Computational medical imaging (radiomics) and potential for immuno-oncology

R. Sun, E. J. Limkin, L. Dercle, S. Reuzé, E. I. Zacharaki, C. Chargari, A. Schernberg, A. S. Dirand, A. Alexis, N. Paragios, Deutsch, C. Ferté, C. Robert

    Research output: Contribution to journalShort surveypeer-review

    15 Citations (Scopus)

    Abstract

    The arrival of immunotherapy has profoundly changed the management of multiple cancers, obtaining unexpected tumour responses. However, until now, the majority of patients do not respond to these new treatments. The identification of biomarkers to determine precociously responding patients is a major challenge. Computational medical imaging (also known as radiomics) is a promising and rapidly growing discipline. This new approach consists in the analysis of high-dimensional data extracted from medical imaging, to further describe tumour phenotypes. This approach has the advantages of being non-invasive, capable of evaluating the tumour and its microenvironment in their entirety, thus characterising spatial heterogeneity, and being easily repeatable over time. The end goal of radiomics is to determine imaging biomarkers as decision support tools for clinical practice and to facilitate better understanding of cancer biology, allowing the assessment of the changes throughout the evolution of the disease and the therapeutic sequence. This review will develop the process of computational imaging analysis and present its potential in immuno-oncology.

    Translated title of the contributionImagerie médicale computationnelle (radiomique) et potentiel en immuno-oncologie
    Original languageEnglish
    Pages (from-to)648-654
    Number of pages7
    JournalCancer/Radiotherapie
    Volume21
    Issue number6-7
    DOIs
    Publication statusPublished - 1 Oct 2017

    Keywords

    • Computational medical imaging
    • Immunology
    • Oncology
    • Radiomics

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