Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke

• Published in: BMC medical imaging 2024-07, Vol.24 (1), p.178-11, Article 178
• Main Authors: Sun, Tao, Yu, Hai-Yun, Zhan, Chun-Hua, Guo, Han-Long, Luo, Mu-Yun
• Format: Article
• Language: English
• Subjects: Aged; Anterior circulation Acute Ischemic Stroke; Blood sugar; Cardiovascular system; Care and treatment; Circulation; Endovascular Procedures - methods; Endovascular treatment; Feature selection; Female; Futile recanalization; Humans; Ischemia; Ischemic Stroke - diagnostic imaging; Ischemic Stroke - surgery; Ischemic Stroke - therapy; Learning algorithms; Machine Learning; Magnetic resonance imaging; Male; Medical Futility; Medical research; Medicine, Experimental; Middle Aged; Nomograms; Patient compliance; Patients; Performance evaluation; Performance prediction; Physicians; Prognosis; Radiomics; Regression analysis; Regression models; Retrospective Studies; Sensitivity analysis; Statistical analysis; Stroke; Tomography, X-Ray Computed - methods
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-024-01365-7

To establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular treatment. A retrospective analysis was conducted on 174 patients who underwent endovascular treatment for acute anterior circulation ischemic stroke between January 2020 and December 2023. FR was defined as successful recanalization but poor prognosis at 90 days (modified Rankin Scale, mRS 4-6). Radiomic features were extracted from non-contrast CT and selected using the least absolute shrinkage and selection operator (LASSO) regression method. Logistic regression (LR) model was used to build models based on radiomic and clinical features. A radiomics-clinical nomogram model was developed, and the predictive performance of the models was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. A total of 174 patients were included. 2016 radiomic features were extracted from non-contrast CT, and 9 features were selected to build the radiomics model. Univariate and stepwise multivariate analyses identified admission NIHSS score, hemorrhagic transformation, NLR, and admission blood glucose as independent factors for building the clinical model. The AUC of the radiomics-clinical nomogram model in the training and testing cohorts were 0.860 (95%CI 0.801-0.919) and 0.775 (95%CI 0.605-0.945), respectively. The radiomics-clinical nomogram model based on non-contrast CT demonstrated satisfactory performance in predicting futile recanalization in patients with anterior circulation acute ischemic stroke.