Abstract:

Objective To discuss the effectiveness of machine learning method combined with magnetic resonance diffusion weighted imaging （DWI） for recognition of the acute ischemic stroke （AIS） patient with onset time within 4.5 h，and to provide the reference for assisted assessment of onset time of AIS patients. Methods A total of 227 AIS patients with complete DWI imaging data were divided into onset time≤ 4.5 h group （n=70） and onset time >4.5 h group （n=157） based on their time from onset to DWI examination. The patients were randomly divided into training set （n=158） and test set （n=69） at a ratio of 7∶3. The regions of interest （ROI） were designated on the DWI images by ITK-SNAP annotation software， and the image features were extracted from the ROI images by the Pyradiomics package.The redundant features with high consistency were removed after evaluating the correlation of each feature by Spearman correlation test. Least absolute shrinkage and selection operator （LASSO） regression model with 10-fold cross-validation was used to recognize the image features of the AIS patients with onset time within 4.5 h. Seven machine learning algorithms， including Support Vector Machines （SVM）， K-Nearest Neighbors （KNN）， Decision Tree， Extra Trees， Random Forest， XGBoost， and LightGBM， were used to train the models. The performance of the models was evaluated by receiver operating characteristic curve （ROC） and the area under the curve （AUC）. Results A total of 107 image features， including 18 first-order features， 14 shape features， and 75 texture features， were extracted from the ROI images， among which 22 features were finally selected for recognition of the AIS patients with onset time within 4.5 h， including 4 first-order features， 6 shape features， and 12 texture features. The XGBoost model yielded the best results in recognizing the AIS patients with onset time within 4.5 h， and the AUC was 0.817， and the accuracy， sensitivity， and specificity were 0.739， 0.733， and 0.814，respectively. Conclusion The XGBoost model based on DWI imaging can effectively recognize the AIS patients with onset time within 4.5 h， which has certain clinical significance in assisting the assessment of onset time of the AIS patients.

Key words: Acute ischemic stroke, Machine learning, Magnetic resonance diffusion weighted imaging, Onset time, XGBoost model