Effect of smoking cessation on quantitative computed tomography in smokers at risk in a lung cancer screening population

A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulfidptosis in the development of COPD could provide a opportunity for primary prediction, targeted prevention, and personalized treatment of the disease.

METHODS

We analyzed the expression profiles of DRGs and immune cell infiltration in COPD patients by using the GSE38974 dataset. According to the DRGs, molecular clusters and related immune cell infiltration levels were explored in individuals with COPD. Next, co-expression modules and cluster-specific differentially expressed genes were identified by the Weighted Gene Co-expression Network Analysis (WGCNA). Comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB), we constructed the ptimal machine learning model.

RESULTS

DE-DRGs, differential immune cells and two clusters were identified. Notable difference in DRGs, immune cell populations, biological processes, and pathway behaviors were noted among the two clusters. Besides, significant differences in DRGs, immune cells, biological functions, and pathway activities were observed between the two clusters.A nomogram was created to aid in the practical application of clinical procedures. The SVM model achieved the best results in differentiating COPD patients across various clusters. Following that, we identified the top five genes as predictor genes via SVM model. These five genes related to the model were strongly linked to traits of the individuals with COPD.

CONCLUSION

Our study demonstrated the relationship between disulfidptosis and COPD and established an optimal machine-learning model to evaluate the subtypes and traits of COPD. DRGs serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in COPD, facilitating the transition from reactive medical services to PPPM in the management of the disease.

GOLD-Grade Specific Disease Characterization and Phenotyping of COPD Using Quantitative Computed Tomography in the Nationwide COSYCONET Multicenter Trial in Germany

INTRODUCTION

The aim of this study was to apply quantitative computed tomography (QCT) for GOLD-grade specific disease characterization and phenotyping of air-trapping, emphysema, and airway abnormalities in patients with chronic obstructive pulmonary disease (COPD) from a nationwide cohort study.

METHODS

As part of the COSYCONET multicenter study, standardized CT in ex- and inspiration, lung function assessment (FEV1/FVC), and clinical scores (BODE index) were prospectively acquired in 525 patients (192 women, 327 men, aged 65.7 ± 8.5 years) at risk for COPD and at GOLD1-4. QCT parameters such as total lung volume (TLV), emphysema index (EI), parametric response mapping (PRM) for emphysema (PRMEmph) and functional small airway disease (PRMfSAD), total airway volume (TAV), wall percentage (WP), and total diameter (TD) were computed using automated software.

RESULTS

TLV, EI, PRMfSAD, and PRMEmph increased incrementally with each GOLD grade (p < 0.001). Aggregated WP5-10 of subsegmental airways was higher from GOLD1 to GOLD3 and lower again at GOLD4 (p < 0.001), whereas TD5-10 was significantly dilated only in GOLD4 (p < 0.001). Fifty-eight patients were phenotyped as "non-airway non-emphysema type," 202 as "airway type," 96 as "emphysema type," and 169 as "mixed type." FEV1/FVC was best in "non-airway non-emphysema type" compared to other phenotypes, while "mixed type" had worst FEV1/FVC (p < 0.001). BODE index was 0.56 ± 0.72 in the "non-airway non-emphysema type" and highest with 2.55 ± 1.77 in "mixed type" (p < 0.001).

CONCLUSION

QCT demonstrates increasing hyperinflation and emphysema depending on the GOLD grade, while airway wall thickening increases until GOLD3 and airway dilatation occur in GOLD4. QCT identifies four disease phenotypes with implications for lung function and prognosis.

CT Imaging With Machine Learning for Predicting Progression to COPD in Individuals at Risk

BACKGROUND

Identifying individuals at risk of progressing to COPD may allow for initiation of treatment to potentially slow the progression of the disease or the selection of subgroups for discovery of novel interventions.

RESEARCH QUESTION

Does the addition of CT imaging features, texture-based radiomic features, and established quantitative CT scan to conventional risk factors improve the performance for predicting progression to COPD in individuals who smoke with machine learning?

STUDY DESIGN AND METHODS

Participants at risk (individuals who currently or formerly smoked, without COPD) from the Canadian Cohort Obstructive Lung Disease (CanCOLD) population-based study underwent CT imaging at baseline and spirometry at baseline and follow-up. Various combinations of CT scan features, texture-based CT scan radiomics (n = 95), and established quantitative CT scan (n = 8), as well as demographic (n = 5) and spirometry (n = 3) measurements, with machine learning algorithms were evaluated to predict progression to COPD. Performance metrics included the area under the receiver operating characteristic curve (AUC) to evaluate the models. DeLong test was used to compare the performance of the models.

RESULTS

Among the 294 at-risk participants who were evaluated (mean age, 65.6 ± 9.2 years; 42% female; mean pack-years, 17.9 ± 18.7), 52 participants (23.7%) in the training data set and 17 participants (23.0%) in the testing data set progressed to spirometric COPD at follow-up (2.5 ± 0.9 years from baseline). Compared with machine learning models with demographics alone (AUC, 0.649), the addition of CT imaging features to demographics (AUC, 0.730; P < .05) or CT imaging features and spirometry to demographics (AUC, 0.877; P < .05) significantly improved the performance for predicting progression to COPD.

INTERPRETATION

Heterogeneous structural changes occur in the lungs of individuals at risk that can be quantified using CT imaging features, and evaluation of these features together with conventional risk factors improves performance for predicting progression to COPD.

Qualitative and quantitative evaluation of computed tomography changes in adults with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor: a retrospective observational study

Introduction: The availability of highly effective triple cystic fibrosis transmembrane conductance regulator (CFTR) modulator combination therapy with elexacaftor-tezacaftor-ivacaftor (ETI) has improved pulmonary outcomes and quality of life of people with cystic fibrosis (pwCF). The aim of this study was to assess computed tomography (CT) changes under ETI visually with the Brody score and quantitatively with dedicated software, and to correlate CT measures with parameters of clinical response. Methods: Twenty two adult pwCF with two consecutive CT scans before and after ETI treatment initiation were retrospectively included. CT was assessed visually employing the Brody score and quantitatively by YACTA, a well-evaluated scientific software computing airway dimensions and lung parenchyma with wall percentage (WP), wall thickness (WT), lumen area (LA), bronchiectasis index (BI), lung volume and mean lung density (MLD) as parameters. Changes in CT metrics were evaluated and the visual and quantitative parameters were correlated with each other and with clinical changes in sweat chloride concentration, spirometry [percent predicted of forced expiratory volume in one second (ppFEV1)] and body mass index (BMI). Results: The mean (SD) Brody score improved with ETI [55 (12) vs. 38 (15); p < 0.001], incl. sub-scores for mucus plugging, peribronchial thickening, and parenchymal changes (all p < 0.001), but not for bronchiectasis (p = 0.281). Quantitatve WP (p < 0.001) and WT (p = 0.004) were reduced, conversely LA increased (p = 0.003), and BI improved (p = 0.012). Lung volume increased (p < 0.001), and MLD decreased (p < 0.001) through a reduction of ground glass opacity areas (p < 0.001). Changes of the Brody score correlated with those of quantitative parameters, exemplarily WT with the sub-score for mucus plugging (r = 0.730, p < 0.001) and peribronchial thickening (r = 0.552, p = 0.008). Changes of CT parameters correlated with those of clinical response parameters, in particular ppFEV1 with the Brody score (r = -0.606, p = 0.003) and with WT (r = -0.538, p = 0.010). Discussion: Morphological treatment response to ETI can be assessed using the Brody score as well as quantitative CT parameters. Changes in CT correlated with clinical improvements. The quantitative analysis with YACTA proved to be an objective, reproducible and simple method for monitoring lung disease, particularly with regard to future interventional clinical trials.

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.

GOLD stage-specific phenotyping of emphysema and airway disease using quantitative computed tomography

Background

In chronic obstructive pulmonary disease (COPD) abnormal lung function is related to emphysema and airway obstruction, but their relative contribution in each GOLD-stage is not fully understood. In this study, we used quantitative computed tomography (QCT) parameters for phenotyping of emphysema and airway abnormalities, and to investigate the relative contribution of QCT emphysema and airway parameters to airflow limitation specifically in each GOLD stage.

Methods

Non-contrast computed tomography (CT) of 492 patients with COPD former GOLD 0 COPD and COPD stages GOLD 1-4 were evaluated using fully automated software for quantitative CT. Total lung volume (TLV), emphysema index (EI), mean lung density (MLD), and airway wall thickness (WT), total diameter (TD), lumen area (LA), and wall percentage (WP) were calculated for the entire lung, as well as for all lung lobes separately. Results from the 3rd-8th airway generation were aggregated (WT3-8, TD3-8, LA3-8, WP3-8). All subjects underwent whole-body plethysmography (FEV1%pred, VC, RV, TLC).

Results

EI was higher with increasing GOLD stages with 1.0 ± 1.8% in GOLD 0, 4.5 ± 9.9% in GOLD 1, 19.4 ± 15.8% in GOLD 2, 32.7 ± 13.4% in GOLD 3 and 41.4 ± 10.0% in GOLD 4 subjects (p < 0.001). WP3-8 showed no essential differences between GOLD 0 and GOLD 1, tended to be higher in GOLD 2 with 52.4 ± 7.2%, and was lower in GOLD 4 with 50.6 ± 5.9% (p = 0.010 - p = 0.960). In the upper lobes WP3-8 showed no significant differences between the GOLD stages (p = 0.824), while in the lower lobes the lowest WP3-8 was found in GOLD 0/1 with 49.9 ± 6.5%, while higher values were detected in GOLD 2 with 51.9 ± 6.4% and in GOLD 3/4 with 51.0 ± 6.0% (p < 0.05). In a multilinear regression analysis, the dependent variable FEV1%pred can be predicted by a combination of both the independent variables EI (p < 0.001) and WP3-8 (p < 0.001).

Conclusion

QCT parameters showed a significant increase of emphysema from GOLD 0-4 COPD. Airway changes showed a different spatial pattern with higher values of relative wall thickness in the lower lobes until GOLD 2 and subsequent lower values in GOLD3/4, whereas there were no significant differences in the upper lobes. Both, EI and WP5-8 are independently correlated with lung function decline.

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.

Structural and functional alterations of subjects with cement dust exposure: A longitudinal quantitative computed tomography-based study

Cement dust exposure (CDE) can be a risk factor for pulmonary disease, causing changes in segmental airways and parenchymal lungs. This study investigates longitudinal alterations in quantitative computed tomography (CT)-based metrics due to CDE. We obtained CT-based airway structural and lung functional metrics from CDE subjects with baseline CT and follow-up CT scans performed three years later. From the CT, we extracted wall thickness (WT) and bifurcation angle (θ) at total lung capacity (TLC) and functional residual capacity (FRC), respectively. We also computed air volume (V_air), tissue volume (V_tissue), global lung shape, percentage of emphysema (Emph%), and more. Clinical measures were used to associate with CT-based metrics. Three years after their baseline, the pulmonary function tests of CDE subjects were similar or improved, but there were significant alterations in the CT-based structural and functional metrics. The follow-up CT scans showed changes in θ at most of the central airways; increased WT at the subgroup bronchi; smaller V_air at TLC at all except the right upper and lower lobes; smaller V_tissue at all lobes in TLC and FRC except for the upper lobes in FRC; smaller global lung shape; and greater Emph% at the right upper and lower lobes. CT-based structural and functional variables are more sensitive to the early identification of CDE subjects, while most clinical lung function changes were not noticeable. We speculate that the significant long-term changes in CT are uniquely observed in CDE subjects, different from smoking-induced structural changes.

COVID-19 pneumonia: Prediction of patient outcome by CT-based quantitative lung parenchyma analysis combined with laboratory parameters

Objectives

To evaluate the prognostic value of fully automatic lung quantification based on spectral computed tomography (CT) and laboratory parameters for combined outcome prediction in COVID-19 pneumonia.

Methods

CT images of 53 hospitalized COVID-19 patients including virtual monochromatic reconstructions at 40-140keV were analyzed using a fully automated software system. Quantitative CT (QCT) parameters including mean and percentiles of lung density, fibrosis index (FIBI_-700, defined as the percentage of segmented lung voxels ≥-700 HU), quantification of ground-glass opacities and well-aerated lung areas were analyzed. QCT parameters were correlated to laboratory and patient outcome parameters (hospitalization, days on intensive care unit, invasive and non-invasive ventilation).

Results

Best correlations were found for laboratory parameters LDH (r = 0.54), CRP (r = 0.49), Procalcitonin (r = 0.37) and partial pressure of oxygen (r = 0.35) with the QCT parameter 75^th percentile of lung density. LDH, Procalcitonin, 75^th percentile of lung density and FIBI-₇₀₀ were the strongest independent predictors of patients’ outcome in terms of days of invasive ventilation. The combination of LDH and Procalcitonin with either 75^th percentile of lung density or FIBI_-700 achieved a r² of 0.84 and 1.0 as well as an area under the receiver operating characteristic curve (AUC) of 0.99 and 1.0 for the prediction of the need of invasive ventilation.

Conclusions

QCT parameters in combination with laboratory parameters could deliver a feasible prognostic tool for the prediction of invasive ventilation in patients with COVID-19 pneumonia.

µCT to quantify muco-obstructive lung disease and effects of neutrophil elastase knockout in mice

Muco-obstructive lung diseases are characterized by airway obstruction and hyperinflation, which can be quantified by imaging. Our aim was to evaluate µCT for longitudinal quantification of muco-obstructive lung disease in β-epithelial Na⁺ channel overexpressing (Scnn1b-TG) mice and of the effects of neutrophil elastase (NE) knockout on its progression. Lungs from wild-type (WT), NE^-/-, Scnn1b-TG, and Scnn1b-TG/NE^-/- mice were scanned with 9 µm resolution at 0, 5, 14 and 60 days of age, and airway and parenchymal disease was quantified. Mucus adhesion lesions (MAL) were persistently increased in Scnn1b-TG compared to WT mice from 0 days (20.25±6.50 vs. 9.60±2.07, P<0.05), and this effect was attenuated in Scnn1b-TG/NE^-/- mice (5.33±3.67, P<0.001). Airway wall area percentage (WA%) was increased in Scnn1b-TG mice compared to WT from 14 days onward (59.2±6.3% vs. 49.8±9.0%, P<0.001) but was similar in Scnn1b-TG/NE^-/- compared to WT at 60 days (46.4±9.2% vs. 45.4±11.5%, P=0.97). Air proportion (Air%) and mean linear intercept (Lm) were persistently increased in Scnn1b-TG compared to WT from 5 days on (53.9±4.5% vs. 30.0±5.5% and 78.82±8.44µm vs. 65.66±4.15µm, respectively, P<0.001), whereas in Scnn1b-TG/NE^-/- Air% and Lm were similar to WT from birth (27.7±5.5% vs.27.2±5.9%, P =0.92 and 61.48±9.20µm vs. 61.70±6.73µm, P=0.93, respectively). Our results suggest that µCT is sensitive to detect the onset and progression of muco-obstructive lung disease and effects of genetic deletion of NE on morphology of airways and lung parenchyma in Scnn1b-TG mice, and that it may serve as a sensitive endpoint for preclinical studies of novel therapeutic interventions for muco-obstructive lung diseases.

Fibroblast Activation Protein-Specific PET/CT Imaging in Fibrotic Interstitial Lung Diseases and Lung Cancer: A Translational Exploratory Study

Interstitial lung diseases (ILDs) comprise over 200 parenchymal lung disorders. Among them, fibrosing ILDs, especially idiopathic pulmonary fibrosis, are associated with a poor prognosis, whereas some other ILDs, such as sarcoidosis, have a much better prognosis. A high proportion manifests as fibrotic ILD (fILD). Lung cancer (LC) is a frequent complication of fILD. Activated fibroblasts are crucial for fibrotic processes in fILD. The aim of this exploratory study was to evaluate the imaging properties of static and dynamic fibroblast activation protein (FAP) inhibitor (FAPI) PET/CT in various types of fILD and to confirm FAP expression in fILD lesions by FAP immunohistochemistry of human fILD biopsy samples and of lung sections of genetically engineered (Nedd4-2-/- ) mice with an idiopathic pulmonary fibrosislike lung disease. Methods: PET scans of 15 patients with fILD and suspected LC were acquired 10, 60, and 180 min after the administration of 150-250 MBq of a 68Ga-labeled FAPI tracer (FAPI-46). In 3 patients, dynamic scans over 40 min were performed instead of imaging after 10 min. The SUVmax and SUVmean of fibrotic lesions and LC were measured and CT-density-corrected. Target-to-background ratios (TBRs) were calculated. PET imaging was correlated with CT-based fibrosis scores. Time-activity curves derived from dynamic imaging were analyzed. FAP immunohistochemistry of 4 human fILD biopsy samples and of fibrotic lungs of Nedd4-2-/- mice was performed. Results: fILD lesions as well as LC showed markedly elevated 68Ga-FAPI uptake (density-corrected SUVmax and SUVmean 60 min after injection: 11.12 ± 6.71 and 4.29 ± 1.61, respectively, for fILD lesions and 16.69 ± 9.35 and 6.44 ± 3.29, respectively, for LC) and high TBR (TBR of density-corrected SUVmax and SUVmean 60 min after injection: 2.30 ± 1.47 and 1.67 ± 0.79, respectively, for fILD and 3.90 ± 2.36 and 2.37 ± 1.14, respectively, for LC). SUVmax and SUVmean decreased over time, with a stable TBR for fILD and a trend toward an increasing TBR in LC. Dynamic imaging showed differing time-activity curves for fILD and LC. 68Ga-FAPI uptake showed a positive correlation with the CT-based fibrosis index. Immunohistochemistry of human biopsy samples and the lungs of Nedd4-2-/- mice showed a patchy expression of FAP in fibrotic lesions, preferentially in the transition zone to healthy lung parenchyma. Conclusion:68Ga-FAPI PET/CT imaging is a promising new imaging modality for fILD and LC. Its potential clinical value for monitoring and therapy evaluation of fILD should be investigated in future studies.

Quantification of pulmonary perfusion abnormalities using DCE-MRI in COPD: comparison with quantitative CT and pulmonary function

Objectives

Pulmonary perfusion abnormalities are prevalent in patients with chronic obstructive pulmonary disease (COPD), are potentially reversible, and may be associated with emphysema development. Therefore, we aimed to evaluate the clinical meaningfulness of perfusion defects in percent (QDP) using DCE-MRI.

Methods

We investigated a subset of baseline DCE-MRIs, paired inspiratory/expiratory CTs, and pulmonary function testing (PFT) of 83 subjects (age = 65.7 ± 9.0 years, patients-at-risk, and all GOLD groups) from one center of the “COSYCONET” COPD cohort. QDP was computed from DCE-MRI using an in-house developed quantification pipeline, including four different approaches: Otsu’s method, k-means clustering, texture analysis, and 80^th percentile threshold. QDP was compared with visual MRI perfusion scoring, CT parametric response mapping (PRM) indices of emphysema (PRM_Emph) and functional small airway disease (PRM_fSAD), and FEV1/FVC from PFT.

Results

All QDP approaches showed high correlations with the MRI perfusion score (r = 0.67 to 0.72, p < 0.001), with the highest association based on Otsu’s method (r = 0.72, p < 0.001). QDP correlated significantly with all PRM indices (p < 0.001), with the strongest correlations with PRM_Emph (r = 0.70 to 0.75, p < 0.001). QDP was distinctly higher than PRM_Emph (mean difference = 35.85 to 40.40) and PRM_fSAD (mean difference = 15.12 to 19.68), but in close agreement when combining both PRM indices (mean difference = 1.47 to 6.03) for all QDP approaches. QDP correlated moderately with FEV1/FVC (r = − 0.54 to − 0.41, p < 0.001).

Conclusion

QDP is associated with established markers of disease severity and the extent corresponds to the CT-derived combined extent of PRM_Emph and PRM_fSAD. We propose to use QDP based on Otsu’s method for future clinical studies in COPD.

Key Points

• QDP quantified from DCE-MRI is associated with visual MRI perfusion score, CT PRM indices, and PFT.

• The extent of QDP from DCE-MRI corresponds to the combined extent of PRM_Emph and PRM_fSAD from CT.

• Assessing pulmonary perfusion abnormalities using DCE-MRI with QDP improved the correlations with CT PRM indices and PFT compared to the quantification of pulmonary blood flow and volume.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08229-6.

Fully Automated Segmentation of Pulmonary Fibrosis Using Different Software Tools

OBJECTIVE

Evaluation of software tools for segmentation, quantification, and characterization of fibrotic pulmonary parenchyma changes will strengthen the role of CT as biomarkers of disease extent, evolution, and response to therapy in idiopathic pulmonary fibrosis (IPF) patients.

METHODS

418 nonenhanced thin-section MDCTs of 127 IPF patients and 78 MDCTs of 78 healthy individuals were analyzed through 3 fully automated, completely different software tools: YACTA, LUFIT, and IMBIO. The agreement between YACTA and LUFIT on segmented lung volume and 80th (reflecting fibrosis) and 40th (reflecting ground-glass opacity) percentile of the lung density histogram was analyzed using Bland-Altman plots. The fibrosis and ground-glass opacity segmented by IMBIO (lung texture analysis software tool) were included in specific regression analyses.

RESULTS

In the IPF-group, LUFIT outperformed YACTA by segmenting more lung volume (mean difference 242 mL, 95% limits of agreement -54 to 539 mL), as well as quantifying higher 80th (76 HU, -6 to 158 HU) and 40th percentiles (9 HU, -73 to 90 HU). No relevant differences were revealed in the control group. The 80th/40th percentile as quantified by LUFIT correlated positively with the percentage of fibrosis/ground-glass opacity calculated by IMBIO (r = 0.78/r = 0.92).

CONCLUSIONS

In terms of segmentation of pulmonary fibrosis, LUFIT as a shape model-based segmentation software tool is superior to the threshold-based YACTA, tool, since the density of (severe) fibrosis is similar to that of the surrounding soft tissues. Therefore, shape modeling as used in LUFIT may serve as a valid tool in the quantification of IPF, since this mainly affects the subpleural space.

Optimizing airway wall segmentation and quantification by reducing the influence of adjacent vessels and intravascular contrast material with a modified integral-based algorithm in quantitative computed tomography

Introduction

Quantitative analysis of multi-detector computed tomography (MDCT) plays an increasingly important role in assessing airway disease. Depending on the algorithms used, airway dimensions may be over- or underestimated, primarily if contrast material was used. Therefore, we tested a modified integral-based method (IBM) to address this problem.

Methods

Temporally resolved cine-MDCT was performed in seven ventilated pigs in breath-hold during iodinated contrast material (CM) infusion over 60s. Identical slices in non-enhanced (NE), pulmonary-arterial (PA), systemic-arterial (SA), and venous phase (VE) were subjected to an in-house software using a standard and a modified IBM. Total diameter (TD), lumen area (LA), wall area (WA), and wall thickness (WT) were measured for ten extra- and six intrapulmonary airways.

Results

The modified IBM significantly reduced TD by 7.6%, LA by 12.7%, WA by 9.7%, and WT by 3.9% compared to standard IBM on non-enhanced CT (p<0.05). Using standard IBM, CM led to a decrease of all airway parameters compared to NE. For example, LA decreased from 80.85±49.26mm² at NE, to 75.14±47.96mm² (-7.1%) at PA (p<0.001), 74.96±48.55mm² (-7.3%) at SA (p<0.001), and to 78.95±48.94mm² (-2.4%) at VE (p = 0.200). Using modified IBM, the differences were reduced to -3.1% at PA, -2.9% at SA and -0.7% at VE (p<0.001; p<0.001; p = 1.000).

Conclusions

The modified IBM can optimize airway wall segmentation and reduce the influence of CM on quantitative CT. This allows a more precise measurement as well as potentially the comparison of enhanced with non-enhanced scans in inflammatory airway disease.

Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison

OBJECTIVE

This study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning-based kernel conversion technique in normalizing kernels for emphysema quantification.

METHODS

A sample of 131 participants underwent LDCT and standard-dose computed tomography (SDCT) at 1- to 2-year intervals. LDCT images were reconstructed with B31f and B50f kernels, and SDCT images were reconstructed with B30f kernels. A deep learning model was used to convert the LDCT image from a B50f kernel to a B31f kernel. Emphysema indices (EIs), lung attenuation at 15th percentile (perc15), and mean lung density (MLD) were calculated. Comparisons among the different kernel types for both LDCT and SDCT were performed using Friedman's test and Bland-Altman plots.

RESULTS

All values of LDCT B50f were significantly different compared with the values of LDCT B31f and SDCT B30f (p < 0.05). Although there was a statistical difference, the variation of the values of LDCT B50f significantly decreased after kernel normalization. The 95% limits of agreement between the SDCT and LDCT kernels (B31f and converted B50f) ranged from - 2.9 to 4.3% and from - 3.2 to 4.4%, respectively. However, there were no significant differences in EIs and perc15 between SDCT and LDCT converted B50f in the non-chronic obstructive pulmonary disease (COPD) participants (p > 0.05).

CONCLUSION

The deep learning-based CT kernel conversion of sharp kernel in LDCT significantly reduced variation in emphysema quantification, and could be used for emphysema quantification.

KEY POINTS

• Low-dose computed tomography with smooth kernel showed adequate performance in quantifying emphysema compared with standard-dose CT. • Emphysema quantification is affected by kernel selection and the application of a sharp kernel resulted in a significant overestimation of emphysema. • Deep learning-based kernel normalization of sharp kernel significantly reduced variation in emphysema quantification.

Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography

IMPORTANCE

Malignancy prediction models based on participant-related characteristics and imaging parameters from low-dose computed tomography (CT) may improve decision-making regarding nodule management and diagnosis in lung cancer screening.

OBJECTIVE

To externally validate 5 malignancy prediction models that were developed in screening settings, compared with 3 models that were developed in clinical settings, in terms of discrimination and absolute risk calibration among participants in the German Lung Cancer Screening Intervention trial.

DESIGN, SETTING, AND PARTICIPANTS

In this population-based diagnostic study, malignancy probabilities were estimated by applying 8 prediction models to data from 1159 participants in the intervention arm of the Lung Cancer Screening Intervention trial, a randomized clinical trial conducted from October 23, 2007, to April 30, 2016, with ongoing follow-up. This analysis considers end points up to 1 year after individuals' last screening visit. Inclusion criteria for participants were at least 1 noncalcified pulmonary nodule detected on any of 5 annual screening visits, receiving a lung cancer diagnosis within the active screening phase of the Lung Cancer Screening Intervention trial, and an unequivocal identification of the malignant nodules. Data analysis was performed from February 1, 2019, through December 5, 2019.

INTERVENTIONS

Five annual rounds of low-dose multislice CT.

MAIN OUTCOMES AND MEASURES

Discrimination ability and calibration of malignancy probabilities estimated by 5 models developed in data from screening studies (4 Pan-Canadian Early Detection of Lung Cancer Study [PanCan] models using a parsimonious approach including nodule spiculation [PanCan-1b] or a comprehensive approach including nodule spiculation [PanCan-2b], and PanCan-2b replacing the nodule diameter variable with mean diameter [PanCan-MD] or volume [PanCan-VOL], as well as a model developed by the UK Lung Cancer Screening trial) and 3 models developed in clinical settings (US Department of Veterans Affairs, Mayo Clinic, and Peking University People's Hospital).

RESULTS

A total of 1159 participants (median [range] age, 57.63 [50.34-71.89] years; 763 [65.8%] men) with 3903 pulmonary nodules were included in this study. For nodules detected in the prevalence round of CT, the PanCan models showed excellent discrimination (PanCan-1b: area under the curve [AUC], 0.93 [95% CI, 0.87-0.99]; PanCan-2b: AUC, 0.94 [95% CI, 0.89-0.99]; PanCan-MD: AUC, 0.94 [95% CI, 0.91-0.98]; PanCan-VOL: AUC, 0.94 [95% CI, 0.90-0.98]), and all of the screening models except PanCan-MD and PanCan-VOL showed acceptable calibration (PanCan-1b: Spiegelhalter z = -1.081; P = .28; PanCan-2b: Spiegelhalter z = 0.436; P = .67; PanCan-MD: Spiegelhalter z = 3.888; P < .001; PanCan-VOL: Spiegelhalter z = 1.978; P = .05; UK Lung Cancer Screening trial: Spiegelhalter z = -1.076; P = .28), whereas the other models showed worse discrimination and calibration, from an AUC of 0.58 (95% CI, 0.46-0.70) for the UK Lung Cancer Screening trial model to an AUC of 0.89 (95% CI, 0.82-0.97) for the Mayo Clinic model.

CONCLUSIONS AND RELEVANCE

This diagnostic study found that PanCan models showed excellent discrimination and calibration in prevalence screenings, confirming their ability to improve nodule management in screening settings, although calibration to nodules detected in follow-up scans should be improved. The models developed by the Mayo Clinic, Peking University People's Hospital, Department of Veterans Affairs, and UK Lung Cancer Screening Trial did not perform as well.

Quantitative CT detects progression in COPD patients with severe emphysema in a 3-month interval

OBJECTIVES

Chronic obstructive pulmonary disease (COPD) is characterized by variable contributions of emphysema and airway disease on computed tomography (CT), and still little is known on their temporal evolution. We hypothesized that quantitative CT (QCT) is able to detect short-time changes in a cohort of patients with very severe COPD.

METHODS

Two paired in- and expiratory CT each from 70 patients with avg. GOLD stage of 3.6 (mean age = 66 ± 7.5, mean FEV1/FVC = 35.28 ± 7.75) were taken 3 months apart and analyzed by fully automatic software computing emphysema (emphysema index (EI), mean lung density (MLD)), air-trapping (ratio expiration to inspiration of mean lung attenuation (E/I MLA), relative volume change between - 856 HU and - 950 HU (RVC_856-950)), and parametric response mapping (PRM) parameters for each lobe separately and the whole lung. Airway metrics measured were wall thickness (WT) and lumen area (LA) for each airway generation and the whole lung.

RESULTS

The average of the emphysema parameters (EI, MLD) increased significantly by 1.5% (p < 0.001) for the whole lung, whereas air-trapping parameters (E/I MLA, RVC_856-950) were stable. PRM_Emph increased from 34.3 to 35.7% (p < 0.001), whereas PRM_Normal decrased from 23.6% to 22.8% (p = 0.012). WT decreased significantly from 1.17 ± 0.18 to 1.14 ± 0.19 mm (p = 0.036) and LA increased significantly from 25.08 ± 4.49 to 25.84 ± 4.87 mm² (p = 0.041) for the whole lung. The generation-based analysis showed heterogeneous results.

CONCLUSION

QCT detects short-time progression of emphysema in severe COPD. The changes were partly different among lung lobes and airway generations, indicating that QCT is useful to address the heterogeneity of COPD progression.

KEY POINTS

• QCT detects short-time progression of emphysema in severe COPD in a 3-month period. • QCT is able to quantify even slight parenchymal changes, which were not detected by spirometry. • QCT is able to address the heterogeneity of COPD, revealing inconsistent changes individual lung lobes and airway generations.

Computed Tomography Imaging for Novel Therapies of Chronic Obstructive Pulmonary Disease

Novel therapeutic options in chronic obstructive pulmonary disease (COPD) require delicate patient selection and thus demand for expert radiologists visually and quantitatively evaluating high-resolution computed tomography (CT) with additional functional acquisitions such as paired inspiratory-expiratory scans or dynamic airway CT. The differentiation between emphysema-dominant and airway-dominant COPD phenotypes by imaging has immediate clinical value for patient management. Assessment of emphysema severity, distribution patterns, and fissure integrity are essential for stratifying patients for different surgical and endoscopic lung volume reduction procedures. This is supported by quantitative software-based postprocessing of CT data sets, which delivers objective emphysema and airway remodelling metrics. However, the significant impact of scanning and reconstruction parameters, as well as intersoftware variability still hamper comparability between sites and studies. In earlier stage COPD imaging, it is less clear as to what extent quantitative CT might impact decision making and therapy follow-up, as emphysema progression is too slow to realistically be useful as a mid-term outcome measure in an individual, and longitudinal data on airway remodelling are still very limited.

Predicting all-cause and lung cancer mortality using emphysema score progression rate between baseline and follow-up chest CT images: A comparison of risk model performances

Purpose

Normalized emphysema score is a protocol-robust CT biomarker of mortality. We aimed to improve mortality prediction by including the emphysema score progression rate–its change over time–into the models.

Method and materials

CT scans from 6000 National Lung Screening Trial CT arm participants were included. Of these, 1810 died (445 lung cancer-specific). The remaining 4190 survivors were sampled with replacement up to 24432 to approximate the full cohort. Three overlapping subcohorts were formed which required participants to have images from specific screening rounds. Emphysema scores were obtained after resampling, normalization, and bullae cluster analysis of the original images. Base models contained solely the latest emphysema score. Progression models included emphysema score progression rate. Models were adjusted by including baseline age, sex, BMI, smoking status, smoking intensity, smoking duration, and previous COPD diagnosis. Cox proportional hazard models predicting all-cause and lung cancer mortality were compared by calculating the area under the curve per year follow-up.

Results

In the subcohort of participants with baseline and first annual follow-up scans, the analysis was performed on 4940 participants (23227 after resampling). Area under the curve for all-cause mortality predictions of the base and progression models 6 years after baseline were 0.564 (0.564 to 0.565) and 0.569 (0.568 to 0.569) when unadjusted, and 0.704 (0.703 to 0.704) to 0.705 (0.704 to 0.705) when adjusted. The respective performances predicting lung cancer mortality were 0.638 (0.637 to 0.639) and 0.643 (0.642 to 0.644) when unadjusted, and 0.724 (0.723 to 0.725) and 0.725 (0.725 to 0.726) when adjusted.

Conclusion

Including emphysema score progression rate into risk models shows no clinically relevant improvement in mortality risk prediction. This is because scan normalization does not adjust for an overall change in lung density. Adjusting for changes in smoking behavior is likely required to make this a clinically useful measure of emphysema progression.

Longitudinal airway remodeling in active and past smokers in a lung cancer screening population

OBJECTIVES

To longitudinally investigate smoking cessation-related changes of quantitative computed tomography (QCT)-based airway metrics in a group of heavy smokers.

METHODS

CT scans were acquired in a lung cancer screening population over 4 years at 12-month intervals in 284 long-term ex-smokers (ES), 405 continuously active smokers (CS), and 31 subjects who quitted smoking within 2 years after baseline CT (recent quitters, RQ). Total diameter (TD), lumen area (LA), and wall percentage (WP) of 1st-8th generation airways were computed using airway analysis software. Inter-group comparison was performed using Mann-Whitney U test or Student's t test (two groups), and ANOVA or ANOVA on ranks with Dunn's multiple comparison test (more than two groups), while Fisher's exact test or chi-squared test was used for categorical data. Multiple linear regression was used for multivariable analysis.

RESULTS

At any time, TD and LA were significantly higher in ES than CS, for example, in 5th-8th generation airways at baseline with 6.24 mm vs. 5.93 mm (p < 0.001) and 15.23 mm² vs. 13.51 mm² (p < 0.001), respectively. RQ showed higher TD (6.15 mm vs. 5.93 mm, n.s.) and significantly higher LA (14.77 mm² vs. 13.51 mm², p < 0.001) than CS after 3 years, and after 4 years. In multivariate analyses, smoking status independently predicted TD, LA, and WP at baseline, at 3 years and 4 years (p < 0.01-0.001), with stronger impact than pack years.

CONCLUSIONS

Bronchial dimensions depend on the smoking status. Smoking-induced airway remodeling can be partially reversible after smoking cessation even in long-term heavy smokers. Therefore, QCT-based airway metrics in clinical trials should consider the current smoking status besides pack years.

KEY POINTS

• Airway lumen and diameter are decreased in active smokers compared to ex-smokers, and there is a trend towards increased airway wall thickness in active smokers. • Smoking-related airway changes improve within 2 years after smoking cessation. • Smoking status is an independent predictor of airway dimensions.