RT info:eu-repo/semantics/article
T1 Synthetic MRI improves radiomics‐based glioblastoma survival prediction
A1 Moya Sáez, Elisa
A1 Navarro González, Rafael
A1 Cepeda, Santiago
A1 Pérez Núñez, Ángel
A1 Luis García, Rodrigo de
A1 Aja Fernández, Santiago
A1 Alberola López, Carlos
K1 Glioblastoma
K1 Synthetic MRI
K1 Radiomics
K1 Survival prediction
K1 32 Ciencias Médicas
K1 33 Ciencias Tecnológicas
AB 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.
PB Wiley
SN 0952-3480
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/54235
UL https://uvadoc.uva.es/handle/10324/54235
LA eng
NO NMR in Biomedicine, 2022, Volume35, Issue 9, e4754
NO Producción Científica
DS UVaDOC
RD 07-ago-2024