiolitis and alveolitis, depending on which structures are most affected. Augmentation of the inflammatory effects of HTLV-1 infected lymphocytes by recruitment of other inflammatory cells in a positive feedback loop is likely to underlie the pathogenesis of HTLV-1 associated pulmonary disease, as has been proposed for HTLV-1 associated myelopathy. In contrast to the conclusions of early case series, HTLV-1 associated pulmonary disease can be associated with significant parenchymal damage, which may progress to bronchiectasis where this involves the airways. Based on our current understanding of HTLV-1 associated pulmonary disease, diagnostic criteria are proposed.

This paper focuses on the thorax disease classification problem in chest X-ray (CXR) images. Different from the generic image classification task, a robust and stable CXR image analysis system should consider the unique characteristics of CXR images. Particularly, it should be able to: 1) automatically focus on the disease-critical regions, which usually are of small sizes; 2) adaptively capture the intrinsic relationships among different disease features and utilize them to boost the multi-label disease recognition rates jointly. In this paper, we propose to learn discriminative features with a two-branch architecture, named ConsultNet, to achieve those two purposes simultaneously. ConsultNet consists of two components. First, an information bottleneck constrained feature selector extracts critical disease-specific features according to the feature importance. Second, a spatial-and-channel encoding based feature integrator enhances the latent semantic dependencies in the feature space. ConsultNet fuses these discriminative features to improve the performance of thorax disease classification in CXRs. Experiments conducted on the ChestX-ray14 and CheXpert dataset demonstrate the effectiveness of the proposed method.

Pediatric cystic lung lesions have long been a source of confusion for clinicians, radiologists, and pathologists. They encompass a wide spectrum of entities with variable prognostic implications, including congenital lung malformations, pulmonary neoplasms, and hereditary conditions. As our understanding of the developmental and genetic origins of these conditions has evolved, revised nomenclature and classifications have emerged in an attempt to bring clarity to the origin of these lesions and guide clinical management. This review discusses cystic lung lesions and the current understanding of their etiopathogenesis., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Existing multi-label medical image learning tasks generally contain rich relationship information among pathologies such as label co-occurrence and interdependency, which is of great importance for assisting in clinical diagnosis and can be represented as the graph-structured data. However, most state-of-the-art works only focus on regression from the input to the binary labels, failing to make full use of such valuable graph-structured information due to the complexity of graph data. In this paper, we propose a novel label co-occurrence learning framework based on Graph Convolution Networks (GCNs) to explicitly explore the dependencies between pathologies for the multi-label chest X-ray (CXR) image classification task, which we term the "CheXGCN". Specifically, the proposed CheXGCN consists of two modules, i.e., the image feature embedding (IFE) module and label co-occurrence learning (LCL) module. Thanks to the LCL model, the relationship between pathologies is generalized into a set of classifier scores by introducing the word embedding of pathologies and multi-layer graph information propagation. During end-to-end training, it can be flexibly integrated into the IFE module and then adaptively recalibrate multi-label outputs with these scores. Extensive experiments on the ChestX-Ray14 and CheXpert datasets have demonstrated the effectiveness of CheXGCN as compared with the state-of-the-art baselines.

Objectives: We develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) in the classification of the pulmonary lesion and identify optimal machine learning methods., Materials and Methods: This retrospective analysis included 201 patients (143 malignancies, 58 benign lesions). Radiomics features were extracted from multiparametric MRI, including T2-weighted imaging (T2WI), T1-weighted imaging (TIWI), and apparent diffusion coefficient (ADC) map. Three feature selection methods, including recursive feature elimination (RFE), t test, and least absolute shrinkage and selection operator (LASSO), and three classification methods, including linear discriminate analysis (LDA), support vector machine (SVM), and random forest (RF) were used to distinguish benign and malignant pulmonary lesions. Performance was compared by AUC, sensitivity, accuracy, precision, and specificity. Analysis of performance differences in three randomly drawn cross-validation sets verified the stability of the results., Results: For most single MR sequences or combinations of multiple MR sequences, RFE feature selection method with SVM classifier had the best performance, followed by RFE with RF. The radiomics model based on multiple sequences showed a higher diagnostic accuracy than single sequence for every machine learning method. Using RFE with SVM, the joint model of T1WI, T2WI, and ADC showed the highest performance with AUC = 0.88 ± 0.02 (sensitivity 83%; accuracy 82%; precision 91%; specificity 79%) in test set., Conclusion: Quantitative radiomics features based on multiparametric MRI have good performance in differentiating lung malignancies and benign lesions. The machine learning method of RFE with SVM is superior to the combination of other feature selection and classifier methods., Key Points: • Radiomics approach has the potential to distinguish between benign and malignant pulmonary lesions. • Radiomics model based on multiparametric MRI has better performance than single-sequence models. • The machine learning methods RFE with SVM perform best in the current cohort.

Background: A challenge in imaging research is a diagnostic classification of study participants. We hypothesised that a structured approach would be efficient and that classification by medical students, residents, and an expert panel whenever necessary would be as valid as classification of all patients by experts., Methods: OPTIMACT is a randomised trial designed to evaluate the effectiveness of replacing chest x-ray for ultra-low-dose chest computed tomography (CT) at the emergency department. We developed a handbook with diagnostic guidelines and randomly selected 240 cases from 2,418 participants enrolled in OPTIMACT. Each case was independently classified by two medical students and, if they disagreed, by the students and a resident in a consensus meeting. Cases without consensus and cases classified as complex were assessed by a panel of medical specialists. To evaluate the validity, 60 randomly selected cases not referred to the panel by the students and the residents were reassessed by the specialists., Results: Overall, the students and, if necessary, residents were able to assign a diagnosis in 183 of the 240 cases (76% concordance; 95% confidence interval [CI] 71-82%). We observed agreement between students and residents versus medical specialists in 50/60 cases (83% concordance; 95% CI 74-93%)., Conclusions: A structured approach in which study participants are assigned diagnostic labels by assessors with increasing levels of medical experience was an efficient and valid classification method, limiting the workload for medical specialists. We presented a viable option for classifying study participants in large-scale imaging trials (Netherlands National Trial Register number NTR6163).

The common CT imaging signs of lung diseases (CISLs) which frequently appear in lung CT images are widely used in the diagnosis of lung diseases. Computer-aided diagnosis (CAD) based on the CISLs can improve radiologists' performance in the diagnosis of lung diseases. Since similarity measure is important for CAD, we propose a multi-level method to measure the similarity between the CISLs. The CISLs are characterized in the low-level visual scale, mid-level attribute scale, and high-level semantic scale, for a rich representation. The similarity at multiple levels is calculated and combined in a weighted sum form as the final similarity. The proposed multi-level similarity method is capable of computing the level-specific similarity and optimal cross-level complementary similarity. The effectiveness of the proposed similarity measure method is evaluated on a dataset of 511 lung CT images from clinical patients for CISLs retrieval. It can achieve about 80% precision and take only 3.6 ms for the retrieval process. The extensive comparative evaluations on the same datasets are conducted to validate the advantages on retrieval performance of our multi-level similarity measure over the single-level measure and the two-level similarity methods. The proposed method can have wide applications in radiology and decision support. Graphical abstract.

Purpose of Review: Occupational exposures remain an underrecognized and preventable cause of lung disease in high-income countries. The present review highlights the emergence of cleaning-related respiratory disease and the re-emergence of silicosis as examples of trends in occupational lung diseases in the 21st century., Recent Findings: Employment trends, such as the shift from large-scale manufacturing to a service economy, the growth of the healthcare sector, and changing consumer products have changed the spectrum of work-related lung diseases. Following decades of progress in reducing traditional hazards such as silica in U.S. workplaces, cases of advanced silicosis have recently re-emerged with the production of engineered stone countertops. With growth in the healthcare and service sectors in the United States, cleaning products have become an important cause of work-related asthma and have recently been associated with an increased risk of chronic obstructive pulmonary disease (COPD) in women. However, these occupational lung diseases largely go unrecognized by practicing clinicians., Summary: The present article highlights how changes in the economy and work structure can lead to new patterns of inhalational workplace hazards and respiratory disease, including cleaning-related respiratory disease and silicosis. Pulmonary clinicians need to be able to recognize and diagnose these occupational lung diseases, which requires a high index of suspicion and a careful occupational history.

Objective: To test the hypothesis that a lung ultrasound severity score (LUSsc) can predict the development of chronic lung disease (CLD) in preterm neonates., Study Design: Preterm infants <30 weeks' gestational age were enrolled in this study. Lung ultrasound (LUS) was performed between 1 and 9 postnatal weeks. All ultrasound studies were done assessing three lung zones on each lung. Each zone was given a score between 0 and 3. A receiver operating characteristic curve was constructed to assess the ability of LUSsc to predict CLD., Results: We studied 27 infants at a median (interquartile range [IQR]) gestational age and birth weight of 26 weeks (25-29) and 780 g (530-1,045), respectively. Median (IQR) postnatal age at the time of LUS studies was 5 (2-8) weeks. Fourteen infants who developed CLD underwent 34 studies. Thirteen infants without CLD underwent 30 studies. Those who developed CLD had a higher LUSsc than those who did not (median [IQR] of scores: 9 [6-12] vs. 3 [1-4], p  < 0.0001). An LUSsc cutoff of 6 has a sensitivity and specificity of 76 and 97% and positive and negative predictive values of 95 and 82%, respectively. Adding gestational age < 27 weeks improved sensitivity and specificity to 86 and 98% and positive and negative predictive values to 97 and 88%., Conclusion: LUSsc between 2 and 8 weeks can predict development of CLD in preterm neonates., Competing Interests: None declared., (Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.)

A novel method to detect and classify several classes of diseased and healthy lung tissue in CT (Computed Tomography), based on the fusion of Riesz and deep learning features, is presented. First, discriminative parametric lung tissue texture signatures are learned from Riesz representations using a one-versus-one approach. The signatures are generated for four diseased tissue types and a healthy tissue class, all of which frequently appear in the publicly available Interstitial Lung Diseases (ILD) dataset used in this article. Because the Riesz wavelets are steerable, they can easily be made invariant to local image rotations, a property that is desirable when analyzing lung tissue micro-architectures in CT images. Second, features from deep Convolutional Neural Networks (CNN) are computed by fine-tuning the Inception V3 architecture using an augmented version of the same ILD dataset. Because CNN features are both deep and non-parametric, they can accurately model virtually any pattern that is useful for tissue discrimination, and they are the de facto standard for many medical imaging tasks. However, invariance to local image rotations is not explicitly implemented and can only be approximated with rotation-based data augmentation. This motivates the fusion of Riesz and deep CNN features, as the two techniques are very complementary. The two learned representations are combined in a joint softmax model for final classification, where early and late feature fusion schemes are compared. The experimental results show that a late fusion of the independent probabilities leads to significant improvements in classification performance when compared to each of the separate feature representations and also compared to an ensemble of deep learning approaches., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: Pulmonary rehabilitation has shown significant benefit for patients with chronic obstructive pulmonary disease (COPD). The effect on non-COPD pulmonary patients is less well established., Objectives: To determine whether pulmonary rehabilitation is also beneficial for non-COPD pulmonary patients., Methods: Clinical and demographic data on non-COPD pulmonary patients who participated in our institutional pulmonary rehabilitation program between January 2009 and December 2016 were collected. Participants engaged in a 60-minute, twice-weekly, ambulatory hospital-based program lasting 12 to 24 sessions. Sessions included both endurance and muscle training as well as healthy lifestyle educational activities. The six-minute walk test (6MWT) and the St. George's Respiratory Questionnaire (SGRQ) were conducted before and after the rehabilitation program., Results: We recruited 214 non-COPD patients, of whom 153 completed at least 12 sessions. Of these, 59 presented with interstitial lung disease (ILD), 18 with non-ILD restrictive lung defects, 25 with asthma, 30 with lung cancer, and 21 with other conditions (e.g., pulmonary hypertension, bronchiectasis) The groups demonstrated significant improvement in 6MWT and in SGRQ scores. Non-COPD patients gained a 61.9 meter (19%) improvement in the 6MWT (P < 0.0001) and 8.3 point reduction in their SGRQ score (P < 0.0001)., Conclusions: Pulmonary rehabilitation is effective in non-COPD pulmonary patients. As such, it should be an integral part of the treatment armament provided to the vast majority of those suffering from chronic respiratory disease.

Objectives. To evaluate the impact of adding a respiratory dimension (a bolt-on dimension) to the EQ-5D-5L health state valuations. Methods. Based on extensive regression and principal component analyses, 2 respiratory bolt-on candidates were formulated: R1, limitations in physical activities due to shortness of breath, and R2, breathing problems. Valuation interviews for the selected bolt-ons were performed with a representative sample from the Dutch general public using the standardized interview protocol and software of the EuroQol group. Hybrid models based on the combined time-tradeoff (TTO) and discrete choice experiment (DCE) data were estimated to assess whether the 5 levels of the respiratory bolt-on led to significant changes in utility values. Results. For each bolt-on candidate, slightly more than 200 valuation interviews were conducted. Mean TTO values and DCE choice probabilities for health states with a level 4 or 5 for the respiratory dimension were significantly lower compared with the same health states in the Dutch EQ-5D-5L valuation study without the respiratory dimension. Results of hybrid models showed that for the bolt-on "limitations in physical activities," the utility decrements were significant for level 3 (-0.055), level 4 (-0.087), and level 5 (-0.135). For "breathing problems," the utility decrements for the same levels were greater (-0.086, -0.219, and -0.327, respectively). Conclusions. The addition of each of the 2 respiratory bolt-ons to the EQ-5D-5L had a significant effect on the valuation of health states with severe levels for the bolt-on. The bolt-on dimension "breathing problems" showed the greatest utility decrements and therefore seems the most appropriate respiratory bolt-on dimension.

In 2005, the National Institutes of Health (NIH) chronic graft-versus-host disease (cGVHD) consensus project provided diagnosis and staging criteria, based mostly on clinical experience and expert opinion. These criteria were revised in 2014, aiming to provide enhanced specificity and clarity. However, the impact of 2014 changes to the original NIH cGVHD severity scoring criteria has not been reported. In this study, 284 patients, prospectively enrolled on the National Cancer Institute's cross-sectional cGVHD natural history study, were scored using the 2005 NIH cGVHD criteria and then rescored according to the 2014 modifications. In comparing the two criteria, 2014 cGVHD global severity scoring resulted in a tendency toward being categorized as milder scores (75 vs. 72% of severe score per 2014, p = 0.0009), with a statistically significant shift in NIH liver and lung scores toward milder categories (p < 0.0001). 2005 and 2014 NIH global severity scores showed a significant association with reduced grip strength (p < 0.0001), reduced joint range of motion (p = 0.0003), and the subspecialist evaluation score (p < 0.0001). Poor survival prediction of the severe NIH lung score is also retained in the new criteria (p = 0.0012). These findings support the use of 2014 cGVHD scoring criteria in continuous efforts to develop better classification systems.

Back Ground: Lung agenesis is a rare congenital anomaly. The main etiology of the disease is unknown whereas genetic, iatrogenic and viral factors as well as vitamin A deficiency during early pregnancy may result in developmental failure of primitive lung bud causing unilateral pulmonary agenesis. Affected patients usually present with variable respiratory symptoms and recurrent chest infection at any age. Plain film demonstrates opaque unilateral lung while chest CT scan can definitely diagnosis the disease. The anomaly has three types. Type I is pulmonary agenesis, type II is called pulmonary aplasia and type III is pulmonary hypoplasia., Cases' Presentation: Six patients with main complaint of dyspnea underwent contrast enhanced chest CT in radiology department of French Medical Institute for Mothers and children, Kabul and were diagnosed lung agenesis. Three patients were categorized as type II pulmonary agenesis (aplasia). Two patients, three months old boy and a seven year- old girl demonstrated right lung aplasia. Another patient boy of eighteen years old presented with left lung aplasia. Two boys of four and seven months of age were classified as type I pulmonary agenesis (agenesis). A boy of one year old was diagnosed pulmonary agenesis type III, right lung hypoplasia., Conclusion: Six patients were diagnosed with pulmonary agenesis by Chest CT scan. The clinicians should consider possibility of congenital pulmonary agenesis in dyspneic patients with opaque unilateral hemithorax in plain film.

Lung sounds convey relevant information related to pulmonary disorders, and to evaluate patients with pulmonary conditions, the physician or the doctor uses the traditional auscultation technique. However, this technique suffers from limitations. For example, if the physician is not well trained, this may lead to a wrong diagnosis. Moreover, lung sounds are non-stationary, complicating the tasks of analysis, recognition, and distinction. This is why developing automatic recognition systems can help to deal with these limitations. In this paper, we compare three machine learning approaches for lung sounds classification. The first two approaches are based on the extraction of a set of handcrafted features trained by three different classifiers (support vector machines, k-nearest neighbor, and Gaussian mixture models) while the third approach is based on the design of convolutional neural networks (CNN). In the first approach, we extracted the 12 MFCC coefficients from the audio files then calculated six MFCCs statistics. We also experimented normalization using zero mean and unity variance to enhance accuracy. In the second approach, the local binary pattern (LBP) features are extracted from the visual representation of the audio files (spectrograms). The features are normalized using whitening. The dataset used in this work consists of seven classes (normal, coarse crackle, fine crackle, monophonic wheeze, polyphonic wheeze, squawk, and stridor). We have also experimentally tested dataset augmentation techniques on the spectrograms to enhance the ultimate accuracy of the CNN. The results show that CNN outperformed the handcrafted feature based classifiers., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Introduction: Video-assisted thoracic surgery (VATS) lung resections are complex procedures with a critical role played by endoscopic staplers in the transection of vessels, bronchi, and lung tissue. This retrospective, observational study compared hospital resource use, costs, and complications of VATS lobectomy procedures for whom powered versus manual endoscopic surgical staplers were used., Methods: Patients ≥ 18 years of age undergoing elective VATS lobectomy during an inpatient admission from January 1, 2012 to September 30, 2016 were identified from the Premier Healthcare Database (first admission = index admission). Use of either powered or manual endoscopic staplers during the index admission was identified from hospital administrative records. Multivariable regression analyses adjusting for patient, hospital, and provider characteristics and hospital-level clustering were carried out to compare the following outcomes between the powered and manual stapler groups: hospital length of stay (LOS), operating room time (ORT), hospital costs, complications (bleeding and/or transfusions, air leak complications, pneumonia, and infection), discharge status, and 30-, 60-, and 90-day all-cause readmissions., Results: The powered and manual stapler groups comprised 659 patients (mean age 66.1 years; 53.6% female) and 3100 patients (mean age 66.7 years; 54.8% female), respectively. In the multivariable analyses, the powered stapler group had shorter LOS (4.9 vs. 5.9 days, P < 0.001), lower total hospital costs ($23,841 vs. $26,052, P = 0.009), and lower rates of combined hemostasis complications (bleeding and/or transfusions; 8.5% vs. 16.0%, P < 0.001) and transfusions (5.4% vs. 10.9%, P = 0.002), compared with the manual stapler group. Other outcomes did not differ significantly between the study groups. Similar trends were observed in subanalyses comparing devices across predominant manufacturers in each group, and in subanalyses of patients with comorbid chronic obstructive pulmonary disease., Conclusion: In this analysis of VATS lobectomy procedures, powered staplers were associated with significant benefits with respect to selected types of hospital resource use, costs, and clinical outcomes when compared with manual staplers., Funding: Johnson & Johnson.

Older persons frequently report respiratory risk factors and symptoms and have a high prevalence of symptomatic lung disease, most commonly obstructive airway disease, interstitial lung disease, and lung cancer. Notably, coexisting nonrespiratory risk factors are also prevalent and may misidentify or modify respiratory diagnoses and their clinical course., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Purpose: Congenital lung abnormalities are rare malformations increasingly detected early by prenatal ultrasound. Whether management of these frequently asymptomatic lesions should be surgical or conservative is an unresolved issue. The necessary prospective studies are limited by the absence of a widely accepted practical classification system. Our aim was to develop a simple, clinically relevant system for classifying and studying congenital lung abnormalities., Materials and Methods: We based our proposed grouping on a detailed analysis of clinical, radiological, and histological data from well-documented cases, plus an extensive review of the literature., Results: The existence of hybrid lesions and common histological findings suggested a unified embryological mechanism-possibly obstruction of developing airways with distal dysplasia. Malformations could be classified by their anatomical and pathological findings; however, a system based on the prenatal ultrasound plus initial chest X-ray findings had greater clinical relevance: Group 1-Congenital solid/cystic lung malformation, Group 2-Congenital hyperlucent lobe, Group 3-Congenital small lung., Conclusions: Pathological classification is academically important but is unnecessarily complex for clinical and research use. Our simple radiological-based system allows unambiguous comparison between the results of different studies and also guides the choice of necessary investigations specific to each group.

Purpose:: To evaluate the changes of caveolin-1 in lung fibroblasts in newborn Wistar rats when exposed to hyperoxic conditions, as well as lung fibroblasts cell cycle., Methods:: One hundred newborn Wistar rats were randomly divided (50 rats/group) into experimental and control groups, exposed to hyperoxic conditions or normal air, respectively. The fraction of inspired oxygen (FiO2) in the experimental group was 90%, whereas this value was 21% in the control group. Lung fibroblasts were collected on days 3, 7, and 14 of the experiment. Caveolin-1 expression dynamics in lung fibroblasts was assayed in each group by immunofluorescence and Western blot analyses. Flow cytometry (FCM) was used to assess the proportions of lung fibroblasts at different stages of the cell cycle., Results:: On day 3, no significant difference in caveolin-1 expression was observed between the hyperoxic and control groups; however, on days 7 and 14, caveolin-1 expression was significantly lower in the hyperoxic group than in the control (P<0.05). No apparent differences were observed in caveolin-1 expression in the control group at the different time points. Using FCM analysis, we showed that the proportion of lung fibroblasts in G0/G1 phase in the hyperoxic group decreased compared to that of the control group on day 7, while the proportion of S-phase cells increased (P<0.05). These differences were more significant when the groups were compared on day 14 (P<0.01)., Conclusion:: After seven days the exposure to hyperoxic conditions, lung fibroblasts proliferated and caveolin-1 expression decreased.

Diffuse cystic lung diseases are characterized by cysts in more than one lung lobe, the cysts originating from various mechanisms, including the expansion of the distal airspaces due to airway obstruction, necrosis of the airway walls, and parenchymal destruction. The progression of these diseases is variable. One essential tool in the evaluation of these diseases is HRCT, because it improves the characterization of pulmonary cysts (including their distribution, size, and length) and the evaluation of the regularity of the cyst wall, as well as the identification of associated pulmonary and extrapulmonary lesions. When combined with clinical and laboratory findings, HRCT is often sufficient for the etiological definition of diffuse lung cysts, avoiding the need for lung biopsy. The differential diagnoses of diffuse cystic lung diseases are myriad, including neoplastic, inflammatory, and infectious etiologies. Pulmonary Langerhans cell histiocytosis, lymphangioleiomyomatosis, lymphocytic interstitial pneumonia, and follicular bronchiolitis are the most common diseases that produce this CT pattern. However, new diseases have been included as potential determinants of this pattern. RESUMO As doenças pulmonares císticas difusas se caracterizam pela presença de cistos envolvendo mais de um lobo pulmonar, que se originam por diversos mecanismos, incluindo dilatação dos espaços aéreos distais por obstrução, necrose das paredes das vias aéreas e destruição do parênquima. Essas doenças apresentam evolução variável. A TCAR é fundamental na avaliação dessas doenças uma vez que permite uma melhor caracterização dos cistos pulmonares, incluindo sua distribuição, tamanho, extensão e regularidade das paredes, assim como a determinação de outras lesões pulmonares e extrapulmonares associadas. Frequentemente a TCAR é suficiente para a definição etiológica dos cistos pulmonares difusos, associada a achados clínicos e laboratoriais, sem a necessidade de realização de biópsia pulmonar. O diagnóstico diferencial das doenças pulmonares císticas difusas é extenso, incluindo etiologias neoplásicas, inflamatórias e infecciosas, sendo as mais frequentes determinantes desse padrão tomográfico a histiocitose pulmonar de células de Langerhans, a linfangioleiomiomatose, a pneumonia intersticial linfocitária e a bronquiolite folicular. Novas etiologias foram incluídas como potenciais determinantes desse padrão.

Objective: To evaluate the role of fine needle aspiration cytology (FNAC) in the diagnosis of pulmonary infections in immunocompromised patients and to identify the imaging pattern of infections on computed tomography (CT)., Materials and Methods: This was a retrospective study of 42 immunocompromised patients who underwent FNAC under image guidance owing to a clinical pulmonary infection. Each patient was evaluated for an underlying immunocompromised condition, cytological diagnosis, CT findings and complications., Results: The most common predisposing condition was diabetes mellitus (n = 11), renal transplant status (n = 11) followed by connective tissue disorders (n = 6) and malignancy (n = 5). There were four patients with renal disease and three had a human immunodeficiency virus (HIV) infection. The most common cytological diagnosis was mucormycosis (n = 13) followed by nocardiosis (n = 8) and necrotising inflammation (n = 7), tuberculosis (n = 6), cryptococcosis (n = 2), aspergillosis(n = 2), histoplasmosis(n = 1) and atypical mycobacterial infection (n = 1). Mucormycosis presented as a pulmonary nodule (n = 7), mass lesion (n = 5) or consolidation (n = 4). The patients with nocardiosis had lung nodules with associated consolidation and cavitation. None of the patients had any major complication., Conclusion: FNA is a relatively reliable, safe and quick method of diagnosing pulmonary infection in immunocompromised patients. Cytomorphological features, when aided by special stains, can accurately detect the specific infection which is potentially treatable. Specific infections may be suggested based on specific imaging patterns., (© 2016 John Wiley & Sons Ltd.)

Context: -Amyloidosis is a heterogeneous group of diseases characterized by the deposition of congophilic amyloid fibrils in the extracellular matrix of tissues and organs. To date, 31 fibril proteins have been identified in humans, and it is now recommended that amyloidoses be named after these fibril proteins. Based on this classification scheme, the most common forms of amyloidosis include systemic AL (formerly primary), systemic AA (formerly secondary), systemic wild-type ATTR (formerly age-related or senile systemic), and systemic hereditary ATTR amyloidosis (formerly familial amyloid polyneuropathy). Three different clinicopathologic forms of amyloidosis can be seen in the lungs: diffuse alveolar-septal amyloidosis, nodular pulmonary amyloidosis, and tracheobronchial amyloidosis., Objective: -To clarify the relationship between the fibril protein-based amyloidosis classification system and the clinicopathologic forms of pulmonary amyloidosis and to provide a useful guide for diagnosing these entities for the practicing pathologist., Data Sources: -This is a narrative review based on PubMed searches and the authors' own experiences., Conclusions: -Diffuse alveolar-septal amyloidosis is usually caused by systemic AL amyloidosis, whereas nodular pulmonary amyloidosis and tracheobronchial amyloidosis usually represent localized AL amyloidosis. However, these generalized scenarios cannot always be applied to individual cases. Because the treatment options for amyloidosis are dependent on the fibril protein-based classifications and whether the process is systemic or localized, the workup of new clinically relevant cases should include amyloid subtyping (preferably with mass spectrometry-based proteomic analysis) and further clinical investigation.

Introduction: Low-dose computed tomography (LDCT) is used for screening for lung cancer (LC) in high-risk patients in the United States. The definition of high risk and the impact of frequent false-positive results of low-dose computed tomography remains a challenge. DNA methylation biomarkers are valuable noninvasive diagnostic tools for cancer detection. This study reports on the evaluation of methylation markers in plasma DNA for LC detection and discrimination of malignant from nonmalignant lung disease., Methods: Circulating DNA was extracted from 3.5-mL plasma samples, treated with bisulfite using a commercially available kit, purified, and assayed by real-time polymerase chain reaction for assessment of DNA methylation of short stature homeobox 2 gene (SHOX2), prostaglandin E receptor 4 gene (PTGER4), and forkhead box L2 gene (FOXL2). In three independent case-control studies these assays were evaluated and optimized. The resultant assay, a triplex polymerase chain reaction combining SHOX2, PTGER4, and the reference gene actin, beta gene (ACTB), was validated using plasma from patients with and without malignant disease., Results: A panel of SHOX2 and PTGER4 provided promising results in three independent case-control studies examining a total of 330 plasma specimens (area under the receiver operating characteristic curve = 91%-98%). A validation study with 172 patient samples demonstrated significant discriminatory performance in distinguishing patients with LC from subjects without malignancy (area under the curve = 0.88). At a fixed specificity of 90%, sensitivity for LC was 67%; at a fixed sensitivity of 90%, specificity was 73%., Conclusions: Measurement of SHOX2 and PTGER4 methylation in plasma DNA allowed detection of LC and differentiation of nonmalignant diseases. Development of a diagnostic test based on this panel may provide clinical utility in combination with current imaging techniques to improve LC risk stratification., (Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

The prevalence of pulmonary disease in patients with peripheral artery disease (PAD) has not been extensively studied. Recent evidence has shown that ∼20% of the patients have an atypical chest transcutaneous oxygen pressure (TcpO2) pattern during exercise, which suggests walking-induced hypoxemia. The main objectives of this study were to: (1) describe in a retrospective way the characteristics of the patients suffering from claudication, who attended a treadmill testing in our laboratory, (2) assess the prevalence of known or unknown pulmonary disease. The second aim of this study was to evaluate the impact of the therapeutic interventions on the walking capacities, after treatment, of the eventually detected pulmonary disorders.We retrospectively analyzed 1482 exercise TcpO2 test results. Patients that had no history of pulmonary disease, but either reported severe dyspnea or showed atypical profiles on their chest exercise-TcpO2, were advised to refer to the department of pneumology for additional investigations.In addition to the 166 patients with a history of pulmonary disease, 158 patients were suspected of unknown pulmonary disease from the result of their TcpO2 test. Many patients (n = 99/158, 62.7%) did not attend a pulmonologist visit. A pulmonary disease was established in 55 (93.2%) of the other 59 patients. Obstructive sleep apnea syndrome (OSAS) was the one and only diagnosis retained in 42/59 patients (71.2%). Among the 47 patients who had a second evaluation of their walking capacity on treadmill, 38 had treatment of the pulmonary disease found, vascular surgery treatment or a severe restricted diet, 9 had no treatment. Only the "treated" group showed a significant improvement in the maximal walking distance on treadmill between the 2 evaluations, 313 ± 251 m to 433 ± 317 m (P = 0.03).This retrospective pilot study underlines the high prevalence of both known and unknown pulmonary disease in patients whose primary complaint was lower limb claudication. Systematic screening and treatment of pulmonary disease in patients with claudication might be justified, to improve walking ability of such patients and possibly reduce or delay the requirement for revascularization. Prospective studies are required to confirm these preliminary results., Competing Interests: The authors have no conflicts of interest to disclose.

Background: Fluorodeoxyglucose (FDG) positron emission tomography (PET) is emerging as an important non- invasive investigation in benign pulmonary conditions too. The aim of this study was to investigate its utility in the diagnosis and monitoring of various benign pulmonary diseases., Methods: In this prospective observational hospital-based study 50 consecutive patients (26 males) with benign lung diseases underwent computed tomography of chest followed by FDG-PET at baseline and after treatment where appropriate. The findings of FDG scan are reported in the context of clinical, histopathological, physiological and radiological findings., Results: All patients showed increased FDG uptake in the lung corresponding to CT findings. Of the 9 patients with sarcoidosis stage 1 (n=1), stage 2 (n=3) and stage 3 (n=5), additional uptake in the myocardium and thyroid was noted in two patients which resulted in a change in the modality of treatment. Repeat FDG scan post-treatment showed decreased uptake in all patients which was consistent with clinico-radiologic, microbiological or spirometry findings. Increased uptake was seen in one patient with pulmonary tuberculosis (TB) and in one patient with TB mediastinal lymphadenopathy at the end of intensive phase discordant with clinical and microbiological response. Of nine cases of idiopathic interstitial pneumonias (IIPs), additional intense FDG uptake was found in two cases which corresponded to the areas of honeycombing., Conclusions: FDG-PET scan can be used as an important adjunct non-invasive investigation in diagnosing and monitoring of various benign lung conditions. It also helps in assessing whole body disease burden which may change therapeutic decisions.

Lung transplantation is a well-established treatment for patients with advanced lung disease. The evaluation of a candidate for transplantation is a complex task and involves a multidisciplinary team that follows the patient beyond the postoperative period. Currently, the mean time on the waiting list for lung transplantation in the state of São Paulo, Brazil, is approximately 18 months. For Brazil as a whole, data from the Brazilian Organ Transplant Association show that, in 2014, there were 67 lung transplants and 204 patients on the waiting list for lung transplantation. Lung transplantation is most often indicated in cases of COPD, cystic fibrosis, interstitial lung disease, non-cystic fibrosis bronchiectasis, and pulmonary hypertension. This comprehensive review aimed to address the major aspects of lung transplantation: indications, contraindications, evaluation of transplant candidates, evaluation of donor candidates, management of transplant recipients, and major complications. To that end, we based our research on the International Society for Heart and Lung Transplantation guidelines and on the protocols used by our Lung Transplant Group in the city of São Paulo, Brazil.

Pulmonary hypertension (PH) due to lung disease (World Health Organization (WHO) group 3) is common, but severe PH, arbitrarily defined as mean pulmonary artery pressure ≥35 mmHg is reported in only a small proportion. Whether these should be treated as patients in WHO group 1 (i.e. pulmonary arterial hypertension) with PH-targeted therapies is unknown. We compared the phenotypic characteristics and outcomes of 118 incident patients with severe PH and lung disease with 74 idiopathic pulmonary arterial hypertension (IPAH) patients, all treated with pulmonary vasodilators. Lung disease patients were older, more hypoxaemic, and had lower gas transfer, worse New York Heart Association functional class and lower 6-min walking distance (6MWD) than IPAH patients. Poorer survival in those with lung disease was driven by the interstitial lung disease (ILD) cohort. In contrast to IPAH, where significant improvements in 6MWD and N-terminal pro-brain natruiretic peptide (NT-proBNP) occurred, PH therapy in severe PH lung disease did not lead to improvement in 6MWD or functional class, but neither was deterioration seen. NT-proBNP decreased from 2200 to 1596 pg·mL(-1) (p=0.015). Response varied by lung disease phenotype, with poorer outcomes in patients with ILD and emphysema with preserved forced expiratory volume in 1 s. Further study is required to investigate whether vasodilator therapy may delay disease progression in severe PH with lung disease., (Copyright ©ERS 2015.)

After a patient encounter, the physician uses two coding systems to bill for the service rendered to the patient. The Current Procedural Terminology (CPT) code is used to describe the encounter or procedure. The International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code is used to describe the diagnosis(es) of the patient. On October 1, 2015, ICD-9-CM coding will end, and all physicians will be required to use a new diagnostic coding system, the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). This article describes the new diagnostic coding system and how it differs from the old system. There are resources and costs involved for physicians and physician practices to prepare properly for ICD-10-CM. Similar to other important events, the more thorough the preparation, the more likely a positive outcome will occur. Resource use is very important in preparation for the transition from ICD-9-CM to ICD-10-CM. Greater familiarity with ICD-10-CM plus a thorough, effective preparation will lead to reduced costs and a smooth transition. Coding descriptor changes and additional codes occur in ICD-10-CM for chronic bronchitis and emphysema, asthma, and respiratory failure. These changes will affect the coding of these diseases and disorders by physicians. Because the number of codes will increase more than fivefold, the complexity of documentation to support ICD-10-CM will increase substantially. The documentation in the patient's chart to support the ICD-10-CM codes used will need to be enhanced. The requirement for accurate and comprehensive documentation cannot be emphasized enough. All of the coding and documentation changes will be a challenge to pulmonary, critical care, and sleep physicians. They must be prepared fully when ICD-10-CM coding begins and ICD-9-CM coding stops abruptly on October 1, 2015.

Background: The detection of Pneumocystis jirovecii DNA in respiratory specimen from individuals who do not have signs or symptoms of pneumonia has been defined as colonization. The role of P. jirovecii colonization in the development or progression of various lung diseases has been reported, but little information about P. jirovecii colonization in patients is available in the People's Republic of China., Objective: To determine the prevalence of P. jirovecii colonization in patients with various pulmonary diseases, including the acute and stable stage of COPD, interstitial lung diseases, cystic fibrosis, and chronic bronchiectasis., Materials and Methods: A loop-mediated isothermal amplification (LAMP) and a conventional polymerase chain reaction (PCR) method for detecting P. jirovecii were developed. Ninety-eight HIV-negative patients who were followed-up and who had undergone bronchoscopy for diagnosis of various underlying respiratory diseases were included in the study. Sputa of these patients were analyzed with LAMP amplification of P. jirovecii gene. In addition, conventional PCR, Giemsa and Gomori's methenamine silver nitrate staining assays were applied to all specimens., Results: The sensitivity and specificity test showed that there was no cross-reaction with other fungi or bacteria in detecting the specific gene of P. jirovecii by LAMP, and the minimum detection limits by LAMP was 50 copies/mL. P. jirovecii DNA was detected in 62 of 98 (63.3%) sputa specimens by LAMP assay and 22.45% (22/98) by conventional PCR. However, no P. jirovecii cysts were found by Giemsa and Gomori's methenamine silver nitrate in all of gene-positive specimens., Conclusion: The results of our study showed that prevalence of P. jirovecii colonization is particularly high in patients with chronic pulmonary diseases in the People's Republic of China, and the LAMP method is better for evaluation of the colonization of P. jirovecii in sputum specimen than conventional PCR.

Increasingly, congenital thoracic malformations (CTMs) are diagnosed on antenatal ultrasound, but we lack the evidence to suggest rational management, not least because descriptive terms are used inconsistently. This review describes a simplified clinical classification of CTMs, and contrasts it with pathological descriptions. The age related presentations of CTM are described, together with the differential diagnoses of cystic masses presenting both antenatally and postnatally. Antenatally diagnosed CTMs rarely require intervention before birth; and urgent treatment is only required postnatally if the baby is symptomatic and does not respond to medical management. The asymptomatic baby with an antenatal diagnosis of a CTM presents a management conundrum. Definitive imaging is with high-resolution computed tomography (HRCT), but the optimal timing of imaging is unclear. Whether surgery should be offered to asymptomatic infants is also unclear; in the medium term, 5 % of asymptomatic babies will require surgery for complications of the disease. The most vexed question is malignant change; the risk in the medium term is probably less than 5 %, but we have no way of delineating a high-risk group. Indeed, malignancy has been described even after complete resection of a CTM. The author's personal management is to advocate surgery in the second year of life for all except for the most trivial CTMs, but many would differ and advocate conservative management. More data are needed if we are to rationalise our approach to these infants.

The spectrum of complications associated with congenital lung malformation is wide. They can range from fetal hydrops in utero to postnatal problems of ventilation, obstruction and infection; presentation may occur from the neonatal period to adulthood. Many lesions will remain asymptomatic while at the other end of the complication spectrum, there is a small risk of neoplasia associated with some forms of cystic lung. A better understanding of the pathology has shown that bronchial atresia/obstruction is the likely hidden pathology underlying many congenital lung lesions leading to downstream cystic maldevelopment. Earlier diagnosis has led to increasing difficulties in ascribing malformations to conventional categories that were originally described in postnatal lungs. It is probably more important to be aware of the potential combination of vascular and airway connections and complications than to try and prescribe a classification of pulmonary lesions associated with rigid definitions., (Copyright © 2015 Elsevier Inc. All rights reserved.)