Deep learning-based radiomics of computed tomography angiography to predict adverse events after initial endovascular repair for acute uncomplicated Stanford type B aortic dissection.

Journal: European journal of radiology

PMID: 38648727

Abstract

PURPOSE: This study aimed to construct a predictive model integrating deep learning-derived radiomic features from computed tomography angiography (CTA) and clinical biomarkers to forecast postoperative adverse events (AEs) in patients with acute uncomplicated Stanford type B aortic dissection (uTBAD) undergoing initial thoracic endovascular aortic repair (TEVAR).

Authors

Xuefang Lu

Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
Wei Gong

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 430079, China.
Wenbing Yang

Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
Zhoufeng Peng

Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
Chao Zheng

School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515 People's Republic of China.
Yunfei Zha

Department of Radiology, Department of Infection Prevention and Control, Renmin Hospital, Wuhan University, Wuhan, China.

Keywords

Acute Disease Aged Aortic Aneurysm, Thoracic Aortic Dissection Computed Tomography Angiography Deep Learning Endovascular Procedures Female Humans Male Middle Aged Postoperative Complications Predictive Value of Tests Radiomics Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (38648727)

Deep learning-based radiomics of computed tomography angiography to predict adverse events after initial endovascular repair for acute uncomplicated Stanford type B aortic dissection.

Abstract

Authors

Keywords

External Resources

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