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Updated: Jul 19, 2025
Endovascular Perforation Model for Subarachnoid Hemorrhage Combined with Magnetic Resonance Imaging MRI
Published on: December 16, 2021
Machine learning prediction model for the rupture status of middle cerebral artery aneurysm in patients with
Mengqi Lin1, Nengzhi Xia1, Ru Lin1
1Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Background:
Hypertension is a common comorbidity in patients with unruptured intracranial aneurysms and is closely associated with the rupture of aneurysms. However, only a few studies have focused on the rupture risk of aneurysms comorbid with hypertension. This retrospective study aimed to construct prediction models for the rupture of middle cerebral artery (MCA) aneurysm associated with hypertension using machine learning (ML) algorithms, and the constructed models were externally validated with multicenter datasets.
Methods:
We included 322 MCA aneurysm patients comorbid with hypertension who were being treated in four hospitals. All participants underwent computed tomography angiography (CTA), and aneurysm morphological features were measured. Clinical characteristics included sex, age, smoking, and hypertension history. Based on the clinical and morphological characteristics, the training datasets (n=277) were used to fit the ML algorithms to construct prediction models, which were externally validated with the testing datasets (n=45). The prediction performances of the models were assessed by receiver operating characteristic (ROC) curves.
Results:
The areas under the ROC curve (AUCs) of the k-nearest-neighbor (KNN), neural network (NNet), support vector machine (SVM) and logistic regression (LR) models in the training datasets were 0.83 [95% confidence interval (CI): 0.78-0.88], 0.87 (95% CI: 0.82-0.92), 0.91 (95% CI: 0.88-0.95), and 0.83 (95% CI: 0.77-0.88), respectively, and in the testing datasets were 0.74 (95% CI: 0.59-0.89), 0.82 (95% CI: 0.69-0.94), 0.73 (95% CI: 0.58-0.88), and 0.76 (95% CI: 0.61-0.90), respectively. The aspect ratio (AR) was ranked as the most important variable in the ML models except for NNet. Further analysis showed that the AR had good diagnostic performance, with AUC values of 0.75 in the training datasets and 0.77 in the testing datasets.
Conclusions:
The ML models performed reasonably accurately in predicting MCA aneurysm rupture comorbid with hypertension. AR was demonstrated as the leading predictor for the rupture of MCA aneurysm with hypertension.
