%0 Journal Article
%A Galldiks, Norbert
%A Zadeh, Gelareh
%A Lohmann, Philipp
%T Artificial Intelligence, Radiomics, and Deep Learning in Neuro-Oncology
%J Neuro-oncology advances
%V 2
%N Supplement_4
%@ 2632-2498
%C Oxford
%I Oxford University Press
%M FZJ-2021-00882
%P iv1 - iv2
%D 2021
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:33521635
%U <Go to ISI:>//WOS:000897684800001
%R 10.1093/noajnl/vdaa179
%U https://juser.fz-juelich.de/record/890309