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Imagerie médicale computationnelle (radiomique) et potentiel en immuno-oncologie

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 noninvasive, 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.
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Contributor : Evangelia Zacharaki <>
Submitted on : Wednesday, December 20, 2017 - 1:08:24 PM
Last modification on : Tuesday, July 20, 2021 - 3:04:33 AM


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Roger Sun, Elaine Limkin, Laurent Dercle, Sylvain Reuzé, Evangelia Zacharaki, et al.. Imagerie médicale computationnelle (radiomique) et potentiel en immuno-oncologie. Cancer Radiothérapie, Elsevier Masson, 2017, 21 (6-7), pp.648-654. ⟨10.1016/j.canrad.2017.07.035⟩. ⟨hal-01668902⟩



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