Development and validation of machine learning prediction model based on computed tomography angiography–derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study

• Published in: European radiology Vol. 30; no. 9; pp. 5170 - 5182
• Main Authors: Chen, Guozhong, Lu, Mengjie, Shi, Zhao, Xia, Shuang, Ren, Yuan, Liu, Zhen, Liu, Xiuxian, Li, Zhiyong, Mao, Li, Li, Xiu Li, Zhang, Bo, Zhang, Long Jiang, Lu, Guang Ming
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020; Springer Nature B.V
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Aneurysm; Aneurysm, Ruptured - diagnostic imaging; Aneurysm, Ruptured - epidemiology; Aneurysms; Angiography; Area Under Curve; Cerebral Angiography - methods; China; Clinical Decision Rules; Computed Tomography; Computed Tomography Angiography - methods; Computer Simulation; Diagnostic Radiology; Female; Hemodynamics; Humans; Imaging; Internal Medicine; Interventional Radiology; Intracranial Aneurysm - diagnostic imaging; Learning algorithms; Logistic Models; Machine Learning; Male; Mathematical models; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Morphology; Multilayer perceptrons; Neural Networks, Computer; Neuroradiology; Parameters; Patients; Performance prediction; Physical characteristics; Prediction models; Radiology; Regression analysis; Retrospective Studies; Rupture; Support Vector Machine; Support vector machines; Surgery; Tomography, X-Ray Computed; Ultrasound; Young Adult; China; Angiography; Tomography, X-ray computed; Intracranial aneurysm; Machine learning; Rupture
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-020-06886-7

Objectives To 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. Methods Patients 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. Results The 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). Conclusion Hemodynamic 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.