Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma

被引：0
|
作者：
Chao Li
Shuo Wang
Angela Serra
Turid Torheim
Jiun-Lin Yan
Natalie R. Boonzaier
Yuan Huang
Tomasz Matys
Mary A. McLean
Florian Markowetz
Stephen J. Price
机构：
[1] University of Cambridge,Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences
[2] Shanghai Jiao Tong University School of Medicine,Department of Neurosurgery, Shanghai General Hospital (originally named “Shanghai First People’s Hospital”)
[3] University of Cambridge,The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics
[4] University of Cambridge,Department of Radiology
[5] Tampere University,Faculty of Medicine and Health Technology
[6] Institute of Biosciences and Medical Technologies (BioMediTech),NeuRoNe Lab, DISA
[7] University of Salerno,MIS
[8] University of Cambridge,Cancer Research UK Cambridge Institute
[9] CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester,Department of Neurosurgery
[10] Chang Gung Memorial Hospital,Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health
[11] Chang Gung University College of Medicine,Wolfson Brain Imaging Centre, Department of Clinical Neurosciences
[12] University College London,undefined
[13] University of Cambridge,undefined
来源：
European Radiology | 2019年 / 29卷
关键词：
Glioblastoma; Magnetic resonance imaging; Machine learning; Survival analysis; Prognosis;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：4718 / 4729
页数：11
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