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.

Journal: European radiology

Published Date: Sep 1, 2020

Abstract

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.

Authors

Guozhong Chen

Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Mengjie Lu

Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Zhao Shi

Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Shuang Xia

Department of Radiology, Tianjin First Central Hospital, Tianjin, China.
Yuan Ren
Zhen Liu

School of Pharmacy, Fudan University, PR China; Analytical Service Unit, WuXi AppTec (Shanghai) Co., Ltd, Shanghai, 200131, PR China.
Xiuxian Liu

School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China.
Zhiyong Li

School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China.
Li Mao

Deepwise AI Lab, Deepwise Inc, No.8 Haidian avenue, Sinosteel International Plaza, Beijing, 100080, China.
Xiu Li Li

Deepwise AI Lab, Beijing, 100089, China.
Bo Zhang

Department of Clinical Pharmacology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China.
Long Jiang Zhang
Guang Ming Lu

Keywords

Adolescent Adult Aged Aged, 80 and over Aneurysm, Ruptured Area Under Curve Cerebral Angiography China Clinical Decision Rules Computed Tomography Angiography Computer Simulation Female Hemodynamics Humans Intracranial Aneurysm Logistic Models Machine Learning Male Middle Aged Neural Networks, Computer Retrospective Studies Support Vector Machine Tomography, X-Ray Computed Young Adult

External Resources

View on PubMed Access via DOI PubMed (32350658)

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.

Abstract

Authors

Keywords

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