RESUMO
BACKGROUND: Airflow obstruction is a hallmark of disease severity and prognosis in bronchiectasis. The relationship between lung microbiota, airway inflammation, and outcomes in bronchiectasis with fixed airflow obstruction (FAO) remains unclear. This study explores these interactions in bronchiectasis patients, with and without FAO, and compares them to those diagnosed with chronic obstructive pulmonary disease (COPD). METHODS: This prospective observational study in Taiwan enrolled patients with either bronchiectasis or COPD. To analyze the lung microbiome and assess inflammatory markers, bronchoalveolar lavage (BAL) samples were collected for 16S rRNA gene sequencing. The study cohort comprised 181 patients: 86 with COPD, 46 with bronchiectasis, and 49 with bronchiectasis and FAO, as confirmed by spirometry. RESULTS: Patients with bronchiectasis, with or without FAO, had similar microbiome profiles characterized by reduced alpha diversity and a predominance of Proteobacteria, distinctly different from COPD patients who exhibited more Firmicutes, greater diversity, and more commensal taxa. Furthermore, compared to COPD and bronchiectasis without FAO, bronchiectasis with FAO showed more severe disease and a higher risk of exacerbations. A significant correlation was found between the presence of Pseudomonas aeruginosa and increased airway neutrophilic inflammation such as Interleukin [IL]-1ß, IL-8, and tumor necrosis factor-alpha [TNF]-α, as well as with higher bronchiectasis severity, which might contribute to an increased risk of exacerbations. Moreover, in bronchiectasis patients with FAO, the ROSE (Radiology, Obstruction, Symptoms, and Exposure) criteria were employed to classify individuals as either ROSE (+) or ROSE (-), based on smoking history. This classification highlighted differences in clinical features, inflammatory profiles, and slight microbiome variations between ROSE (-) and ROSE (+) patients, suggesting diverse endotypes within the bronchiectasis with FAO group. CONCLUSION: Bronchiectasis patients with FAO may exhibit two distinct endotypes, as defined by ROSE criteria, characterized by greater disease severity and a lung microbiome more similar to bronchiectasis without FAO than to COPD. The significant correlation between Pseudomonas aeruginosa colonization and increased airway neutrophilic inflammation, as well as disease severity, underscores the clinical relevance of microbial patterns. This finding reinforces the potential role of these patterns in the progression and exacerbations of bronchiectasis with FAO.
Assuntos
Bronquiectasia , Pulmão , Microbiota , Humanos , Bronquiectasia/microbiologia , Bronquiectasia/diagnóstico , Feminino , Masculino , Estudos Prospectivos , Microbiota/fisiologia , Pessoa de Meia-Idade , Idoso , Pulmão/microbiologia , Pulmão/fisiopatologia , Doença Pulmonar Obstrutiva Crônica/microbiologia , Doença Pulmonar Obstrutiva Crônica/diagnóstico , Doença Pulmonar Obstrutiva Crônica/fisiopatologia , Estudos de Coortes , Taiwan/epidemiologiaRESUMO
Advanced bronchoscopic lung volume reduction treatment (BLVR) is now a routine care option for treating patients with severe emphysema. Patterns of low attenuation clusters indicating emphysema and functional small airway disease (fSAD) on paired CT, which may provide additional insights to the target selection of the segmental or subsegmental lobe of the treatments, require further investigation. The low attenuation clusters (LACS) were segmented to identify the scalar and spatial distribution of the lung destructions, in terms of 10 fractions scales of low attenuation density (LAD) located in upper lobes and lower lobes. The LACs of functional small airway disease (fSAD) were delineated by applying the technique of parametric response map (PRM) on the co-registered CT image data. Both emphysematous LACs of inspiratory CT and fSAD LACs on expiratory CT were used to derive the coefficients of the predictive model for estimating the airflow limitation. The voxel-wise severity is then predicted using the regional LACs on the co-registered CT to indicate the functional localization, namely, the bullous parametric response map (BPRM). A total of 100 subjects, 88 patients with mild to very severe COPD and 12 control participants with normal lung functions (FEV1/FVC % > 70%), were evaluated. Pearson's correlations between FEV1/FVC% and LAV%HU-950 of severe emphysema are - 0.55 comparing to - 0.67 and - 0.62 of LAV%HU-856 of air-trapping and LAV%fSAD respectively. Pearson's correlation between FEV1/FVC% and FEV1/FVC% predicted by the proposed model using LAD% of HU-950 and fSAD on BPRM is 0.82 (p < 0.01). The result of the Bullous Parametric Response Map (BPRM) is capable of identifying the less functional area of the lung, where the BLVR treatment is aimed at removing from a hyperinflated area of emphysematous regions.
Assuntos
Enfisema , Doença Pulmonar Obstrutiva Crônica , Enfisema Pulmonar , Volume Expiratório Forçado/fisiologia , Humanos , Pulmão/diagnóstico por imagem , Doença Pulmonar Obstrutiva Crônica/diagnóstico por imagem , Enfisema Pulmonar/diagnóstico por imagemRESUMO
BACKGROUND: This paper proposes a semantic segmentation algorithm that provides the spatial distribution patterns of pulmonary ground-glass nodules with solid portions in computed tomography (CT) images. METHODS: The proposed segmentation algorithm, anatomy packing with hierarchical segments (APHS), performs pulmonary nodule segmentation and quantification in CT images. In particular, the APHS algorithm consists of two essential processes: hierarchical segmentation tree construction and anatomy packing. It constructs the hierarchical segmentation tree based on region attributes and local contour cues along the region boundaries. Each node of the tree corresponds to the soft boundary associated with a family of nested segmentations through different scales applied by a hierarchical segmentation operator that is used to decompose the image in a structurally coherent manner. The anatomy packing process detects and localizes individual object instances by optimizing a hierarchical conditional random field model. Ninety-two histopathologically confirmed pulmonary nodules were used to evaluate the performance of the proposed APHS algorithm. Further, a comparative study was conducted with two conventional multi-label image segmentation algorithms based on four assessment metrics: the modified Williams index, percentage statistic, overlapping ratio, and difference ratio. RESULTS: Under the same framework, the proposed APHS algorithm was applied to two clinical applications: multi-label segmentation of nodules with a solid portion and surrounding tissues and pulmonary nodule segmentation. The results obtained indicate that the APHS-generated boundaries are comparable to manual delineations with a modified Williams index of 1.013. Further, the resulting segmentation of the APHS algorithm is also better than that achieved by two conventional multi-label image segmentation algorithms. CONCLUSIONS: The proposed two-level hierarchical segmentation algorithm effectively labelled the pulmonary nodule and its surrounding anatomic structures in lung CT images. This suggests that the generated multi-label structures can potentially serve as the basis for developing related clinical applications.
Assuntos
Algoritmos , Processamento de Imagem Assistida por Computador/métodos , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/patologia , Tomografia Computadorizada por Raios X , Humanos , Pulmão/diagnóstico por imagem , Pulmão/patologiaRESUMO
Target lung tissue selection remains a challenging task to perform for treating severe emphysema with lung volume reduction (LVR). In order to target the treatment candidate, the percentage of low attenuation volume (LAV%) representing the proportion of emphysema volume to whole lung volume is measured using computed tomography (CT) images. Although LAV% have shown to have a correlation with lung function in patients with chronic obstructive pulmonary disease (COPD), similar measurements of LAV% in whole lung or lobes may have large variations in lung function due to emphysema heterogeneity. The functional information of regional emphysema destruction is required for supporting the choice of optimal target. The purpose of this study is to develop an emphysema heterogeneity descriptor for the three-dimensional emphysematous bullae according to the size variations of emphysematous density (ED) and their spatial distribution. The second purpose is to derive a predictive model of airflow limitation based on the regional emphysema heterogeneity. Deriving the bullous representation and grouping them into four scales in the upper and lower lobes, a predictive model is computed using the linear model fitting to estimate the severity of lung function. A total of 99 subjects, 87 patients with mild to very severe COPD (Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage I~IV) and 12 control participants with normal lung functions (forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) > 0.7) were evaluated. The final model was trained with stratified cross-validation on randomly selected 75% of the dataset (n = 76) and tested on the remaining dataset (n = 23). The dispersed cases of LAV% inconsistent with their lung function outcome were evaluated, and the correlation study suggests that comparing to LAV of larger bullae, the widely spread smaller bullae with equivalent LAV has a larger impact on lung function. The testing dataset has the correlation of r = -0.76 (p < 0.01) between the whole lung LAV% and FEV1/FVC, whereas using two ED % of scales and location-dependent variables to predict the emphysema-associated FEV1/FVC, the results shows their correlation of 0.82 (p < 0.001) with clinical FEV1/FVC.
Assuntos
Pulmão , Modelos Biológicos , Doença Pulmonar Obstrutiva Crônica , Enfisema Pulmonar , Tomografia Computadorizada por Raios X , Idoso , Idoso de 80 Anos ou mais , Feminino , Volume Expiratório Forçado , Humanos , Pulmão/diagnóstico por imagem , Pulmão/fisiopatologia , Masculino , Pessoa de Meia-Idade , Doença Pulmonar Obstrutiva Crônica/diagnóstico por imagem , Enfisema Pulmonar/diagnóstico por imagem , Enfisema Pulmonar/fisiopatologia , Testes de Função Respiratória , Estudos Retrospectivos , Índice de Gravidade de DoençaRESUMO
INTRODUCTION: Histological subtypes of lung adenocarcinomas (ADCs) classified by the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) system have been investigated using radiomic approaches. However, the results have had limitations since <80% of invasive lung ADCs were heterogeneous, with two or more subtypes. To reduce the influence of heterogeneity during radiomic analysis, computed tomography (CT) images of lung ADCs with near-pure ADC subtypes were analyzed to extract representative radiomic features of different subtypes. METHODS: We enrolled 95 patients who underwent complete resection for lung ADC and a pathological diagnosis of a "near-pure" (≥70%) IASLC/ATS/ERS histological subtype. Conventional histogram/morphological features and complex radiomic features (grey-level-based statistical features and component variance-based features) of thin-cut CT data of tumor regions were analyzed. A prediction model based on leave-one-out cross-validation (LOOCV) and logistic regression (LR) was used to classify all five subtypes and three pathologic grades (lepidic, acinar/papillary, micropapillary/solid) of ADCs. The validation was performed using 36 near-pure ADCs in a later cohort. RESULTS: A total of 31 lepidic, 14 papillary, 32 acinar, 10 micropapillary, and 8 solid ADCs were analyzed. With 21 conventional and complex radiomic features, for 5 subtypes and 3 pathological grades, the prediction models achieved accuracy rates of 84.2% (80/95) and 91.6% (87/95), respectively, while accuracy was 71.6% and 85.3%, respectively, if only conventional features were used. The accuracy rate for the validation set (nâ¯=â¯36) was 83.3% (30/36) and 94.4% (34/36) in 5 subtypes and 3 pathological grades, respectively, using conventional and complex features, while it was 66.7% and 77.8% only using conventional features, respectively. CONCLUSION: Lung ADC with high purity pathological subtypes demonstrates strong stratification of radiomic values, which provide basic information for accurate pathological subtyping and image parcellation of tumor sub-regions.