Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease

Abstract

BACKGROUNDCurrently recommended traditional spirometry outputs do not reflect the relative contributions of emphysema and airway disease to airflow obstruction. We hypothesized that machine-learning algorithms can be trained on spirometry data to identify these structural phenotypes.METHODSParticipants enrolled in a large multicenter study (COPDGene) were included. The data points from expiratory flow-volume curves were trained using a deep-learning model to predict structural phenotypes of chronic obstructive pulmonary disease (COPD) on CT, and results were compared with traditional spirometry metrics and an optimized random forest classifier. Area under the receiver operating characteristic curve (AUC) and weighted F-score were used to measure the discriminative accuracy of a fully convolutional neural network, random forest, and traditional spirometry metrics to phenotype CT as normal, emphysema-predominant (>5% emphysema), airway-predominant (Pi10 > median), and mixed phenotypes. Similar comparisons were made for the detection of functional small airway disease phenotype (>20% on parametric response mapping).RESULTSAmong 8980 individuals, the neural network was more accurate in discriminating predominant emphysema/airway phenotypes (AUC 0.80, 95%CI 0.79-0.81) compared with traditional measures of spirometry, FEV1/FVC (AUC 0.71, 95%CI 0.69-0.71), FEV1% predicted (AUC 0.70, 95%CI 0.68-0.71), and random forest classifier (AUC 0.78, 95%CI 0.77-0.79). The neural network was also more accurate in discriminating predominant emphysema/small airway phenotypes (AUC 0.91, 95%CI 0.90-0.92) compared with FEV1/FVC (AUC 0.80, 95%CI 0.78-0.82), FEV1% predicted (AUC 0.83, 95%CI 0.80-0.84), and with comparable accuracy with random forest classifier (AUC 0.90, 95%CI 0.88-0.91).CONCLUSIONSStructural phenotypes of COPD can be identified from spirometry using deep-learning and machine-learning approaches, demonstrating their potential to identify individuals for targeted therapies.TRIAL REGISTRATIONClinicalTrials.gov NCT00608764.FUNDINGThis study was supported by NIH grants K23 HL133438 and R21EB027891 and an American Thoracic Foundation 2018 Unrestricted Research Grant. The COPDGene study is supported by NIH grants NHLBI U01 HL089897 and U01 HL089856. The COPDGene study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprising AstraZeneca, Boehringer-Ingelheim, GlaxoSmithKline, Novartis, and Sunovion.

Keywords: COPD; Pulmonology.

Conflict of interest statement

Conflict of interest: JMR reports holding stock in VIDA Diagnostics and personal fees from Boehringer Ingelheim. PJC reports personal fees and grant support from GlaxoSmithKline and personal fees from Novartis in the past 3 years. SPB has served on advisory boards for Sunovion and GlaxoSmithKline.

Figures

Figure 1. CONSORT diagram.

Figure 2. Classification of structural phenotypes — normal and mixed emphysema/airway disease.

Classification performance of normal (A) (emphysema <5% and medium size airway disease < median Pi10) and mixed (B) (emphysema >5% and medium size airway disease > median Pi10) groups. The results show per-class area under the curve (AUC) of the FCN model versus random forest classifier and logistic regression models with FEV1/FVC and FEV1% predicted measurements. Results shown for the hold-out test data set. FEV1, forced expiratory volume in the first second; FVC, forced vital capacity; FCN, fully convolutional network; Pi10, airway wall area measurement.

Figure 3. Classification of structural phenotypes — emphysema and airway disease.

Classification performance of airway disease predominant (A) (emphysema <5% and medium size airway disease > median Pi10) and emphysema predominant (B) (emphysema >5% and medium size airway disease < median Pi10) groups. The results show per-class area under the curve (AUC) of the FCN model versus random forest classifier and logistic regression models with FEV1/FVC and FEV1% predicted measurements. Results shown for the hold-out test data set. FEV1, forced expiratory volume in the first second; FVC, forced vital capacity; FCN, fully convolutional network; Pi10, airway wall area measurement.

Figure 4. Classification of structural phenotypes — normal and mixed emphysema/small airway disease.

Classification performance of normal (A) (emphysema <5% and small airway disease <20%) and mixed (B) (emphysema >5% and small airway disease >20%) groups. The results show per-class area under the curve (AUC) of the FCN model versus random forest classifier and logistic regression models with FEV1/FVC and FEV1% predicted measurements. Results shown for the hold-out test data set. FEV1, forced expiratory volume in the first second; FVC, forced vital capacity; FCN, fully convolutional network. Small airway disease was defined by parametric response mapping.

Figure 5. Classification of structural phenotypes — emphysema and small airway disease.

Classification performance of airway disease predominant (A) (emphysema <5% and small airway disease >20%) and emphysema predominant (B) (emphysema >5% and small airway disease <20%) groups. The results show per-class area under the curve (AUC) of the FCN model versus random forest classifier and logistic regression models with FEV1/FVC and FEV1% predicted measurements. Results shown for the hold-out test data set. FEV1, forced expiratory volume in the first second; FVC, forced vital capacity; FCN, fully convolutional network. Small airway disease was defined by parametric response mapping.