Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab

Neuro-Oncology

Volumn 18, Issue 12, 2016, Pages 1680-1687

  Chang, Ken ^a   Zhang, Biqi ^a   Guo, Xiaotao ^b   Zong, Min ^b   Rahman, Rifaquat ^a   Sanchez, David ^a   Winder, Nicolette ^a   Reardon, David A ^a   Zhao, Binsheng ^b   Wen, Patrick Y ^a   Huang, Raymond Y ^a  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

^b New York Presbyterian Columbia University Medical Center   (United States)

Author keywords

Bevacizumab; Glioblastoma; Machine learning; Recurrent; Survival

Indexed keywords

BEVACIZUMAB; ANGIOGENESIS INHIBITOR; BIOLOGICAL MARKER;

ADULT; ARTICLE; CANCER PROGNOSIS; CANCER RECURRENCE; COHORT ANALYSIS; CONTRAST ENHANCEMENT; CONTROLLED STUDY; FEMALE; GLIOBLASTOMA; HISTOGRAM; HUMAN; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MODEL; MULTIMODAL IMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; OVERALL SURVIVAL; PROGRESSION FREE SURVIVAL; TUMOR VOLUME; BRAIN NEOPLASMS; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; DIFFUSION WEIGHTED IMAGING; DISEASE FREE SURVIVAL; MIDDLE AGED; MORTALITY; PATHOLOGY; PROCEDURES; PROPORTIONAL HAZARDS MODEL; SURVIVAL ANALYSIS;

ANGIOGENESIS INHIBITORS; BEVACIZUMAB; BIOMARKERS; BRAIN NEOPLASMS; DIFFUSION MAGNETIC RESONANCE IMAGING; DISEASE-FREE SURVIVAL; FEMALE; GLIOBLASTOMA; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; MULTIMODAL IMAGING; PROPORTIONAL HAZARDS MODELS; SURVIVAL ANALYSIS; TUMOR BURDEN;

EID: 84992559802     PISSN: 15228517     EISSN: 15235866     Source Type: Journal    
DOI: 10.1093/neuonc/now086     Document Type: Article

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