Machine learning based characterisation of glioma shows best performance with post-contrast T1 and diffusion imaging

Motivation: Accurate glioma classification currently relies on tissue diagnosis, which has associated surgical risks. Machine learning based classification of MR images may enable non-invasive glioma characterisation. Goal(s): Our aim was to assess which imaging modalities provided optimal training data to increase accuracy of machine learning based glioma characterisation. Approach: A pyRadiomics based pipeline predicted tumour grade and IDH-mutation status with XGBoost on a glioblastoma-rich dataset. 10 structural and advanced MR acquisitions were used as model input and a systematic search for the most informative MR modalities was performed. Results: The classifier performed best when the model was trained on post-contrast T1 and diffusion imaging. Impact: We found post-contrast T1 and diffusion imaging to be the most informative MR modalities for machine learning based glioma characterisation. This result will benefit scientists in making well-informed choices on how to train their machine learning models for glioma classification.