Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme

BACKGROUND: For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression‐free survival (PFS) on the basis of clinical, pathological, semantic MRI‐based, and FET‐PET/CT‐deri...

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Detalles Bibliográficos
Autores principales: Peeken, Jan C., Goldberg, Tatyana, Pyka, Thomas, Bernhofer, Michael, Wiestler, Benedikt, Kessel, Kerstin A., Tafti, Pouya D., Nüsslin, Fridtjof, Braun, Andreas E., Zimmer, Claus, Rost, Burkhard, Combs, Stephanie E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Clinical Cancer Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6346243/
https://www.ncbi.nlm.nih.gov/pubmed/30561851
http://dx.doi.org/10.1002/cam4.1908

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6346243/
https://www.ncbi.nlm.nih.gov/pubmed/30561851
http://dx.doi.org/10.1002/cam4.1908

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