PET/MRI Radiomics in Patients With Brain Metastases

Although a variety of imaging modalities are used or currently being investigated for patients with brain tumors including brain metastases, clinical image interpretation to date uses only a fraction of the underlying complex, high-dimensional digital information from routinely acquired imaging data...

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Bibliographic Details
Published in:Frontiers in neurology 2020-02, Vol.11, p.1-1
Main Authors: Lohmann, Philipp, Kocher, Martin, Ruge, Maximillian I, Visser-Vandewalle, Veerle, Shah, N Jon, Fink, Gereon R, Langen, Karl-Josef, Galldiks, Norbert
Format: Article
Language:English
Subjects:
amino acid PET
artificial intelligence
brain tumors
deep learning
machine learning
Neurology
textural features
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Summary:Although a variety of imaging modalities are used or currently being investigated for patients with brain tumors including brain metastases, clinical image interpretation to date uses only a fraction of the underlying complex, high-dimensional digital information from routinely acquired imaging data. The growing availability of high-performance computing allows the extraction of quantitative imaging features from medical images that are usually beyond human perception. Using machine learning techniques and advanced statistical methods, subsets of such imaging features are used to generate mathematical models that represent characteristic signatures related to the underlying tumor biology and might be helpful for the assessment of prognosis or treatment response, or the identification of molecular markers. The identification of appropriate, characteristic image features as well as the generation of predictive or prognostic mathematical models is summarized under the term radiomics. This review summarizes the current status of radiomics in patients with brain metastases.
ISSN:1664-2295
1664-2295
DOI:10.3389/fneur.2020.00001