A clinical–radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study

A clinical–radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study

Key points Noncontrast computed tomography (NCCT) is valuable for predicting hemorrhagic transformation (HT) after intravenous thrombolysis (IVT) treatment. Machine learning is vital for predicting HT. NCCT radiomics integrated with clinical factors could facilitate predicting HT.

Bibliographic Details
Main Authors: Huanhuan Ren, Haojie Song, Jingjie Wang, Hua Xiong, Bangyuan Long, Meilin Gong, Jiayang Liu, Zhanping He, Li Liu, Xili Jiang, Lifeng Li, Hanjian Li, Shaoguo Cui, Yongmei Li
Format: Article
Language:English
Published: SpringerOpen 2023-03-01
Series:Insights into Imaging
Subjects:
Acute ischemic stroke
Hemorrhagic transformation
Noncontrast computed tomography
Radiomics
Machine learning
Online Access:https://doi.org/10.1186/s13244-023-01399-5

Internet

https://doi.org/10.1186/s13244-023-01399-5

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