%0 Journal Article
%T Combining multimodal imaging and treatment features improves machine learning©\based prognostic assessment in patients with glioblastoma multiforme
%A Andreas E. Braun
%A Benedikt Wiestler
%A Burkhard Rost
%A Claus Zimmer
%A Fridtjof N¨¹sslin
%A Jan C. Peeken
%A Kerstin A. Kessel
%A Michael Bernhofer
%A Pouya D. Tafti
%A Stephanie E. Combs
%A Tatyana Goldberg
%A Thomas Pyka
%J Archive of "Cancer Medicine".
%D 2019
%R 10.1002/cam4.1908
%X 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©\derived information. Finally, the value of adding treatment features was evaluated
%K biomarker
%K FET©\PET
%K glioblastoma
%K machine learning
%K MRI
%K prognostic model
%K VASARI
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6346243/