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Fulltext A novel CT-emphysema index/FEV1 approach of phenotyping COPD to predict mortality

Loh LC, Ong CK, Koo HJ, Lee SM, Lee JS, Oh YM, et al.

Int J Chron Obstruct Pulmon Dis, 2018;13:2543-2550.
PMID: 30174423 DOI: 10.2147/COPD.S165898

Background: COPD-associated mortality was examined using a novel approach of phenotyping COPD based on computed tomography (CT)-emphysema index from quantitative CT (QCT) and post-bronchodilator (BD) forced expiratory volume in 1 second (FEV1) in a local Malaysian cohort.

Patients and methods: Prospectively collected data of 112 eligible COPD subjects (mean age, 67 years; male, 93%; mean post-BD FEV1, 45.7%) was available for mortality analysis. Median follow-up time was 1,000 days (range, 60-1,400). QCT and clinicodemographic data were collected at study entry. Based on CT-emphysema index and post-BD FEV1% predicted, subjects were categorized into "emphysema-dominant," "airway-dominant," "mild mixed airway-emphysema," and "severe mixed airway-emphysema" diseases.

Results: Sixteen patients (14.2%) died of COPD-associated causes. There were 29 (25.9%) "mild mixed," 23 (20.5%) "airway-dominant," 15 (13.4%) "emphysema-dominant," and 45 (40.2%) "severe mixed" cases. "Mild mixed" disease was proportionately more in Global Initiative for Chronic Obstructive Lung Disease (GOLD) Group A, while "severe mixed" disease was proportionately more in GOLD Groups B and D. Kaplan-Meier survival estimates showed increased mortality risk with "severe mixed" disease (log rank test, p=0.03) but not with GOLD groups (p=0.08). Univariate Cox proportionate hazard analysis showed that age, body mass index, long-term oxygen therapy, FEV1, forced volume capacity, COPD Assessment Test score, modified Medical Research Council score, St Georges' Respiratory Questionnaire score, CT-emphysema index, and "severe mixed" disease (vs "mild mixed" disease) were associated with mortality. Multivariate Cox analysis showed that age, body mass index, and COPD Assessment Test score remain independently associated with mortality.

Conclusion: "Severe mixed airway-emphysema" disease may predict COPD-associated mortality. Age, body mass index, and COPD Assessment Test score remain as key mortality risk factors in our cohort.
Radiomics approach for survival prediction in chronic obstructive pulmonary disease

Cho YH, Seo JB, Lee SM, Kim N, Yun J, Hwang JE, et al.

Eur Radiol, 2021 Oct;31(10):7316-7324.
PMID: 33847809 DOI: 10.1007/s00330-021-07747-7

OBJECTIVES: To apply radiomics analysis for overall survival prediction in chronic obstructive pulmonary disease (COPD), and evaluate the performance of the radiomics signature (RS).
METHODS: This study included 344 patients from the Korean Obstructive Lung Disease (KOLD) cohort. External validation was performed on a cohort of 112 patients. In total, 525 chest CT-based radiomics features were semi-automatically extracted. The five most useful features for survival prediction were selected by least absolute shrinkage and selection operation (LASSO) Cox regression analysis and used to generate a RS. The ability of the RS for classifying COPD patients into high or low mortality risk groups was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis.
RESULTS: The five features remaining after the LASSO analysis were %LAA-950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA18mm. The RS demonstrated a C-index of 0.774 in the discovery group and 0.805 in the validation group. Patients with a RS greater than 1.053 were classified into the high-risk group and demonstrated worse overall survival than those in the low-risk group in both the discovery (log-rank test, < 0.001; hazard ratio [HR], 5.265) and validation groups (log-rank test, < 0.001; HR, 5.223). For both groups, RS was significantly associated with overall survival after adjustments for patient age and body mass index.
CONCLUSIONS: A radiomics approach for survival prediction and risk stratification in COPD patients is feasible, and the constructed radiomics model demonstrated acceptable performance. The RS derived from chest CT data of COPD patients was able to effectively identify those at increased risk of mortality.
KEY POINTS: • A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA-950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA18mm were selected to generate a radiomics model. • A radiomics model for predicting survival of COPD patients demonstrated reliable performance with a C-index of 0.774 in the discovery group and 0.805 in the validation group. • Radiomics approach was able to effectively identify COPD patients with an increased risk of mortality, and patients assigned to the high-risk group demonstrated worse overall survival in both the discovery and validation groups.
Fulltext Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease

Yun J, Cho YH, Lee SM, Hwang J, Lee JS, Oh YM, et al.

Sci Rep, 2021 07 26;11(1):15144.
PMID: 34312450 DOI: 10.1038/s41598-021-94535-4

Heterogeneous clinical manifestations and progression of chronic obstructive pulmonary disease (COPD) affect patient health risk assessment, stratification, and management. Pulmonary function tests are used to diagnose and classify the severity of COPD, but they cannot fully represent the type or range of pathophysiologic abnormalities of the disease. To evaluate whether deep radiomics from chest computed tomography (CT) images can predict mortality in patients with COPD, we designed a convolutional neural network (CNN) model for extracting representative features from CT images and then performed random survival forest to predict survival in COPD patients. We trained CNN-based binary classifier based on six-minute walk distance results (> 440 m or not) and extracted high-throughput image features (i.e., deep radiomics) directly from the last fully connected layer of it. The various sizes of fully connected layers and combinations of deep features were experimented using a discovery cohort with 344 patients from the Korean Obstructive Lung Disease cohort and an external validation cohort with 102 patients from Penang General Hospital in Malaysia. In the integrative analysis of discovery and external validation cohorts, with combining 256 deep features from the coronal slice of the vertebral body and two sagittal slices of the left/right lung, deep radiomics for survival prediction achieved concordance indices of 0.8008 (95% CI, 0.7642-0.8373) and 0.7156 (95% CI, 0.7024-0.7288), respectively. Deep radiomics from CT images could be used to predict mortality in COPD patients.
Identification of chronic obstructive pulmonary disease subgroups in 13 Asian cities

Kim WJ, Gupta V, Nishimura M, Makita H, Idolor L, Roa C, et al.

Int J Tuberc Lung Dis, 2018 07 01;22(7):820-826.
PMID: 29914609 DOI: 10.5588/ijtld.17.0524

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition that can differ in its clinical manifestation, structural changes and response to treatment.
OBJECTIVE: To identify subgroups of COPD with distinct phenotypes, evaluate the distribution of phenotypes in four related regions and calculate the 1-year change in lung function and quality of life according to subgroup.
METHODS: Using clinical characteristics, we performed factor analysis and hierarchical cluster analysis in a cohort of 1676 COPD patients from 13 Asian cities. We compared the 1-year change in forced expiratory volume in one second (FEV1), modified Medical Research Council dyspnoea scale score, St George's Respiratory Questionnaire (SGRQ) score and exacerbations according to subgroup derived from cluster analysis.
RESULTS: Factor analysis revealed that body mass index, Charlson comorbidity index, SGRQ total score and FEV1 were principal factors. Using these four factors, cluster analysis identified three distinct subgroups with differing disease severity and symptoms. Among the three subgroups, patients in subgroup 2 (severe disease and more symptoms) had the most frequent exacerbations, most rapid FEV1 decline and greatest decline in SGRQ total score.
CONCLUSION: Three subgroups with differing severities and symptoms were identified in Asian COPD subjects.