Journal Article

FZJ-2021-00882

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Artificial Intelligence, Radiomics, and Deep Learning in Neuro-Oncology

Galldiks, N. (Corresponding author)FZJ* ; Zadeh, G. ; Lohmann, P.FZJ*

2021
Oxford University Press Oxford

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Please use a persistent id in citations: http://hdl.handle.net/2128/27085  doi:10.1093/noajnl/vdaa179

Abstract: Besides the histomolecular evaluation of tissue samples obtained from resection or biopsy, neuroimaging forms the basis for the diagnosis of brain cancer. Contrast-enhanced MRI is the method of choice for brain tumor diagnostics, treatment planning, and follow-up. Currently, advanced MRI techniques as well as amino acid PET are increasingly applied, generating a large variety of imaging parameters for brain tumor diagnostics. This is also driven by the increasing availability of hybrid PET/CT and PET/MRI scanners. Evaluation of the complex, multiparametric imaging data can be achieved by methods from the emerging field of artificial intelligence, potentially supporting physicians in clinical routine. For example, time-consuming steps such as manual detection and segmentation of lesions can be performed fully automatically. Since computer-aided image analysis is independent of the experience level of the evaluating physician, the results are more standardized and improve the inter-institutional comparability.

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Contributing Institute(s):

1. Physik der Medizinischen Bildgebung (INM-4)
2. Kognitive Neurowissenschaften (INM-3)

Research Program(s):

1. 5252 - Brain Dysfunction and Plasticity (POF4-525) (POF4-525)
2. 5253 - Neuroimaging (POF4-525) (POF4-525)
3. DFG project 428090865 - Radiomics basierend auf MRT und Aminosäure PET in der Neuroonkologie (428090865) (428090865)

Appears in the scientific report 2021

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