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Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study

We aimed to develop a framework relying on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumour burden.

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Main Authors: Vollmuth, Philipp (Author) , Pflüger, Irada (Author) , Petersen, Jens (Author) , Neuberger, Ulf (Author) , Brugnara, Gianluca (Author) , Schell, Marianne (Author) , Keßler, Tobias (Author) , Foltyn-Dumitru, Martha (Author) , Harting, Inga (Author) , Sahm, Felix (Author) , Prager, Marcel (Author) , Nowosielski, Martha (Author) , Wick, Antje (Author) , Radbruch, Alexander (Author) , Debus, Jürgen (Author) , Schlemmer, Heinz-Peter (Author) , Heiland, Sabine (Author) , Platten, Michael (Author) , Deimling, Andreas von (Author) , Wick, Wolfgang (Author) , Bendszus, Martin (Author) , Maier-Hein, Klaus H. (Author)
Format: Article (Journal)
Language:English
Published: April 2, 2019
In: The lancet. Oncology
Year: 2019, Volume: 20, Issue: 5, Pages: 728-740
ISSN:1474-5488
DOI:10.1016/S1470-2045(19)30098-1
Online Access:Resolving-System, Volltext: https://doi.org/10.1016/S1470-2045(19)30098-1
Verlag: http://www.sciencedirect.com/science/article/pii/S1470204519300981
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Author Notes:Philipp Kickingereder, Fabian Isensee, Irada Tursunova, Jens Petersen, Ulf Neuberger, David Bonekamp, Gianluca Brugnara, Marianne Schell, Tobias Kessler, Martha Foltyn, Inga Harting, Felix Sahm, Marcel Prager, Martha Nowosielski, Antje Wick, Marco Nolden, Alexander Radbruch, Jürgen Debus, Heinz-Peter Schlemmer, Sabine Heiland, Michael Platten, Andreas von Deimling, Martin J van den Bent, Thierry Gorlia, Wolfgang Wick, Martin Bendszus, Klaus H Maier-Hein
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Summary:We aimed to develop a framework relying on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumour burden.
Item Description:Gesehen am 16.10.2019
Physical Description:Online Resource
ISSN:1474-5488
DOI:10.1016/S1470-2045(19)30098-1

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