Abstract #1575
An alternative approach for the automatic prediction of therapy response from MRI data sets in small cohorts of experimental High Grade Gliomas
Ania Bentez 1,2 , Gerardo Pelez-Brioso 1,2 , Alexandra Borges 3 , Pilar Lpez-Larrubia 1 , Sebastin Cerdn 1 , and Manuel Snchez-Montas 2
1
Instituto de Investigaciones Biomdicas
"Alberto Sols", Madrid, Madrid, Spain,
2
Computer
Science and Engineering, Escuela Politcnica Superior,
Universidad Autnoma de Madrid, Madrid, Madrid, Spain,
3
Instituto
Portugus de Oncologia Centro de Lisboa, Lisboa,
Portugal
MRI is presently one of the most important non-invasive
methods to investigate and diagnose High Grade Gliomas (HGG)
with the automatic classification of medical images into
different pathological categories or grades playing an
important role. A common problem to both approaches is
many times, the small size of individual observations,
while the data set from each individual is very large.
We propose here an interesting protocol to predict
therapy response in an animal HGG model, from the MRIs
obtained during the first two days of anti-VGEF
treatment. This approach in combination with LDA
predicts therapy response outperformimg the classical
approaches.
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