Fine-Grained Classification Reveals Angiopathological Heterogeneity of Port Wine Stains Using OCT and OCTA Features.

Accurate classification of port wine stains (PWS, vascular malformations present at birth), is critical for subsequent treatment planning. However, the current method of classifying PWS based on the external skin appearance rarely reflects the underlying angiopathological heterogeneity of PWS lesions, resulting in inconsistent outcomes with the common vascular-targeted photodynamic therapy (V-PDT) treatments. Conversely, optical coherence tomography angiography (OCTA) is an ideal tool for visualizing the vascular malformations of PWS. Previous studies have shown no significant correlation between OCTA quantitative metrics and the PWS subtypes determined by the current classification approach. In this study, we propose a novel fine-grained classification method for PWS that integrates OCT and OCTA imaging. Utilizing a machine learning-based approach, we subdivided PWS into five distinct subtypes by unearthing the heterogeneity of hypodermic histopathology and vessel structures. Six quantitative metrics, encompassing vascular morphology and depth information of PWS lesions, were designed and statistically analyzed to evaluate angiopathological differences among the subtypes. Our classification reveals significant distinctions across all metrics compared to conventional skin appearance-based subtypes, demonstrating its ability to accurately capture angiopathological heterogeneity. This research marks the first attempt to classify PWS based on angiopathology, potentially guiding more effective subtyping and treatment strategies for PWS.