Phenotyping of COPD with MRI in comparison to same-day CT in a multi-centre trial.

OBJECTIVES: A prospective, multi-centre study to evaluate concordance of morphologic lung MRI and CT in chronic obstructive pulmonary disease (COPD) phenotyping for airway disease and emphysema. METHODS: A total of 601 participants with COPD from 15 sites underwent same-day morpho-functional chest MRI and paired inspiratory-expiratory CT. Two readers systematically scored bronchial wall thickening, bronchiectasis, centrilobular nodules, air trapping and lung parenchyma defects in each lung lobe and determined COPD phenotype. A third reader acted as adjudicator to establish consensus. Inter-modality and inter-reader agreement were assessed using Cohen's kappa (im-κ and ir-κ). RESULTS: The mean combined MRI score for bronchiectasis/bronchial wall thickening was 4.5/12 (CT scores, 2.2/12 for bronchiectasis and 6/12 for bronchial wall thickening; im-κ, 0.04-0.3). Expiratory right/left bronchial collapse was observed in 51 and 47/583 on MRI (62 and 57/599 on CT; im-κ, 0.49-0.52). Markers of small airways disease on MRI were 0.15/12 for centrilobular nodules (CT, 0.34/12), 0.94/12 for air trapping (CT, 0.9/12) and 7.6/12 for perfusion deficits (CT, 0.37/12 for mosaic attenuation; im-κ, 0.1-0.41). The mean lung defect score on MRI was 1.3/12 (CT emphysema score, 5.8/24; im-κ, 0.18-0.26). Airway-/emphysema/mixed COPD phenotypes were assigned in 370, 218 and 10 of 583 cases on MRI (347, 218 and 34 of 599 cases on CT; im-κ, 0.63). For all examined features, inter-reader agreement on MRI was lower than on CT. CONCLUSION: Concordance of MRI and CT for phenotyping of COPD in a multi-centre setting was substantial with variable inter-modality and inter-reader concordance for single diagnostic key features. CLINICAL RELEVANCE STATEMENT: MRI of lung morphology may well serve as a radiation-free imaging modality for COPD in scientific and clinical settings, given that its potential and limitations as shown here are carefully considered. KEY POINTS: • In a multi-centre setting, MRI and CT showed substantial concordance for phenotyping of COPD (airway-/emphysema-/mixed-type). • Individual features of COPD demonstrated variable inter-modality concordance with features of pulmonary hypertension showing the highest and bronchiectasis showing the lowest concordance. • For all single features of COPD, inter-reader agreement was lower on MRI than on CT.

Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT.

OBJECTIVES: To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity. MATERIALS AND METHODS: Paired inspiratory/expiratory CT and clinical data from COPDGene and COSYCONET cohort studies were included. COPDGene data served as training/validation/test data sets (N = 3144/786/1310) and COSYCONET as external test set (N = 446). To differentiate low-risk (healthy/minimal disease, [GOLD 0]) from COPD patients (GOLD 1-4), the self-supervised DL model learned semantic information from 50 × 50 × 50 voxel samples from segmented intact lungs. An anomaly detection approach was trained to quantify lung abnormalities related to COPD, as regional deviations. Four supervised DL models were run for comparison. The clinical and radiological predictive power of the proposed anomaly score was assessed using linear mixed effects models (LMM). RESULTS: The proposed approach achieved an area under the curve of 84.3 ± 0.3 (p < 0.001) for COPDGene and 76.3 ± 0.6 (p < 0.001) for COSYCONET, outperforming supervised models even when including only inspiratory CT. Anomaly scores significantly improved fitting of LMM for predicting lung function, health status, and quantitative CT features (emphysema/air trapping; p < 0.001). Higher anomaly scores were significantly associated with exacerbations for both cohorts (p < 0.001) and greater dyspnea scores for COPDGene (p < 0.001). CONCLUSION: Quantifying heterogeneous COPD manifestations as anomaly offers advantages over supervised methods and was found to be predictive for lung function impairment and morphology deterioration. CLINICAL RELEVANCE STATEMENT: Using deep learning, lung manifestations of COPD can be identified as deviations from normal-appearing chest CT and attributed an anomaly score which is consistent with decreased pulmonary function, emphysema, and air trapping. KEY POINTS: • A self-supervised DL anomaly detection method discriminated low-risk individuals and COPD subjects, outperforming classic DL methods on two datasets (COPDGene AUC = 84.3%, COSYCONET AUC = 76.3%). • Our contrastive task exhibits robust performance even without the inclusion of expiratory images, while voxel-based methods demonstrate significant performance enhancement when incorporating expiratory images, in the COPDGene dataset. • Anomaly scores improved the fitting of linear mixed effects models in predicting clinical parameters and imaging alterations (p < 0.001) and were directly associated with clinical outcomes (p < 0.001).

GOLD-grade specific disease characterization and phenotyping of COPD using quantitative computed tomography in the nationwide COSYCONET multicenter trial in Germany.

INTRODUCTION: 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 (192women, 327men, aged 65.7±8.5y) at risk for COPD and at GOLD1-4. QCT parameters 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). 58 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 dependent on GOLD grade, while airway wall thickening increases until GOLD 3 and airway dilatation occurs in GOLD4. QCT identifies four disease phenotypes with implications for lung function and prognosis.

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.

Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules.

OBJECTIVES: To assess the value of quantitative computed tomography (QCT) of the whole lung and nodule-bearing lobe regarding pulmonary nodule malignancy risk estimation. METHODS: A total of 251 subjects (median [IQR] age, 65 (57-73) years; 37% females) with pulmonary nodules on non-enhanced thin-section CT were retrospectively included. Twenty percent of the nodules were malignant, the remainder benign either histologically or at least 1-year follow-up. CT scans were subjected to in-house software, computing parameters such as mean lung density (MLD) or peripheral emphysema index (pEI). QCT variable selection was performed using logistic regression; selected variables were integrated into the Mayo Clinic and the parsimonious Brock Model. RESULTS: Whole-lung analysis revealed differences between benign vs. malignant nodule groups in several parameters, e.g. the MLD (-766 vs. -790 HU) or the pEI (40.1 vs. 44.7 %). The proposed QCT model had an area-under-the-curve (AUC) of 0.69 (95%-CI, 0.62-0.76) based on all available data. After integrating MLD and pEI into the Mayo Clinic and Brock Model, the AUC of both clinical models improved (AUC, 0.91 to 0.93 and 0.88 to 0.91, respectively). The lobe-specific analysis revealed that the nodule-bearing lobes had less emphysema than the rest of the lung regarding benign (EI, 0.5 vs. 0.7 %; p < 0.001) and malignant nodules (EI, 1.2 vs. 1.7 %; p = 0.001). CONCLUSIONS: Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant; hereby the nodule-bearing lobes have less emphysema. QCT variables could improve the risk assessment of incidental pulmonary nodules. KEY POINTS: • Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant. • The nodule-bearing lobes have less emphysema compared to the rest of the lung. • QCT variables could improve the risk assessment of incidental pulmonary nodules.

Reproducibility of pulmonary magnetic resonance angiography in adults with muco-obstructive pulmonary disease.

BACKGROUND: Recent studies support magnetic resonance angiography (MRA) as a diagnostic tool for pulmonary arterial disease. PURPOSE: To determine MRA image quality and reproducibility, and the dependence of MRA image quality and reproducibility on disease severity in patients with chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF). MATERIAL AND METHODS: Twenty patients with COPD (mean age 66.5 ± 8.9 years; FEV1% = 42.0 ± 13.3%) and 15 with CF (mean age 29.3 ± 9.3 years; FEV1% = 66.6 ± 15.8%) underwent morpho-functional chest magnetic resonance imaging (MRI) including time-resolved MRA twice one month apart (MRI1, MRI2), and COPD patients underwent non-contrast computed tomography (CT). Image quality was assessed visually using standardized subjective 5-point scales. Contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were measured by regions of interest. Disease severity was determined by spirometry, a well-evaluated chest MRI score, and by computational CT emphysema index (EI) for COPD. RESULTS: Subjective image quality was diagnostic for all MRA at MRI1 and MRI2 (mean score = 4.7 ± 0.6). CNR and SNR were 4 43.8 ± 8.7 and 50.5 ± 8.7, respectively. Neither image quality score nor CNR or SNR correlated with FEV1% or chest MRI score for COPD and CF (r = 0.239-0.248). CNR and SNR did not change from MRI1 to MRI2 (P = 0.434-0.995). Further, insignificant differences in CNR and SNR between MRA at MRI1 and MRI2 did not correlate with FEV1% nor chest MRI score in COPD and CF (r = -0.238-0.183), nor with EI in COPD (r = 0.100-0.111). CONCLUSION: MRA achieved diagnostic quality in COPD and CF patients and was highly reproducible irrespective of disease severity. This supports MRA as a robust alternative to CT in patients with underlying muco-obstructive lung disease.

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 ß-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 with 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 with 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 with 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 with WT from 5 days on (53.9 ± 4.5% vs. 30.0 ± 5.5% and 78.82 ± 8.44 µm vs. 65.66 ± 4.15 µm, 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 ± 9.20 µm vs. 61.70 ± 6.73 µm, 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.

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 80th percentile threshold. QDP was compared with visual MRI perfusion scoring, CT parametric response mapping (PRM) indices of emphysema (PRMEmph) and functional small airway disease (PRMfSAD), 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 PRMEmph (r = 0.70 to 0.75, p < 0.001). QDP was distinctly higher than PRMEmph (mean difference = 35.85 to 40.40) and PRMfSAD (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 PRMEmph and PRMfSAD. 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 PRMEmph and PRMfSAD 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.

Echo Time-Dependent Observed Lung T1 in Patients With Chronic Obstructive Pulmonary Disease in Correlation With Quantitative Imaging and Clinical Indices.

BACKGROUND: There is a clinical need for imaging-derived biomarkers for the management of chronic obstructive pulmonary disease (COPD). Observed pulmonary T1 (T1 (TE)) depends on the echo-time (TE) and reflects regional pulmonary function. PURPOSE: To investigate the potential diagnostic value of T1 (TE) for the assessment of lung disease in COPD patients by determining correlations with clinical parameters and quantitative CT. STUDY TYPE: Prospective non-randomized diagnostic study. POPULATION: Thirty COPD patients (67.7 ± 6.6 years). Data from a previous study (15 healthy volunteers [26.2 ± 3.9 years) were used as reference. FIELD STRENGTH/SEQUENCE: Study participants were examined at 1.5 T using dynamic contrast-enhanced three-dimensional gradient echo keyhole perfusion sequence and a multi-echo inversion recovery two-dimensional UTE (ultra-short TE) sequence for T1 (TE) mapping at TE1-5  = 70 µsec, 500 µsec, 1200 µsec, 1650 µsec, and 2300 µsec. ASSESSMENT: Perfusion images were scored by three radiologists. T1 (TE) was automatically quantified. Computed tomography (CT) images were quantified in software (qCT). Clinical parameters including pulmonary function testing were also acquired. STATISTICAL TESTS: Spearman rank correlation coefficients (ρ) were calculated between T1 (TE) and perfusion scores, clinical parameters and qCT. A P-value <0.05 was considered statistically significant. RESULTS: Median values were T1 (TE1-5 ) = 644 ± 78 msec, 835 ± 92 msec, 835 ± 87 msec, 831 ± 131 msec, 893 ± 220 msec, all significantly shorter than previously reported in healthy subjects. A significant increase of T1 was observed from TE1 to TE2 , with no changes from TE2 to TE3 (P = 0.48), TE3 to TE4 (P = 0.94) or TE4 to TE5 (P = 0.02) which demonstrates an increase at shorter TEs than in healthy subjects. Moderate to strong Spearman's correlations between T1 and parameters including the predicted diffusing capacity for carbon monoxide (DLCO, ρ < 0.70), mean lung density (MLD, ρ < 0.72) and the perfusion score (ρ > -0.69) were found. Overall, correlations were strongest at TE2 , weaker at TE1 and rarely significant at TE4 -TE5 . DATA CONCLUSION: In COPD patients, the increase of T1 (TE) with TE occurred at shorter TEs than previously found in healthy subjects. Together with the lack of correlation between T1 and clinical parameters of disease at longer TEs, this suggests that T1 (TE) quantification in COPD patients requires shorter TEs. The TE-dependence of correlations implies that T1 (TE) mapping might be developed further to provide diagnostic information beyond T1 at a single TE. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

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.

A calibration CT mini-lung-phantom created by 3-D printing and subtractive manufacturing.

We describe the creation and characterization of a calibration CT mini-lung-phantom incorporating simulated airways and ground-glass densities. Ten duplicate mini-lung-phantoms with Three-Dimensional (3-D) printed tubes simulating airways and gradated density polyurethane foam blocks were designed and built. Dimensional accuracy and CT numbers were measured using micro-CT and clinical CT scanners. Micro-CT images of airway tubes demonstrated an average dimensional variation of 0.038 mm from nominal values. The five different densities of incorporated foam blocks, simulating ground-glass, showed mean CT numbers (±standard deviation) of -897.0 ± 1.5, -844.1 ± 1.5, -774.1 ± 2.6, -695.3 ± 1.6, and -351.0 ± 3.7 HU, respectively. Three-Dimensional printing and subtractive manufacturing enabled rapid, cost-effective production of ground-truth calibration mini-lung-phantoms with low inter-sample variation that can be scanned simultaneously with the patient undergoing lung quantitative CT.

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 (RVC856-950)), 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, RVC856-950) were stable. PRMEmph increased from 34.3 to 35.7% (p < 0.001), whereas PRMNormal 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.

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.

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

OBJECTIVE: To longitudinally evaluate effects of smoking cessation on quantitative CT in a lung cancer screening cohort of heavy smokers over 4 years. METHODS: After 4 years, low-dose chest CT was available for 314 long-term ex-smokers (ES), 404 continuous smokers (CS) and 39 recent quitters (RQ) who quitted smoking within 2 years after baseline CT. CT acquired at baseline and after 3 and 4 years was subjected to well-evaluated densitometry software, computing mean lung density (MLD) and 15th percentile of the lung density histogram (15TH). RESULTS: At baseline, active smokers showed significantly higher MLD and 15TH (-822±35 and -936±25 HU, respectively) compared to ES (-831±31 and -947±22 HU, p<0.01-0.001). After 3 years, CS again had significantly higher MLD and 15TH (-801±29 and -896±23 HU) than ES (-808±27 and -906±20 HU, p<0.01-0.001) but also RQ (-813±20 and -909±15 HU, p<0.05-0.001). Quantitative CT parameters did not change significantly after 4 years. Importantly, smoking status independently predicted MLD at baseline and year 3 (p<0.001) in multivariate analysis. CONCLUSION: On quantitative CT, lung density is higher in active smokers than ex-smokers, and sustainably decreases after smoking cessation, reflecting smoking-induced inflammation. Interpretations of quantitative CT data within clinical trials should consider smoking status. KEY POINTS: • Lung density is higher in active smokers than ex-smokers. • Lung density sustainably decreases after smoking cessation. • Impact of smoking cessation on lung density is independent of potentially confounding factors. • Smoke-induced pulmonary inflammation and particle deposition influence lung density on CT.

Detection of inspiratory recruitment of atelectasis by automated lung sound analysis as compared to four-dimensional computed tomography in a porcine lung injury model.

BACKGROUND: Cyclic recruitment and de-recruitment of atelectasis (c-R/D) is a contributor to ventilator-induced lung injury (VILI). Bedside detection of this dynamic process could improve ventilator management. This study investigated the potential of automated lung sound analysis to detect c-R/D as compared to four-dimensional computed tomography (4DCT). METHODS: In ten piglets (25 ± 2 kg), acoustic measurements from 34 thoracic piezoelectric sensors (Meditron ASA, Norway) were performed, time synchronized to 4DCT scans, at positive end-expiratory pressures of 0, 5, 10, and 15 cmH2O during mechanical ventilation, before and after induction of c-R/D by surfactant washout. 4DCT was post-processed for within-breath variation in atelectatic volume (Δ atelectasis) as a measure of c-R/D. Sound waveforms were evaluated for: 1) dynamic crackle energy (dCE): filtered crackle sounds (600-700 Hz); 2) fast Fourier transform area (FFT area): spectral content above 500 Hz in frequency and above -70 dB in amplitude in proportion to the total amount of sound above -70 dB amplitude; and 3) dynamic spectral coherence (dSC): variation in acoustical homogeneity over time. Parameters were analyzed for global, nondependent, central, and dependent lung areas. RESULTS: In healthy lungs, negligible values of Δ atelectasis, dCE, and FFT area occurred. In lavage lung injury, the novel dCE parameter showed the best correlation to Δ atelectasis in dependent lung areas (R2 = 0.88) where c-R/D took place. dCE was superior to FFT area analysis for each lung region examined. The analysis of dSC could predict the lung regions where c-R/D originated. CONCLUSIONS: c-R/D is associated with the occurrence of fine crackle sounds as demonstrated by dCE analysis. Standardized computer-assisted analysis of dCE and dSC seems to be a promising method for depicting c-R/D.

Changes of Emphysema Parameters over the Respiratory Cycle During Free Breathing: Preliminary Results Using Respiratory Gated 4D-CT.

The purpose of this research was to evaluate respiratory gated CT of the lung in patients with COPD for analysis of parenchymal characteristics who were potential candidates for volume reduction surgery. Eleven patients with clinically known emphysematous disease underwent a respiratory gated, free-breathing 64-multislice-CT (Aquilion 64, Toshiba). Retrospective image reconstruction was performed similar to cardiac CT at every 10% of the respiratory loop, resulting in 10 complete volumetric datasets at 10 equidistant time points. All images were transferred onto a PC for calculation of the total lung volume, emphysema volume, emphysema index, and mean lung density. Complete datasets could be successfully reconstructed in all patients. The mean lung volume increased from 6.9 L to 7.5 L over the respiratory cycle. Emphysema volume increased from 1.6 L to 2.0 L and emphysema index from 22.6% to 26.5% from expiration to inspiration. In conclusion, respiratory gated chest 4D-CT allows for combined morphologic and functional image analysis, which can provide new insight into functional impairment and individual treatment planning.

Simultaneous assessment of airway instability and respiratory dynamics with low-dose 4D-CT in chronic obstructive pulmonary disease: a technical note.

BACKGROUND: Advanced-stage chronic obstructive pulmonary disease (COPD) is associated with severely altered respiratory dynamics. Dynamic airway instability is usually diagnosed by invasive bronchoscopy. Cine-computed tomography (CT) may be used alternatively, but is limited to predefined anatomical positions. Also, a paradoxical diaphragmatic motion has been described in patients with emphysema. OBJECTIVES: As the airways and chest wall show inherently high contrast to airway lumen and lung tissue, low-dose CT acquisitions potentially suffice for depicting tracheobronchial and chest wall motion. Therefore, we propose low-dose dynamic respiratory-gated multidetector CT (4D-CT) of the whole chest as a new method to assess respiratory dynamics. METHODS: 4D-CT was performed in 3 patients (52, 62 and 76 years old) with suspected tracheal instability due to COPD or tracheal stenosis at minimal pitch (0.09) and radiation exposure (1.4-1.9 mSv) during regular tidal breathing registered by a belt system. Image reconstruction involved a raw data-based iterative algorithm (1.5-mm slice thickness, 1.0-mm z-axis increment, 5% respiratory increment), resulting in a stack of 6,700 images, which were evaluated with a 4D-viewing tool. RESULTS: An excessive dynamic collapse of the trachea in combination with tracheobronchomalacia (TBM) of the main-stem and segmental bronchi, and a paradoxical diaphragmatic motion were demonstrated in 1 case. Moreover, we detected a saber-sheath trachea and main-stem TBM in another case. The third case showed a fixed tracheal stenosis. CONCLUSIONS: 4D-CT provides unprecedented z-axis coverage and time-resolved volumetric datasets of the whole chest. Airway instability, stenosis and paradoxical diaphragmatic motion may be assessed simultaneously, preceding interventions such as airway stabilization or lung volume reduction.

Delta-radiomics features of ADC maps as early predictors of treatment response in lung cancer.

OBJECTIVE: Investigate the feasibility of detecting early treatment-induced tumor tissue changes in patients with advanced lung adenocarcinoma using diffusion-weighted MRI-derived radiomics features. METHODS: This prospective observational study included 144 patients receiving either tyrosine kinase inhibitors (TKI, n = 64) or platinum-based chemotherapy (PBC, n = 80) for the treatment of pulmonary adenocarcinoma. Patients underwent diffusion-weighted MRI the day prior to therapy (baseline, all patients), as well as either + 1 (PBC) or + 7 and + 14 (TKI) days after treatment initiation. One hundred ninety-seven radiomics features were extracted from manually delineated tumor volumes. Feature changes over time were analyzed for correlation with treatment response (TR) according to CT-derived RECIST after 2 months and progression-free survival (PFS). RESULTS: Out of 14 selected delta-radiomics features, 6 showed significant correlations with PFS or TR. Most significant correlations were found after 14 days. Features quantifying ROI heterogeneity, such as short-run emphasis (p = 0.04(pfs)/0.005(tr)), gradient short-run emphasis (p = 0.06(pfs)/0.01(tr)), and zone percentage (p = 0.02(pfs)/0.01(tr)) increased in patients with overall better TR whereas patients with worse overall response showed an increase in features quantifying ROI homogeneity, such as normalized inverse difference (p = 0.01(pfs)/0.04(tr)). Clustering of these features allows stratification of patients into groups of longer and shorter survival. CONCLUSION: Two weeks after initiation of treatment, diffusion MRI of lung adenocarcinoma reveals quantifiable tissue-level insights that correlate well with future treatment (non-)response. Diffusion MRI-derived radiomics thus shows promise as an early, radiation-free decision-support to predict efficacy and potentially alter the treatment course early. CRITICAL RELEVANCE STATEMENT: Delta-Radiomics texture features derived from diffusion-weighted MRI of lung adenocarcinoma, acquired as early as 2 weeks after initiation of treatment, are significantly correlated with RECIST TR and PFS as obtained through later morphological imaging. KEY POINTS: Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management.

How do deep-learning models generalize across populations? Cross-ethnicity generalization of COPD detection.

OBJECTIVES: To evaluate the performance and potential biases of deep-learning models in detecting chronic obstructive pulmonary disease (COPD) on chest CT scans across different ethnic groups, specifically non-Hispanic White (NHW) and African American (AA) populations. MATERIALS AND METHODS: Inspiratory chest CT and clinical data from 7549 Genetic epidemiology of COPD individuals (mean age 62 years old, 56-69 interquartile range), including 5240 NHW and 2309 AA individuals, were retrospectively analyzed. Several factors influencing COPD binary classification performance on different ethnic populations were examined: (1) effects of training population: NHW-only, AA-only, balanced set (half NHW, half AA) and the entire set (NHW + AA all); (2) learning strategy: three supervised learning (SL) vs. three self-supervised learning (SSL) methods. Distribution shifts across ethnicity were further assessed for the top-performing methods. RESULTS: The learning strategy significantly influenced model performance, with SSL methods achieving higher performances compared to SL methods (p < 0.001), across all training configurations. Training on balanced datasets containing NHW and AA individuals resulted in improved model performance compared to population-specific datasets. Distribution shifts were found between ethnicities for the same health status, particularly when models were trained on nearest-neighbor contrastive SSL. Training on a balanced dataset resulted in fewer distribution shifts across ethnicity and health status, highlighting its efficacy in reducing biases. CONCLUSION: Our findings demonstrate that utilizing SSL methods and training on large and balanced datasets can enhance COPD detection model performance and reduce biases across diverse ethnic populations. These findings emphasize the importance of equitable AI-driven healthcare solutions for COPD diagnosis. CRITICAL RELEVANCE STATEMENT: Self-supervised learning coupled with balanced datasets significantly improves COPD detection model performance, addressing biases across diverse ethnic populations and emphasizing the crucial role of equitable AI-driven healthcare solutions. KEY POINTS: Self-supervised learning methods outperform supervised learning methods, showing higher AUC values (p < 0.001). Balanced datasets with non-Hispanic White and African American individuals improve model performance. Training on diverse datasets enhances COPD detection accuracy. Ethnically diverse datasets reduce bias in COPD detection models. SimCLR models mitigate biases in COPD detection across ethnicities.