INTRODUCTION Magnetic Resonance Imaging (MRI) is routinely used in the assessment of children’s brain tumours. Reduced diffusion and increased perfusion on MRI are commonly associated with higher grade but there is a lack of quantitative data linking these parameters to survival. Machine learning is increasingly being used to develop diagnostic tools but its use in survival analysis is rare. In this study we combine quantitative parameters from diffusion and perfusion MRI with machine learning to develop a model of survival for paediatric brain tumours. METHOD: 69 children from 4 centres (Birmingham, Liverpool, Nottingham, Newcastle) underwent MRI with diffusion and perfusion (dynamic susceptibility contrast) at diagnosis. Images were processed to form ADC, cerebral blood volume (CBV) and vessel leakage correction (K2) parameter maps. Parameter mean, standard deviation and heterogeneity measures (skewness and kurtosis) were calculated from tumour and whole brain and used in iterative Bayesian survival analysis. The features selected were used for k-means clustering and differences in survival between clusters assessed by Kaplan-Meier and Cox-regression. RESULTS Bayesian analysis revealed the 5 top features determining survival to be tumour volume, ADC kurtosis, CBV mean, K2 mean and whole brain CBV mean. K-means clustering using these features showed two distinct clusters (high- and low-risk) which bore significantly different survival characteristics (Hazard Ratio = 5.6). DISCUSSION AND CONCLUSION Diffusion and perfusion MRI can be used to aid the prediction of survival in children’s brain tumours. Tumour perfusion played a particularly important role in predicting survival despite being less routinely measured than diffusion.

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Divisions: College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences
Additional Information: © The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact
Uncontrolled Keywords: Cancer Research,Oncology,Clinical Neurology
Publication ISSN: 1523-5866
Last Modified: 11 Jan 2024 08:16
Date Deposited: 14 Dec 2020 09:20
Full Text Link:
Related URLs: https://academi ... /iii356/6018670 (Publisher URL)
PURE Output Type: Meeting abstract
Published Date: 2020-12-04
Accepted Date: 2020-12-01
Authors: Grist, James T
Withey, Stephanie
Bennett, Christopher
Rose, Heather
MacPherson, Lesley
Oates, Adam
Powell, Stephen
Novak, Jan (ORCID Profile 0000-0001-5173-3608)
Abernethy, Laurence
Pizer, Barry
Bailey, Simon
Mitra, Dipayan
Arvanitis, Theodoros N
Auer, Dorothee P
Avula, Shivaram
Grundy, Richard
Peet, Andrew C


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