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Background: Determining the rupture risk of unruptured intracranial aneurysm is crucial for treatment strategy. The purpose of this study was to predict the rupture risk of middle cerebral artery (MCA) aneurysms using a machine learning technique. Methods: We retrospectively reviewed 403 MCA aneurysms and randomly partitioned them into the training and testing datasets with a ratio of 8:2. A generalized linear model with logit link was developed using training dataset to predict the aneurysm rupture risk based on the clinical variables and morphological features manually measured from computed tomography angiography. To facilitate the clinical application, we further constructed an easy-to-use nomogram based on the developed model. Results: Ruptured MCA aneurysm had larger aneurysm size, aneurysm height, perpendicular height, aspect ratio, size ratio, bottleneck factor, and height-width ratio. Presence of a daughter-sac was more common in ruptured than in unruptured MCA aneurysms. Six features, including aneurysm multiplicity, lobulations, size ratio, bottleneck factor, height-width ratio, and aneurysm angle, were adopted in the model after feature selection. The model achieved a relatively good performance with areas under the receiver operating characteristic curves of 0.77 in the training dataset and 0.76 in the testing dataset. The nomogram provided a visual interpretation of our model, and the rupture risk probability of MCA aneurysms can be directly read from it. Conclusion: Our model can be used to predict the rupture risk of MCA aneurysm. [ABSTRACT FROM AUTHOR]

Objective: Radiomics and morphological features were associated with aneurysms rupture. However, the multicentral study of their predictive power for specific-located aneurysms rupture is rare. We aimed to determine robust radiomics features related to middle cerebral artery (MCA) aneurysms rupture and evaluate the additional value of combining morphological and radiomics features in the classification of ruptured MCA aneurysms. Methods: A total of 632 patients with 668 MCA aneurysms (423 ruptured aneurysms) from five hospitals were included. Radiomics and morphological features of aneurysms were extracted on computed tomography angiography images. The model was developed using a training dataset (407 patients) and validated with the internal (152 patients) and external validation (73 patients) datasets. The support vector machine method was applied for model construction. Optimal radiomics, morphological, and clinical features were used to develop the radiomics model (R-model), morphological model (M-model), radiomics-morphological model (RM-model), clinical-morphological model (CM-model), and clinical-radiomics-morphological model (CRM-model), respectively. A comprehensive nomogram integrating clinical, morphological, and radiomics predictors was generated. Results: We found seven radiomics features and four morphological predictors of MCA aneurysms rupture. The R-model obtained an area under the receiver operating curve (AUC) of 0.822 (95% CI, 0.776, 0.867), 0.817 (95% CI, 0.744, 0.890), and 0.691 (95% CI, 0.567, 0.816) in the training, temporal validation, and external validation datasets, respectively. The RM-model showed an AUC of 0.848 (95% CI, 0.810, 0.885), 0.865 (95% CI, 0.807, 0.924), and 0.721 (95% CI, 0.601, 0.841) in the three datasets. The CRM-model obtained an AUC of 0.856 (95% CI, 0.820, 0.892), 0.882 (95% CI, 0.828, 0.936), and 0.738 (95% CI, 0.618, 0.857) in the three datasets. The CRM-model and RM-model outperformed the CM-model and M-model in the internal datasets (p < 0.05), respectively. But these differences were not statistically significant in the external dataset. Decision curve analysis indicated that the CRM-model obtained the highest net benefit for most of the threshold probabilities. Conclusion: Robust radiomics features were determined related to MCA aneurysm rupture. The RM-model exhibited good ability in classifying ruptured MCA aneurysms. Integrating radiomics features into conventional models might provide additional value in ruptured MCA aneurysms classification. [ABSTRACT FROM AUTHOR]

Mirror intracranial aneurysms with different rupture status is a useful model to investigate features associated with aneurysm rupture. Morphological and hemodynamic analyses of ruptured middle cerebral artery (MCA) mirror aneurysms are rarely reported. The purpose of this study was to determine the morphological or hemodynamic characteristics associated with ruptured MCA mirror aneurysms. We performed a retrospective analysis of consecutive 317 patients with MCA aneurysms. Ruptured MCA mirror aneurysms (1 ruptured and mirror unruptured aneurysm) were included. In the matched pairs of ruptured and unruptured mirror aneurysms, 13 morphological parameters were measured using 3-dimensional computed tomography angiography and 6 hemodynamic parameters were evaluated using high-resolution computational fluid dynamic simulations. The association of morphological and hemodynamic characteristics with the rupture of MCA mirror aneurysms was determined. A total of 20 (6.31%) patients with 40 MCA mirror aneurysms were included in this study. There were significant differences in morphological and hemodynamic parameters between the ruptured and unruptured mirror aneurysms. Irregular aneurysms were 3 times more common in the ruptured aneurysms than in the unruptured aneurysms. A larger aneurysm (P = 0.025), a higher aneurysm (P = 0.020), a larger size ratio (P = 0.009), a higher bottleneck ratio (P = 0.033), an irregular aneurysm (P = 0.022), a higher maximum intra-aneurysmal wall shear stress (WSS; P = 0.020), and a lower normalized average WSS (P = 0.008) were associated with MCA mirror aneurysm rupture. Larger aneurysms, a larger size ratio, irregular aneurysms, a lower spatial average WSS, and a higher maximum WSS may contribute to evaluating the risk of rupture of MCA aneurysms independent of patient characteristics. [ABSTRACT FROM AUTHOR]