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A3903 - Lung Elasticity Distribution as an Imaging Bio Marker for COPD Lung Physiology
Author Block: A. Santhanam, K. Hasse, D. Low; Department of Radiation Oncology, University of California, Los angeles, Los Angeles, CA, United States.
Background: Lung tissue elasticity is an effective spatial representation for COPD phenotypes and pathophysiology. As a biomarker, lung tissue elasticity will lead to new end points for clinical trials and new targeted treatment for COPD subgroups. Methods: 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. 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. Our elasticity estimation was formulated as an iterative process, where the elasticity was optimized based on it’s ability to reconstruct the ground-truth. The imaging biomarker, YM1-3, derived from the optimized elasticity distribution was compared with RA950 using confusion matrix and area under the ROC curve analysis. Findings: The estimated elasticity had 90% accuracy when representing the ground-truth DVFs. The YM1-3biomarker had higher diagnostic accuracy (86% versus 71%), higher sensitivity (0.875 versus 0.5), and a higher AUROC curve (0.917 versus 0.875) than compared to RA950. Interpretation: While the lung tissue elasticity is expected to be an effective spatial indicator of lung pathophysiology, the YM1-3 biomarker may be a better indicator for diagnostic purposes than RA950.