Abstract #3784

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

Gabriel Oliveira-Stahl¹, Marianna Inglese^2,3, Steffi Thust^4,5,6,7, and Matthew Grech-Sollars^8,9

¹Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, ²Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy, ³Department of Surgery and Cancer, Imperial College London, London, United Kingdom, ⁴Precision Imaging Beacon, Medical School, University of Nottingham, Nottingham, United Kingdom, ⁵Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom, ⁶Radiology Department, Queen’s Medical Centre, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom, ⁷Department of Brain Rehabilitation and Repair, Institute of Neurology, University College London, London, United Kingdom, ⁸Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, United Kingdom, ⁹Department of Computer Science, University College London, London, United Kingdom

Synopsis

Keywords: Diagnosis/Prediction, Tumor, Neuro-oncology

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.

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