| Literature DB >> 33882246 |
Morgan Michalet1,2, David Azria1,2, Marion Tardieu2, Hichem Tibermacine2, Stéphanie Nougaret2.
Abstract
Radiomics is the extraction of a significant number of quantitative imaging features with the aim of detecting information in correlation with useful clinical outcomes. Features are extracted, after delineation of an area of interest, from a single or a combined set of imaging modalities (including X-ray, US, CT, PET/CT and MRI). Given the high dimensionality, the analytical process requires the use of artificial intelligence algorithms. Firstly developed for diagnostic performance in radiology, it has now been translated to radiation oncology mainly to predict tumor response and patient outcome but other applications have been developed such as dose painting, prediction of side-effects, and quality assurance. In gynecological cancers, most studies have focused on outcomes of cervical cancers after chemoradiation. This review highlights the role of this new tool for the radiation oncologists with particular focus on female GU oncology.Entities:
Mesh:
Year: 2021 PMID: 33882246 PMCID: PMC9327766 DOI: 10.1259/bjr.20210032
Source DB: PubMed Journal: Br J Radiol ISSN: 0007-1285 Impact factor: 3.629