A comparative study of HRCT image metrics and PFT values for characterization of ILD and COPD

The Gene Expression Biomarkers for Chronic Obstructive Pulmonary Disease and Interstitial Lung Disease

COPD (chronic obstructive pulmonary disease) and ILD (interstitial lung disease) are two common respiratory diseases. They share similar clinical traits but require different therapeutic treatments. Identifying the biomarkers that are differentially expressed between them will not only help the diagnosis of COPD and ILD, but also provide candidate drug targets that may facilitate the development of new treatment for COPD and ILD. Due to the irreversible complex pathological changes of COPD, there are very limited therapeutic options for COPD patients. In this study, we analyzed the gene expression profiles of two datasets: one training dataset that includes 144 COPD patients and 194 ILD patients, and one test dataset that includes 75 COPD patients and 61 ILD patients. Advanced feature selection methods, mRMR (minimal Redundancy Maximal Relevance) and incremental feature selection (IFS), were applied to identify the 38-gene biomarker. An SVM (support vector machine) classifier was built based on the 38-gene biomarker. Its accuracy, sensitivity, and specificity on training dataset evaluated by leave one out cross-validation were 0.905, 0.896, and 0.912, respectively. And on independent test dataset, the accuracy, sensitivity, and specificity on were as great as and were 0.904, 0.933, and 0.869, respectively. The biological function analysis of the 38 genes indicated that many of them can be potential treatment targets that may benefit COPD and ILD patients.

Multivariate modeling using quantitative CT metrics may improve accuracy of diagnosis of bronchiolitis obliterans syndrome after lung transplantation

BACKGROUND

To assess how quantitative CT (qCT) metrics compare to pulmonary function testing (PFT) and semi-quantitative image scores (SQS) to diagnose bronchiolitis obliterans syndrome (BOS), manifestation of chronic lung allograft dysfunction after lung transplantation (LTx), according to the type of LTx (unilateral or bilateral).

METHODS

Paired inspiratory-expiratory CT scans and PFTs of 176 LTx patients were analyzed retrospectively, and separated into BOS (78) and non-BOS (98) cohorts. SQS were assessed by 2 radiologists and graded (0-3) for features including mosaic attenuation and bronchiectasis. qCT metrics included lung volumes and air trapping volumes. Multivariate logistic regression (MVLR) and support vector machines (SVM) were used for the classification task.

RESULTS

MVLR and SVM models using PFT metrics demonstrated highest accuracy for bilateral LTx (max AUC 0.771), whereas models using qCT metrics-only outperformed models using SQS or PFTs in unilateral LTx (max AUC 0.817), to diagnose BOS. Adding PC (principal components) from qCT on top of PFT improved model diagnostic accuracy for all transplant types.

CONCLUSIONS

Combinations of qCT metrics augment the diagnostic performance of PFTs, are superior to SQS to predict BOS status, and outperform PFTs in the unilateral LTx group. This suggests that latent information on paired volumetric CT may allow early diagnosis of BOS in LTx patients, particularly in unilateral LTx.

Comparison of spirometric thresholds in diagnosing smoking-related airflow obstruction

BACKGROUND

Diagnosis of chronic obstructive pulmonary disease is based on detection of airflow obstruction on spirometry. There is no consensus regarding using a fixed threshold to define airflow obstruction versus using the lower limit of normal (LLN) adjusted for age. We compared the accuracy and discrimination of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommended fixed ratio of forced expiratory volume in the first second/forced vital capacity<0.70 with LLN in diagnosing smoking-related airflow obstruction using CT-defined emphysema and gas trapping as the disease gold standard.

METHODS

Data from a large multicentre study (COPDGene), which included current and former smokers (age range 45-80 years) with and without airflow obstruction, were analysed. Concordance between spirometric thresholds was measured. The accuracy of the thresholds in diagnosing emphysema and gas trapping was assessed using quantitative CT as gold standard.

RESULTS

7743 subjects were included. There was very good agreement between the two spirometric cutoffs (κ=0.85; 95% CI 0.83 to 0.86, p<0.001). 7.3% were discordant. Subjects with airflow obstruction by fixed ratio only had a greater degree of emphysema (4.1% versus 1.2%, p<0.001) and gas trapping (19.8% vs 7.5%, p<0.001) than those positive by LLN only, and also smoking controls without airflow obstruction (4.1% vs 1.9% and 19.8% vs 10.9%, respectively, p<0.001). On follow-up, the fixed ratio only group had more exacerbations than smoking controls.

CONCLUSIONS

Compared with the fixed ratio, the use of LLN fails to identify a number of patients with significant pulmonary pathology and respiratory morbidity.

Quantifying heterogeneity in emphysema from high-resolution computed tomography: a lung tissue research consortium study

RATIONALE AND OBJECTIVES

To quantify spatial distribution of emphysema using high-resolution computed tomography (HRCT), we applied semiautomated analysis with internal attenuation calibration to measure regional air volume, tissue volume, and fractional tissue volume (FTV = tissue/[air + tissue] volume) in well-characterized patients studied by the Lung Tissue Research Consortium (LTRC).

METHODS

HRCT was obtained at supine end-inspiration and end-expiration, and prone end-inspiration from 31 patients with mild, moderate, severe, or very severe emphysema (stages II-V, forced expiratory volume at 1 second >75%, 51%-75%, 21%-50% and ≤20% predicted, respectively). Control data were from 20 healthy non-smokers (stage I). Each lobe was analyzed separately. Heterogeneity of FTV was assessed from coefficients of variation (CV) within and among lobes, and the kurtosis and skewness of FTV histograms.

RESULTS

In emphysema, lobar air volume increased up to 177% above normal except in the right middle lobe. Lobar tissue volume increased up to 107% in mild-moderate stages then normalized in advanced stages. Normally, FTV was up to 82% higher in lower than upper lobes. In mild-moderate emphysema, lobar FTV increased by up to 74% above normal at supine inspiration. In severe emphysema, FTV declined below normal in all lobes and positions in correlation with pulmonary function (P < .05). Markers of FTV heterogeneity increased steadily with disease stage in correlation with pulmonary function (P < .05); the pattern is distinct from that seen in interstitial lung disease (ILD).

CONCLUSION

CT-derived biomarkers differentiate the spatial patterns of emphysema distribution and heterogeneity from that in ILD. Early emphysema is associated with elevated tissue volume and FTV, consistent with hyperemia, inflammation or atelectasis.

Computer-aided detection of early interstitial lung diseases using low-dose CT images

This study aims to develop a new computer-aided detection (CAD) scheme to detect early interstitial lung disease (ILD) using low-dose computed tomography (CT) examinations. The CAD scheme classifies each pixel depicted on the segmented lung areas into positive or negative groups for ILD using a mesh-grid-based region growth method and a multi-feature-based artificial neural network (ANN). A genetic algorithm was applied to select optimal image features and the ANN structure. In testing each CT examination, only pixels selected by the mesh-grid region growth method were analyzed and classified by the ANN to improve computational efficiency. All unselected pixels were classified as negative for ILD. After classifying all pixels into the positive and negative groups, CAD computed a detection score based on the ratio of the number of positive pixels to all pixels in the segmented lung areas, which indicates the likelihood of the test case being positive for ILD. When applying to an independent testing dataset of 15 positive and 15 negative cases, the CAD scheme yielded the area under receiver operating characteristic curve (AUC = 0.884 ± 0.064) and 80.0% sensitivity at 85.7% specificity. The results demonstrated the feasibility of applying the CAD scheme to automatically detect early ILD using low-dose CT examinations.