Geometrical determinants of cerebral artery fenestration for cerebral infarction - Yuqian Mei; Xiaoqin Chen; Yao Zhang; Yanling Wang; Bo Wu; Mingcheng Hu; Quan Bao (PeerJ)
Purpose Few data are available on the causality of cerebral artery fenestration (CAF) triggering cerebral infarction (CI) and this study aims to identify representative morphological features that can indicate risks. Methods A cohort comprising 89 patients diagnosed with CAF were enrolled from a total of 9,986 cranial MR angiographies. These patients were categorized into Infarction Group (n = 55) and Control Group (n = 34) according to infarction events. These two groups are divided into two subgroups depending on fenestration location (basilar artery or other cerebravascular location), respectively, i.e., BA Infarction Group (n = 37), BA Control Group (n = 23), Non_BA Infarction Group (n = 18), Non_BA Control Group (n = 11). This study firstly defined 12 indices to quantify the morphological characteristics of fenestration per se and its connecting arteries. The data were evaluated using either the independent sample t-test or the Mann–Whitney U test. Conducting univariate and multivariate logistic regression analyses to ascertain potential independent predictors of CI. Results The initiation angle φ1 and confluence angle φ2 at the fenestration in the Infarction Group are both smaller compared to the Control Group, but only the Infarction Group and BA Infarction Group have significant difference (p < 0.05). The maximum left fenestration axis (fAL) and the left tortuosity index (TIL) were greater in the Infarction Group for CAFs than those in the Control Group (p < 0.05). In contrast, the maximum right fenestration axis (fAR) and the right tortuosity index (TIR) were smaller than those in Control Group (p < 0.05). The logistic regression analysis revealed that φ2 (AUC = 0.68, p = 0.02), fAL (AUC = 0.72, p < 0.01), and fAR (AUC = 0.70, p < 0.01) serve as independent risk factors influencing the occurrence of CI. The regression predictive model achieved an AUC of 0.83, enabling accurate classification of 77.5% of cases, indicating a robust predictive performance of the model. Conclusion Morphological results demonstrated a left-leaning type of fenestration with more narrow fenestration terminals indicating a higher risk of CI occurrence. Furthermore, the regression predictive model established in this study demonstrates a good predictive performance, enabling early prediction of CI occurrence in fenestrated patients and facilitating early diagnosis of CI.