Journal Article FZJ-2020-01448

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PET/MRI Radiomics in Patients With Brain Metastases

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2020
Frontiers Research Foundation Lausanne

Frontiers in neurology 11, 1 () [10.3389/fneur.2020.00001]

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Abstract: 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.

Classification:

Contributing Institute(s):
  1. Kognitive Neurowissenschaften (INM-3)
  2. Physik der Medizinischen Bildgebung (INM-4)
  3. Jara-Institut Quantum Information (INM-11)
Research Program(s):
  1. 572 - (Dys-)function and Plasticity (POF3-572) (POF3-572)
  2. DFG project 428090865 - Radiomics basierend auf MRT und Aminosäure PET in der Neuroonkologie (428090865)

Appears in the scientific report 2020
Database coverage:
Medline ; Creative Commons Attribution CC BY 4.0 ; DOAJ ; OpenAccess ; Clarivate Analytics Master Journal List ; DOAJ Seal ; IF < 5 ; JCR ; NCBI Molecular Biology Database ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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Dokumenttypen > Aufsätze > Zeitschriftenaufsätze
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Institutssammlungen > INM > INM-3
Institutssammlungen > INM > INM-4
Workflowsammlungen > Öffentliche Einträge
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Open Access

 Datensatz erzeugt am 2020-03-10, letzte Änderung am 2023-05-22


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