The value of radiomics-based hyperdense middle cerebral artery sign in predicting hemorrhagic transformation in acute ischemic stroke patients undergoing endovascular treatment
ObjectiveTo establish and validate a model based on hyperdense middle cerebral artery sign (HMCAS) radiomics features for predicting hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) after endovascular treatment (EVT).MethodsPatients with AIS who presented with HMCAS on no...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Neurology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2024.1492089/full |
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| Summary: | ObjectiveTo establish and validate a model based on hyperdense middle cerebral artery sign (HMCAS) radiomics features for predicting hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) after endovascular treatment (EVT).MethodsPatients with AIS who presented with HMCAS on non-contrast computed tomography (NCCT) at admission and underwent EVT at three comprehensive hospitals between June 2020 and January 2024 were recruited for this retrospective study. A radiomics model was constructed using the HMCAS radiomics features most strongly associated with HT. In addition, clinical and radiological independent factors associated with HT were identified. Subsequently, a combined model incorporating radiomics features and independent risk factors was developed via multivariate logistic regression and presented as a nomogram. The models were evaluated via receiver operating characteristic curve, calibration curve, and decision curve analysis.ResultsOf the 118 patients, 71 (60.17%) developed HT. The area under the curve (AUC) of the radiomics model was 0.873 (95% CI 0.797–0.935) in the training cohort and 0.851 (95%CI 0.721–0.942) in the test cohort. The Alberta Stroke Program Early CT score (ASPECTS) was the only independent predictor among 24 clinical and 4 radiological variables. The combined model further improved the predictive performance, with an AUC of 0.911 (95%CI 0.850–0.960) in the training cohort and 0.877 (95%CI 0.753–0.960) in the test cohort. Decision curve analysis demonstrated that the combined model had greater clinical utility for predicting HT.ConclusionHMCAS-based radiomics is expected to be a reliable tool for predicting HT risk stratification in AIS patients after EVT. |
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| ISSN: | 1664-2295 |