Improving rupture status prediction for intracranial aneurysms using wall shear stress informatics.

Background: Wall shear stress (WSS) plays a crucial role in the natural history of intracranial aneurysms (IA). However, spatial variations among WSS have rarely been utilized to correlate with IAs' natural history. This study aims to establish the feasibility of using spatial patterns of WSS data to predict IAs' rupture status (i.e., ruptured versus unruptured).

Methods: "Patient-specific" computational fluid dynamics (CFD) simulations were performed for 112 IAs; each IA's rupture status was known from medical records. Recall that CFD-simulated hemodynamics data (wall shear stress and its derivatives) are located on unstructured meshes. Hence, we mapped WSS data from an unstructured grid onto a unit disk (i.e., a uniformly sampled polar coordinate system); data in a uniformly sampled polar system is equivalent to image data. Mapped WSS data (onto the unit disk) were readily available for Radiomics analysis to extract spatial patterns of WSS data. We named this innovative technology "WSS-informatics" (i.e., using informatics techniques to analyze WSS data); the usefulness of WSS-informatics was demonstrated during the predictive modeling of IAs' rupture status.

Results: None of the conventional WSS parameters correlated to IAs' rupture status. However, WSS-informatics metrics were discriminative (p-value < 0.05) to IAs' rupture status. Furthermore, predictive models with WSS-informatics features could significantly improve the prediction performance (area under the receiver operating characteristic curve [AUROC]: 0.78 vs. 0.85; p-value < 0.01).

Conclusion: The proposed innovations enabled the first study to use spatial patterns of WSS data to improve the predictive modeling of IAs' rupture status.