Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques-Reference-Cited by-同舟云学术

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Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques

Published:2015-07-16 Issue:3 Volume:18 Page:417-425
ISSN:1522-8517
Container-title:Neuro-Oncology
language:en
Short-container-title:Neuro Oncol

Author:

Macyszyn Luke,Akbari Hamed,Pisapia Jared M.,Da Xiao,Attiah Mark,Pigrish Vadim,Bi Yingtao,Pal Sharmistha,Davuluri Ramana V.,Roccograndi Laura,Dahmane Nadia,Martinez-Lage Maria,Biros George,Wolf Ronald L.,Bilello Michel,O'Rourke Donald M.,Davatzikos Christos

Funder

NIH

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Clinical Neurology,Oncology

Link

http://academic.oup.com/neuro-oncology/article-pdf/18/3/417/7640370/nov127.pdf

Reference42 articles.

1. Pattern Analysis of Dynamic Susceptibility Contrast-enhanced MR Imaging Demonstrates Peritumoral Tissue Heterogeneity

2. Gliomas: Predicting Time to Progression or Survival with Cerebral Blood Volume Measurements at Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging

3. Apparent diffusion coefficient histogram analysis stratifies progression-free and overall survival in patients with recurrent GBM treated with bevacizumab: a multi-center study

4. Identifying the survival subtypes of glioblastoma by quantitative volumetric analysis of MRI

5. Imaging descriptors improve the predictive power of survival models for glioblastoma patients

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