Abstract
1) Objectives: Machine learning for binary glioma grading have been extensively used on anatomical MRI, especially using the BraTS dataset. The relevance of radiomic criteria based on multimodal imaging, including diffusion, perfusion and spectroscopy data is to be explored, as multimodal datasets are scarce, and there is no common benchmark for performance comparison.
2) Material and methods: Poitiers University Hospital provides 123 multimodal patient data. We computed 124 features and let a recursive feature elimination algorithm (RFE) yield a relevant, reduced subset of features. We trained a SVM classifier on this subset. We proposed a method to adapt the BraTS dataset to allow performance comparison with the literature. We got a performance reference point by training on anatomical data only, and showed improvements when multimodalities were added. We explored the feature relevance through the RFE subset. The RFE subset is not constant and induce variability in the performances. To smooth the variability, we applied the RFE algorithm 100 times and incremented the selected features, resulting in a global feature ranking. We also show the best classifier reached on these 100 trainings and its feature subset.
3) Results: The best classifier reached 86.5% accuracy, with a mean accuracy on 100 trainings of 78.6%. The rankings shows that anatomical and perfusion sequences are the most relevant for glioma grading, especially T1 post-gadolinium, cerebral blood volume and flow. Intensity and texture features are frequently selected, while anisotropic diffusion coefficient, time to peak and mean time transit mappings seem irrelevant.
4) Conclusion: Multimodal radiomics improve the classification and are consistent with the radiological analysis.