Abstract #4476
Combining Multi-Centre Conventional and Diffusion MR Texture for the Characterisation of Childhood Brain Tumours
S. Tantisatirapong 1 , N. P. Davies 1,2 , D. Rodriguez 3 , L. Abernethy 4 , D. P. Auer 3,5 , C. A. Clark 6,7 , R. Grundy 3,5 , T. Jaspan 5 , D. Hargrave 7 , L. MacPherson 2 , M. O. Leach 8 , G. S. Payne 8 , B. L. Pizer 4 , S. Bailey 9 , A. C. Peet 1,10 , and T. N. Arvanitis 10,11
1
University of Birmingham, Edgbaston,
Birmingham, United Kingdom,
2
University
Hospitals Birmingham NHS Foundation Trust, Birmingham,
United Kingdom,
3
University
of Nottingham, Nottingham, United Kingdom,
4
Alder
Hey Childrens NHS Foundation Trust, Liverpool, United
Kingdom,
5
University
Hospital Nottingham, Nottingham, United Kingdom,
6
University
College London, London, United Kingdom,
7
Great
Ormond Street Hospital, London, United Kingdom,
8
The
Institute of Cancer Research and Royal Marsden Hospital,
Sutton, United Kingdom,
9
Northern
Institute for Cancer Research, Newcastle University,
Newcastle upon Tyne, United Kingdom,
10
Birmingham
Childrens Hospital NHS Foundation Trust, Birmingham,
United Kingdom,
11
Institute
of Digital Healthcare, WMG, University of Warwick,
Coventry, United Kingdom
This paper presents integration of multimodal MR image
based texture analysis which take advantage of
complementary information derived from structural and
diffusion MR images. A supervised machine learning
approach is used to achieve image analysis based on
textural features from individual image types of T2,
T1-post contrast and ADC, as well their combination, in
order to characterize the multicenter dataset of the
most common pediatric brain tumors; medulloblastomas
(MB), pilocytic astrocytomas (PA), and ependymomas (EP).
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