Objective To construct a prediction model of malignant edema after thrombectomy for acute stroke using ma-chine learning based on the radiomic features of diffusion weighted imaging(DWI)infarction and cerebrospinal fluid.Meth-ods A total of 155 acute stroke patients receiving MRI were retrospectively enrolled.The DWI infarction and cerebrospinal fluid were segmented using software.The AK software was used to extract the radiomic features and to reduce the dimensionality.The the minimum redundancy and maximum correlation feature selection method was used to determine the radiomic features.The support vector machine classifier was used to construct prediction model of malignant edema.Results The subset with 10 features(including 7 DWI infarction features and 3 cerebrospinal fluid features)obtained the highest average the area under curve(AUC)value after screening,and was designated as the final feature subset for model analysis.The ROC analysis showed that the AUC of predicting malignant edema in training set was 0.975,and the sensitivity and specificity were 0.903,0.968,re-spectively,and the AUC of predicting malignant edema in test set was 893,and the sensitivity and specificity were 0.868,0.903,respectively.Conclusion Machine learning based on DWI infarction and cerebrospinal fluid can accurately predict the malignant edema after thrombectomy for acute stroke,and can provide guidance for clinical early intervention.
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