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
| العنوان: | 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 |
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| المؤلفون: | Zhen Liu, Long Jiang Zhang, Bo Zhang, Li Mao, Xiu Li Li, Shuang Xia, Zhao Shi, Yuan Ren, Mengjie Lu, Guo-Zhong Chen, Xiuxian Liu, Zhiyong Li, Guangming Lu |
| المصدر: | European Radiology. 30:5170-5182 |
| بيانات النشر: | Springer Science and Business Media LLC, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Male, Support Vector Machine, Computed Tomography Angiography, medicine.medical_treatment, Hemodynamics, Aneurysm, Ruptured, computer.software_genre, Logistic regression, 030218 nuclear medicine & medical imaging, Machine Learning, 0302 clinical medicine, Medicine, Computed tomography angiography, Neuroradiology, Aged, 80 and over, medicine.diagnostic_test, Interventional radiology, General Medicine, Middle Aged, Area Under Curve, 030220 oncology & carcinogenesis, Female, Radiology, Adult, China, medicine.medical_specialty, Adolescent, Machine learning, Young Adult, 03 medical and health sciences, Aneurysm, Clinical Decision Rules, Humans, Computer Simulation, Radiology, Nuclear Medicine and imaging, Aged, Retrospective Studies, business.industry, Intracranial Aneurysm, Clipping (medicine), medicine.disease, Cerebral Angiography, Logistic Models, Angiography, Neural Networks, Computer, Artificial intelligence, Tomography, X-Ray Computed, business, computer |
| الوصف: | 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.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.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).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.• 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. |
| تدمد: | 1432-1084 0938-7994 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1dbcb9900c85379bda6737ddeacd0a4Test https://doi.org/10.1007/s00330-020-06886-7Test |
| حقوق: | CLOSED |
| رقم الانضمام: | edsair.doi.dedup.....d1dbcb9900c85379bda6737ddeacd0a4 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14321084 09387994 |
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