論文ID: oa.2018-0095
Objective: Thin-walled regions of cerebral aneurysms are areas of risk for rupture, particularly during surgical procedures. Prediction of thin-walled regions before surgery can lead to safer treatment, avoiding interactions with thin-walled regions. It is considered that blood flow influences aneurysm wall thickness reduction. The objective of this study was to establish a parameter to accurately identify thin-walled regions using computational fluid dynamics (CFD) analysis.
Methods: The surgical field was photographed during craniotomy in 50 patients with unruptured middle cerebral artery aneurysms and red regions of the aneurysm wall were compared with the color of the parent vessel and defined as a thin-walled region. CFD analysis was performed and the distribution map of wall shear stress divergence (WSSD*) was compared to the surgical image of the cerebral aneurysms.
Results: The WSSDmax region and thin-walled region were coinciding in 41 (82.0%) of the 50 patients. There was a significant difference (P = 0.00022) between the patients with and without coincidence between the WSSDmax and thin-walled regions, and the threshold, sensitivity, specificity, and area under the curve (AUC) on receiver operating characteristic (ROC) analysis of WSSDmax were 0.230, 0.900, 0.875, and 0.883, respectively.
Conclusion: High-WSSD regions tended to be coinciding with thin-walled regions, suggesting that WSSDmax is useful to identify thin-walled regions of cerebral aneurysms.
