CT Image-Derived Fluctuations in Total Curvature Characterize Aortic Disease Evolution and Clinical Outcomes

Abstract

AbstractClinical imaging modalities are a mainstay of modern disease management, but the full utilization of imaging-based treatment algorithms remains elusive. Aortic disease management has been defined by anatomic scalars, even though aortic disease progression initiates complex shape changes which are difficult to quantify. We present an imaging-based geometric descriptor, inspired by fundamental ideas from topology and soft-matter physics, that captures dynamic shape evolution. The fluctuation in total curvature (δK) captures heterogeneous morphologic evolution by characterizing local shape changes. We discover that aortic morphology evolves with a power-law like behavior with rapidly increasingδKforming the hallmark of aortic disease. Classification accuracy for predicting aortic disease state (normal, mildly diseased with successful surgery, and severely diseased with failed surgical outcomes) approaches92.8 ± 1.7%. The analysis ofδKcan be applied on any 3D geometric structure and thus may be extended to other clinical problems of characterizing disease through captured anatomic changes.