Central Role of CT in Management of Pulmonary Fibrosis

With the approval of antifibrotic medications to treat patients with idiopathic pulmonary fibrosis and progressive pulmonary fibrosis, radiologists have an integral role in diagnosing these entities and guiding treatment decisions. CT features of early pulmonary fibrosis include irregular thickening of interlobular septa, pleura, and intralobular linear structures, with subsequent progression to reticular abnormality, traction bronchiectasis or bronchiolectasis, and honeycombing. CT patterns of fibrotic lung disease can often be reliably classified on the basis of the CT features and distribution of the condition. Accurate identification of usual interstitial pneumonia (UIP) or probable UIP patterns by radiologists can obviate the need for a tissue sample-based diagnosis. Other entities that can appear as a UIP pattern must be excluded in multidisciplinary discussion before a diagnosis of idiopathic pulmonary fibrosis is made. Although the imaging findings of nonspecific interstitial pneumonia and fibrotic hypersensitivity pneumonitis can overlap with those of a radiologic UIP pattern, these entities can often be distinguished by paying careful attention to the radiologic signs. Diagnostic challenges may include misdiagnosis of fibrotic lung disease due to pitfalls such as airspace enlargement with fibrosis, paraseptal emphysema, recurrent aspiration, and postinfectious fibrosis. The radiologist also plays an important role in identifying complications of pulmonary fibrosis-pulmonary hypertension, acute exacerbation, infection, and lung cancer in particular. In cases in which there is uncertainty regarding the clinical and radiologic diagnoses, surgical biopsy is recommended, and a multidisciplinary discussion among clinicians, radiologists, and pathologists can be used to address diagnosis and management strategies. This review is intended to help radiologists diagnose and manage pulmonary fibrosis more accurately, ultimately aiding in the clinical management of affected patients. ©RSNA, 2024 Supplemental material is available for this article.

Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes

Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. Methods: We trained an MIL algorithm using a pooled dataset (n = 2,143) and tested it in three independent populations: data from a prior publication (n = 127), a single-institution clinical cohort (n = 239), and a national registry of patients with pulmonary fibrosis (n = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [n = 127] and 0.79 [n = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; P < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (-88 ml/yr vs. -45 ml/yr; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.

Chronic rhinosinusitis and olfaction after highly effective modulator therapy: The role of individual sinus inflammation

KEY POINTS

Individual sinus opacification (ISO) is measurable via a convolutional neural network approach. ISO decreased through 2 years after highly effective modulator therapy was initiated. In adults with cystic fibrosis, ISO did not correlate with quality of life or olfaction.

Local heterogeneity of normal lung parenchyma and small airways disease are associated with COPD severity and progression

BACKGROUND

Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline.

METHODS

PRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) were generated from CT scans collected as part of the COPDGene study (n = 8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRMNorm and PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1 decline using a machine learning model.

RESULTS

Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSAD and PRMNorm were independently associated with the amount of emphysema. Readouts χfSAD (β of 0.106, p < 0.001) and VfSAD (β of 0.065, p = 0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1 decline over five years with an AUC of 0.69.

CONCLUSIONS

We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression.

Deep Learning-based Fibrosis Extent on Computed Tomography Predicts Outcome of Fibrosing Interstitial Lung Disease Independent of Visually Assessed Computed Tomography Pattern

Rationale: Radiologic pattern has been shown to predict survival in patients with fibrosing interstitial lung disease. The additional prognostic value of fibrosis extent by quantitative computed tomography (CT) is unknown. Objectives: We hypothesized that fibrosis extent provides information beyond visually assessed CT pattern that is useful for outcome prediction. Methods: We performed a retrospective analysis of chest CT, demographics, longitudinal pulmonary function, and transplantation-free survival among participants in the Pulmonary Fibrosis Foundation Patient Registry. CT pattern was classified visually according to the 2018 usual interstitial pneumonia criteria. Extent of fibrosis was objectively quantified using data-driven textural analysis. We used Kaplan-Meier plots and Cox proportional hazards and linear mixed-effects models to evaluate the relationships between CT-derived metrics and outcomes. Results: Visual assessment and quantitative analysis were performed on 979 enrollment CT scans. Linear mixed-effect modeling showed that greater baseline fibrosis extent was significantly associated with the annual rate of decline in forced vital capacity. In multivariable models that included CT pattern and fibrosis extent, quantitative fibrosis extent was strongly associated with transplantation-free survival independent of CT pattern (hazard ratio, 1.04; 95% confidence interval, 1.04-1.05; P < 0.001; C statistic = 0.73). Conclusions: The extent of lung fibrosis by quantitative CT is a strong predictor of physiologic progression and survival, independent of visually assessed CT pattern.

Imaging of pulmonary fibrosis in children: A review, with proposed diagnostic criteria

Computed tomography (CT) imaging findings of pulmonary fibrosis are well established for adults and have been shown to correlate with prognosis and outcome. Recognition of fibrotic CT findings in children is more limited. With approved treatments for adult pulmonary fibrosis, it has become critical to define CT criteria for fibrosis in children, to identify patients in need of treatment and those eligible for clinical trials. Understanding how pediatric fibrosis compares with idiopathic pulmonary fibrosis and other causes of fibrosis in adults is increasingly important as these patients transition to adult care teams. Here, we review what is known regarding the features of pulmonary fibrosis in children compared with adults. Pulmonary fibrosis in children may be associated with genetic surfactant dysfunction disorders, autoimmune systemic disorders, and complications after radiation, chemotherapy, transplantation, and other exposures. Rather than a basal-predominant usual interstitial pneumonia pattern with honeycombing, pediatric fibrosis is primarily characterized by reticulation, traction bronchiectasis, architectural distortion, or cystic lucencies/abnormalities. Ground-glass opacities are more frequent in children with fibrotic interstitial lung disease than adults, and disease distribution appears more diffuse, without clearly defined axial or craniocaudal predominance. Following discussion and consensus amongst a panel of expert radiologists, pathologists and physicians, distinctive disease features were integrated to develop criteria for the first global Phase III trial in children with pulmonary fibrosis.

Quantification of Emphysema Progression at CT Using Simultaneous Volume, Noise, and Bias Lung Density Correction

Background CT attenuation is affected by lung volume, dosage, and scanner bias, leading to inaccurate emphysema progression measurements in multicenter studies. Purpose To develop and validate a method that simultaneously corrects volume, noise, and interscanner bias for lung density change estimation in emphysema progression at CT in a longitudinal multicenter study. Materials and Methods In this secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, lung function data were obtained from participants who completed baseline and 5-year follow-up visits from January 2008 to August 2017. CT emphysema progression was measured with volume-adjusted lung density (VALD) and compared with the joint volume-noise-bias-adjusted lung density (VNB-ALD). Reproducibility was studied under change of dosage protocol and scanner model with repeated acquisitions. Emphysema progression was visually scored in 102 randomly selected participants. A stratified analysis of clinical characteristics was performed that considered groups based on their combined lung density change measured by VALD and VNB-ALD. Results A total of 4954 COPDGene participants (mean age, 60 years ± 9 [SD]; 2511 male, 2443 female) were analyzed (1329 with repeated reduced-dose acquisition in the follow-up visit). Mean repeatability coefficients were 30 g/L ± 0.46 for VALD and 14 g/L ± 0.34 for VNB-ALD. VALD measurements showed no evidence of differences between nonprogressors and progressors (mean, -5.5 g/L ± 9.5 vs -8.6 g/L ± 9.6; P = .11), while VNB-ALD agreed with visual readings and showed a difference (mean, -0.67 g/L ± 4.8 vs -4.2 g/L ± 5.5; P < .001). Analysis of progression showed that VNB-ALD progressors had a greater decline in forced expiratory volume in 1 second (-42 mL per year vs -32 mL per year; Tukey-adjusted P = .002). Conclusion Simultaneously correcting volume, noise, and interscanner bias for lung density change estimation in emphysema progression at CT improved repeatability analyses and agreed with visual readings. It distinguished between progressors and nonprogressors and was associated with a greater decline in lung function metrics. Clinical trial registration no. NCT00608764 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Goo in this issue.

Quantitative CT of Normal Lung Parenchyma and Small Airways Disease Topologies are Associated With COPD Severity and Progression

Objectives

Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients, and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline.

Materials and Methods

PRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) were generated from CT scans collected as part of the COPDGene study (n=8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRMNorm and PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1 decline using a machine learning model.

Results

Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSAD and PRMNorm were independently associated with the amount of emphysema. Readouts χfSAD (β of 0.106, p<0.001) and VfSAD (β of 0.065, p=0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1 decline over five years with an AUC of 0.69.

Conclusions

We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression.

Pirfenidone in fibrotic hypersensitivity pneumonitis: a double-blind, randomised clinical trial of efficacy and safety

BACKGROUND

Fibrotic hypersensitivity pneumonitis (FHP) is an irreversible lung disease with high morbidity and mortality. We sought to evaluate the safety and effect of pirfenidone on disease progression in such patients.

METHODS

We conducted a single-centre, randomised, double-blinded, placebo-controlled trial in adults with FHP and disease progression. Patients were assigned in a 2:1 ratio to receive either oral pirfenidone (2403 mg/day) or placebo for 52 weeks. The primary end point was the mean absolute change in the per cent predicted forced vital capacity (FVC%). Secondary end points included progression-free survival (PFS, time to a relative decline ≥10% in FVC and/or diffusing capacity of the lung for carbon monoxide (DLCO), acute respiratory exacerbation, a decrease of ≥50 m in the 6 min walk distance, increase or introduction of immunosuppressive drugs or death), change in FVC slope and mean DLCO%, hospitalisations, radiological progression of lung fibrosis and safety.

RESULTS

After randomising 40 patients, enrolment was interrupted by the COVID-19 pandemic. There was no significant between-group difference in FVC% at week 52 (mean difference -0.76%, 95% CI -6.34 to 4.82). Pirfenidone resulted in a lower rate of decline in the adjusted FVC% at week 26 and improved PFS (HR 0.26, 95% CI 0.12 to 0.60). Results for other secondary end points showed no significant difference between groups. No deaths occurred in the pirfenidone group and one death (respiratory) occurred in the placebo group. There were no treatment-emergent serious adverse events.

CONCLUSIONS

The trial was underpowered to detect a difference in the primary end point. Pirfenidone was found to be safe and improved PFS in patients with FHP.

TRIAL REGISTRATION MUMBER

NCT02958917.

Heterogeneity and Progression of Chronic Obstructive Pulmonary Disease: Emphysema-Predominant and Non-Emphysema-Predominant Disease

While variation in emphysema severity between patients with chronic obstructive pulmonary disease (COPD) is well-recognized, clinically applicable definitions of the emphysema-predominant disease (EPD) and non-emphysema-predominant disease (NEPD) subtypes have not been established. To study the clinical relevance of the EPD and NEPD subtypes, we tested the association of these subtypes with prospective decline in forced expiratory volume in 1 second (FEV1) and mortality among 3,427 subjects with Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric grade 2-4 COPD at baseline in the Genetic Epidemiology of COPD (COPDGene) Study, an ongoing national multicenter study that started in 2007. NEPD was defined as airflow obstruction with less than 5% computed tomography (CT) quantitative densitometric emphysema at -950 Hounsfield units, and EPD was defined as airflow obstruction with 10% or greater CT emphysema. Mixed-effects models for FEV1 demonstrated larger average annual FEV1 loss in EPD subjects than in NEPD subjects (-10.2 mL/year; P < 0.001), and subtype-specific associations with FEV1 decline were identified. Cox proportional hazards models showed higher risk of mortality among EPD patients versus NEPD patients (hazard ratio = 1.46, 95% confidence interval: 1.34, 1.60; P < 0.001). To determine whether the NEPD/EPD dichotomy is captured by previously described COPDGene subtypes, we used logistic regression and receiver operating characteristic (ROC) curve analysis to predict NEPD/EPD membership using these previous subtype definitions. The analysis generally showed excellent discrimination, with areas under the ROC curve greater than 0.9. The NEPD and EPD COPD subtypes capture important aspects of COPD heterogeneity and are associated with different rates of disease progression and mortality.

Deep Learning Assessment of Progression of Emphysema and Fibrotic Interstitial Lung Abnormality

Rationale: Although studies have evaluated emphysema and fibrotic interstitial lung abnormality individually, less is known about their combined progression. Objectives: To define clinically meaningful progression of fibrotic interstitial lung abnormality in smokers without interstitial lung disease and evaluate the effects of fibrosis and emphysema progression on mortality. Methods: Emphysema and pulmonary fibrosis were assessed on the basis of baseline and 5-year follow-up computed tomography scans of 4,450 smokers in the COPDGene Study using deep learning algorithms. Emphysema was classified as absent, trace, mild, moderate, confluent, or advanced destructive. Fibrosis was expressed as a percentage of lung volume. Emphysema progression was defined as an increase by at least one grade. A hybrid distribution and anchor-based method was used to determine the minimal clinically important difference in fibrosis. The relationship between progression and mortality was evaluated using multivariable shared frailty models using an age timescale. Measurements and Main Results: The minimal clinically important difference for fibrosis was 0.58%. On the basis of this threshold, 2,822 (63%) had progression of neither emphysema nor fibrosis, 841 (19%) had emphysema progression alone, 512 (12%) had fibrosis progression alone, and 275 (6.2%) had progression of both. Compared with nonprogressors, hazard ratios for mortality were 1.42 (95% confidence interval, 1.11-1.82) in emphysema progressors, 1.49 (1.14-1.94) in fibrosis progressors, and 2.18 (1.58-3.02) in those with progression of both emphysema and fibrosis. Conclusions: In smokers without known interstitial lung disease, small changes in fibrosis may be clinically significant, and combined progression of emphysema and fibrosis is associated with increased mortality.

Quantitative CT Evaluation of Emphysema Progression over 10 Years in the COPDGene Study

Background Long-term studies of chronic obstructive pulmonary disease (COPD) can evaluate emphysema progression. Adjustment for differences in equipment and scanning protocols of individual CT examinations have not been studied extensively. Purpose To evaluate emphysema progression in current and former smokers in the COPDGene cohort over three imaging points obtained at 5-year intervals accounting for individual CT parameters. Materials and Methods Current and former cigarette smokers enrolled between 2008 and 2011 from the COPDGene study were prospectively followed for 10 years between 2008 and 2020. Extent of emphysema as adjusted lung density (ALD) from quantitative CT was measured at baseline and at 5- and 10-year follow-up. Linear mixed models adjusted for CT technical characteristics were constructed to evaluate emphysema progression. Mean annual changes in ALD over consecutive 5-year study periods were estimated by smoking status and baseline emphysema. Results Of 8431 participants at baseline (mean age, 60 years ± 9 [SD]; 3905 female participants), 4913 were at 5-year follow-up and 1544 participants were at 10-year follow-up. There were 4134 (49%) participants who were current smokers, and 4449 (53%) participants had more than trace emphysema at baseline. Current smokers with more than trace emphysema showed the largest decline in ALD, with mean annual decreases of 1.4 g/L (95% CI: 1.2, 1.5) in the first 5 years and 0.9 g/L (95% CI: 0.7, 1.2) in the second 5 years. Accounting for CT noise, field of view, and scanner model improved model fit for estimation of emphysema progression (P < .001 by likelihood ratio test). Conclusion Evaluation at CT of emphysema progression in the COPDGene study showed that, during the span of 10 years, participants with pre-existing emphysema who continued smoking had the largest decline in ALD. Adjusting for CT equipment and protocol factors improved these longitudinal estimates. Clinical trial registration no. NCT00608764 © RSNA, 2023 Supplemental material is available for this article. See the editorial by Parraga and Kirby in this issue.

Serum Antibodies to Peptidylarginine Deiminase-4 in Rheumatoid Arthritis Associated-Interstitial Lung Disease Are Associated with Decreased Lung Fibrosis and Improved Survival

OBJECTIVE

Interstitial lung disease (ILD) is a leading cause of mortality in rheumatoid arthritis (RA), particularly in those with the usual interstitial pneumonia subtype (RA-UIP). Serum antibodies to peptidylarginine deiminase type 4 (anti-PAD4), particularly a subset that cross-react with PAD3 (PAD3/4XR), have been associated with imaging evidence of ILD. We aimed to determine the specificity of anti-PAD4 antibodies in RA-ILD and to examine associations with markers of ILD severity.

METHODS

48 RA-ILD and 31 idiopathic pulmonary fibrosis (IPF) patients were identified from the National Jewish Health Biobank. RA-ILD subtype was defined by imaging pattern on high-resolution chest computed tomography (CT), and serum was tested for anti-PAD4 and anti-PAD3/4XR antibodies. Antibody prevalence, measures of ILD severity (% predicted forced vital capacity, FVC; % predicted diffusion capacity carbon monoxide, DLCO; quantitative CT fibrosis) and mortality were compared between groups.

RESULTS

Anti-PAD4 antibodies were present in 9/48 (19%) subjects with RA-ILD and no subjects with IPF. Within RA-ILD, anti-PAD4 antibodies were found almost exclusively in RA-UIP (89%). Within RA-UIP subjects, % predicted FVC was higher in anti-PAD4+ subjects, and this finding was most strongly associated with anti-PAD3/4XR antibodies. In addition, quantitative CT fibrosis score was lower in anti-PAD4+ RA-UIP subjects, including those with mono-reactive anti-PAD4 antibodies and anti-PAD3/4XR antibodies. Anti-PAD4+ RA-UIP subjects also exhibited decreased mortality.

CONCLUSION

We demonstrate the presence of serum anti-PAD4 antibodies in a subset of patients with RA-UIP that were notably associated with better lung function, less fibrosis and decreased mortality.

Machine learning in radiology: the new frontier in interstitial lung diseases

Challenges for the effective management of interstitial lung diseases (ILDs) include difficulties with the early detection of disease, accurate prognostication with baseline data, and accurate and precise response to therapy. The purpose of this Review is to describe the clinical and research gaps in the diagnosis and prognosis of ILD, and how machine learning can be applied to image biomarker research to close these gaps. Machine-learning algorithms can identify ILD in at-risk populations, predict the extent of lung fibrosis, correlate radiological abnormalities with lung function decline, and be used as endpoints in treatment trials, exemplifying how this technology can be used in care for people with ILD. Advances in image processing and analysis provide further opportunities to use machine learning that incorporates deep-learning-based image analysis and radiomics. Collaboration and consistency are required to develop optimal algorithms, and candidate radiological biomarkers should be validated against appropriate predictors of disease outcomes.

Quantitative Interstitial Abnormality Progression and Outcomes in the Genetic Epidemiology of COPD and Pittsburgh Lung Screening Study Cohorts

BACKGROUND

The risk factors and clinical outcomes of quantitative interstitial abnormality progression over time have not been characterized.

RESEARCH QUESTIONS

What are the associations of quantitative interstitial abnormality progression with lung function, exercise capacity, and mortality? What are the demographic and genetic risk factors for quantitative interstitial abnormality progression?

STUDY DESIGN AND METHODS

Quantitative interstitial abnormality progression between visits 1 and 2 was assessed from 4,635 participants in the Genetic Epidemiology of COPD (COPDGene) cohort and 1,307 participants in the Pittsburgh Lung Screening Study (PLuSS) cohort. We used multivariable linear regression to determine the risk factors for progression and the longitudinal associations between progression and FVC and 6-min walk distance, and Cox regression models for the association with mortality.

RESULTS

Age at enrollment, female sex, current smoking status, and the MUC5B minor allele were associated with quantitative interstitial abnormality progression. Each percent annual increase in quantitative interstitial abnormalities was associated with annual declines in FVC (COPDGene: 8.5 mL/y; 95% CI, 4.7-12.4 mL/y; P < .001; PLuSS: 9.5 mL/y; 95% CI, 3.7-15.4 mL/y; P = .001) and 6-min walk distance, and increased mortality (COPDGene: hazard ratio, 1.69; 95% CI, 1.34-2.12; P < .001; PLuSS: hazard ratio, 1.28; 95% CI, 1.10-1.49; P = .001).

INTERPRETATION

The objective, longitudinal measurement of quantitative interstitial abnormalities may help identify people at greatest risk for adverse events and most likely to benefit from early intervention.

Quantitative computed tomography predicts outcomes in idiopathic pulmonary fibrosis

BACKGROUND AND OBJECTIVE

Prediction of disease course in patients with progressive pulmonary fibrosis remains challenging. The purpose of this study was to assess the prognostic value of lung fibrosis extent quantified at computed tomography (CT) using data-driven texture analysis (DTA) in a large cohort of well-characterized patients with idiopathic pulmonary fibrosis (IPF) enrolled in a national registry.

METHODS

This retrospective analysis included participants in the Australian IPF Registry with available CT between 2007 and 2016. CT scans were analysed using the DTA method to quantify the extent of lung fibrosis. Demographics, longitudinal pulmonary function and quantitative CT metrics were compared using descriptive statistics. Linear mixed models, and Cox analyses adjusted for age, gender, BMI, smoking history and treatment with anti-fibrotics were performed to assess the relationships between baseline DTA, pulmonary function metrics and outcomes.

RESULTS

CT scans of 393 participants were analysed, 221 of which had available pulmonary function testing obtained within 90 days of CT. Linear mixed-effect modelling showed that baseline DTA score was significantly associated with annual rate of decline in forced vital capacity and diffusing capacity of carbon monoxide. In multivariable Cox proportional hazard models, greater extent of lung fibrosis was associated with poorer transplant-free survival (hazard ratio [HR] 1.20, p < 0.0001) and progression-free survival (HR 1.14, p < 0.0001).

CONCLUSION

In a multi-centre observational registry of patients with IPF, the extent of fibrotic abnormality on baseline CT quantified using DTA is associated with outcomes independent of pulmonary function.

Sex Differences in Airways at Chest CT: Results from the COPDGene Cohort

Background The prevalence of chronic obstructive pulmonary disease (COPD) in women is fast approaching that in men, and women experience greater symptom burden. Although sex differences in emphysema have been reported, differences in airways have not been systematically characterized. Purpose To evaluate whether structural differences in airways may underlie some of the sex differences in COPD prevalence and clinical outcomes. Materials and Methods In a secondary analyses of a multicenter study of never-, current-, and former-smokers enrolled from January 2008 to June 2011 and followed up longitudinally until November 2020, airway disease on CT images was quantified using seven metrics: airway wall thickness, wall area percent, and square root of the wall thickness of a hypothetical airway with internal perimeter of 10 mm (referred to as Pi10) for airway wall; and lumen diameter, airway volume, total airway count, and airway fractal dimension for airway lumen. Least-squares mean values for each airway metric were calculated and adjusted for age, height, ethnicity, body mass index, pack-years of smoking, current smoking status, total lung capacity, display field of view, and scanner type. In ever-smokers, associations were tested between each airway metric and postbronchodilator forced expiratory volume in 1 second (FEV1)-to-forced vital capacity (FVC) ratio, modified Medical Research Council dyspnea scale, St George's Respiratory Questionnaire score, and 6-minute walk distance. Multivariable Cox proportional hazards models were created to evaluate the sex-specific association between each airway metric and mortality. Results In never-smokers (n = 420), men had thicker airway walls than women as quantified on CT images for segmental airway wall area percentage (least-squares mean, 47.68 ± 0.61 [standard error] vs 45.78 ± 0.55; difference, -1.90; P = .02), whereas airway lumen dimensions were lower in women than men after accounting for height and total lung capacity (segmental lumen diameter, 8.05 mm ± 0.14 vs 9.05 mm ± 0.16; difference, -1.00 mm; P < .001). In ever-smokers (n = 9363), men had greater segmental airway wall area percentage (least-squares mean, 52.19 ± 0.16 vs 48.89 ± 0.18; difference, -3.30; P < .001), whereas women had narrower segmental lumen diameter (7.80 mm ± 0.05 vs 8.69 mm ± 0.04; difference, -0.89; P < .001). A unit change in each of the airway metrics (higher wall or lower lumen measure) resulted in lower FEV1-to-FVC ratio, more dyspnea, poorer respiratory quality of life, lower 6-minute walk distance, and worse survival in women compared with men (all P < .01). Conclusion Airway lumen sizes quantified at chest CT were smaller in women than in men after accounting for height and lung size, and these lower baseline values in women conferred lower reserves against respiratory morbidity and mortality for equivalent changes compared with men. © RSNA, 2022 Online supplemental material is available for this article.

Extent of fibrosis and lung function decline in patients with systemic sclerosis and interstitial lung disease: data from the SENSCIS trial

Objective

To assess associations between the extent of fibrotic interstitial lung disease (ILD) and forced vital capacity (FVC) at baseline and change in FVC over 52 weeks in patients with systemic sclerosis-associated ILD (SSc-ILD) in the SENSCIS trial.

Methods

We used generalised additive models, which involve few assumptions and allow for interaction between non-linear effects, to assess associations between the extent of fibrotic ILD on high-resolution computed tomography (HRCT), and the interplay of extent of fibrotic ILD on HRCT and FVC % predicted, at baseline and FVC decline over 52 weeks.

Results

In the placebo group (n = 288), there was weak evidence of a modest association between a greater extent of fibrotic ILD at baseline and a greater decline in FVC % predicted at week 52 (r: -0.09 [95% CI -0.2, 0.03]). Higher values of both the extent of fibrotic ILD and FVC % predicted at baseline tended to be associated with greater decline in FVC % predicted at week 52. In the nintedanib group (n = 288), there was no evidence of an association between the extent of fibrotic ILD at baseline and decline in FVC % predicted at week 52 (r: 0.01 [95% CI: -0.11, 0.12]) or between the interplay of extent of fibrotic ILD and FVC % predicted at baseline and decline in FVC % predicted at week 52.

Conclusion

Data from the SENSCIS trial suggest that patients with SSc-ILD are at risk of ILD progression and benefit from nintedanib largely irrespective of their extent of fibrotic ILD at baseline.

Trial registration

ClinicalTrials.gov, https://clinicaltrials.gov, NCT02597933.

Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study

Background Visual assessment remains the standard for evaluating emphysema at CT; however, it is time consuming, is subjective, requires training, and is affected by variability that may limit sensitivity to longitudinal change. Purpose To evaluate the clinical and imaging significance of increasing emphysema severity as graded by a deep learning algorithm on sequential CT scans in cigarette smokers. Materials and Methods A secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study participants was performed and included baseline and 5-year follow-up CT scans from 2007 to 2017. Emphysema was classified automatically according to the Fleischner emphysema grading system at baseline and 5-year follow-up using a deep learning model. Baseline and change in clinical and imaging parameters at 5-year follow-up were compared in participants whose emphysema progressed versus those who did not. Kaplan-Meier analysis and multivariable Cox regression were used to assess the relationship between emphysema score progression and mortality. Results A total of 5056 participants (mean age, 60 years ± 9 [SD]; 2566 men) were evaluated. At 5-year follow-up, 1293 of the 5056 participants (26%) had emphysema progression according to the Fleischner grading system. This group demonstrated progressive airflow obstruction (forced expiratory volume in 1 second [percent predicted]: -3.4 vs -1.8), a greater decline in 6-minute walk distance (-177 m vs -124 m), and greater progression in quantitative emphysema extent (adjusted lung density: -1.4 g/L vs 0.5 g/L; percentage of lung voxels with CT attenuation less than -950 HU: 0.6 vs 0.2) than those with nonprogressive emphysema (P < .001 for each). Multivariable Cox regression analysis showed a higher mortality rate in the group with emphysema progression, with an estimated hazard ratio of 1.5 (95% CI: 1.2, 1.8; P < .001). Conclusion An increase in Fleischner emphysema grade on sequential CT scans using an automated deep learning algorithm was associated with increased functional impairment and increased risk of mortality. ClinicalTrials.gov registration no. NCT00608764 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Grenier in this issue.

Clinical Validation and Extension of an Automated, Deep Learning-Based Algorithm for Quantitative Sinus CT Analysis

BACKGROUND AND PURPOSE

Sinus CT is critically important for the diagnosis of chronic rhinosinusitis. While CT is sensitive for detecting mucosal disease, automated methods for objective quantification of sinus opacification are lacking. We describe new measurements and further clinical validation of automated CT analysis using a convolutional neural network in a chronic rhinosinusitis population. This technology produces volumetric segmentations that permit calculation of percentage sinus opacification, mean Hounsfield units of opacities, and percentage of osteitis.

MATERIALS AND METHODS

Demographic and clinical data were collected retrospectively from adult patients with chronic rhinosinusitis, including serum eosinophil count, Lund-Kennedy endoscopic scores, and the SinoNasal Outcomes Test-22. CT scans were scored using the Lund-Mackay score and the Global Osteitis Scoring Scale. CT images were automatically segmented and analyzed for percentage opacification, mean Hounsfield unit of opacities, and percentage osteitis. These readouts were correlated with visual scoring systems and with disease parameters using the Spearman ρ.

RESULTS

Eighty-eight subjects were included. The algorithm successfully segmented 100% of scans and calculated features in a diverse population with CT images obtained on different scanners. A strong correlation existed between percentage opacification and the Lund-Mackay score (ρ = 0.85, P < .001). Both percentage opacification and the Lund-Mackay score exhibited moderate correlations with the Lund-Kennedy score (ρ = 0.58, P < .001, and ρ = 0.58, P < .001, respectively). The percentage osteitis correlated moderately with the Global Osteitis Scoring Scale (ρ = 0.48, P < .001).

CONCLUSIONS

Our quantitative processing of sinus CT images provides objective measures that correspond well to established visual scoring methods. While automation is a clear benefit here, validation may be needed in a prospective, multi-institutional setting.

Deep Learning-based Outcome Prediction in Progressive Fibrotic Lung Disease Using High-resolution Computed Tomography

RATIONALE Reliable outcome prediction in patients with fibrotic lung disease using baseline high-resolution computed tomography (HRCT) data remains challenging. OBJECTIVES To evaluate the prognostic accuracy of a deep learning algorithm (SOFIA), trained and validated in the identification of UIP-like features on HRCT (UIP probability), in a large cohort of well characterised patients with progressive fibrotic lung disease, drawn from a national registry. METHODS SOFIA and radiologist-UIP probabilities were converted to PIOPED-based UIP probability categories (UIP not included in the differential: 0-4%, low probability of UIP: 5-29%, intermediate probability of UIP: 30-69%, high probability of UIP: 70-94%, and pathognomonic for UIP:95-100%) and their prognostic utility assessed using Cox proportional hazards modelling. MEASUREMENTS AND MAIN RESULTS On multivariable analysis adjusting for age, gender, guideline based radiologic diagnosis and disease severity (using total ILD extent on HRCT, %predicted FVC, DLco or the CPI), only SOFIA-UIP probability PIOPED categories predicted survival. SOFIA-PIOPED UIP probability categories remained prognostically significant in patients considered indeterminate (n=83) by expert radiologist consensus (HR1.73, p<0.0001, 95%CI 1.40-2.14). In patients undergoing surgical lung biopsy (SLB) (n=86), after adjusting for guideline-based histologic pattern and total ILD extent on HRCT, only SOFIA-PIOPED probabilities were predictive of mortality (HR1.75, p<0.0001, 95%CI 1.37-2.25). CONCLUSIONS Deep learning-based UIP probability on HRCT provides enhanced outcome prediction in patients with progressive fibrotic lung disease when compared to expert radiologist evaluation or guideline-based histologic pattern. In principle this tool may be useful in multidisciplinary characterisation of fibrotic lung disease. The utility of this technology as a decision support system when ILD expertise is unavailable requires further investigation.

Quantitative imaging analysis detects subtle airway abnormalities in symptomatic military deployers

Background

Exposure to inhalational hazards during post-9/11 deployment to Southwest Asia and Afghanistan puts military personnel at risk for respiratory symptoms and disease. Pulmonary function and qualitative chest high resolution computed tomography (HRCT) are often normal in “deployers” with persistent respiratory symptoms. We explored the utility of quantitative HRCT imaging markers of large and small airways abnormalities, including airway wall thickness, emphysema, and air trapping, in symptomatic deployers with clinically-confirmed lung disease compared to controls.

Methods

Chest HRCT images from 45 healthy controls and 82 symptomatic deployers with asthma, distal lung disease or both were analyzed using Thirona Lung quantification software to calculate airway wall thickness (by Pi10), emphysema (by percentage of lung volume with attenuation < -950 Hounsfield units [LAA%-950]), and three parameters of air trapping (expiratory/inspiratory total lung volume and mean lung density ratios, and LAA%-856). SAS v.9.4 was used to compare demographic and clinical characteristics between deployers and controls using Chi-Square, Fisher Exact or t-tests. Linear regression was used to assess relationships between pulmonary function and quantitative imaging findings.

Results

Gender and smoking status were not statistically significantly different between groups, but deployers were significantly younger than controls (42 vs 58 years, p < 0.0001), had higher body mass index (31 vs 28 kg/m², p = 0.01), and had fewer total smoking pack-years (8 vs. 26, p = 0.007). Spirometric measures were not statistically significantly different between groups. Pi10 and LAA%-950 were significantly elevated in deployers compared to controls in unadjusted analyses, with the emphysema measure remaining significantly higher in deployers after adjustment for age, sex, smoking, BMI, and expiratory total lung volume. Air trapping parameters were more common in control images, likely due to differences in age and smoking between groups. Among deployers, LAA%-950 and Pi10 were significantly correlated with spirometric markers of obstruction based on ratio of forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) and/or percent predicted FEV1.

Conclusions

Quantitative chest HRCT imaging analysis identifies emphysema in deployers with asthma and distal lung disease, and may be useful in detecting and monitoring deployment-related lung disease in a population where spirometry is typically normal.

Olfactory dysfunction in cystic fibrosis: Impact of CFTR modulator therapy

BACKGROUND

Elexacaftor-tezacaftor-ivacaftor (ETI) improves pulmonary health and chronic rhinosinusitis (CRS) for people with cystic fibrosis (PwCF), however its impact on olfaction has not been investigated. Olfactory dysfunction impairs quality-of-life (QOL). This study evaluated the impact of ETI on multiple olfactory metrics.

METHODS

Adult PwCF/CRS with CF transmembrane conductance regulator genotype F508del/F508del or F508del/minimal function who clinically initiated ETI participated in a prospective, observational study. Endpoints included changes after 6 months of ETI in quantitative olfactory function (Smell Identification Test, SIT), olfactory QOL (Questionnaire of Olfactory Disorders, QOD) and percent olfactory cleft opacification (%OCO), representing superior nasal cavity inflammation where afferent olfactory neurons are concentrated.

RESULTS

30 PwCF/CRS met inclusion criteria; 25 completed the study. Mean ETI adherence was 93%. At baseline, participants were hyposmic (mean SIT 31.3), had significant %OCO (mean 65.6%), yet reported non-impaired olfactory QOL (mean QOD 6.1). At follow-up, mean SIT worsened mildly (p=0.009), mean %OCO remained stable (p=0.46), and mean QOD improved modestly (p=0.008). No outcomes were impacted by prior modulator use, genotype, nasal polyps, or CF-related diabetes. Prior sinus surgery was associated with QOD improvement (p=0.04). Increased (worse) baseline QOD scores and %OCO were associated with greater improvements (p<0.003), but not SIT (p=0.44).

CONCLUSIONS

ETI was not associated with improvement in quantitative olfaction or olfactory cleft opacification after 6 months. PwCF/CRS have hyposmia but do not report impairment in olfactory QOL. Further study to investigate mechanisms explaining olfactory dysfunction and whether olfaction improves with greater duration of ETI or in younger age groups is warranted.

Ground glass and fibrotic change in children with surfactant protein C dysfunction mutations

INTRODUCTION

Therapeutics exist to treat fibrotic lung disease in adults, but these have not been investigated in children. Defining biomarkers for pediatric fibrotic lung disease in children is crucial for clinical trials. Children with surfactant protein C (SFTPC) dysfunction mutations develop fibrotic lung disease over time. We evaluated chest computed tomography (CT) changes over time in children with SFTPC dysfunction mutations.

METHODS

We performed an institutional review board-approved retrospective review of children with SFTPC dysfunction mutations. We collected demographic and clinical information. Chest CT scans were evaluated using visual and computerized scores. Chest CT scores and pulmonary function tests were reviewed.

RESULTS

Eleven children were included. All children presented in infancy and four children suffered from respiratory failure requiring mechanical ventilation. Those who performed pulmonary function tests had stable forced vital capacities over time by percent predicted, but increased forced vital capacity in liters. CT findings evolved over time in most patients with earlier CT scans demonstrating ground glass opacities and later CT scans with more fibrotic features. In a pilot analysis, data-driven textural analysis software identified fibrotic features in children with SFTPC dysfunction that increased over time and correlated with visual CT scores.

DISCUSSION

We describe 11 children with SFTPC dysfunction mutations. Increases in forced vital capacity over time suggest that these children experience lung growth and that therapeutic intervention may maximize lung growth. Ground glass opacities are the primary early imaging findings while fibrotic features dominate later. CT findings suggest the development of and increases in fibrotic features that may serve as potential biomarkers for antifibrotic therapeutic trials.

Persistent, Progressive Pulmonary Fibrosis and Epithelial Remodeling in Mice

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic interstitial lung disease with underlying mechanisms that have been primarily investigated in mice after intratracheal instillation of a single dose of bleomycin. However, the model has significant limitations, including transient fibrosis that spontaneously resolves and its failure to fully recapitulate the epithelial remodeling in the lungs of patients with IPF. Thus, there remains an unmet need for a preclinical model with features that more closely resemble the human disease. Repetitive intratracheal instillation of bleomycin has previously been shown to recapitulate some of these features, but the instillation procedure is complex, and the long-term consequences on epithelial remodeling and fibrosis persistence and progression remain poorly understood. Here, we developed a simplified repetitive bleomycin instillation strategy consisting of three bi-weekly instillations that leads to persistent and progressive pulmonary fibrosis. Lung histology demonstrates increased collagen deposition, fibroblast accumulation, loss of type I and type II alveolar epithelial cells within fibrotic areas, bronchiolization of the lung parenchyma with CCSP+ cells, remodeling of the distal lung into cysts reminiscent of simple honeycombing, and accumulation of hyperplastic transitional KRT8+ epithelial cells. Micro-computed tomographic imaging demonstrated significant traction bronchiectasis and subpleural fibrosis. Thus, the simplified repetitive bleomycin instillation strategy leads to progressive fibrosis and recapitulates the histological and radiographic characteristics of IPF. Compared with the single bleomycin instillation model, we suggest that the simplified repetitive instillation model may be better suited to address mechanistic questions about IPF pathogenesis and preclinical studies of antifibrotic drug candidates.

Emphysema Progression and Lung Function Decline Among Angiotensin Converting Enzyme Inhibitors and Angiotensin-Receptor Blockade Users in the COPDGene Cohort

BACKGROUND

Attenuation of transforming growth factor β by blocking angiotensin II has been shown to reduce emphysema in a murine model. General population studies have demonstrated that the use of angiotensin converting enzyme inhibitors (ACEis) and angiotensin-receptor blockers (ARBs) is associated with reduction of emphysema progression in former smokers and that the use of ACEis is associated with reduction of FEV₁ progression in current smokers.

RESEARCH QUESTION

Is use of ACEi and ARB associated with less progression of emphysema and FEV₁ decline among individuals with COPD or baseline emphysema?

METHODS

Former and current smokers from the Genetic Epidemiology of COPD Study who attended baseline and 5-year follow-up visits, did not change smoking status, and underwent chest CT imaging were included. Adjusted linear mixed models were used to evaluate progression of adjusted lung density (ALD), percent emphysema (%total lung volume <-950 Hounsfield units [HU]), 15th percentile of the attenuation histogram (attenuation [in HU] below which 15% of voxels are situated plus 1,000 HU), and lung function decline over 5 years between ACEi and ARB users and nonusers in those with spirometry-confirmed COPD, as well as all participants and those with baseline emphysema. Effect modification by smoking status also was investigated.

RESULTS

Over 5 years of follow-up, compared with nonusers, ACEi and ARB users with COPD showed slower ALD progression (adjusted mean difference [aMD], 1.6; 95% CI, 0.34-2.9). Slowed lung function decline was not observed based on phase 1 medication (aMD of FEV₁ % predicted, 0.83; 95% CI, -0.62 to 2.3), but was when analysis was limited to consistent ACEi and ARB users (aMD of FEV₁ % predicted, 1.9; 95% CI, 0.14-3.6). No effect modification by smoking status was found for radiographic outcomes, and the lung function effect was more pronounced in former smokers. Results were similar among participants with baseline emphysema.

INTERPRETATION

Among participants with spirometry-confirmed COPD or baseline emphysema, ACEi and ARB use was associated with slower progression of emphysema and lung function decline.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov.

Comparison of CT Lung Density Measurements between Standard Full-Dose and Reduced-Dose Protocols

Purpose

To evaluate the reproducibility and predicted clinical outcomes of CT-based quantitative lung density measurements using standard fixed-dose (FD) and reduced-dose (RD) scans.

Materials and Methods

In this retrospective analysis of prospectively acquired data, 1205 participants (mean age, 65 years ± 9 [standard deviation]; 618 men) enrolled in the COPDGene study who underwent FD and RD CT image acquisition protocols between November 2014 and July 2017 were included. Of these, the RD scans of 640 participants were also reconstructed using iterative reconstruction (IR). Median filtering was applied to the RD scans (RD-MF) to investigate an alternative noise reduction strategy. CT attenuation at the 15th percentile of the lung CT histogram (Perc15) was computed for all image types (FD, RD, RD-MF, and RD-IR). Reproducibility coefficients were calculated to quantify the measurement differences between FD and RD scans. The ability of Perc15 to predict chronic obstructive pulmonary disease (COPD) diagnosis and exacerbation frequency was investigated using receiver operating characteristic analysis.

Results

The Perc15 reproducibility coefficients with and without volume adjustment were as follows: RD, 29.43 HU ± 0.62 versus 32.81 HU ± 1.70; RD-MF, 7.42 HU ± 0.42 versus 19.40 HU ± 2.65; and RD-IR, 7.10 HU ± 0.52 versus 22.46 HU ± 3.91. Receiver operating characteristic curve analysis indicated that Perc15 on volume-adjusted FD and RD scans were both predictive for COPD diagnosis (area under the receiver operating characteristic curve [AUC]: FD, 0.724 ± 0.045; RD, 0.739 ± 0.045) and for having one or more exacerbation per year (AUCs: FD, 0.593 ± 0.068; RD, 0.589 ± 0.066). Similar trends were observed when volume adjustment was not applied.

Conclusion

A combination of volume adjustment and noise reduction filtering improved the reproducibility of lung density measurements computed using serial FD and RD CT scans.Supplemental material is available for this article.© RSNA, 2021.

Relationship between Emphysema Progression at CT and Mortality in Ever-Smokers: Results from the COPDGene and ECLIPSE Cohorts

Background The relationship between emphysema progression and long-term outcomes is unclear. Purpose To determine the relationship between emphysema progression at CT and mortality among participants with emphysema. Materials and Methods In a secondary analysis of two prospective observational studies, COPDGene (clinicaltrials.gov, NCT00608764) and Evaluation of Chronic Obstructive Pulmonary Disease Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE; clinicaltrials.gov, NCT00292552), emphysema was measured at CT at two points by using the volume-adjusted lung density at the 15th percentile of the lung density histogram (hereafter, lung density perc15) method. The association between emphysema progression rate and all-cause mortality was analyzed by using Cox regression adjusted for ethnicity, sex, baseline age, pack-years, and lung density, baseline and change in smoking status, forced expiratory volume in 1 second, and 6-minute walk distance. In COPDGene, respiratory mortality was analyzed by using the Fine and Gray method. Results A total of 5143 participants (2613 men [51%]; mean age, 60 years ± 9 [standard deviation]) in COPDGene and 1549 participants (973 men [63%]; mean age, 62 years ± 8) in ECLIPSE were evaluated, of which 2097 (40.8%) and 1179 (76.1%) had emphysema, respectively. Baseline imaging was performed between January 2008 and December 2010 for COPDGene and January 2006 and August 2007 for ECLIPSE. Follow-up imaging was performed after 5.5 years ± 0.6 in COPDGene and 3.0 years ± 0.2 in ECLIPSE, and mortality was assessed over the ensuing 5 years in both. For every 1 g/L per year faster rate of decline in lung density perc15, all-cause mortality increased by 8% in COPDGene (hazard ratio [HR], 1.08; 95% CI: 1.01, 1.16; P = .03) and 6% in ECLIPSE (HR, 1.06; 95% CI: 1.00, 1.13; P = .045). In COPDGene, respiratory mortality increased by 22% (HR, 1.22; 95% CI: 1.13, 1.31; P < .001) for the same increase in the rate of change in lung density perc15. Conclusion In ever-smokers with emphysema, emphysema progression at CT was associated with increased all-cause and respiratory mortality. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Lee and Park in this issue.

Fleischner Society Visual Emphysema CT Patterns Help Predict Progression of Emphysema in Current and Former Smokers: Results from the COPDGene Study

Background The correlation between visual emphysema patterns and subsequent progression of disease may provide a way to enrich a study population for treatment trials of emphysema. Purpose To evaluate the potential relationship between emphysema visual subtypes and progression of emphysema and gas trapping. Materials and Methods Current and former smokers with and without chronic obstructive pulmonary disease (COPD) enrolled in the prospective Genetic Epidemiology of COPD (COPDGene) study (ClinicalTrials.gov identifier: NCT02445183) between 2008 and 2011 had their Fleischner Society visual CT scores assessed at baseline, quantitative inspiratory, and expiratory CT and at 5 years. They also underwent pulmonary function testing at baseline CT and at 5 years. The dependent variables were inspiratory lung density at 15th percentile (adjusted for lung volume) as a measure of emphysema and percentage of lung volume with attenuation less than -856 HU at expiratory CT as a measure of air trapping. Statistical analysis used a linear mixed model, adjusted for age, height, sex, race, smoking status, and scanner make. Results A total of 4166 participants (mean age, 60 years ± 9 [standard deviation]; 2091 [50%] men) were evaluated. In participants with COPD (1655 participants, 40%), those with visual presence of mild, moderate, and confluent emphysema at baseline CT showed a mean decline in lung density of 4.6 g/L ± 1.1 (P < .001), 6.7 g/L ± 1.1 (P < .001), and 6.4 g/L ± 1.2 (P < .001), respectively, compared with 2.4 g/L ± 1.3 (P < .001) for those with trace emphysema. For participants without COPD, those with visual presence of mild and moderate emphysema at baseline CT showed a mean decline in lung density of 3.6 g/L ± 1.0 (P < .001) and 3.1 g/L ± 1.6 (P < .001), respectively, compared with 1.8 g/L ± 1.0 (P < .001) for those with trace emphysema. Conclusion The pattern of parenchymal emphysema at baseline CT was an independent predictor of subsequent progression of emphysema in participants who are current or former cigarette smokers with and without chronic obstructive pulmonary disease. © RSNA, 2020 Online supplemental material is available for this article.

Development and Progression of Radiologic Abnormalities in Individuals at Risk for Familial Interstitial Lung Disease

Rationale: The preclinical natural history of progressive lung fibrosis is poorly understood.Objectives: Our goals were to identify risk factors for interstitial lung abnormalities (ILA) on high-resolution computed tomography (HRCT) scans and to determine progression toward clinical interstitial lung disease (ILD) among subjects in a longitudinal cohort of self-reported unaffected first-degree relatives of patients with familial interstitial pneumonia.Methods: Enrollment evaluation included a health history and exposure questionnaire and HRCT scans, which were categorized by visual assessment as no ILA, early/mild ILA, or extensive ILA. The study endpoint was met when ILA were extensive or when ILD was diagnosed clinically. Among subjects with adequate study time to complete 5-year follow-up HRCT, the proportion with ILD events (endpoint met or radiographic ILA progression) was calculated.Measurements and Main Results: Among 336 subjects, the mean age was 53.1 (SD, 9.9) years. Those with ILA (early/mild [n = 74] or extensive [n = 3]) were older, were more likely to be ever smokers, had shorter peripheral blood mononuclear cell telomeres, and were more likely to carry the MUC5B risk allele. Self-reported occupational or environmental exposures, including aluminum smelting, lead, birds, and mold, were independently associated with ILA. Among 129 subjects with sufficient study time, 25 (19.4%) had an ILD event by 5 years after enrollment; of these, 12 met the study endpoint and another 13 had radiologic progression of ILA. ILD events were more common among those with early/mild ILA at enrollment (63.3% vs. 6.1%; P < 0.0001).Conclusions: Rare and common environmental exposures are independent risk factors for radiologic abnormalities. In 5 years, progression of ILA occurred in most individuals with early ILA detected at enrollment.

Volumetric assessment of paranasal sinus opacification on computed tomography can be automated using a convolutional neural network

BACKGROUND

Computed tomography (CT) plays a key role in evaluation of paranasal sinus inflammation, but improved, and standardized, objective assessment is needed. Computerized volumetric analysis has benefits over visual scoring, but typically relies on manual image segmentation, which is difficult and time-consuming, limiting practical applicability. We hypothesized that a convolutional neural network (CNN) algorithm could perform automatic, volumetric segmentation of the paranasal sinuses on CT, enabling efficient, objective measurement of sinus opacification. In this study we performed initial clinical testing of a CNN for fully automatic quantitation of paranasal sinus opacification in the diagnostic workup of patients with chronic upper and lower airway disease.

METHODS

Sinus CT scans were collected on 690 patients who underwent imaging as part of multidisciplinary clinical workup at a tertiary care respiratory hospital between April 2016 and November 2017. A CNN was trained to perform automatic segmentation using a subset of CTs (n = 180) that were segmented manually. A nonoverlapping set (n = 510) was used for testing. CNN opacification scores were compared with Lund-MacKay (LM) visual scores, pulmonary function test results, and other clinical variables using Spearman correlation and linear regression.

RESULTS

CNN scores were correlated with LM scores (rho = 0.82, p < 0.001) and with forced expiratory volume in 1 second (FEV₁ ) percent predicted (rho = -0.21, p < 0.001), FEV₁ /forced vital capacity ratio (rho = -0.27, p < 0.001), immunoglobulin E (rho = 0.20, p < 0.001), eosinophil count (rho = 0.28, p < 0.001), and exhaled nitric oxide (rho = 0.40, p < 0.001).

CONCLUSION

Segmentation of the paranasal sinuses on CT can be automated using a CNN, providing truly objective, volumetric quantitation of sinonasal inflammation.

Flow profile characteristics in Fontan circulation are associated with the single ventricle dilation and function: principal component analysis study

The Fontan circulation is characterized as a nonpulsatile flow propagation without a pressure-generating ventricle. However, flow through the Fontan circulation still exhibits oscillatory waves as a result of pressure changes generated by the systemic single ventricle. Identification of discrete flow patterns through the Fontan circuit may be important to understand single ventricle performance. Ninety-seven patients with Fontan circulation underwent phase-contrast MRI of the right pulmonary artery, yielding subject-specific flow waveforms. Principal component (PC) analysis was performed on preprocessed flow waveforms. Principal components were then correlated with standard MRI indices of function, volume, and aortopulmonary collateral flow. The first principal component (PC) described systolic versus diastolic-dominant flow through the Fontan circulation, accounting for 31.3% of the variance in all waveforms. The first PC correlated with end-diastolic volume (R = 0.34, P = 0.001), and end-systolic volume (R = 0.30, P = 0.003), cardiac index (R = 0.51, P < 0.001), and the amount of aortopulmonary collateral flow (R = 0.25, P = 0.027)-lower ventricular volumes and a smaller volume of collateral flow-were associated with diastolic-dominant cavopulmonary flow. The second PC accounted for 19.5% of variance and described late diastolic acceleration versus deceleration and correlated with ejection fraction-diastolic deceleration was associated with higher ejection fraction. Principal components describing the diastolic flow variations in pulmonary arteries are related to the single ventricle function and volumes. Particularly, diastolic-dominant flow without late acceleration appears to be related to preserved ventricular volume and function, respectively.NEW & NOTEWORTHY The exact physiological significance of flow oscillations of phasic and temporal flow variations in Fontan circulation is unknown. With the use of principal component analysis, we discovered that flow variations in the right pulmonary artery of Fontan patients are related to the single ventricle function and volumes. Particularly, diastolic-dominant flow without late acceleration appears to be related to more ideal ventricular volume and systolic function, respectively.

Inter- and intra-software reproducibility of computed tomography lung density measurements

PURPOSE

Multiple commercial, open-source, and academic software tools exist for objective quantification of lung density in computed tomography (CT) images. The purpose of this study was to evaluate the intersoftware reproducibility of CT lung density measurements.

METHODS

Computed tomography images from 50 participants from the COPDGene^TM cohort study were randomly selected for analysis; n = 10 participants across each global initiative for chronic obstructive lung disease (GOLD) grade (GOLD 0-IV). Academic-based groups (n = 4) and commercial vendors (n = 4) participated anonymously to generate CT lung density measurements using their software tools. Computed tomography total lung volume (TLV), percentage of the low attenuation areas in the lung with Hounsfield unit (HU) values below -950HU (LAA₉₅₀ ), and the HU value corresponding to the 15th percentile on the parenchymal density histogram (Perc15) were included in the analysis. The intersoftware bias and reproducibility coefficient (RDC) was generated with and without quality assurance (QA) for manual correction of the lung segmentation; intrasoftware bias and RDC was also generated by repeated measurements on the same images.

RESULTS

Intersoftware mean bias was within ±0.22 mL, ±0.46%, and ±0.97 HU for TLV, LAA₉₅₀ and Perc15, respectively. The RDC was 0.35 L, 1.2% and 1.8 HU for TLV, LAA₉₅₀ and Perc15, respectively. Intersoftware RDC remained unchanged following QA: 0.35 L, 1.2% and 1.8 HU for TLV, LAA₉₅₀ and Perc15, respectively. All software investigated had an intrasoftware RDC of 0. The RDC was comparable for TLV, LAA₉₅₀ and Perc15 measurements, respectively, for academic-based groups/commercial vendor-based software tools: 0.39 L/0.32 L, 1.2%/1.2%, and 1.7 HU/1.6 HU. Multivariable regression analysis showed that academic-based software tools had greater within-subject standard deviation of TLV than commercial vendors, but no significant differences between academic and commercial groups were found for LAA₉₅₀ or Perc15 measurements.

CONCLUSIONS

Computed tomography total lung volume and lung density measurement bias and reproducibility was reported across eight different software tools. Bias was negligible across vendors, reproducibility was comparable for software tools generated by academic-based groups and commercial vendors, and segmentation QA had negligible impact on measurement variability between software tools. In summary, results from this study report the amount of additional measurement variability that should be accounted for when using different software tools to measure lung density longitudinally with well-standardized image acquisition protocols. However, intrasoftware reproducibility was deterministic for all cases so use of the same software tool to reduce variability for serial studies is highly recommended.

Deep Learning Enables Automatic Classification of Emphysema Pattern at CT

BackgroundPattern of emphysema at chest CT, scored visually by using the Fleischner Society system, is associated with physiologic impairment and mortality risk.PurposeTo determine whether participant-level emphysema pattern could predict impairment and mortality when classified by using a deep learning method.Materials and MethodsThis retrospective analysis of Genetic Epidemiology of COPD (COPDGene) study participants enrolled between 2007 and 2011 included those with baseline CT, visual emphysema scores, and survival data through 2018. Participants were partitioned into nonoverlapping sets of 2407 for algorithm training, 100 for validation and parameter tuning, and 7143 for testing. A deep learning algorithm using convolutional neural network and long short-term memory architectures was trained to classify pattern of emphysema according to Fleischner criteria. Deep learning scores were compared with visual scores and clinical parameters including pulmonary function tests. Cox proportional hazard models were used to evaluate relationships between emphysema scores and survival. The algorithm was also tested by using CT and clinical data in 1962 participants enrolled in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE) study.ResultsA total of 7143 COPDGene participants (mean age ± standard deviation, 59.8 years ± 8.9; 3734 men and 3409 women) were evaluated. Deep learning emphysema classifications were associated with impaired pulmonary function tests, 6-minute walk distance, and St George's Respiratory Questionnaire at univariate analysis (P < .001 for each). Testing in the ECLIPSE cohort showed similar associations (P < .001). In the COPDGene test cohort, deep learning emphysema classification improved the fit of linear mixed models in the prediction of these clinical parameters compared with visual scoring (P < .001). Compared with participants without emphysema, mortality was greater in participants classified by the deep learning algorithm as having any grade of emphysema (adjusted hazard ratios were 1.5, 1.7, 2.9, 5.3, and 9.7, respectively, for trace, mild, moderate, confluent, and advanced destructive emphysema; P < .05).ConclusionDeep learning automation of the Fleischner grade of emphysema at chest CT is associated with clinical measures of pulmonary insufficiency and the risk of mortality.© RSNA, 2019Online supplemental material is available for this article.

Risk factors for disease progression in idiopathic pulmonary fibrosis

In this retrospective study of a randomised trial of simtuzumab in idiopathic pulmonary fibrosis (IPF), prodromal decline in forced vital capacity (FVC) was significantly associated with increased risk of mortality, respiratory and all-cause hospitalisations, and categorical disease progression. Predictive modelling of progression-free survival event risk was used to assess the effect of population enrichment for patients at risk of rapid progression of IPF; C-index values were 0.64 (death), 0.69 (disease progression), and 0.72 (adjudicated respiratory hospitalisation) and 0.76 (all-cause hospitalisation). Predictive modelling may be a useful tool for improving efficiency of clinical trials with categorical end points.

Lung and airway shape in neuroendocrine cell hyperplasia of infancy

BACKGROUND

Neuroendocrine cell hyperplasia of infancy (NEHI) is a rare lung disease associated with significant air trapping. Although chest CT is crucial in establishing a diagnosis, CT and biopsy findings do not reveal airway abnormalities to explain the air trapping.

OBJECTIVE

We compared lung and airway morphology obtained from chest CT scans in children with NEHI and control children. In the children with NEHI, we explored relationships between lung and airway shape and lung function.

MATERIALS AND METHODS

We performed a retrospective review of children with NEHI who underwent clinical chest CT. We identified control children of similar size and age. We created lung masks and airway skeletons using semi-automated software and compared them using statistical shape modeling methods. Then we calculated a logistic regression model using lung and airway shape to differentiate NEHI from controls, and we compared shape model parameters to lung function measurements.

RESULTS

Airway and lung shapes were statistically different between children with NEHI and controls. We noted a broad lung apex in the children with NEHI and a significantly increased apical anterior-posterior lung diameter. A logistic regression model including lung shape was 90% accurate in differentiating children with NEHI from controls. Correlation coefficients were significant between lung function values and lung and airway shape.

CONCLUSION

Lung and airway shapes were different between children with NEHI and control children in this cohort. Children with NEHI had an increased anteroposterior diameter of their lungs that might be useful in the diagnostic criteria.

Quantitative high-resolution computed tomography fibrosis score: performance characteristics in idiopathic pulmonary fibrosis

We evaluated performance characteristics and estimated the minimal clinically important difference (MCID) of data-driven texture analysis (DTA), a high-resolution computed tomography (HRCT)-derived measurement of lung fibrosis, in subjects with idiopathic pulmonary fibrosis (IPF).

The study population included 141 subjects with IPF from two interventional clinical trials who had both baseline and nominal 54- or 60-week follow-up HRCT. DTA scores were computed and compared with forced vital capacity (FVC), diffusing capacity of the lung for carbon monoxide, distance covered during a 6-min walk test and St George's Respiratory Questionnaire scores to assess the method's reliability, validity and responsiveness. Anchor- and distribution-based methods were used to estimate its MCID.

DTA had acceptable reliability in subjects appearing stable according to anchor variables at follow-up. Correlations between the DTA score and other clinical measurements at baseline were moderate to weak and in the hypothesised directions. Acceptable responsiveness was demonstrated by moderate to weak correlations (in the directions hypothesised) between changes in the DTA score and changes in other parameters. Using FVC as an anchor, MCID was estimated to be 3.4%.

Quantification of lung fibrosis extent on HRCT using DTA is reliable, valid and responsive, and an increase of ∼3.4% represents a clinically important change.

Nintedanib reduces pulmonary fibrosis in a model of rheumatoid arthritis-associated interstitial lung disease

Rheumatoid arthritis (RA)-associated interstitial lung disease (RA-ILD) develops in ~20% of patients with RA. SKG mice, which are genetically prone to development of autoimmune arthritis, develop a pulmonary interstitial pneumonia that resembles human cellular and fibrotic nonspecific interstitial pneumonia. Nintedanib, a tyrosine kinase inhibitor approved for treatment of idiopathic pulmonary fibrosis, has been shown to reduce the decline in lung function. Therefore, we investigated the effect of nintedanib on development of pulmonary fibrosis and joint disease in female SKG mice with arthritis induced by intraperitoneal injection of zymosan (5 mg). Nintedanib (60 mg·kg-1·day-1 via oral gavage) was started 5 or 10 wk after injection of zymosan. Arthritis and lung fibrosis outcome measures were assessed after 6 wk of treatment with nintedanib. A significant reduction in lung collagen levels, determined by measuring hydroxyproline levels and staining for collagen, was observed after 6 wk in nintedanib-treated mice with established arthritis and lung disease. Early intervention with nintedanib significantly reduced development of arthritis based on joint assessment and high-resolution μ-CT. This study impacts the RA and ILD fields by facilitating identification of a therapeutic treatment that may improve both diseases. As this model replicates the characteristics of RA-ILD, the results may be translatable to the human disease.

CT-based Visual Classification of Emphysema: Association with Mortality in the COPDGene Study

Purpose

To determine whether visually assessed patterns of emphysema at CT might provide a simple assessment of mortality risk among cigarette smokers.

Materials and Methods

Of the first 4000 cigarette smokers consecutively enrolled between 2007 and 2011 in this COPDGene study, 3171 had data available for both visual emphysema CT scores and survival. Each CT scan was retrospectively visually scored by two analysts using the Fleischner Society classification system. Severity of emphysema was also evaluated quantitatively by using percentage lung volume occupied by low-attenuation areas (voxels with attenuation of −950 HU or less) (LAA-950). Median duration of follow-up was 7.4 years. Regression analysis for the relationship between imaging patterns and survival was based on the Cox proportional hazards model, with adjustment for age, race, sex, height, weight, pack-years of cigarette smoking, current smoking status, educational level, LAA-950, and (in a second model) forced expiratory volume in 1 second (FEV₁).

Results

Observer agreement in visual scoring was good (weighted κ values, 0.71–0.80). There were 519 deaths in the study cohort. Compared with subjects who did not have visible emphysema, mortality was greater in those with any grade of emphysema beyond trace (adjusted hazard ratios, 1.7, 2.5, 5.0, and 4.1, respectively, for mild centrilobular emphysema, moderate centrilobular emphysema, confluent emphysema, and advanced destructive emphysema, P < .001). This increased mortality generally persisted after adjusting for LAA-950.

Conclusion

The visual presence and severity of emphysema is associated with significantly increased mortality risk, independent of the quantitative severity of emphysema.

Online supplemental material is available for this article.

Idiopathic Pulmonary Fibrosis: Data-driven Textural Analysis of Extent of Fibrosis at Baseline and 15-Month Follow-up

Purpose To evaluate associations between pulmonary function and both quantitative analysis and visual assessment of thin-section computed tomography (CT) images at baseline and at 15-month follow-up in subjects with idiopathic pulmonary fibrosis (IPF). Materials and Methods This retrospective analysis of preexisting anonymized data, collected prospectively between 2007 and 2013 in a HIPAA-compliant study, was exempt from additional institutional review board approval. The extent of lung fibrosis at baseline inspiratory chest CT in 280 subjects enrolled in the IPF Network was evaluated. Visual analysis was performed by using a semiquantitative scoring system. Computer-based quantitative analysis included CT histogram-based measurements and a data-driven textural analysis (DTA). Follow-up CT images in 72 of these subjects were also analyzed. Univariate comparisons were performed by using Spearman rank correlation. Multivariate and longitudinal analyses were performed by using a linear mixed model approach, in which models were compared by using asymptotic χ² tests. Results At baseline, all CT-derived measures showed moderate significant correlation (P < .001) with pulmonary function. At follow-up CT, changes in DTA scores showed significant correlation with changes in both forced vital capacity percentage predicted (ρ = -0.41, P < .001) and diffusing capacity for carbon monoxide percentage predicted (ρ = -0.40, P < .001). Asymptotic χ² tests showed that inclusion of DTA score significantly improved fit of both baseline and longitudinal linear mixed models in the prediction of pulmonary function (P < .001 for both). Conclusion When compared with semiquantitative visual assessment and CT histogram-based measurements, DTA score provides additional information that can be used to predict diminished function. Automatic quantification of lung fibrosis at CT yields an index of severity that correlates with visual assessment and functional change in subjects with IPF. ^© RSNA, 2017.