Binary Glioma Grading: Radiomics versus Pre-trained CNN Features

Abstract

Determining the malignancy of glioma is highly important for initial therapy planning. In current clinical practice, often a biopsy is performed to verify tumour grade which involves risks and can negatively impact overall survival. To avoid biopsy, non-invasive tumour characterisation based on MRI is preferred and to improve accuracy and efficiency, the use of computer-aided diagnosis (CAD) systems is investigated. Existing radiomics CAD techniques often rely on manual segmentation and are trained and evaluated on data from one clinical centre. Therefore, there is a need for accurate and automatic CAD systems that are robust to large variations in imaging protocols between different institutions. In this study, we extract features from T1ce MRI with a pre-trained CNN and compare their predictive power with hand-engineered radiomics features for binary grade prediction. Performance was evaluated on the BRATS 2017 database containing MRI and manual segmentation data of 285 patients from multiple institutions. State-of-the-art performance with an AUC of \(96.4\%\) was achieved with radiomics features extracted from manually segmented tumour volumes. Pre-trained CNN features had a strong predictive value as well and an AUC score of \(93.5\%\) could be obtained when propagating the tumour region of interest (ROI). Additionally, using a pre-trained CNN as feature extractor, we were able to design an accurate, automatic, fast and robust binary glioma grading system achieving an AUC score of \(91.1\%\) without requiring ROI annotations.