Abstract
Diabetic Macular Edema (DME) and macular edema secondary to retinal occlusion (RVO) are the two most common retinal vascular causes of visual impairment and leading cause of worldwide vision loss. The blood-retinal barrier is the key barrier for maintaining fluid balance within the retinal tissue. Vascular Endothelial Growth Factor (VEGF) has a significant role in the permeability of the blood-retinal barrier, which also leads to appearance of leakage foci. Intravitreal anti-VEGF therapy is the current gold standard treatment and has been demonstrated to improve macular thickening, improve vision acuity and reduce vascular leakage. However, treatment response and required dosing interval can vary widely across patients. Given the role of the blood-retinal barrier and vascular leakage in the pathogenesis of these disorders, the goal of this study was to present and evaluate new computer extracted features relating to morphology, spatial architecture and tortuosity of vessels and leakages from baseline ultra-widefield fluorescein angiography (UWFA) images. Specifically, we sought to evaluate the role of these computer extracted features from baseline UWFA images. Notably, these UWFA images were obtained from IRB-approved PERMEATE clinical trial [1], [2] to distinguish eyes tolerating extended dosing intervals (n = 16) who are referred to as non-rebounders and those who require more frequent dosing (n = 12) and are called rebounders based on visual acuity loss with extended dosing challenges. A total of 64 features encapsulating different morphological and geometrical attributes of leakage patches including the anatomical (shape, size, density, area, minor and major axis, orientation, area, extent ratio, perimeter, radii) and geometrical characteristics (the proximity of each leakage foci to main vessels, to other leakage foci and to optical disc) as well as 54 tortuosity features (tortuosity of whole vessel network, local tortuosity of vessels in the vicinity of leakage foci) were extracted. The most significant and predictive biomarkers related to treatment response were proximity of leakage nodes to major and minor eye vessels as well as local vasculature tortuosity in the vicinity of the leakages. The imaging features were then used in conjunction with a Linear Discriminant Analysis (LDA) classifier to distinguish rebounders from non-rebounders. The 3-fold cross-validated Area Under Curve (AUC) was found to be 0.82 for the morphological based features and 0.85 for the tortuosity based features. Our findings suggest higher variation in leakage node proximity to retinal vessels in eyes tolerating extended interval dosing. In contrast, eyes with increased local vascular tortuosity demonstrated less tolerance of increased dosing interval. Moreover, a class activation map generated by a deep learning model identified regions that corresponded to regions of leakages proximal to the vessels, providing confirmation of the validity of predictive image features extracted from these regions in this study.