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REVIEW  HIGH-GRADE GLIOMA 

The Quarterly Journal of Nuclear Medicine and Molecular Imaging 2018 September;62(3):272-80

DOI: 10.23736/S1824-4785.18.03095-9

Copyright © 2018 EDIZIONI MINERVA MEDICA

lingua: Inglese

Radiomics derived from amino-acid PET and conventional MRI in patients with high-grade gliomas

Philipp LOHMANN 1 , Martin KOCHER 1, 2, Jan STEGER 3, Norbert GALLDIKS 1, 3, 4

1 Institute of Neuroscience and Medicine (INM-3, -4), Forschungszentrum Juelich, Juelich, Germany; 2 Department of Stereotaxy and Functional Neurosurgery, University of Cologne, Cologne, Germany; 3 Department of Neurology, University of Cologne, Cologne, Germany; 4 Center of Integrated Oncology (CIO), Universities of Cologne and Bonn, Cologne, Germany



Radiomics is a technique that uses high-throughput computing to extract quantitative features from tomographic medical images such as MRI and PET that usually are beyond visual perception. Importantly, the radiomics approach can be performed using neuroimages that have already been acquired during the routine follow-up of the patients allowing an additional data evaluation at low cost. In Neuro-Oncology, these features can potentially be used for differential diagnosis of newly diagnosed cerebral lesions suggestive for brain tumors or for the prediction of response to a neurooncological treatment option. Furthermore, especially in the light of the recent update of the World Health Organization classification of brain tumors, radiomics also has the potential to non-invasively assess important prognostic and predictive molecular markers such as a mutation in the isocitrate dehydrogenase gene or a 1p/19q codeletion which are not accessible by conventional visual interpretation of MRI or PET findings. This review summarizes the current status of the rapidly evolving field of radiomics with a special focus on patients with high-grade gliomas.


KEY WORDS: Machine learning - Glioma - Positron-emission tomography - Magnetic resonance imaging

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