Deep Convolutional Neural Networks for Molecular Subtyping of Gliomas Using Magnetic Resonance Imaging

TL;DR

This study demonstrates that deep convolutional neural networks can effectively classify glioma molecular subtypes from MRI images, offering a noninvasive diagnostic tool with performance comparable or superior to radiomics methods.

Contribution

The paper introduces a hierarchical DCNN model that processes multimodal MRI data for noninvasive glioma subtyping, showing improved accuracy over traditional radiomics approaches.

Findings

DCNN achieved AUCs of 0.89 and 0.85 in key classification tasks.

The model outperformed radiomics in predictive accuracy.

Sufficient, balanced training data is crucial for optimal performance.

Abstract

Knowledge of molecular subtypes of gliomas can provide valuable information for tailored therapies. This study aimed to investigate the use of deep convolutional neural networks (DCNNs) for noninvasive glioma subtyping with radiological imaging data according to the new taxonomy announced by the World Health Organization in 2016. Methods: A DCNN model was developed for the prediction of the five glioma subtypes based on a hierarchical classification paradigm. This model used three parallel, weight-sharing, deep residual learning networks to process 2.5-dimensional input of trimodal MRI data, including T1-weighted, T1-weighted with contrast enhancement, and T2-weighted images. A data set comprising 1,016 real patients was collected for evaluation of the developed DCNN model. The predictive performance was evaluated via the area under the curve (AUC) from the receiver operating characteristic analysis. For comparison, the performance of a radiomics-based approach was also evaluated. Results: The AUCs of the DCNN model for the four classification tasks in the hierarchical classification paradigm were 0.89, 0.89, 0.85, and 0.66, respectively, as compared to 0.85, 0.75, 0.67, and 0.59 of the radiomics approach. Conclusion: The results showed that the developed DCNN model can predict glioma subtypes with promising performance, given sufficient, non-ill-balanced training data.