Computed Tomography Radiomics Features Predict Change in Lung Density and Rate of Emphysema Progression

Abstract

Rationale: Emphysema progression is heterogeneous. Predicting temporal changes in lung density and detecting rapid progressors may facilitate the selection of individuals for targeted therapies. Objectives: To test whether computed tomography (CT) radiomics can be used to predict changes in lung density and detect rapid progressors. Methods: We extracted radiomics features from inspiratory chest CT in 4,575 subjects with and without airflow obstruction at enrollment, who completed a follow-up visit at approximately 5 years. We quantified emphysema using adjusted lung density (ALD) and estimated emphysema progression as the annualized change in ALD (DALD/yr) between visits. We categorized participants into rapid progressors (.1% DALD/yr) and stable disease (<1% DALD/yr). A gradient boosting model was used 1) to predict ALD at 5 years and 2) to identify rapid progressors. Four models using demographics (base clinical model), CT density, radiomics, and combined features (clinical, radiomics, and CT density) were evaluated and tested. Results: There were 1,773 (38.8%) rapid progressors. For predicting ALD at 5 years in the 20% held-out data, the base model explained 31% of the variance (adjusted R2 = 0.31), whereas R2 was 0.74 for the CT density model, 0.66 for the radiomics-only model, and 0.77 for the combined-features model. For detecting rapid progressors, the base model (area under the receiver operating characteristic curve [AUC], 0.57 [95% confidence interval (CI), 0.53–0.61]) was outperformed by the radiomics-only model (AUC, 0.73 [95% CI, 0.69–0.76]; D = 0.15; P, 0.001) and the combined model (AUC, 0.74 [95% CI, 0.71–0.77]; D = 0.17; P, 0.001). Conclusions: Parenchymal and airway radiomics features derived from inspiratory scans can be used to predict temporal changes in lung density and help identify rapid progressors.