Machine learning for grading prediction and survival analysis in high grade glioma.
Authors
Affiliations (7)
Affiliations (7)
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Branch of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), No.50, Zhenxin Road, Taizhou, 317502, China.
- School of Science & School of Medicine, Guangxi University of Science and Technology, Liuzhou, 545006, China.
- School of Public Health, Youjiang Medical University For Nationalities, Baise, 533000, China.
- Department of Radiation Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.
- Department of International Education College, Hainan Medical University, Haikou, 571199, China.
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Branch of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), No.50, Zhenxin Road, Taizhou, 317502, China. [email protected].
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Branch of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), No.50, Zhenxin Road, Taizhou, 317502, China. [email protected].
Abstract
We developed and validated a magnetic resonance imaging (MRI)-based radiomics model for the classification of high-grade glioma (HGG) and determined the optimal machine learning (ML) approach. This retrospective analysis included 184 patients (59 grade III lesions and 125 grade IV lesions). Radiomics features were extracted from MRI with T1-weighted imaging (T1WI). The least absolute shrinkage and selection operator (LASSO) feature selection method and seven classification methods including logistic regression, XGBoost, Decision Tree, Random Forest (RF), Adaboost, Gradient Boosting Decision Tree, and Stacking fusion model were used to differentiate HGG. Performance was compared on AUC, sensitivity, accuracy, precision and specificity. In the non-fusion models, the best performance was achieved by using the XGBoost classifier, and using SMOTE to deal with the data imbalance to improve the performance of all the classifiers. The Stacking fusion model performed the best, with an AUC = 0.95 (sensitivity of 0.84; accuracy of 0.85; F1 score of 0.85). MRI-based quantitative radiomics features have good performance in identifying the classification of HGG. The XGBoost method outperforms the classifiers in the non-fusion model and the Stacking fusion model outperforms the non-fusion model.