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.