Clot-based radiomics model for cardioembolic stroke prediction with CT imaging before recanalization: a multicenter study

ObjectivesTo develop a clot-based radiomics model using CT imaging radiomic features and machine learning to identify cardioembolic (CE) stroke before mechanical thrombectomy (MTB) in patients with acute ischemic stroke (AIS).Materials and methodsThis retrospective four-center study consecutively included 403 patients with AIS who sequentially underwent CT and MTB between April 2016 and July 2021. These were grouped into training, testing, and external validation cohorts. Thrombus-extracted radiomic features and basic information were gathered to construct a machine learning model to predict CE stroke. The radiological characteristics and basic information were used to build a routine radiological model. A combined radiomics and radiological features model was also developed. The performances of all models were evaluated and compared in the validation cohort. A histological analysis helped further assess the proposed model in all patients.ResultsThe radiomics model yielded an area under the curve (AUC) of 0.838 (95% confidence interval [CI], 0.771–0.891) for predicting CE stroke in the validation cohort, significantly higher than the radiological model (AUC, 0.713; 95% CI, 0.636–0.781; p = 0.007) but similar to the combined model (AUC, 0.855; 95% CI, 0.791–0.906; p = 0.14). The thrombus radiomic features achieved stronger correlations with red blood cells (|rmax|, 0.74 vs. 0.32) and fibrin and platelet (|rmax|, 0.68 vs. 0.18) than radiological characteristics.ConclusionThe proposed CT-based radiomics model could reliably predict CE stroke in AIS, performing better than the routine radiological method.Key Points • Admission CT imaging could offer valuable information to identify the acute ischemic stroke source by radiomics analysis. • The proposed CT imaging–based radiomics model yielded a higher area under the curve (0.838) than the routine radiological method (0.713; p = 0.007). • Several radiomic features showed significantly stronger correlations with two main thrombus constituents (red blood cells, |r max |, 0.74; fibrin and platelet, |r max |, 0.68) than routine radiological characteristics.