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

页码：4718 / 4729

页数：11

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