Predicting Functional Outcomes of Endovascular Thrombectomy in Acute Ischemic Stroke Using a Clinical-Radiomics Nomogram
Year: 2024
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Endovascular thrombectomy (EVT) is recommended for acute ischemic stroke due to large-vessel occlusion. However, approximately 50% of patients still experience poor outcomes after the procedure. This study aimed to assess whether a nomogram model that integrates computed tomography angiography radiomics features and clinical variables can predict EVT outcomes in patients with acute ischemic stroke. METHODS: A total of 159 patients undergoing EVT were randomly divided into training and validation groups at a 7:3 ratio. A modified Rankin Scale score ≤ 2 at 90 days indicated a favorable outcome. We used univariate and multivariate logistic regression to identify analytic and radiomics predictors and create predictive models. Model performance was evaluated using the area under the curve, Hosmer-Lemeshow test, and decision curve analysis for discrimination, calibration, and clinical utility. RESULTS: A 19-feature radiomics signature reached an area under the curve of 0.79. Combining it with age, baseline National Institutes of Health Stroke Scale score, diabetes, and statin use increased the area under the curve of the clinical-radiomics nomogram to 0.85. Both decision curve and calibration curve analyses showed strong performance. CONCLUSIONS: Combining a radiomics nomogram with clinical predictors could effectively forecast EVT outcomes in patients with acute anterior circulation large vessel occlusion stroke.
Source: World Neurosurgery
Authors: Yuan Zhang, Tingting Zheng, Hao Wang, Jie Zhu, Shaofeng Duan, Bin Song
Institution: Fudan University
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,920
Award: G8658246070
OpenAlex ID: W4403965846