PeerJ · 2025
Geometrical determinants of cerebral artery fenestration for cerebral infarction.
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Mei Yuqian, Chen Xiaoqin, Zhang Yao, Wang Yanling, Wu Bo, Hu Mingcheng, Bao Quan
Аннотация
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 φ 1 and confluence angle φ 2 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 φ 2 (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.
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 φ 1 and confluence angle φ 2 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 φ 2 (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.