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Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study

Authors :
Jayachandran Preetha, Chandrakanth
Meredig, Hagen
Brugnara, Gianluca
Mahmutoglu, Mustafa A
Foltyn, Martha
Isensee, Fabian
Kessler, Tobias
Pflüger, Irada
Schell, Marianne
Neuberger, Ulf
Petersen, Jens
Wick, Antje
Heiland, Sabine
Debus, Jürgen
Platten, Michael
Idbaih, Ahmed
Brandes, Alba A
Winkler, Frank
van den Bent, Martin J
Nabors, Burt
Stupp, Roger
Maier-Hein, Klaus H
Gorlia, Thierry
Tonn, Jörg-Christian
Weller, Michael
Wick, Wolfgang
Bendszus, Martin
Vollmuth, Philipp
Source :
The Lancet Digital Health; December 2021, Vol. 3 Issue: 12 pe784-e794, 11p
Publication Year :
2021

Abstract

Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibility and diagnostic value of synthetic post-contrast T1-weighted MRI generated from pre-contrast MRI sequences through deep convolutional neural networks (dCNN) for tumour response assessment in neuro-oncology.

Details

Language :
English
ISSN :
25897500
Volume :
3
Issue :
12
Database :
Supplemental Index
Journal :
The Lancet Digital Health
Publication Type :
Periodical
Accession number :
ejs58092945
Full Text :
https://doi.org/10.1016/S2589-7500(21)00205-3