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Abstract #3928

Synthetic MRI aids in glioblastoma survival prediction

Rafael Navarro-González1, Elisa Moya-Sáez1, Rodrigo de Luis-García1, Santiago Aja-Fernández1, and Carlos Alberola-López1
1University of Valladolid, Valladolid, Spain

Synopsis

Radiomics systems for survival prediction in glioblastoma multiforme could enhance patient management, personalizing its treatment and obtaining better outcomes. However, these systems are data-demanding multimodality images. Thus, synthetic MRI could improve radiomics systems by retrospectively completing databases or replacing artifacted images. In this work we analyze the replacement of an acquired modality by a synthesized counterversion for predicting survival with an independent radiomic system. Results prove that a model fed with the synthesized modality achieves similar performance compared to using the acquired modality, and better performance than using a corrupted modality or a model trained from scratch without this modality.

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