Potential Value of Expiratory CT in Quantitative Assessment of Pulmonary Vessels in COPD

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).

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

Purpose

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

Patients and Methods

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

Results

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

Conclusion

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

Trial Registration

NCT01245933.

Contributions of Emphysema and Functional Small Airway Disease on Intrapulmonary Vascular Volume in COPD

Background

Previous studies have demonstrated that there is a certain correlation between emphysema and changes in pulmonary small blood vessels in patients with chronic obstructive pulmonary disease (COPD), but most of them were limited to the investigation of the inspiratory phase. The emphysema indicators need to be further optimized. Based on the parametric response mapping (PRM) method, this study aimed to investigate the effect of emphysema and functional small airway disease on intrapulmonary vascular volume (IPVV).

Methods

This retrospective study enrolled 63 healthy subjects and 47 COPD patients, who underwent both inspiratory and expiratory CT scans of the chest and pulmonary function tests (PFTs). Inspiratory and expiratory IPVV were measured by using an automatic pulmonary vessels integration segmentation approach, the ratio of emphysema volume (Emph%), functional small airway disease volume (fsAD%), and normal areas volume (Normal%) were quantified by the PRM method for biphasic CT scans. The participants were grouped according to PFTs. Analysis of variance (ANOVA) and Kruskal–Wallis H-test were used to analyze the differences in indicators between different groups. Then, Spearman’s rank correlation coefficients were used to analyze the correlation between Emph%, fsAD%, Normal%, PFTs, and IPVV. Finally, multiple linear regression was applied to analyze the effects of Emph% and fsAD% on IPVV.

Results

Differences were found in age, body mass index (BMI), smoking index, FEV1%, FEV1/forced vital capacity (FVC), expiratory IPVV, IPVV relative value, IPVV difference value, Emph%, fsAD%, and Normal% between the groups (P<0.05). A strong correlation was established between the outcomes of PFTs and quantitative CT indexes. Finally, the effect of Emph% was more significant than that of fsAD% on expiratory IPVV, IPVV difference value, and IPVV relative value.

Conclusion

IPVV may have a potential value in assessing COPD severity and is significantly affected by emphysema.