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Objectives: Predicting the risk of rupture of small intracranial aneurysms remains challenging. The irregular pulsation of aneurysms detected by four-dimensional CT angiography (4D-CTA) could be an imaging marker of aneurysm vulnerability. We aimed to investigate the association of irregular pulsation with small aneurysm rupture. Materials and Methods: This was a prospective study on intracranial aneurysms detected by 4D-CTA from October 2017 to January 2020. A total of 242 consecutive patients with 316 aneurysms were enrolled. Irregular pulsation was defined as a temporary focal protuberance on more than 3 consecutive frames of the 20 phases in the RR interval. Small aneurysms were defined as those <7 mm. Univariate and multivariate analyses were performed to determine the independent predictors of small aneurysm rupture. Results: A total of 169 patients with 217 small intracranial aneurysms were included. Fourteen (6.5%) of the aneurysms had ruptured, and 77 (35.5%) had irregular pulsation. There were no significant differences in age, sex, hypertension, smoking, diabetes, drinking, or hyperlipidemia between the ruptured and unruptured aneurysm groups. The univariate analysis showed that smaller vessel size (p = 0.008), larger size ratio (p = 0.003), larger aspect ratio (p = 0.006), larger flow angle (p = 0.001), large vessel angle (p = 0.004), middle cerebral artery aneurysms (p = 0.046), anterior cerebral artery/posterior communicating artery/posterior circulation aneurysm (p = 0.006), irregular aneurysm (p = 0.001), and t presence of irregular pulsation (p = 0.001) were associated with small aneurysm rupture. The multivariate analysis showed that the presence of irregular pulsation (p = 0.003), anterior cerebral artery/posterior communicating artery/posterior circulation aneurysms (p = 0.014), and larger flow angle (p = 0.006) was independently associated with aneurysm rupture. Multivariate analysis of predictors of the irregular pulsation of small aneurysms showed that the aneurysm rupture (p = 0.022), irregular aneurysm (p < 0.001), and large size ratio (p = 0.005) were independently associated with the presence of irregular pulsation. Conclusions: The ruptured small aneurysms more often had irregular pulsation. The irregular pulsation was independently associated with aneurysm rupture and may help evaluate the risk of rupture of small intracranial aneurysms. [ABSTRACT FROM AUTHOR]

Background: Aneurysmal subarachnoid hemorrhage (SAH) is a devastating disease. Anterior communicating artery (ACoA) aneurysm is the most frequent location of intracranial aneurysms. The purpose of this study is to predict the clinical outcome at discharge after rupture of ACoA aneurysms using the random forest machine learning technique. Methods: A total of 607 patients with ruptured ACoA aneurysms were included in this study between December 2007 and January 2016. In addition to basic clinical variables, 12 aneurysm morphologic parameters were evaluated. A multivariate logistic regression analysis was performed to determine the independent predictors of poor outcome. Of the 607 patients, 485 patients were randomly selected for training and the remaining for internal testing. The random forest model was developed using the training data set. An additional 202 patients from February 2016 to December 2017 were collected for externally validating the model. The prediction performance of the random forest model was compared with two radiologists. Results: Patients' age (odds ratio [OR] = 1.04), ventilated breathing status (OR = 4.23), World Federation of Neurosurgical Societies (WFNS) grade (OR = 2.13), and Fisher grade (OR = 1.50) are significantly associated with poor outcome. None of the investigated morphological parameters of ACoA aneurysm is an independent predictor of poor outcome. The developed random forest model achieves sensitivities of 78.3% for internal test and 73.8% for external test. The areas under receiver operating characteristic (ROC) curve of the random forest model were 0.90 for the internal test and 0.84 for the external test. Both sensitivities and areas under ROC curves of our model are superior to those of two raters in both internal and external tests. Conclusions: The random forest model presents good performance in predicting the outcome after rupture of ACoA aneurysms, which may aid in clinical decision making. [ABSTRACT FROM AUTHOR]