Machine learning-based differentiation between multiple sclerosis and glioma WHO II°-IV° using O-(2-...
Machine learning-based differentiation between multiple sclerosis and glioma WHO II°-IV° using O-(2-[18F] fluoroethyl)-L-tyrosine positron emission tomography
About this item
Full title
Author / Creator
Kebir, Sied , Rauschenbach, Laurèl , Weber, Manuel , Lazaridis, Lazaros , Schmidt, Teresa , Keyvani, Kathy , Schäfer, Niklas , Milia, Asma , Umutlu, Lale , Pierscianek, Daniela , Stuschke, Martin , Forsting, Michael , Sure, Ulrich , Kleinschnitz, Christoph , Antoch, Gerald , Colletti, Patrick M. , Rubello, Domenico , Herrmann, Ken , Herrlinger, Ulrich , Scheffler, Björn , Bundschuh, Ralph A. and Glas, Martin
Publisher
New York: Springer US
Journal title
Language
English
Formats
Publication information
Publisher
New York: Springer US
Subjects
More information
Scope and Contents
Contents
Introduction
This study aimed to test the diagnostic significance of FET-PET imaging combined with machine learning for the differentiation between multiple sclerosis (MS) and glioma II°-IV°.
Methods
Our database was screened for patients in whom FET-PET imaging was performed for the diagnostic workup of newly diagnosed lesions evident on...
Alternative Titles
Full title
Machine learning-based differentiation between multiple sclerosis and glioma WHO II°-IV° using O-(2-[18F] fluoroethyl)-L-tyrosine positron emission tomography
Authors, Artists and Contributors
Author / Creator
Rauschenbach, Laurèl
Weber, Manuel
Lazaridis, Lazaros
Schmidt, Teresa
Keyvani, Kathy
Schäfer, Niklas
Milia, Asma
Umutlu, Lale
Pierscianek, Daniela
Stuschke, Martin
Forsting, Michael
Sure, Ulrich
Kleinschnitz, Christoph
Antoch, Gerald
Colletti, Patrick M.
Rubello, Domenico
Herrmann, Ken
Herrlinger, Ulrich
Scheffler, Björn
Bundschuh, Ralph A.
Glas, Martin
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_miscellaneous_2481646636
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2481646636
Other Identifiers
ISSN
0167-594X
E-ISSN
1573-7373
DOI
10.1007/s11060-021-03701-1
