Open AccessArticle
Multicenter DSC–MRI-Based Radiomics Predict IDH Mutation in Gliomas
by
1,*
, 1
, 2
, 1, 3, 4,5, 4,5, 3, 2,6,† and 1,7,†
1
Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 70013 Heraklion, Greece
2
Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, UCL, London WC1N 3BG, UK
3
Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University, Stanford, CA 94305, USA
4
Department of Radiology, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
5
Department of Neuroradiology, University Medical Centre, 1000 Ljubljana, Slovenia
6
Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London WC1N 3BG, UK
7
Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
*
Author to whom correspondence should be addressed.
†
These authors contributed equally as last authors.
Academic Editor: Brigitta G. Baumert
Received: 24 June 2021
/
Revised: 25 July 2021
/
Accepted: 31 July 2021
/
Published: 5 August 2021
Simple Summary
Significant efforts have been put toward developing MRI-based radiogenomics for IDH status subtyping predictions; however, in the vast majority of these approaches, the external validation sets are absent. Another limitation in current studies is the lack of explainability in radiomics models, which hampers clinical trust and translation. Motivated by these challenges, we proposed a multicenter DSC–MRI-based radiomics study based on an independent exploratory set, which was externally validated on two independent cohorts, for IDH mutation status prediction. Our results demonstrated that DSC–MRI radiogenomics in gliomas, coupled with dynamic-based image standardization techniques, hold the potential to provide (a) increased predictive performance by offering models that generalize well, (b) reasoning behind the IDH mutation status predictions, and (c) interpretability of the radiomics features’ impacts in model performance.