Imaging-based clusters in current smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)

Haghighi, Babak; Choi, Seock Hwan; Choi, Jiwoong; Hoffman, Eric A.; Comellas, Alejandro P.; Newell, John D.; Barr, R. Graham; Bleecker, Eugene R.; Cooper, Christopher B.; Couper, David J.; Han, Mei Lan K.; Hansel, Nadia N.; Kanner, Richard E.; Kazerooni, Ella A.; Kleerup, Eric; Martínez, Fernando J.; O’Neal, Wanda K.; Rennard, Stephen I.; Woodruff, Prescott G.; Lin, Ching Long

doi:10.1186/s12931-018-0888-7

Cited by 23 publications

(29 citation statements)

References 35 publications

(46 reference statements)

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“…We analyzed former smokers independently from current smokers because smoking status may have significant effect on lung density, and cessation of smoking can be deemed as an effective intervention (32), resulting in different progression patterns. The imagingbased variables were also employed in cross-sectional cluster analysis (13,14) and these variables were found to be a sensitive means of differentiating subgroups in COPD and asthma cohorts (11,14). However, evidence has shown that the disease progression depends on the subject's current stage and even at the same disease stage the progression patterns may differ (3).…”

Section: Discussionmentioning

confidence: 99%

“…The separate quantification of emphysema and small airway disease can dissociate the emphysematous region(s) from the air-trapped region(s) (3). fSAD% and Emph% with the respective air-fraction threshold of 90% and 98.5% were employed to eliminate the effects of scanners (13)(14)(15). Tissue fractions at TLC (β tissue TLC ) and at RV (β tissue RV ) (14) were introduced to measure the proportion of tissue volume in each voxel for detection of tissue destruction and inflammation.…”

Section: Derivation Of Qct Imaging-based Metricsmentioning

confidence: 99%

“…The cross-sectional cluster members were divided between neighboring longitudinal clusters due to different progression patterns. Cross-sectional clustering, though being able to derive meaningful clusters for disease phenotyping (11,13,14), Values expressed as mean (SD) or number (%). Kruskal-Wallis tests were performed for continuous variables.…”

Section: Comparison Between Longitudinal Clustering and Cross-sectionmentioning

confidence: 99%

“…Cluster analysis of clinical variables by Burgel et al (10) concluded that COPD patients with similar airflow obstruction may belong to different phenotypes and may differ in symptoms, outcomes, age and comorbidities. Besides clinical variables, qCT imaging-based machine learning of diseased lungs has advanced recently, and multiscale imaging-based cluster analyses have provided clinically meaningful clusters in both asthma and COPD cohorts (11)(12)(13)(14). By considering inter-site and inter-subject variations through normalization schemes (15,16), their approach enabled the analysis of datasets acquired by multiple centers utilizing multiple make and model CT scanners.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we performed a longitudinal cluster analysis of former smokers based on the imaging-derived variables at two visits with a one-year interval. We focused on former smokers because smoking status may introduce confounding factors that interfere with qCT quantification (13,14). The feature vector for each subject contains the baseline imagingderived variables and their percentages of change over one year.…”

Section: Introductionmentioning

confidence: 99%

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Longitudinal Imaging-Based Clusters in Former Smokers of the COPD Cohort Associate with Clinical Characteristics: the SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)

Zou

Choi

et al. 2020

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Background: Quantitative CT (qCT) imaging-based cluster analysis identified clinically meaningful COPD former-smoker subgroups (clusters) based on cross-sectional data. We aimed to identify progression clusters for former smokers using longitudinal data.Patients and Methods: We selected 472 former smokers from SPIROMICS with a baseline visit and a one-year follow-up visit. A total of 150 qCT imaging-based variables, comprising 75 variables at baseline and their corresponding progression rates, were derived from the respective inspiration and expiration scans of the two visits. The COPD progression clusters identified were then associated with subject demography, clinical variables and biomarkers.Results: COPD severities at baseline increased with increasing cluster number. Cluster 1 patients were an obese subgroup with rapid progression of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%), and preserved FEV1%predicted but impairment of FEV1/FVC ratio over one year. Cluster 2 exhibited a decrease of fSAD% and Emph%, an increase of tissue fraction at total lung capacity, airway narrowing, and a reduced post-bronchodilator FEV1%predicted but preserved FEV1/FVC ratio over one year. Cluster 3 showed rapid expansion of Emph% and an attenuation of fSAD%. Cluster 4 demonstrated severe emphysema and fSAD and significant structural alterations at baseline with rapid progression of fSAD% over one year. Subjects with different progression patterns in the same cross-sectional cluster were identified by longitudinal clustering.Conclusions: qCT imaging-based metrics at two visits for former smokers allow for the derivation of four statistically stable clusters associated with unique progression patterns and clinical characteristics. Use of baseline variables and their progression rates enables identification of longitudinal clusters, resulting in a refinement of cross-sectional clusters.

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Section: Discussionmentioning

confidence: 99%

Section: Derivation Of Qct Imaging-based Metricsmentioning

confidence: 99%

Section: Comparison Between Longitudinal Clustering and Cross-sectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Longitudinal Imaging-Based Clusters in Former Smokers of the COPD Cohort Associate with Clinical Characteristics: the SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)

Zou

Choi

et al. 2020

Preprint

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Direct estimation of regional lung volume change from paired and single CT images using residual regression neural network

et al. 2023

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BackgroundChest computed tomography (CT) enables characterization of pulmonary diseases by producing high‐resolution and high‐contrast images of the intricate lung structures. Deformable image registration is used to align chest CT scans at different lung volumes, yielding estimates of local tissue expansion and contraction.PurposeWe investigated the utility of deep generative models for directly predicting local tissue volume change from lung CT images, bypassing computationally expensive iterative image registration and providing a method that can be utilized in scenarios where either one or two CT scans are available.MethodsA residual regression convolutional neural network, called Reg3DNet+, is proposed for directly regressing high‐resolution images of local tissue volume change (i.e., Jacobian) from CT images. Image registration was performed between lung volumes at total lung capacity (TLC) and functional residual capacity (FRC) using a tissue mass‐ and structure‐preserving registration algorithm. The Jacobian image was calculated from the registration‐derived displacement field and used as the ground truth for local tissue volume change. Four separate Reg3DNet+ models were trained to predict Jacobian images using a multifactorial study design to compare the effects of network input (i.e., single image vs. paired images) and output space (i.e., FRC vs. TLC). The models were trained and evaluated on image datasets from the COPDGene study. Models were evaluated against the registration‐derived Jacobian images using local, regional, and global evaluation metrics.ResultsStatistical analysis revealed that both factors – network input and output space – were significant determinants for change in evaluation metrics. Paired‐input models performed better than single‐input models, and model performance was better in the output space of FRC rather than TLC. Mean structural similarity index for paired‐input models was 0.959 and 0.956 for FRC and TLC output spaces, respectively, and for single‐input models was 0.951 and 0.937. Global evaluation metrics demonstrated correlation between registration‐derived Jacobian mean and predicted Jacobian mean: coefficient of determination (r2) for paired‐input models was 0.974 and 0.938 for FRC and TLC output spaces, respectively, and for single‐input models was 0.598 and 0.346. After correcting for effort, registration‐derived lobar volume change was strongly correlated with the predicted lobar volume change: for paired‐input models r2 was 0.899 for both FRC and TLC output spaces, and for single‐input models r2 was 0.803 and 0.862, respectively.ConclusionsConvolutional neural networks can be used to directly predict local tissue mechanics, eliminating the need for computationally expensive image registration. Networks that use paired CT images acquired at TLC and FRC allow for more accurate prediction of local tissue expansion compared to networks that use a single image. Networks that only require a single input image still show promising results, particularly after correcting for effort, and allow for local tissue expansion estimation in cases where multiple CT scans are not available. For single‐input networks, the FRC image is more predictive of local tissue volume change compared to the TLC image.

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Machine learning and in silico methods

2021

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Imaging-based clusters in current smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)

Cited by 23 publications

References 35 publications

Longitudinal Imaging-Based Clusters in Former Smokers of the COPD Cohort Associate with Clinical Characteristics: the SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)

Longitudinal Imaging-Based Clusters in Former Smokers of the COPD Cohort Associate with Clinical Characteristics: the SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)

Direct estimation of regional lung volume change from paired and single CT images using residual regression neural network

Machine learning and in silico methods

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