UCLA

Objective:

Lung tissue elasticity is an effective spatial representation for Chronic Obstructive Pulmonary Disease phenotypes and pathophysiology. We investigated a novel imaging biomarker based on the voxel-by-voxel distribution of lung tissue elasticity. Our approach combines imaging and biomechanical modeling to characterize tissue elasticity.

Methods:

We acquired 4DCT images for 13 lung cancer patients with known COPD diagnoses based on GOLD 2017 criteria. Deformation vector fields (DVFs) from the deformable registration of end-inhalation and end-exhalation breathing phases were taken to be the ground-truth. A linear elastic biomechanical model was assembled from end-exhalation datasets with a density-guided initial elasticity distribution. The elasticity estimation was formulated as an iterative process, where the elasticity was optimized based on its ability to reconstruct the ground-truth. An imaging biomarker (denoted YM_1-3) derived from the optimized elasticity distribution, was compared with the current gold standard, RA₉₅₀ using confusion matrix and area under the receiver operating characteristic (AUROC) curve analysis.

Results:

The estimated elasticity had 90 % accuracy when representing the ground-truth DVFs. The YM_1-3 biomarker had higher diagnostic accuracy (86% vs 71 %), higher sensitivity (0.875 vs 0.5), and a higher AUROC curve (0.917 vs 0.875) as compared to RA₉₅₀. Along with acting as an effective spatial indicator of lung pathophysiology, the YM_1-3 biomarker also proved to be a better indicator for diagnostic purposes than RA₉₅₀.

Conclusions:

Overall, the results suggest that, as a biomarker, lung tissue elasticity will lead to new end points for clinical trials and new targeted treatment for COPD subgroups.

Advances in knowledge:

The derivation of elasticity information directly from 4DCT imaging data is a novel method for performing lung elastography. The work demonstrates the need for a mechanics-based biomarker for representing lung pathophysiology.