Lobar Lung Density Embeddings with a Transformer Encoder (LobTe) to Predict Emphysema Progression in COPD

Abstract

Emphysema is defined as an abnormal alveolar wall destruction exhibits varied extent and distribution within the lung, leading to heterogeneous spatial emphysema distribution. The progression of emphysema leads to decreased gas exchange, resulting in clinical worsening, and has been associated with higher mortality. Despite the ability to diagnose emphysema on CT scans there are no methods to predict its evolution. Our study aims to propose and validate a novel prognostic lobe-based transformer (LobTe) model capable of capturing the complexity and spatial variability of emphysema progression. This model predicts the evolution of emphysema based on %LAA-950 measurements, thereby enhancing our understanding of Chronic Obstructive Pulmonary Disease (COPD). LobTe is specifically tailored to address the spatial heterogeneity in lung destruction via a transformer encoder using lobe embedding fingerprints to maintain global attention according to lobes’ positions. We trained and tested our model using data from 4,612 smokers, both with and without COPD, across all GOLD stages, who had complete baseline and 5-year follow-up data. Our findings from 1,830 COPDGene participants used for testing demonstrate the model’s effectiveness in predicting lung density evolution based on %LAA-950, achieving a Root Mean Squared Error (RMSE) of 2.957%, a correlation coefficient ($\rho$) of 0.643 and a coefficient of determination ($R^2$) of 0.36. The model’s capability to predict changes in lung density over five years from baseline CT scans highlights its potential in the early identification of patients at risk of emphysema progression. Our results suggest that image embeddings derived from baseline CT scans effectively forecast emphysema progression by quantifying lung tissue loss.