Combining machine learning with radiomics features in predicting outcomes after mechanical thrombectomy in patients with acute ischemic stroke. (October 2022)

• Record Type: Journal Article
• Authors: Li, Yan; Liu, Yongchang; Hong, Zhen; Wang, Ying; Lu, Xiuling
• Is Part Of: Computer methods and programs in biomedicine. Volume 225(2022)
• Publication Date: 2022-10
• Subjects: Diffusion-weighted imaging -- Radiomics -- Machine learning -- Support vector machine -- Acute ischemic stroke -- Prognosis; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107093
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The model based on the characteristics of imaging omics and machine learning has high predictive efficiency for the prognosis of acute stroke after mechanical thrombectomy. Minimum absolute contraction and selection operator regression model screened the best radiomics features. Six features highly correlated with prognosis of acute stroke after mechanical thrombectomy. The support vector machine classifier established a useful prediction model in stroke patients receiving mechanical thrombectomy. Abstract: Background and Objective: Some patients with mechanical thrombectomy will have a poor prognosis. This study establishes a model for predicting the prognosis after mechanical thrombectomy in acute stroke based on diffusion-weighted imaging (DWI) omics characteristics. Methods: A total of 260 stroke patients receiving mechanical thrombectomy in our hospital were randomly divided into a training set ( n = 182) and a test set ( n = 78) in a 7:3 ratio. The regions of interest (ROI) of the imaging features of the DWI infarct area were extracted, and the minimum absolute contraction and selection operator regression model were used to screen the best radiomics features. A support vector machine classifier established the prediction model of the prognosis after mechanical thrombectomy of acute stroke based on the selected features. The prediction efficiency of the model was evaluated by the receiver operating characteristic (ROC) curve. Results: A total of 1936 radiomic features were extracted, and six features highly correlated with prognosis were screened after dimensionality reduction. Based on the DWI model, the ROC analysis showed that the area under the curve (AUC) for correct prediction in the training and test sets was 0.945 and 0.920, respectively. Conclusion: The model based on the characteristics of radiomics and machine learning has high predictive efficiency for the prognosis of acute stroke after mechanical thrombectomy, which can be used to guide personalized clinical treatment.