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Título
Synthetic MRI improves radiomics‐based glioblastoma survival prediction
Autor
Año del Documento
2022
Editorial
Wiley
Descripción
Producción Científica
Documento Fuente
NMR in Biomedicine, 2022, Volume35, Issue 9, e4754
Zusammenfassung
Glioblastoma is an aggressive and fast-growing brain tumor with poor prognosis. Predicting the expected survival of patients with glioblastoma is a key task for efficient treatment and surgery planning. Survival predictions could be enhanced by means of a radiomic system. However, these systems demand high numbers of multicontrast images, the acquisitions of which are time consuming, giving rise to patient discomfort and low healthcare system efficiency. Synthetic MRI could favor deployment of radiomic systems in the clinic by allowing practitioners not only to reduce acquisition time, but also to retrospectively complete databases or to replace artifacted images. In this work we analyze the replacement of an actually acquired MR weighted image by a synthesized version to predict survival of glioblastoma patients with a radiomic system. Each synthesized version was realistically generated from two acquired images with a deep learning synthetic MRI approach based on a convolutional neural network. Specifically, two weighted images were considered for the replacement one at a time, a T2w and a FLAIR, which were synthesized from the pairs T1w and FLAIR, and T1w and T2w, respectively. Furthermore, a radiomic system for survival prediction, which can classify patients into two groups (survival >480 days and 480 days), was built. Results show that the radiomic system fed with the synthesized image achieves similar performance compared with using the acquired one, and better performance than a model that does not include this image. Hence, our results confirm that synthetic MRI does add to glioblastoma survival prediction within a radiomics-based approach.
Materias Unesco
32 Ciencias Médicas
33 Ciencias Tecnológicas
Palabras Clave
Glioblastoma
Synthetic MRI
Radiomics
Survival prediction
ISSN
0952-3480
Revisión por pares
SI
DOI
Patrocinador
Ministerio de Ciencia e Innovación (research grants RTI 2018-094569-B-I00, PRE2019-089176 y PID2020-115339RB-I0)
Asociación Española Contra el Cáncer (subvención PRDVL19001MOYA)
Asociación Española Contra el Cáncer (subvención PRDVL19001MOYA)
Propietario de los Derechos
© 2022 The Author(s)
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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