Diagnosis Of Pulmonary Disease Research Articles

Clinical guideline highlights for the hospitalist: GOLD COPD update 2024.

Guideline Title: Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease (2024 Report) Release Date: December 4, 2023 Prior versions: Initial report was released in 2001 with major revisions in 2006, 2011, 2017, and 2023. This 2024 release is an update to the 2023 major revision Developer: Global Initiative for Chronic Obstructive Lung Disease (GOLD) Funding source: GOLD Target population: Adults with a diagnosis of or at risk for COPD.

Endobronchial ultrasound-guided transbronchial needle aspiration for diagnosing thoracic lesions: a retrospective cohort study.

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a minimally invasive technique for biopsy of lung, peri-pulmonary tissue and lymph nodes under real-time ultrasound-guided biopsy. It is used in the diagnosis and/or staging of benign and malignant pulmonary and non-pulmonary diseases. Our study is based on a large sample size, in a diversified population which provides a representative real-world cohort for analysis. Patients who underwent EBUS-TBNA procedure between September 2019 and August 2022 were included in this retrospective study. For cases diagnosed as benign and unclassified lesions by EBUS-TBNA, the final diagnosis was determined by further invasive surgery or a combination of therapy and clinical follow-up for at least 6 months. A total of 618 patients were included in the study, including 182 females (29.4%) and 436 males (70.6%). The mean age of all patients was 61.9 ± 10.5 years. These patients were successfully punctured by EBUS-TBNA to obtain pathological results. The pathological diagnosis results of EBUS-TBNA were compared with the final clinical diagnosis results as follows: 133 cases (21.5%) of benign lesions and 485 cases (78.5%) of malignant lesions were finally diagnosed. Among them, the pathological diagnosis was obtained by EBUS-TBNA in 546 patients (88.3%) (464 malignant lesions and 82 benign conditions), while EBUS-TBNA was unable to define diagnosis in 72 patients (11.6%). 20/72 non-diagnostic EBUS-TBNA were true negative. The overall diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of EBUS-TBNA were 91.3%, 100%, 100%, 27.8%, and 91.6% [95% confidence interval (CI): 89.1-93.6%], respectively. In this study, only one case had active bleeding without serious complications during the EBUS-TBNA procedure. Given its low invasiveness, high diagnostic accuracy, and safety, EBUS-TBNA is worth promoting in thoracic lesions.

AI-based diagnosis of chronic obstructive pulmonary disease from low-dose CT images

Background: Chronic obstructive pulmonary disease (COPD) is a group of diseases characterized by airflow blockage. It is one of the leading causes of global mortality and is primarily attributed to smoking. COPD patients are usually diagnosed by spirometry test. Although regarded as the gold standard for COPD diagnosis, the spirometry test carries contraindications, thus prompting the development of low-dose computed tomography (low-dose CT) scan as an alternative for COPD screening. However, a practical limitation of diagnosing COPD from CT images is its reliance on the expertise of a skilled radiologist. Objective: To address this limitation, we aimed to develop a deep-learning model for the automated classification of COPD and non-COPD from low-dose CT images. Materials and methods: We examined the potential of a convolutional neural network for identifying COPD. Our dataset consisted of 10,000 low-dose CT images obtained from a lung cancer screening program, involving both ex-smokers and current smokers deemed at high risk of lung cancer. Spirometry data served as the ground truth for defining COPD. We used 90% of the datasets for training and 10% for testing. Results: Our developed model achieved notable performance metrics: an area under the receiver operating characteristic curve (AUC) of 0.97, an accuracy of 0.89, a precision of 0.85, a recall of 0.96, and an F1-score of 0.90. Conclusion: Our study demonstrates the potential of deep learning models to augment clinical assessments and improve the diagnosis of COPD, thereby enhancing diagnostic accuracy and efficiency. The findings suggest the feasibility of integrating this technology into routine lung cancer screening programs for COPD detection.

Association Between ACE2 and Lung Diseases.

Angiotensin-converting enzyme 2 (ACE2) is an important regulator of the Renin-Angiotensin System (RAS). Additionally, it has been identified as a functional receptor for the Coronavirus. Research indicates that ACE2 plays a role in the regulation of cardiovascular systems by modulating blood pressure and electrolyte balance. Its role in pulmonary diseases has also garnered significant attention due to the widespread prevalence of Coronavirus. There is solid evidence linking ACE2 to other pulmonary diseases, including chronic obstructive pulmonary disease, acute respiratory distress syndrome, allergic asthma, among others. However, the exact pathological and physiological mechanisms of ACE2 in these diseases remain elusive. Our research aims to review and explore the latest advancements in ACE2-related studies in pulmonary diseases. These findings have the potential to open new avenues for utilizing ACE2 as a potential biomarker for early diagnosis and monitoring of pulmonary diseases.

RNA modifications in pulmonary diseases.

Threatening public health, pulmonary disease (PD) encompasses diverse lung injuries like chronic obstructive PD, pulmonary fibrosis, asthma, pulmonary infections due to pathogen invasion, and fatal lung cancer. The crucial involvement of RNA epigenetic modifications in PD pathogenesis is underscored by robust evidence. These modifications not only shape cell fates but also finely modulate the expression of genes linked to disease progression, suggesting their utility as biomarkers and targets for therapeutic strategies. The critical RNA modifications implicated in PDs are summarized in this review, including N6-methylation of adenosine, N1-methylation of adenosine, 5-methylcytosine, pseudouridine (5-ribosyl uracil), 7-methylguanosine, and adenosine to inosine editing, along with relevant regulatory mechanisms. By shedding light on the pathology of PDs, these summaries could spur the identification of new biomarkers and therapeutic strategies, ultimately paving the way for early PD diagnosis and treatment innovation.

Is there a role for lung or bronchial biopsies for the diagnosis of mycobacterial pulmonary disease in patients with bronchiectasis?

BackgroundWorkup of bronchiectasis patients mandates microbiological characterization often being sought via Bronchoscopy. However, whether to perform bronchial or lung biopsies, is unknown, especially for the diagnosis of NTM pulmonary disease. We aimed to assess the current practice and yield of the different bronchoscopic procedures in this setting. MethodsData from an adult cohort with bronchiectasis referred for bronchoscopy for microbiologic sampling was reviewed, including demographics, etiology, imaging and results of the different bronchoscopic procedures performed. Results127 subjects were analyzed (mean age 61, 56% female). BAL culture was positive in 44%. Frequent pathogens were Hemophilus Influenza (20%), pseudomonas aeruginosa (8%) and Staphylococcus aureus (7%). NTM and tuberculosis were found in 6% and 1.5% respectively. BAL cytology was sent in 125 procedures, EBB was performed in 51 patients (40%) and TBLB in 38 patients (30%). BAL cytology and both EBB and TBB (including tissue cultures) had no benefit over BAL with respect to microbiological diagnosis, including identification of mycobacterial disease. ConclusionsIn adult subjects with Non-CF bronchiectasis requiring bronchoscopy for microbiological characterization, BAL cytology and lung tissue biopsies were frequently performed but were of minimal additional benefit over BAL culture (including for mycobacterial pulmonary disease), and are most likely futile.

Time to diagnosis of nontuberculous mycobacterial pulmonary disease and longitudinal changes on CT before diagnosis

BackgroundThe healthcare burden of nontuberculous mycobacterial pulmonary disease (NTM-PD) is increasing, but the diagnosis remains challenging and sometimes requires considerable time. This nested case-control study aims to clarify the time to diagnosis of NTM-PD, the factors that affect diagnosis and diagnostic delay, and changes in CT findings before diagnosis. Patients and methodsWe retrospectively analyzed 187 patients suspected of having NTM-PD based on computed tomography (CT) findings at our institution between January 2019 and September 2020. We investigated the time to diagnosis of NTM-PD for all suspected and diagnosed patients. Multivariate analyses identified the factors affecting diagnosis and diagnostic delay over 6 months. We also evaluated longitudinal changes in CT findings during the observation period using CT scoring system. ResultsThe median times to diagnosis of NTM-PD were 71.8 months in all suspected patients and 3.2 months in only the diagnosed patients. Multivariable analysis showed that severity of the cavity domain of the CT score and anti-glycopeptidolipid (GPL)-core immunoglobulin A (IgA) antibody positivity were significantly associated with establishing the diagnosis. A low CT score in the cavity domain was a risk factor for delayed diagnosis. In patients with delayed diagnosis, the total CT score was less severe than that in the early diagnosis patients at their first visits; however, it had deteriorated prior to the diagnosis. ConclusionThe diagnosis of NTM-PD sometimes required several years, and the absence or mild cavitation predicted a diagnostic delay. Of concern, a delay in diagnosis can result in a delay in treatment.

The application of impulse oscillometry system based on machine learning algorithm in the diagnosis of chronic obstructive pulmonary disease.

Diagnosing chronic obstructive pulmonary disease (COPD) using Impulse Oscillometry (IOS) is challenging due to the high level of clinical expertise it demands from doctors , which limits the clinical application of IOS in screening. The primary aim of this study is to develop a COPD diagnostic model based on machine learning algorithms using IOS test results. 
Approach:Feature selection was conducted to identify the optimal subset of features from the original feature set, which significantly enhanced the classifier's performance. Additionally, secondary features area of reactance (AX) were derived from the original features based on clinical theory, further enhancing the performance of the classifier. The performance of the model was analyzed and validated using various classifiers and hyperparameter settings to identify the optimal classifier. We collected 528 clinical data examples from the China-Japan Friendship Hospital for training and validating the model. 
Main results:The proposed model achieved reasonably accurate diagnostic results in the clinical data (accuracy=0.920, specificity=0.941, precision=0.875, recall=0.875). 
Significance:The results of this study demonstrate that the proposed classifier model, feature selection method, and derived secondary feature AX provide significant auxiliary support in reducing the requirement for clinical experience in COPD diagnosis using IOS.&#xD.

Application of Sensing Devices in the Detection of Oral, Pulmonary, and Gastrointestinal Diseases

Biomedical sensing technology is developing at a tremendous pace and is expected to become an effective clinical tool for the diagnosis and monitoring of human health. The development of sensing devices has successfully transformed the specific sensor prototype designed in the laboratory into a commercially feasible clinical disease detection device. Recently, sensing devices have been accelerated and extended to various fields beyond disease detection, including the measurement of gastrointestinal physiological parameters such as pH, VOC detection, small-molecule gas sensing, and noninvasive screening of oral and lung diseases such as oral cancer, gastric cancer, and other major diseases. In this review, the applications of sensors and electronic nose devices in the diagnosis and monitoring of oral, pulmonary, and gastrointestinal diseases are reviewed, as well as the design and application of sensor materials in disease markers and in situ detection. This article also introduces the practical application of sensing devices in human disease detection, critically analyzes their detection mechanisms and clinical utility, and discusses their future development in medicine. We believe that this review will help readers, especially practitioners in the medical field, provide ideas for the development of sensing devices.

Material composition characterization from computed tomography via self-supervised learning promotes pulmonary disease diagnosis

Material composition characterization from computed tomography via self-supervised learning promotes pulmonary disease diagnosis

Pulmonary Sclerosing Pneumocytoma - A Comprehensive Exploration of Clinical, Radiological, and Surgical Dimensions By IJISRT

Objectives: The study findings have important Clinical implications, especially in the preoperative evaluation of Lung Nodules and aid in the radiological diagnosis based on CT features. Pulmonary Sclerosing pneumocytoma is a Benign, rare tumor of the lung that represents a diagnostic challenge due to the Non-specific CT findings. The Aim of this study was to present a 10-year experience with sclerosing pneumocytoma of a large center of CHINA for the diagnosis and treatment of pulmonary diseases, and to emphasize differential diagnostic dilemmas as a potential source of errors.  Material and Methods: This represents a retrospective study of 31 patients diagnosed and treated with sclerosing pneumocytoma in the 10-year period. The study analyzed various Variables, including Gender, Age, Smoking history, Reason for CT, Nodule location, Nodule shape, Clinical symptoms, Calcifications, and Surgical Resections.  Results: Sclerosing pneumocytoma was more frequently diagnosed in females (93.5%). The patients ranged in age from 28 to 68. Most of the patients (77.4%) were asymptomatic. 30 patients had no history of smoking (96.8%). Mean Nodule size was 14.4mm. Most nodules have Round, oval and smooth margin, with majority of Nodules have location in the lower lobes of both lungs. Most nodules were peripherally situated (54.83%). VATS with lobectomy performed in 25 (80.64%) patients while VATS with wedge resection performed in 6(19.64%) patients, without post-surgical complications and Normal follow up.

A novel method in COPD diagnosing using respiratory signal generation based on CycleGAN and machine learning

Objective The main goal of this research is to use distinctive features in respiratory sounds for diagnosing Chronic Obstructive Pulmonary Disease (COPD). This study develops a classification method by utilizing inverse transforms to effectively identify COPD based on unique respiratory features while comparing the classification performance of various optimal algorithms. Method Respiratory sounds are divided into individual breathing cycles. In the data standardization and augmentation phase, the CycleGAN model enhances data diversity. Comprehensive analyses for these segments are then implemented using various Wavelet families and different spectral transformations representing characteristic signals. Advanced convolutional neural networks, including VGG16, ResNet50, and InceptionV3, are used for the classification task. Results The results of this study demonstrate the effectiveness of the mentioned method. Notably, the best-performing method utilizes Wavelet Bior1.3 after standardization in combination with InceptionV3, achieving a remarkable 99.75% F1-score, the gold standard for classification accuracy. Conclusion Inverse transformation techniques combined with deep learning models show significant accuracy in detecting COPD disease. These findings suggest the feasibility of early COPD diagnosis through AI-powered characterization of acoustic features. Motivation and Significance The motivation behind this research stems from the urgent need for early and accurate diagnosis of Chronic Obstructive Pulmonary Disease (COPD). COPD is a respiratory disease that poses many difficulties when detected late, potentially causing severe harm to the patient’s quality of life and increasing the healthcare burden. Timely identification and intervention are crucial to reduce the progression of the disease and improve patient outcomes.

Diagnostic insights from chemometric analysis of hemogram inflammatory indexes in male smokers with and without asthma or chronic obstructive pulmonary disease.

Diagnosis of asthma and chronic obstructive pulmonary disease (COPD) becomes difficult in a primary healthcare center due to ambiguous interpretation of spirometry and lack of facility to access established biomarkers. While routine hematological indices are easily available and accessible. The study aimed to evaluate the role of different hemogram indexes in males in COPD, asthma, and healthy smokers. Lung function tests and complete blood count (CBC) were done for 50 male subjects each from asthma, COPD, and healthy smokers. Multivariate analysis (MVA) was performed on blood indices data set. Receiver operating characteristic (ROC) curve was plotted to observe the performance of indexes. Pearson correlation was used to establish association between the lung function and blood indices. Most of the indices were elevated in COPD. Whereas, asthma patients showed a significant increase in eosinophil basophil ratio (EBR), lymphocyte-monocyte ratio (LMR), and mean platelet volume-platelet count ratio (MPR). Orthogonal (O)- Partial Least-Squares Discriminant Analysis (PLSDA) and variable importance in projection (VIP) score established EBR, neutrophil-lymphocyte ratio (NLR) and LMR, as discriminants for asthma. Whereas, Systemic Inflammatory Response Index (SIRI), NLR and EBR were the key variables for COPD. NLR (r = -0.73, p < 0.001) and SIRI (r = -0.71, p < 0.001) were found to be negatively correlated with forced expiratory volume in 1 s (FEV1) percentage of the predicted value (%pred) in asthma and COPD, respectively. EBR showed the sensitivity and specificity of 96% and 86% respectively in asthma. NLR was having sensitivity of 82% and 90% specificity in COPD. Our study in males shows routine hematological indices as being cost-effective, feasible, and seem to have tremendous potential as screening markers among chronic respiratory diseases in a primary healthcare center.

Spirometry test values can be estimated from a single chest radiograph.

Physical measurements of expiratory flow volume and speed can be obtained using spirometry. These measurements have been used for the diagnosis and risk assessment of chronic obstructive pulmonary disease and play a crucial role in delivering early care. However, spirometry is not performed frequently in routine clinical practice, thereby hindering the early detection of pulmonary function impairment. Chest radiographs (CXRs), though acquired frequently, are not used to measure pulmonary functional information. This study aimed to evaluate whether spirometry parameters can be estimated accurately from single frontal CXR without image findings using deep learning. Forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and FEV1/FVC as spirometry measurements as well as the corresponding chest radiographs of 11,837 participants were used in this study. The data were randomly allocated to the training, validation, and evaluation datasets at an 8:1:1 ratio. A deep learning network was pretrained using ImageNet. The input and output information were CXRs and spirometry test values, respectively. The training and evaluation of the deep learning network were performed separately for each parameter. The mean absolute error rate (MAPE) and Pearson's correlation coefficient (r) were used as the evaluation indices. The MAPEs between the spirometry measurements and AI estimates for FVC, FEV1 and FEV1/FVC were 7.59% (r = 0.910), 9.06% (r = 0.879) and 5.21% (r = 0.522), respectively. A strong positive correlation was observed between the measured and predicted indices of FVC and FEV1. The average accuracy of >90% was obtained in each estimation of spirometry indices. Bland-Altman analysis revealed good agreement between the estimated and measured values for FVC and FEV1. Frontal CXRs contain information related to pulmonary function, and AI estimation performed using frontal CXRs without image findings could accurately estimate spirometry values. The network proposed for estimating pulmonary function in this study could serve as a recommendation for performing spirometry or as an alternative method, suggesting its utility.

Contemporary Insights into the Diagnosis and Treatment of Chronic Obstructive Pulmonary Disease (COPD)

Contemporary Insights into the Diagnosis and Treatment of Chronic Obstructive Pulmonary Disease (COPD)

Comparative study of bone mineral density, phenotypes of emphysema in patients with or without established diagnosis of chronic airway disease

Background: Bone thinning and emphysema are established diseases caused by tobacco-related products proven beyond doubt. A paucity of literature available to establish the association between them. Objectives: To explore the association between the different emphysema subtypes and the presence of low bone mineral density among a population of active or former smokers with and without an established diagnosis of chronic obstructive pulmonary disease (COPD). Methods: 100 active and former smokers with chronic airway disease attending the Pulmonology Outpatient Department were identified, and they were administered clinical questionnaires, pulmonary function tests, chest computed tomography, and DEXA scans. Appropriate statistical tests were performed to analyze the association between the different emphysema subtypes, low bone mineral density, and smoking in the study participants. Results: Out of 100 samples, 80 % of the patients had low BMD (25 had osteoporosis, and 55 had osteopenia). Emphysema was more frequent and severe in patients with osteoporosis (63.2 vs. 36.8%, p=&amp;lt;0.01). For analyzing significant co-factors, a multivariable analysis was done, which yielded Body Mass Index (Odds ratio of 0.89 with a 95% Confidence Interval of 0.67–1.23) and the presence of centrilobular emphysema as most commonly associated with osteoporosis. (Odds ratio of 35.25, with a 95% confidence interval of 3.23 to 401.63). Conclusion: Patients having increased Body Mass Index (BMI) and the presence of centrilobular emphysema are having osteoporosis irrespective of their smoking status, which implicates BMI, and centrilobular emphysema has to be considered while evaluating COPD patients.

Factors Affecting Mortality and Distribution of Co-morbidities in Chronic Obstructive Pulmonary Disease

Objectives: Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide. This study aims to identify the rules associated with major comorbidities in patients with COPD and analyze how they affect death, thereby suggesting patient management measures that are efficient and can improve the quality of medical care.Methods: For this purpose, among the Korea Disease Control and Prevention Agency’s discharge injury in-depth survey data of more than 3 million cases from 2006 to 2021, adult patients aged 19 years or older with a principal diagnosis of chronic obstructive pulmonary disease were targeted. &lt;i&gt;χ&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt;-test and independent sample t-test were performed to analyze the distribution of deaths by demographic characteristics, and ARM (Association Rule Mining) was analyzed for association rules for comorbidities. Logistic regression analysis was performed on factors influencing death.Results: As a result of the analysis, the mortality rate was high in patients over 75 years of age, patients admitted to the emergency room, and patients discharged from medical institutions with more than 1,000 beds. The prevalence of comorbidities was high for E11 (Type 2 diabetes mellitus) and I10 (essential hypertension). As a result of analyzing the impact on death based on major association rules, other sepsis (A41) and essential hypertension (I10), other sepsis (A41) and pneumonia, organism unspecified (J18), sequelae of tuberculosis (B90) and J96 (respiratory Failure, NEC), sequelae of tuberculosis (B90), and I27 (other pulmonary heart diseases) were found to have a high probability of death.Conclusions: Based on the major association rules affecting death, we propose to apply them to a clinical decision support system for efficient patient management and personalized treatment.

Optimized fuzzy K-nearest neighbor approach for accurate lung cancer prediction based on radial endobronchial ultrasonography

Radial endobronchial ultrasonography (R-EBUS) has been a surge in the development of new ultrasonography for the diagnosis of pulmonary diseases beyond the central airway. However, it faces challenges in accurately pinpointing the location of abnormal lesions. Therefore, this study proposes an improved machine learning model aimed at distinguishing between malignant lung disease (MLD) from benign lung disease (BLD) through R-EBUS features. An enhanced manta ray foraging optimization based on elite perturbation search and cyclic mutation strategy (ECMRFO) is introduced at first. Experimental validation on 29 test functions from CEC 2017 demonstrates that ECMRFO exhibits superior optimization capabilities and robustness compared to other competing algorithms. Subsequently, it was combined with fuzzy k-nearest neighbor for the classification prediction of BLD and MLD. Experimental results indicate that the proposed modal achieves a remarkable prediction accuracy of up to 99.38%. Additionally, parameters such as R-EBUS1 Circle-dense sign, R-EBUS2 Hemi-dense sign, R-EBUS5 Onionskin sign and CCT5 mediastinum lymph node are identified as having significant clinical diagnostic value.

Early Diagnosis and Treatment of Chronic Obstructive Pulmonary Disease: The Costs and Benefits of Case Finding.

Early Diagnosis and Treatment of Chronic Obstructive Pulmonary Disease: The Costs and Benefits of Case Finding.

Estimating lung function from computed tomography at the patient and lobe level using machine learning.

Automated estimation of Pulmonary function test (PFT) results from Computed Tomography (CT) could advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases. Estimating lung function per lobe, which cannot be done with PFTs, would be helpful for risk assessment for pulmonary resection surgery and bronchoscopic lung volume reduction. To automatically estimate PFT results from CT and furthermore disentangle the individual contribution of pulmonary lobes to a patient's lung function. We propose I3Dr, a deep learning architecture for estimating global measures from an image that can also estimate the contributions of individual parts of the image to this global measure. We apply it to estimate the separate contributions of each pulmonary lobe to a patient's total lung function from CT, while requiring only CT scans and patient level lung function measurements for training. I3Dr consists of a lobe-level and a patient-level model. The lobe-level model extracts all anatomical pulmonary lobes from a CT scan and processes them in parallel to produce lobe level lung function estimates that sum up to a patient level estimate. The patient-level model directly estimates patient level lung function from a CT scan and is used to re-scale the output of the lobe-level model to increase performance. After demonstrating the viability of the proposed approach, the I3Dr model is trained and evaluated for PFT result estimation using a large data set of 8433 CT volumes for training, 1775 CT volumes for validation, and 1873 CT volumes fortesting. First, we demonstrate the viability of our approach by showing that a model trained with a collection of digit images to estimate their sum implicitly learns to assign correct values to individual digits. Next, we show that our models can estimate lobe-level quantities, such as COVID-19 severity scores, pulmonary volume (PV), and functional pulmonary volume (FPV) from CT while only provided with patient-level quantities during training. Lastly, we train and evaluate models for producing spirometry and diffusion capacity of carbon mono-oxide (DLCO) estimates at the patient and lobe level. For producing Forced Expiratory Volume in one second (FEV1), Forced Vital Capacity (FVC), and DLCO estimates, I3Dr obtains mean absolute errors (MAE) of 0.377L, 0.297L, and 2.800mL/min/mm Hg respectively. We release the resulting algorithms for lung function estimation to the research community at https://grand-challenge.org/algorithms/lobe-wise-lung-function-estimation/ CONCLUSIONS: I3Dr can estimate global measures from an image, as well as the contributions of individual parts of the image to this global measure. It offers a promising approach for estimating PFT results from CT scans and disentangling the individual contribution of pulmonary lobes to a patient's lung function. The findings presented in this work may advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases as well as in risk assessment for pulmonary resection surgery and bronchoscopic lung volumereduction.