Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance

PURPOSE: Surveillance of patients with high-grade glioma (HGG) and identification of disease progression remain a major challenge in neurooncology. This study aimed to develop a support vector machine (SVM) classifier, employing combined longitudinal structural and perfusion MRI studies, to classify...

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Detalles Bibliográficos
Autores principales: Siakallis, Loizos, Sudre, Carole H., Mulholland, Paul, Fersht, Naomi, Rees, Jeremy, Topff, Laurens, Thust, Steffi, Jager, Rolf, Cardoso, M. Jorge, Panovska-Griffiths, Jasmina, Bisdas, Sotirios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Functional Neuroradiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589799/
https://www.ncbi.nlm.nih.gov/pubmed/34047805
http://dx.doi.org/10.1007/s00234-021-02719-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589799/
https://www.ncbi.nlm.nih.gov/pubmed/34047805
http://dx.doi.org/10.1007/s00234-021-02719-6

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