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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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