• Guozhong Chen, Mengjie Lu, Zhao Shi, Shuang Xia, Yuan Ren, Zhen Liu, Xiuxian Liu, Zhiyong Li, Li Mao, Xiu Li Li, Bo Zhang, Long Jiang Zhang, and Guang Ming Lu.
• Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
• Eur Radiol. 2020 Sep 1; 30 (9): 5170-5182.

ObjectivesTo build models based on conventional logistic regression (LR) and machine learning (ML) algorithms combining clinical, morphological, and hemodynamic information to predict individual rupture status of unruptured intracranial aneurysms (UIAs), afterwards tested in internal and external validation datasets.MethodsPatients with intracranial aneurysms diagnosed by computed tomography angiography and confirmed by invasive cerebral angiograph or clipping surgery were included. The prediction models were developed based on clinical, aneurysm morphological, and hemodynamic parameters by conventional LR and ML methods.ResultsThe training, internal validation, and external validation cohorts were composed of 807 patients, 200 patients, and 108 patients, respectively. The area under curves (AUCs) of conventional LR models 1 (clinical), 2 (clinical and aneurysm morphological), and 3 (clinical, aneurysm morphological and hemodynamic characteristics) were 0.608, 0.765, and 0.886, respectively (all p < 0.05). The AUCs of ML models using random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM) were 0.871, 0.851, and 0.863, respectively. There were no difference among AUCs of conventional LR, RF, and SVM (all p > 0.05/6), while the AUC of MLP was lower than that of conventional LR (p = 0.0055).ConclusionHemodynamic parameters play an important role in the prediction performance of the models. ML methods cannot outperform conventional LR in prediction models for rupture status of UIAs integrating clinical, aneurysm morphological, and hemodynamic parameters.Key Points• The addition of hemodynamic parameters can improve prediction performance for rupture status of unruptured intracranial aneurysms. • Machine learning algorithms cannot outperform conventional logistic regression in prediction models for rupture status integrating clinical, aneurysm morphological, and hemodynamic parameters. • Models integrating clinical, aneurysm morphological, and hemodynamic parameters may help choose the optimal management.

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