Measuring pulmonary function in COPD using quantitative chest computed tomography analysis

Reference formulas for chest CT-derived lobar volumes in the lung-healthy general population

INTRODUCTION

Lung hyperinflation, a key contributor to dyspnea in chronic obstructive pulmonary disease (COPD), can be quantified via chest computed tomography (CT). Establishing reference equations for lobar volumes and total lung volume (TLV) can aid in evaluating lobar hyperinflation, especially for targeted lung volume reduction therapies.

METHODS

The Imaging in Lifelines study (ImaLife) comprises 11,729 participants aged 45 and above with analyzed inspiratory low-dose thoracic CT scans. Lung and lobar volumes were measured using an automatic AI-based segmentation algorithm (LungSeg). For the main analysis, participants were excluded if they had self-reported COPD/asthma, lung disease on CT, airflow obstruction on lung function testing, were currently smoking, aged over 80 years, or had height outside the 99% confidence interval. Reference equations for TLV and lobar volumes were determined using linear regression considering age and height, stratified by sex. For the subanalysis, participants who were currently smoking or experiencing airflow obstruction were compared to the group of the main analysis.

RESULTS

The study included 7306 lung-healthy participants, 97.5% Caucasian, 43.6% men, with mean age of 60.3 ± 9.5 years. Lung and lobar volumes generally increased with age and height. Men consistently had higher volumes than women when adjusted for height. R2 values ranged from 7.8 to 19.9%. In smokers and those with airway obstruction, volumes were larger than in lung-healthy groups, with the largest increases measured in the upper lobes.

CONCLUSION

The established reference equations for CT-derived TLV and lobar volumes provide a standardized interpretation for individuals aged 45 to 80 of Northern European descent.

KEY POINTS

Question Lobar lung volumes can be derived from inspiratory CT scans, but healthy-lung reference values are lacking. Findings Lung and lobar volumes generally increased with age and height. Reference equations for lung/lobar volumes were derived from a sizeable lung-healthy population. Clinical relevance This study provides reference equations for inspiratory CT-derived lung and lobar volumes in a lung-healthy population, potentially useful for assessing candidates for lung volume reduction therapies, for lobe removal in lung cancer patients, and in case of restrictive pulmonary diseases.

Prediction of Clinical Bronchiectasis from Asymptomatic Radiological Bronchiectasis

Background

Under persistent inflammation, asymptomatic radiological bronchiectasis (ARB) may develop into clinical bronchiectasis (CB). Although CB has been extensively studied, the potential for ARB to evolve into CB remains largely unexplored. Whether the ARB could progress to CB and the risk factors to speed up the process are poorly understood.

Methods

This was an observational cohort study. 370 patients with radiological bronchiectasis were included in Wuhan Union Hospital in 2018. 296 ARB patients were followed up in 2022 to verify if they progressed to CB and divided the development and validation of clinical prediction models into a training set (n=207) and a validation set (n=89) by the ratio of 7:3. LASSO algorithm and multivariable logistic regression analysis were performed to construct a new nomogram model. ROC, a calibration and decision curve were used to assess the predictive performance of our new prediction model.

Results

370 patients (74, 20% with CB) were finally included. Compared with ARB, CB had lower BMI, Bhalla score, FEV1% predicted, greater extent and degree of bronchodilation, more lobes with mucus plugs, greater thickness of bronchodilation, greater likelihood of pulmonary heart disease and chronic obstructive pulmonary disease (COPD), and lower likelihood of hypertension and coronary artery disease (P<0.05). In 2022, 60 out of 296 ARB patients progressed to CB. Age, FEV1% predicted, COPD, heart failure (HF), degree of bronchiectasis, number of lobes with bronchiectasis and number of lung segments with mucus plugs were risk factors. The AUCs of the prediction model were 0.866 (95% CI, 0.802-0.931) in the training set and 0.860 (95% CI, 0.770-0.949) in the validation set.

Conclusion

ARB may progress to CB under the risk factors, including age, FEV1% predicted, COPD, HF and CT images including degree of bronchiectasis, number of lobes with bronchiectasis and number of lung segments with mucus plugs), based on which the nomogram model is a convenient and efficient tool for follow-up management and preventing CB in patients with ARB.

Effect of adaptive statistical iterative reconstruction-V algorithm and deep learning image reconstruction algorithm on image quality and emphysema quantification in COPD patients under ultra-low-dose conditions

PURPOSE

To explore the effect of different reconstruction algorithms (ASIR-V and DLIR) on image quality and emphysema quantification in chronic obstructive pulmonary disease (COPD) patients under ultra-low-dose scanning conditions.

MATERIALS AND METHODS

This prospective study with patient consent included 62 COPD patients. Patients were examined by pulmonary function test (PFT), standard-dose CT (SDCT) and ultra-low-dose CT (ULDCT). SDCT images were reconstructed with filtered-back-projection (FBP), while ULDCT images were reconstructed using FBP, 30%ASIR-V, 60%ASIR-V, 90%ASIR-V, low-strength (DLIR-L), medium-strength (DLIR-M) and high-strength DLIR (DLIR-H) to form 8 image sets. Images were analysed using a commercial computer aided diagnosis (CAD) software. Parameters such as image noise, lung volume (LV), emphysema index (EI), mean lung density (MLD) and 15th percentile of lung density (PD15) were measured. Two radiologists evaluated tracheal and pulmonary artery image quality using a 5-point scale. Measurements were compared and the correlation between EI and PFT indices was analysed.

RESULT

ULDCT used 0.46 ± 0.22 mSv in radiation dose, 93.8% lower than SDCT (P < .001). There was no difference in LV and MLD among image groups (P > .05). ULDCT-ASIR-V90% and ULDCT-DLIR-M had similar image noise and EI and PD15 values to SDCT-FBP, and ULDCT-DLIR-M and ULDCT-DLIR-H had similar subjective scores to SDCT-FBP (all P > .05). ULDCT-DLIR-M provided the best correlation between EI and the FEV1/FVC and FEV1% indices in PFT, and the lowest deviations with SDCT-FBP in both EI and PD15.

CONCLUSION

DLIR-M provides the best image quality and emphysema quantification for COPD patients in ULDCT.

ADVANCES IN KNOWLEDGE

Ultra-low-dose CT scanning combined with DLIR-M reconstruction is comparable to standard dose images for quantitative analysis of emphysema and image quality.

Dual-phase computed tomography imaging for lung function assessment in patients with early-stage non-small cell lung cancer

Background

Early-stage non-small cell lung cancer (NSCLC) requires accurate preoperative lung function assessment, but traditional tests have limitations in evaluating regional lung function. This study aimed to evaluate the efficacy of dual-phase computed tomography (CT) imaging for preoperative lung function assessment in patients with early-stage NSCLC.

Methods

Sixty patients (28 men and 32 women; mean age 55.47±10.30 years) with early-stage NSCLC were prospectively included in this study. The data utilized in this study were retrospectively analyzed from prospectively collected clinical data. Each patient underwent dual-phase (inspiratory and respiratory) CT using dual-energy computed tomography (DECT) before surgery. The DECT parameters, including volume, iodine content, and iodine concentration (iodine content per unit volume) for each lung and lobe in both phases, were collected by a dual-energy workstation and the eXamine software. Semi-automatic lobe segmentation was achieved through the DE Lung Isolation function in the eXamine. Pulmonary function tests (PFTs) before surgery were conducted as the reference standard to assess the accuracy of DECT in lung function evaluation. The correlation between DECT parameters and PFT metrics was analyzed using Spearman correlation.

Results

DECT can achieve stable and reliable semi-automatic lung lobe segmentation. The median iodine concentration in each lobe showed that the left lung had slightly lower values than the right, with the left upper lobe having lower concentrations than the left lower lobe. In the right lung, the middle lobe had the lowest concentration, while the lower lobe had the highest. The Spearman correlation analysis indicated that multiple parameters from DECT correlated with lung function indices measured by PFT (P<0.05). Dual-phase functional CT imaging can accurately measure lung function.

Conclusions

Dual-phase CT imaging provides a comprehensive and precise preoperative lung function evaluation of patients who underwent operations for early-stage NSCLC.

Trends and hotspots of energy-based imaging in thoracic disease: a bibliometric analysis

OBJECTIVE

To conduct a bibliometric analysis of the prospects and obstacles associated with dual- and multi-energy CT in thoracic disease, emphasizing its current standing, advantages, and areas requiring attention.

METHODS

The Web of Science Core Collection was queried for relevant publications in dual- and multi-energy CT and thoracic applications without a limit on publication date or language. The Bibliometrix packages, VOSviewer, and CiteSpace were used for data analysis. Bibliometric techniques utilized were co-authorship analyses, trend topics, thematic map analyses, thematic evolution analyses, source's production over time, corresponding author's countries, and a treemap of authors' keywords.

RESULTS

A total of 1992 publications and 7200 authors from 313 different sources were examined in this study. The first available document was published in November 1982, and the most cited article was cited 1200 times. Siemens AG in Germany emerged as the most prominent author affiliation, with a total of 221 published articles. The most represented scientific journals were the "European Radiology" (181 articles, h-index = 46), followed by the "European Journal of Radiology" (148 articles, h-index = 34). Most of the papers were from Germany, the USA, or China. Both the keyword and topic analyses showed the history of dual- and multi-energy CT and the evolution of its application hotspots in the chest.

CONCLUSION

Our study illustrates the latest advances in dual- and multi-energy CT and its increasingly prominent applications in the chest, especially in lung parenchymal diseases and coronary artery diseases. Photon-counting CT and artificial intelligence will be the emerging hot technologies that continue to develop in the future.

CRITICAL RELEVANCE STATEMENT

This study aims to provide valuable insights into energy-based imaging in chest disease, validating the clinical application of multi-energy CT together with photon-counting CT and effectively increasing utilization in clinical practice.

KEY POINTS

Bibliometric analysis is fundamental to understanding the current and future state of dual- and multi-energy CT. Research trends and leading topics included coronary artery disease, pulmonary embolism, and radiation dose. All analyses indicate a growing interest in the use of energy-based imaging techniques for thoracic applications.

Predicting Acute Exacerbation Phenotype in Chronic Obstructive Pulmonary Disease Patients Using VGG-16 Deep Learning

INTRODUCTION

Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impact on hospitalizations, morbidity, and mortality of patients. This study aimed to develop a model for predicting acute exacerbation in COPD patients (AECOPD) based on deep-learning (DL) features.

METHODS

We performed a retrospective study on 219 patients with COPD who underwent inspiratory and expiratory HRCT scans. By recording the acute respiratory events of the previous year, these patients were further divided into non-AECOPD group and AECOPD group according to the presence of acute exacerbation events. Sixty-nine quantitative CT (QCT) parameters of emphysema and airway were calculated by NeuLungCARE software, and 2,000 DL features were extracted by VGG-16 method. The logistic regression method was employed to identify AECOPD patients, and 29 patients of external validation cohort were used to access the robustness of the results.

RESULTS

The model 3-B achieved an area under the receiver operating characteristic curve (AUC) of 0.933 and 0.865 in the testing cohort and external validation cohort, respectively. Model 3-I obtained AUC of 0.895 in the testing cohort and AUC of 0.774 in the external validation cohort. Model 7-B combined clinical characteristics, QCT parameters, and DL features achieved the best performance with an AUC of 0.979 in the testing cohort and demonstrating robust predictability with an AUC of 0.932 in the external validation cohort. Likewise, model 7-I achieved an AUC of 0.938 and 0.872 in the testing cohort and external validation cohort, respectively.

CONCLUSIONS

DL features extracted from HRCT scans can effectively predict acute exacerbation phenotype in COPD patients.

Computed tomography (CT) quantitative assessment of single lobe emphysema correlates with chronic obstructive pulmonary disease (COPD) severity: a cross-sectional study with retrospective data collection

Background

In the past, many researchers have studied the correlation between quantitative parameters of computed tomography (CT) and parameters of pulmonary function test (PFT) in patients with chronic obstructive pulmonary disease (COPD) with good results. Most of these studies have focused on the whole-lung level. In this study, we analyzed the biphasic CT lung volume parameters and the percentage of emphysema volume in different lobes of the lungs of patients with different grades of COPD and assessed their relationship with different lung function indices.

Methods

We retrospectively collected patients who underwent PFTs at The First Affiliated Hospital of Guangzhou Medical University from 1 July 2019 to 27 January 2020, and underwent chest respiratory dual-phase CT scans within 1 week, including 112 non-COPD patients and 297 COPD patients. We quantified the biphasic CT lung volume parameters and the percentage of emphysema volume in different lobes using a pulmonary image analysis tool. One-way analysis of variance (ANOVA) and Kruskal-Wallis H method were used to compare the quantitative CT parameters of each lung lobe in different groups. The correlation between quantitative CT parameters of different lung lobes and lung function indices was assessed using multiple linear regression.

Results

Among the 3 biphasic CT lung volume parameters, only volume change/inspiratory lung volume (∆LV/LVin) in the non-COPD control, mildly to moderately severe, and severe to extremely severe groups had statistical differences in each lobe level (all P<0.05). Correlation was significant between LVin and different lung function indices and between low attenuation areas percent below the threshold of -950 in the inspiratory phase [low attenuation area below -950 in the inspiratory phase (%LAA-950in)] and lung function indices in the left lower lobe (all P<0.05). There was statistically significant correlation between expiratory lung volume and ∆LV/LVin and lung function indices in the right lower lung (all P≤0.001). In the remaining lobes, LVin, expiratory lung volume, ∆LV/LVin, and %LAA-950in correlated with only some of the lung function indices.

Conclusions

The percentage of emphysema volume did not differ between lobes in the non-COPD control and severe to extremely severe COPD populations. LVin and %LAA-950in in the left upper lobe, expiratory lung volume and ∆LV/LVin in the right lower lobe were more reflective of the changes in lung function indices of the patients, whereas the correlation of the 3 biphasic CT lung volume parameters and the percentage of emphysema volume in the upper lobes of both lungs and the right middle lung with lung function indices was unclear.

Pulmonary volumes and signs of chronic airflow limitation in quantitative computed tomography

BACKGROUND

Computed tomography (CT) offers pulmonary volumetric quantification but is not commonly used in healthy individuals due to radiation concerns. Chronic airflow limitation (CAL) is one of the diagnostic criteria for chronic obstructive pulmonary disease (COPD), where early diagnosis is important. Our aim was to present reference values for chest CT volumetric and radiodensity measurements and explore their potential in detecting early signs of CAL.

METHODS

From the population-based Swedish CArdioPulmonarybioImage Study (SCAPIS), 294 participants aged 50-64, were categorized into non-CAL (n = 258) and CAL (n = 36) groups based on spirometry. From inspiratory and expiratory CT images we compared lung volumes, mean lung density (MLD), percentage of low attenuation volume (LAV%) and LAV cluster volume between groups, and against reference values from static pulmonary function test (PFT).

RESULTS

The CAL group exhibited larger lung volumes, higher LAV%, increased LAV cluster volume and lower MLD compared to the non-CAL group. Lung volumes significantly deviated from PFT values. Expiratory measurements yielded more reliable results for identifying CAL compared to inspiratory. Using a cut-off value of 0.6 for expiratory LAV%, we achieved sensitivity, specificity and positive/negative predictive values of 72%, 85% and 40%/96%, respectively.

CONCLUSION

We present volumetric reference values from inspiratory and expiratory chest CT images for a middle-aged healthy cohort. These results are not directly comparable to those from PFTs. Measures of MLD and LAV can be valuable in the evaluation of suspected CAL. Further validation and refinement are necessary to demonstrate its potential as a decision support tool for early detection of COPD.

Expiratory Venous Volume and Arterial Tortuosity are Associated with Disease Severity and Mortality Risk in Patients with COPD: Results from COSYCONET

Purpose

The aim of this study was to evaluate the association between computed tomography (CT) quantitative pulmonary vessel morphology and lung function, disease severity, and mortality risk in patients with chronic obstructive pulmonary disease (COPD).

Patients and Methods

Participants of the prospective nationwide COSYCONET cohort study with paired inspiratory-expiratory CT were included. Fully automatic software, developed in-house, segmented arterial and venous pulmonary vessels and quantified volume and tortuosity on inspiratory and expiratory scans. The association between vessel volume normalised to lung volume and tortuosity versus lung function (forced expiratory volume in 1 sec [FEV1]), air trapping (residual volume to total lung capacity ratio [RV/TLC]), transfer factor for carbon monoxide (TLCO), disease severity in terms of Global Initiative for Chronic Obstructive Lung Disease (GOLD) group D, and mortality were analysed by linear, logistic or Cox proportional hazard regression.

Results

Complete data were available from 138 patients (39% female, mean age 65 years). FEV1, RV/TLC and TLCO, all as % predicted, were significantly (p < 0.05 each) associated with expiratory vessel characteristics, predominantly venous volume and arterial tortuosity. Associations with inspiratory vessel characteristics were absent or negligible. The patterns were similar for relationships between GOLD D and mortality with vessel characteristics. Expiratory venous volume was an independent predictor of mortality, in addition to FEV1.

Conclusion

By using automated software in patients with COPD, clinically relevant information on pulmonary vasculature can be extracted from expiratory CT scans (although not inspiratory scans); in particular, expiratory pulmonary venous volume predicted mortality.

Trial Registration

NCT01245933.

Novel Surface Topographic Assessment of Lung Volume and Pulmonary Function Tests in Idiopathic Scoliosis: A Preliminary Study

OBJECTIVE

Severe spinal deformity results in restrictive pulmonary disease from thoracic distortions and lung-volume limitations. Though spirometry and body plethysmography are widely accepted tests for pulmonary function tests (PFTs), they are time-consuming and require patient compliance. This study investigates whether surface topographic [surface topography (ST)] measurements of body volume difference (BVD) and torso volume difference between maximum inhale and exhale correlate to values determined on PFTs.

METHODS

This study included patients with idiopathic scoliosis and thoracic/thoracolumbar curves ≥40 degrees. Patients received ST scans, clinical examinations, and EOS biplanar radiographs on the same day. PFTs were performed within 3 months of ST/radiographic analysis. Univariate linear regression analysis was used to examine relationships between BVD, PFT values, and mean curves.

RESULTS

Sixteen patients (14.6 ± 2.2 y, 69% females) with idiopathic scoliosis and mean thoracic/thoracolumbar curves of 62 degrees ± 15˚ degrees (45 degrees to 93 degrees) were assessed. BVD displayed statistically high-positive positive correlations with forced vital capacity ( R = 0.863, P < 0.0001), forced expiratory volume in 1 second ( R = 0.870, P < 0.001), vital capacity ( R = 0.802, P < 0.0001), and TLC ( R = 0.831, P < 0.0001. Torso volume difference showed similarly high positive correlations to forced vital capacity, forced expiratory volume in 1 second, vital capacity, and TLC, but not residual volume. No correlations emerged between the mean thoracic/thoracolumbar curve and BVD or PFT values.

CONCLUSION

This study strongly endorses further investigation into ST scanning as an alternative to traditional PFTs for assessing pulmonary volumes. The noncontact and noninvasive nature of ST scanning presents a valuable alternative method for analyzing thoracic volume, particularly beneficial for patients unable to cooperate with standard PFTs.

LEVEL OF EVIDENCE

Level II-prognostic.

Lung volume measurement using chest CT in COVID-19 patients: a cohort study in Japan

OBJECTIVE

This study aimed to investigate the utility of CT quantification of lung volume for predicting critical outcomes in COVID-19 patients.

METHODS

This retrospective cohort study included 1200 hospitalised patients with COVID-19 from 4 hospitals. Lung fields were extracted using artificial intelligence-based segmentation, and the percentage of the predicted (%pred) total lung volume (TLC (%pred)) was calculated. The incidence of critical outcomes and posthospitalisation complications was compared between patients with low and high CT lung volumes classified based on the median percentage of predicted TLCct (n=600 for each). Prognostic factors for residual lung volume loss were investigated in 208 patients with COVID-19 via a follow-up CT after 3 months.

RESULTS

The incidence of critical outcomes was higher in the low TLCct (%pred) group than in the high TLCct (%pred) group (14.2% vs 3.3%, p<0.0001). Multivariable analysis of previously reported factors (age, sex, body mass index and comorbidities) demonstrated that CT-derived lung volume was significantly associated with critical outcomes. The low TLCct (%pred) group exhibited a higher incidence of bacterial infection, heart failure, thromboembolism, liver dysfunction and renal dysfunction than the high TLCct (%pred) group. TLCct (%pred) at 3 months was similarly divided into two groups at the median (71.8%). Among patients with follow-up CT scans, lung volumes showed a recovery trend from the time of admission to 3 months but remained lower in critical cases at 3 months.

CONCLUSION

Lower CT lung volume was associated with critical outcomes, posthospitalisation complications and slower improvement of clinical conditions in COVID-19 patients.

Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is underdiagnosed with the current gold standard measure pulmonary function test (PFT). A more sensitive and simple option for early detection and severity evaluation of COPD could benefit practitioners and patients.

METHODS

In this multicenter retrospective study, frontal chest X-ray (CXR) images and related clinical information of 1055 participants were collected and processed. Different deep learning algorithms and transfer learning models were trained to classify COPD based on clinical data and CXR images from 666 subjects, and validated in internal test set based on 284 participants. External test including 105 participants was also performed to verify the generalization ability of the learning algorithms in diagnosing COPD. Meanwhile, the model was further used to evaluate disease severity of COPD by predicting different grads.

RESULTS

The Ensemble model showed an AUC of 0.969 in distinguishing COPD by simultaneously extracting fusion features of clinical parameters and CXR images in internal test, better than models that used clinical parameters (AUC = 0.963) or images (AUC = 0.946) only. For the external test set, the AUC slightly declined to 0.934 in predicting COPD based on clinical parameters and CXR images. When applying the Ensemble model to determine disease severity of COPD, the AUC reached 0.894 for three-classification and 0.852 for five-classification respectively.

CONCLUSION

The present study used DL algorithms to screen COPD and predict disease severity based on CXR imaging and clinical parameters. The models showed good performance and the approach might be an effective case-finding tool with low radiation dose for COPD diagnosis and staging.

Quantification of airway wall contrast enhancement on virtual monoenergetic images from spectral computed tomography

OBJECTIVES

Quantitative computed tomography (CT) plays an increasingly important role in phenotyping airway diseases. Lung parenchyma and airway inflammation could be quantified by contrast enhancement at CT, but its investigation by multiphasic examinations is limited. We aimed to quantify lung parenchyma and airway wall attenuation in a single contrast-enhanced spectral detector CT acquisition.

METHODS

For this cross-sectional retrospective study, 234 lung-healthy patients who underwent spectral CT in four different contrast phases (non-enhanced, pulmonary arterial, systemic arterial, and venous phase) were recruited. Virtual monoenergetic images were reconstructed from 40-160 keV, on which attenuations of segmented lung parenchyma and airway walls combined for 5th-10th subsegmental generations were assessed in Hounsfield Units (HU) by an in-house software. The spectral attenuation curve slope between 40 and 100 keV (λHU) was calculated.

RESULTS

Mean lung density was higher at 40 keV compared to that at 100 keV in all groups (p < 0.001). λHU of lung attenuation was significantly higher in the systemic (1.7 HU/keV) and pulmonary arterial phase (1.3 HU/keV) compared to that in the venous phase (0.5 HU/keV) and non-enhanced (0.2 HU/keV) spectral CT (p < 0.001). Wall thickness and wall attenuation were higher at 40 keV compared to those at 100 keV for the pulmonary and systemic arterial phase (p ≤ 0.001). λHU for wall attenuation was significantly higher in the pulmonary arterial (1.8 HU/keV) and systemic arterial (2.0 HU/keV) compared to that in the venous (0.7 HU/keV) and non-enhanced (0.3 HU/keV) phase (p ≤ 0.002).

CONCLUSIONS

Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition, and may separate arterial and venous enhancement. Further studies are warranted to analyze spectral CT for inflammatory airway diseases.

KEY POINTS

• Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition. • Spectral CT may separate arterial and venous enhancement of lung parenchyma and airway wall. • The contrast enhancement can be quantified by calculating the spectral attenuation curve slope from virtual monoenergetic images.

Deep Learning for Estimating Lung Capacity on Chest Radiographs Predicts Survival in Idiopathic Pulmonary Fibrosis

Background Total lung capacity (TLC) has been estimated with use of chest radiographs based on time-consuming methods, such as planimetric techniques and manual measurements. Purpose To develop a deep learning-based, multidimensional model capable of estimating TLC from chest radiographs and demographic variables and validate its technical performance and clinical utility with use of multicenter retrospective data sets. Materials and Methods A deep learning model was pretrained with use of 50 000 consecutive chest CT scans performed between January 2015 and June 2017. The model was fine-tuned on 3523 pairs of posteroanterior chest radiographs and plethysmographic TLC measurements from consecutive patients who underwent pulmonary function testing on the same day. The model was tested with multicenter retrospective data sets from two tertiary care centers and one community hospital, including (a) an external test set 1 (n = 207) and external test set 2 (n = 216) for technical performance and (b) patients with idiopathic pulmonary fibrosis (n = 217) for clinical utility. Technical performance was evaluated with use of various agreement measures, and clinical utility was assessed in terms of the prognostic value for overall survival with use of multivariable Cox regression. Results The mean absolute difference and within-subject SD between observed and estimated TLC were 0.69 L and 0.73 L, respectively, in the external test set 1 (161 men; median age, 70 years [IQR: 61-76 years]) and 0.52 L and 0.53 L in the external test set 2 (113 men; median age, 63 years [IQR: 51-70 years]). In patients with idiopathic pulmonary fibrosis (145 men; median age, 67 years [IQR: 61-73 years]), greater estimated TLC percentage was associated with lower mortality risk (adjusted hazard ratio, 0.97 per percent; 95% CI: 0.95, 0.98; P < .001). Conclusion A fully automatic, deep learning-based model estimated total lung capacity from chest radiographs, and the model predicted survival in idiopathic pulmonary fibrosis. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Sorkness in this issue.

A Computer-based Analysis for Identification and Quantification of Small Airway Disease in Lung Computed Tomography Images: A Comprehensive Review for Radiologists

Computed tomography (CT) imaging is being increasingly used in clinical practice for detailed characterization of lung diseases. Respiratory diseases involve various components of the lung, including the small airways. Evaluation of small airway disease on CT images is challenging as the airways cannot be visualized directly by a CT scanner. Small airway disease can manifest as pulmonary air trapping (AT). Although AT may be sometimes seen as mosaic attenuation on expiratory CT images, it is difficult to identify diffuse AT visually. Computer technology advances over the past decades have provided methods for objective quantification of small airway disease on CT images. Quantitative CT (QCT) methods are being rapidly developed to quantify underlying lung diseases with greater precision than subjective visual assessment of CT images. A growing body of evidence suggests that QCT methods can be practical tools in the clinical setting to identify and quantify abnormal regions of the lung accurately and reproducibly. This review aimed to describe the available methods for the identification and quantification of small airway disease on CT images and to discuss the challenges of implementing QCT metrics in clinical care for patients with small airway disease.

Quantitative Computed Tomography: What Clinical Questions Can it Answer in Chronic Lung Disease?

Quantitative computed tomography (QCT) has recently gained an important role in the functional assessment of chronic lung disease. Its capacity in diagnostic, staging, and prognostic evaluation in this setting is similar to that of traditional pulmonary function testing. Furthermore, it can demonstrate lung injury before the alteration of pulmonary function test parameters, and it enables the classification of disease phenotypes, contributing to the customization of therapy and performance of comparative studies without the intra- and inter-observer variation that occurs with qualitative analysis. In this review, we address technical issues with QCT analysis and demonstrate the ability of this modality to answer clinical questions encountered in daily practice in the management of patients with chronic lung disease.

Treatable traits in COPD patients

There is no justification for a therapeutic nihilism in clinical practice because current management (pharmacological and non-pharmacological) of the patients with chronic obstructive pulmonary disease according to treatable traits is effective in decreasing their respiratory symptoms, increasing their exercise tolerance and capacity, improving their quality of life, preventing (and treating) many of their exacerbations and decreasing their mortality.

Evaluation of spirometry-gated computed tomography to measure lung volumes in emphysema patients

Introduction

In emphysema patient being evaluated for bronchoscopic lung volume reduction (BLVR), accurate measurement of lung volumes is important. Total lung capacity (TLC) and residual volume (RV) are commonly measured by body plethysmography but can also be derived from chest computed tomography (CT). Spirometry-gated CT scanning potentially improves the agreement of CT and body plethysmography. The aim of this study was to compare lung volumes derived from spirometry-gated CT and “breath-hold-coached” CT to the reference standard: body plethysmography.

Methods

In this single-centre retrospective cohort study, emphysema patients being evaluated for BLVR underwent body plethysmography, inspiration (TLC) and expiration (RV) CT scan with spirometer guidance (“gated group”) or with breath-hold-coaching (“non-gated group”). Quantitative analysis was used to calculate lung volumes from the CT.

Results

200 patients were included in the study (mean±sd age 62±8 years, forced expiratory flow in 1 s 29.2±8.7%, TLC 7.50±1.46 L, RV 4.54±1.07 L). The mean±sd CT-derived TLC was 280±340 mL lower compared to body plethysmography in the gated group (n=100), and 590±430 mL lower for the non-gated group (n=100) (both p<0.001). The mean±sd CT-derived RV was 300±470 mL higher in the gated group and 700±720 mL higher in the non-gated group (both p<0.001). Pearson correlation factors were 0.947 for TLC gated, 0.917 for TLC non-gated, 0.823 for RV gated, 0.693 for RV non-gated, 0.539 for %RV/TLC gated and 0.204 for %RV/TLC non-gated. The differences between the gated and non-gated CT results for TLC and RV were significant for all measurements (p<0.001).

Conclusion

In severe COPD patients with emphysema, CT-derived lung volumes are strongly correlated to body plethysmography lung volumes, and especially for RV, more accurate when using spirometry gating.