Machine learning-based prediction of glioma grading

Objective: Gliomas are among the most common and heterogeneous primary tumours of the central nervous system. Accurate grading is essential for treatment planning and prognosis, yet conventional histopathological approaches are limited by subjectivity and poor reproducibility. This study aimed to develop a machine learning–based prediction model that integrates clinical and molecular characteristics to improve early glioma grading, thereby enhancing diagnostic accuracy and supporting individualized treatment strategies. Methods: An efficient prediction model for low-grade gliomas (LGGs) and glioblastoma (grade IV, GBM) was developed by utilizing the clinical and molecular characteristics of gliomas from The Cancer Genome Atlas (TCGA) dataset. A novel integration of recursive feature elimination (RFE) with random forest (RF) and elastic net regression (ENR) was implemented to select features efficiently. Additionally, the synthetic minority oversampling technique (SMOTE) was applied to balance the training set, and K-nearest neighbours (KNN), support vector machine (SVM), and other algorithms were optimized through random-search hyper-parameter optimization (HPO) with five-fold cross-validation, yielding nine distinct machine learning (ML) models. Ultimately, by applying the voting and stacking algorithms, 34 ensemble learning models were constructed. Furthermore, all the models were externally validated using the Chinese Glioma Genome Atlas (CGGA) dataset. Finally, SHapley Additive exPlanations (SHAP) analysis was conducted to elucidate the prediction processes of the ensemble models. Results: Feature selection revealed 11 key grading features, including Tumour Protein 53 (TP53) and Isocitrate Dehydrogenase 1 (IDH1). Among the 9 basic models constructed by combining optimization techniques such as SMOTE, the RF model had the best performance (Area Under Curve (AUC) of 0.916 for TCGA and 0.797 for CGGA). Among the 34 integrated models constructed, the Voting25 model integrating RF, Extreme Gradient Boosting (XGBoost), and KNN achieved AUC values of 0.928 and 0.794, respectively, on the TCGA and CGGA datasets, demonstrating overall optimal predictive performance. Conclusion: Eleven key features have been identified that facilitate molecular detection and personalized targeted therapy for glioma. Nine models were developed and optimized, and the RF model was observed to provide the best performance, potentially guiding future ML-related research in glioma. Additionally, the voting ensemble method, which integrates RF, XGBoost, and KNN, was shown to achieve superior performance, thereby enhancing both accuracy and robustness. Finally, all the models were successfully validated on the CGGA dataset, indicating strong generalizability.