PT  - JOURNAL ARTICLE
AU  - Li, Minda
AU  - Jiang, Jingxuan
AU  - Gu, Hongmei
AU  - Hu, Su
AU  - Wang, Jingli
AU  - Hu, Chunhong
TI  - CT-Based Intrathrombus and Perithrombus Radiomics for Prediction of Prognosis after Endovascular Thrombectomy: A Retrospective Study across 2 Centers
AID  - 10.3174/ajnr.A8522
DP  - 2025 Mar 20
TA  - American Journal of Neuroradiology
4099  - http://www.ajnr.org/content/early/2025/03/19/ajnr.A8522.short
4100  - http://www.ajnr.org/content/early/2025/03/19/ajnr.A8522.full
AB  - BACKGROUND AND PURPOSE: Complications from endovascular thrombectomy (EVT) can negatively affect clinical outcomes, making the development of a more precise and objective prediction model essential. This research aimed to assess the effectiveness of radiomics features derived from presurgical CT scans in predicting the prognosis post-EVT in patients with acute ischemic stroke.MATERIALS AND METHODS: This investigation included 336 patients with acute ischemic stroke from 2 medical centers from March 2018 to March 2024. The participants were split into a training cohort of 161 patients and a validation cohort of 175 patients. Patient outcomes were rated with the mRS: 0–2 for good, 3–6 for poor. A total of 428 radiomics features were derived from intrathrombus and perithrombus regions in noncontrast CT and CTA images. Feature selection was conducted using a least absolute shrinkage and selection operator regression model. The efficacy of 8 different supervised learning models was assessed using the area under the curve (AUC) of the receiver operating characteristic curve.RESULTS: Among all models tested in the validation cohort, the logistic regression algorithm for the combined model achieved the highest AUC (0.87; 95% CI, 0.81–0.92), outperforming other algorithms. The combined use of radiomics features from both the intrathrombus and perithrombus regions significantly enhanced diagnostic accuracy over models using features from a single region (0.81 versus 0.70, 0.77), highlighting the benefit of integrating data from both regions for improved prediction.CONCLUSIONS: The findings suggest that a combined radiomics model based on CT serves as a potent approach to assessing the prognosis following EVT. The logistic regression model, in particular, proved to be both effective and stable, offering critical insights for the management of stroke.AUCarea under the curveEVTendovascular thrombectomyKNNk-nearest neighborsLASSOleast absolute shrinkage and selection operatorLightGBMLight Gradient-Boosting MachineLRlogistic regressionMLPmultilayer perceptronRadradiomicsRFrandom forestSVMsupport vector machineXGBoosteXtreme Gradient Boosting