What question did this study set out to answer?

The research aims to develop a non-invasive method for predicting glioma grade and 1p/19q codeletion using MRI data.

February 19, 2026Open Access

Radiomics-Driven Hybrid Deep Learning for MRI-Based Prediction of Glioma Grade and 1p/19q Codeletion

Key Points

The research aims to develop a non-invasive method for predicting glioma grade and 1p/19q codeletion using MRI data.
Utilized a radiomics framework combining MRI features with machine learning (ML) and deep learning (DL) techniques.
Derived high-dimensional radiomic features from preoperative MRI scans.
Compared multiple ML classifiers with DL models, including CNNs and a hybrid CNN-LSTM model.
Employed five-fold cross-validation and an independent test dataset for model validation.
The hybrid CNN-LSTM model achieved 88.1% accuracy and an AUC of 0.93, outperforming traditional ML and single DL models.
Radiomic features related to tumor heterogeneity and morphology significantly influenced model performance.

Abstract

Background: Correct preoperative evaluation of glioma grade and molecular profile is a prerequisite for tailored treatment strategies. Specifically, the 1p/19q codeletion status represents a major prognostic and therapeutic marker in low-grade gliomas (LGGs). Nevertheless, its assessment is presently performed through invasive histopathological and genetic studies, thus underlining the need for non-invasive alternative approaches. Methods: We introduce a non-invasive radiomics framework that combines quantitative MRI features with sophisticated ML and DL approaches for glioma grading and 1p/19q codeletion status prediction. High-dimensional radiomic features characterizing tumor geometry, intensity, and texture were derived from preoperative MRI-based tumor delineations. Features were normalized and optimized using correlation-based feature selection. Several traditional ML classifiers were compared and contrasted with DL models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and a CNN-Long Short-Term Memory (LSTM) hybrid model tailored to exploit both spatial feature hierarchies and feature correlations. Model validation was conducted using five-fold cross-validation and an independent test dataset, with accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) metrics. Results: Among all the models tested, the hybrid CNN-LSTM model performed the best, with an accuracy of 88.1% and an AUC of 0.93, outperforming conventional ML approaches and single-model DL architectures. Explainability analysis showed that the radiomic features of tumor heterogeneity and morphology had the most prominent impact on model performance. Conclusions: These findings indicate that the combination of radiomic features with hybrid DL models is capable of making non-invasive predictions of glioma grade and 1p/19q codeletion status. The new computational model has the potential to be used as a supplementary approach in precision neuro-oncology.

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