In vivoevaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature | Zendy

Hamed Akbari | Zendy; Spyridon Bakas | Zendy; Jared Pisapia | Zendy; MacLean P. Nasrallah | Zendy; Martin Rozycki | Zendy; María Martínez-Lage | Zendy; Jennifer J.D. Morrissette | Zendy; Nadia Dahmane | Zendy; Donald M. O’Rourke | Zendy; Christos Davatzikos | Zendy

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In vivoevaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature

Author(s) -

Hamed Akbari,

Spyridon Bakas,

Jared Pisapia,

MacLean P. Nasrallah,

Martin Rozycki,

María Martínez-Lage,

Jennifer J.D. Morrissette,

Nadia Dahmane,

Donald M. O’Rourke,

Christos Davatzikos

Publication year - 2018

Publication title -

neuro-oncology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 4.005

H-Index - 125

eISSN - 1523-5866

pISSN - 1522-8517

DOI - 10.1093/neuonc/noy033

Subject(s) - in vivo , epidermal growth factor receptor , glioblastoma , mutation , computational biology , cancer research , pathology , cancer , biology , medicine , genetics , gene

Epidermal growth factor receptor variant III (EGFRvIII) is a driver mutation and potential therapeutic target in glioblastoma. Non-invasive in vivo EGFRvIII determination, using clinically acquired multiparametric MRI sequences, could assist in assessing spatial heterogeneity related to EGFRvIII, currently not captured via single-specimen analyses. We hypothesize that integration of subtle, yet distinctive, quantitative imaging/radiomic patterns using machine learning may lead to non-invasively determining molecular characteristics, and particularly the EGFRvIII mutation.

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