1. 基于术前 MRI 图像构建影像组学与深度学习 的机器学习模型预测胶质瘤 IDH-1 基因表达的 研究.
- Author
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胡哲, 王玉红, 刘晓龙, 于笑, 王皆欢, 刘德国, 王唯伟, and 陈月芹
- Abstract
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 hased 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 T2 WI fat suppression sequence in routine MRI exhibits higher predictive performance in determining the IDH-1 gene expression status in gliomas. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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