Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

Abstract : Medical image processing and analysis (also known as Radiomics) is a rapidly growing discipline that maps digital medical images into quantitative data, with the end goal of generating imaging biomarkers as decision support tools for clinical practice. The use of imaging data from routine clinical work-up has tremendous potential in improving cancer care by heightening understanding of tumor biology and aiding in the implementation of precision medicine. As a noninvasive method of assessing the tumor and its microenvironment in their entirety, radiomics allows the evaluation and monitoring of tumor characteristics such as temporal and spatial heterogeneity. One can observe a rapid increase in the number of computational medical imaging publications - milestones that have highlighted the utility of imaging biomarkers in oncology. Nevertheless, the use of radiomics as clinical biomarkers still necessitates amelioration and standardization in order to achieve routine clinical adoption. This Review addresses the critical issues to ensure the proper development of radiomics as a biomarker and facilitate its implementation in clinical practice.
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Annals of Oncology, Oxford University Press (OUP), 2017, 28 (6), pp.1191 - 1206. 〈10.1093/annonc/mdx034〉
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Elaine Limkin, Roger Sun, Laurent Dercle, Evangelia Zacharaki, Charlotte Robert, et al.. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Annals of Oncology, Oxford University Press (OUP), 2017, 28 (6), pp.1191 - 1206. 〈10.1093/annonc/mdx034〉. 〈hal-01648559〉

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