TY  - JOUR
AU  - Galldiks, Norbert
AU  - Zadeh, Gelareh
AU  - Lohmann, Philipp
TI  - Artificial Intelligence, Radiomics, and Deep Learning in Neuro-Oncology
JO  - Neuro-oncology advances
VL  - 2
IS  - Supplement_4
SN  - 2632-2498
CY  - Oxford
PB  - Oxford University Press
M1  - FZJ-2021-00882
SP  - iv1 - iv2
PY  - 2021
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:33521635
UR  - <Go to ISI:>//WOS:000897684800001
DO  - DOI:10.1093/noajnl/vdaa179
UR  - https://juser.fz-juelich.de/record/890309
ER  -