DWI-Based Radiomics Predicts the Functional Outcome of Endovascular Treatment in Acute Basilar Artery Occlusion

Abstract

BACKGROUND AND PURPOSE: Endovascular treatment is a reference treatment for acute basilar artery occlusion (ABAO). However, no established and specific methods are available for the preoperative screening of patients with ABAO suitable for endovascular treatment. This study explores the potential value of DWI-based radiomics in predicting the functional outcomes of endovascular treatment in ABAO. MATERIALS AND METHODS:Patients with ABAO treated with endovascular treatment from the BASILAR registry (91 patients in the training cohort) and the hospitals in the Northwest of China (31 patients for the external testing cohort) were included in this study. The Mann-Whitney U test, random forests algorithm, and least absolute shrinkage and selection operator were used to reduce the feature dimension. A machine learning model was developed on the basis of the training cohort to predict the prognosis of endovascular treatment. The performance of the model was evaluated on the independent external testing cohort. RESULTS:A subset of radiomics features (n ¼ 6) was used to predict the functional outcomes in patients with ABAO. The areas under the receiver operating characteristic curve of the radiomics model were 0.870 and 0.781 in the training cohort and testing cohort, respectively. The accuracy of the radiomics model was 77.4%, with a sensitivity of 78.9%, specificity of 75%, positive predictive value of 83.3%, and negative predictive value of 69.2% in the testing cohort.CONCLUSIONS: DWI-based radiomics can predict the prognosis of endovascular treatment in patients with ABAO, hence allowing a potentially better selection of patients who are most likely to benefit from this treatment.ABBREVIATIONS: ABAO ¼ acute basilar artery occlusion; AUC ¼ area under the receiver operating characteristic curve; CAPS ¼ critical area perfusion score; EVT ¼ endovascular treatment; LASSO ¼ least absolute shrinkage and selection operator; ML ¼ machine learning; pc ¼ posterior circulation; pcASCO ¼ posterior circulation ASPECTS-Collaterals score; RF ¼ radiomics feature

CBF Profile in Computed Tomography Perfusion-Based AutoMIStar Software Predicts Futile Recanalization After Basilar Artery Thrombectomy

“…[37][38][39] Recently, machine learning-based evaluation of NCCT pc-ASPECTS regions and diffusion-weighted imaging-derived radiomics both have shown promising preliminary results in predicting the functional outcomes of patients with PC stroke or BAO. 40,41 Moving forward, the application of AI algorithms in accurately interpreting perfusion imaging and predicting FR in BAO stroke may represent the next frontier in this field.…”