Assistant Professor The University of Chicago Chicago, Illinois, United States
Introduction:: Maximum aortic diameter (l) has been the mainstay metric for aortic disease management and surgical intervention for decades. While this size metric has been effective at separating patient populations into groupings associated with required intervention and monitoring for eventual intervention respectively, size alone ignores the role of complex geometric shape and how it may be further indicative of disease state. Analyses of aortic shape in addition to size have been discussed at length in literature, however the representations and methods to convey shape are siloed and often times highly subjective.
Materials and Methods:: Machine learning methodologies offer a means to explore high dimensional feature spaces and can identify a sparse set of metrics which result in a division of patient responses parametrized by physical and interpretable variables. Additionally, by introducing a modeling workflow which contains engineered parameters designed to describe aortic shape, in addition to traditional size metrics, the exploration of shape dependence on intervention outcomes becomes more comprehensive across size-shape parameter spaces.
Results, Conclusions, and Discussions:: The engineered parameters presented primarily incorporate estimations of integrated Gaussian surface curvature (δK) into features that contain both the magnitude and spatial variation in shape of the aortas. The models discussed accurately classify over one hundred and fifty thoracic endovascular aortic repair (TEVAR) patient outcomes as a function of size and shape factors [l, f(δK)] and suggest the underlying dependence on pathology shape on the success of the intervention outcome.
