CT-Based Intrathrombus and Perithrombus Radiomics for Prediction of Prognosis after Endovascular Thrombectomy: A Retrospective Study across 2 Centers.

Abstract

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.