Clot-based radiomics features predict first pass effect in acute ischemic stroke

Abstract

Purpose

Our aim was to evaluate the performance of clot-based radiomics features (RFs) for predicting first pass effect (FPE) in patients with acute ischemic stroke (AIS). The secondary purpose was to search for any other variables associated with FPE.

Materials and methods

Patients who underwent mechanical thrombectomy (MT) for anterior circulation large vessel stroke in a single center were retrospectively reviewed. Patients were divided into two groups: FPE and non-FPE. Two observers extracted RFs from the clot on pretreatment noncontrast computed tomography (NCCT) images. Demographic, clinical, periprocedural, and RFs were compared between the groups and receiver operating characteristic (ROC) curves were constructed. Logistic regression analysis was used to determine the independent predictors of FPE.

Results

Fifty-two patients (27 female, 25 male; mean age 64.50 ± 15.15) who were treated by stent retrievers as the first option were included in the study. FPE was achieved in 25 patients (25/52, 48.1%). Twelve RFs were significantly different between patients with FPE and non-FPE. The long-run low gray-level emphasis (odds ratio = 44.24, p = 0.003) and the zone percentage (odds ratio = 16.88, p = 0.017) were found as independent predictors of FPE. Female sex and a baseline ASPECT score of >8.5 were the other independent variables to predict FPE. The diagnostic accuracy to predict FPE was observed as 83% when using all independent predictors in our predictive model.

Conclusions

Clot-based RFs on NCCT may help to estimate the success of the intended outcome of MT in patients with AIS.

Keywords

Ischemic stroke thrombectomy first pass effect artificial intelligence

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