ArticleStroke (Hoboken, N.J.)2023
Ability of Radiomics Versus Humans in Predicting First-Pass Effect After Endovascular Treatment in the ESCAPE-NA1 Trial.
Article in Stroke (Hoboken, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.AJNR. American journal of neuroradiology · 2024Article
- Review
- Can Aneurysm Wall Radiomics Help Predict Rupture Risk?Stroke (Hoboken, N.J.) · 2023Article
- Impact of vessel tortuosity and radiological thrombus characteristics on the choice of first-line thrombectomy strategy: Results from the ESCAPE-NA1 trial.European stroke journal · 2023Article
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Authors and funding
24 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: First-pass effect (FPE), that is, achieving reperfusion with a single thrombectomy device pass, is associated with better clinical outcomes in patients with acute stroke. FPE is therefore increasingly used as a marker of device and procedural efficacy. We aimed to evaluate the ability of thrombus-based radiomics models to predict FPE in patients undergoing endovascular thrombectomy and compare performance with experts and nonradiomics thrombus characteristics. Methods: Patients with thin-slice noncontrast computed tomography and computed tomography angiography from the ESCAPE-NA1 (Efficacy and Safety of Nerinetide for the Treatment of Acute Ischemic Stroke) trial were included. Thrombi were manually segmented on all images. Data were randomly split into a derivation set that included a training and a validation subset and an independent test set. Radiomics features were extracted from the derivation set. The machine learning models were compared with 3 expert stroke physicians in predicting FPE in the test set using area under the receiver operating characteristic curves. Results: Thin-slice images of 554 patients were divided into a derivation set (training [n=388] and validation [n=55]) and a test set (n=111). A radiomics model using the combination of noncontrast computed tomography, computed tomography angiography, and noncontrast computed tomography-computed tomography angiography difference achieved the highest performance (area under the curve, 0.74 [95% CI, 0.64-0.84]) for prediction of FPE. This was higher than the mean area under the curve of the 3 experts (0.62 [95% CI, 0.53-0.71], Conclusion: A radiomics-based machine learning model of thrombus characteristics from noncontrast computed tomography and computed tomography angiography performs better than experts and traditional nonradiomics imaging features in predicting FPE in patients with acute stroke treated with endovascular thrombectomy.
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