Machine learning approach to differentiation of peripheral schwannomas and neurofibromas: A multi-center study | Zendy

Michael Zhang | Zendy; Elizabeth Tong | Zendy; Sam Wong | Zendy; Forrest Hamrick | Zendy; Maryam Mohammadzadeh | Zendy; Vaishnavi Rao | Zendy; Courtney Pendleton | Zendy; Brandon W. Smith | Zendy; Nicholas F. Hug | Zendy; Sandip Biswal | Zendy; Jayne Seekins | Zendy; Sandy Napel | Zendy; Robert J. Spinner | Zendy; Mark A. Mahan | Zendy; Kristen W. Yeom | Zendy; Thomas J. Wilson | Zendy

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Machine learning approach to differentiation of peripheral schwannomas and neurofibromas: A multi-center study

Author(s) -

Michael Zhang,

Elizabeth Tong,

Sam Wong,

Forrest Hamrick,

Maryam Mohammadzadeh,

Vaishnavi Rao,

Courtney Pendleton,

Brandon W. Smith,

Nicholas F. Hug,

Sandip Biswal,

Jayne Seekins,

Sandy Napel,

Robert J. Spinner,

Mark A. Mahan,

Kristen W. Yeom,

Thomas J. Wilson

Publication year - 2021

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/noab211

Subject(s) - radiomics , random forest , logistic regression , medicine , artificial intelligence , machine learning , support vector machine , magnetic resonance imaging , radiology , computer science

Non-invasive differentiation between schwannomas and neurofibromas is important for appropriate management, preoperative counseling, and surgical planning, but has proven difficult using conventional imaging. The objective of this study was to develop and evaluate machine learning approaches for differentiating peripheral schwannomas from neurofibromas.

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