AUTHOR=Yusuying Shadamu , Lu Yao , Zhang Shun , Wang Junjie , Chen Juan , Wang Daming , Lu Jun , Qi Peng 

TITLE=CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion

JOURNAL=Frontiers in Neurology

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1152730

DOI=10.3389/fneur.2023.1152730

ISSN=1664-2295

ABSTRACT=<sec><title>Background and aims</title><p>Secondary embolization (SE) during mechanical thrombectomy (MT) for cerebral large vessel occlusion (LVO) could reduce the anterior blood flow and worsen clinical outcomes. The current SE prediction tools have limited accuracy. In this study, we aimed to develop a nomogram to predict SE following MT for LVO based on clinical features and radiomics extracted from computed tomography (CT) images.</p></sec><sec><title>Materials and methods</title><p>A total of 61 patients with LVO stroke treated by MT at Beijing Hospital were included in this retrospective study, of whom 27 developed SE during the MT procedure. The patients were randomly divided (7:3) into training (<italic>n</italic> = 42) and testing (<italic>n</italic> = 19) cohorts. The thrombus radiomics features were extracted from the pre-interventional thin-slice CT images, and the conventional clinical and radiological indicators associated with SE were recorded. A support vector machine (SVM) learning model with 5-fold cross-verification was used to obtain the radiomics and clinical signatures. For both signatures, a prediction nomogram for SE was constructed. The signatures were then combined using the logistic regression analysis to construct a combined clinical radiomics nomogram.</p></sec><sec><title>Results</title><p>In the training cohort, the area under the receiver operating characteristic curve (AUC) of the nomograms was 0.963 for the combined model, 0.911 for the radiomics, and 0.891 for the clinical model. Following validation, the AUCs were 0.762 for the combined model, 0.714 for the radiomics model, and 0.637 for the clinical model. The combined clinical and radiomics nomogram had the best prediction accuracy in both the training and test cohort.</p></sec><sec><title>Conclusion</title><p>This nomogram could be used to optimize the surgical MT procedure for LVO based on the risk of developing SE.</p></sec>