Literature DB >> 26195699

Can We Predict Bevacizumab Responders in Patients With Glioblastoma?

Tina M Mayer1.   

Abstract

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Year:  2015        PMID： 26195699     DOI： 10.1200/JCO.2015.62.3637

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN： 0732-183X            Impact factor:   44.544


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  3 in total

1.  Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response.

Authors:  Philipp Kickingereder; Michael Götz; John Muschelli; Antje Wick; Ulf Neuberger; Russell T Shinohara; Martin Sill; Martha Nowosielski; Heinz-Peter Schlemmer; Alexander Radbruch; Wolfgang Wick; Martin Bendszus; Klaus H Maier-Hein; David Bonekamp
Journal:  Clin Cancer Res       Date:  2016-10-10       Impact factor: 12.531

2.  Validation of diffusion MRI phenotypes for predicting response to bevacizumab in recurrent glioblastoma: post-hoc analysis of the EORTC-26101 trial.

Authors:  Marianne Schell; Irada Pflüger; Gianluca Brugnara; Fabian Isensee; Ulf Neuberger; Martha Foltyn; Tobias Kessler; Felix Sahm; Antje Wick; Martha Nowosielski; Sabine Heiland; Michael Weller; Michael Platten; Klaus H Maier-Hein; Andreas Von Deimling; Martin J Van Den Bent; Thierry Gorlia; Wolfgang Wick; Martin Bendszus; Philipp Kickingereder
Journal:  Neuro Oncol       Date:  2020-11-26       Impact factor: 12.300

3.  The role of c-Met and VEGFR2 in glioblastoma resistance to bevacizumab.

Authors:  Bruno Carvalho; José Manuel Lopes; Roberto Silva; Joana Peixoto; Dina Leitão; Paula Soares; Ana Catarina Fernandes; Paulo Linhares; Rui Vaz; Jorge Lima
Journal:  Sci Rep       Date:  2021-03-16       Impact factor: 4.379
  3 in total

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