Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke.

Journal: BMC medical imaging

Published Date: Jul 19, 2024

Abstract

OBJECTIVE: To establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular treatment.

Authors

Tao Sun

Janssen Research & Development, LLC, Raritan, NJ, USA.
Hai-Yun Yu

First Clinical Medical College, Gannan Medical University, Ganzhou, Jiangxi, China.
Chun-Hua Zhan

Department of Medical Ultrasonics, The Third Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China.
Han-Long Guo

First Clinical Medical College, Gannan Medical University, Ganzhou, Jiangxi, China.
Mu-Yun Luo

Department of Neurosurgery, The First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China. lmy771230@163.com.

Keywords

Aged Endovascular Procedures Female Humans Ischemic Stroke Machine Learning Male Medical Futility Middle Aged Nomograms Prognosis Radiomics Retrospective Studies Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (39030494)

Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke.

Abstract

Authors

Keywords

External Resources

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