Study on predicting IDH-1 gene expression in gliomas using machine learning models based on imagomics and deep learning based on preoperative MRI images
Objective To investigate value of preoperative magnetic resonance imaging(MRI)T2 fat suppression sequence in predicting isocitrate dehydrogenase(IDH)-1 gene expression in gliomas.Methods 124 patients with glioma,who were confirmed by histopathology,were collected.Regions of interest(ROI)was delineated using ITK-SNAP software.Pyradiomics package was used to extract radiomic features from the imaging data,while a pre-trained ResNet50 deep learning model was employed to extract deep learning features.Feature selection was performed using the Pearson correlation coefficient and the Least Absolute Shrinkage and Selection Operator(LASSO)regression model.Model performance was evaluated through 10-fold cross-validation.Traditional radiomics,deep transfer learning,and fusion models were separately constructed based on support vector machine(SVM),k-nearest neighbors(KNN),and random forest(RF)machine learning algorithms.The predictive performance of each model was assessed using receiver operating characteristic(ROC)curve.Results The area under curve(AUC)for the machine learning models SVM,KNN,and RF based on radiomic features were 0.699,0.628,and 0.616,respectively.For the machine learning models SVM,KNN,and RF based on deep transfer learning features,the AUC values were 0.853,0.753,and 0.807,respectively.The machine learning models SVM,KNN,and RF based on fusion features achieved AUC values of 0.868,0.818,and 0.787,respectively.Conclusion The SVM fusion model based on the T2WI fat suppression sequence in routine MRI exhibits higher predictive performance in determining the IDH-1 gene expression status in gliomas.
维普
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