Ovarian cancer: An update on imaging in the era of radiomics - 25/09/19

Doi : 10.1016/j.diii.2018.11.007

S. Nougaret ^a,^g,^⁎ , M. Tardieu ^a, H.A. Vargas ^b, C. Reinhold ^c, S. Vande Perre ^d, N. Bonanno ^e, E. Sala ^f, I. Thomassin-Naggara ^d
^a IRCM, Montpellier Cancer Research Institute, 34295 Montpellier, France
^b Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
^c Department of Radiology, McGill University Health Center, Montreal, Canada
^d Department of Radiology, Hopital Tenon, Assistance publique–Hôpitaux de Paris Sorbonne Universités, Institut des Sciences du Calcul et de Données (ISCD), 75020 Paris, France
^e Medical Imaging Department, Mater Dei Hospital, Msida, Malta
^f Department of Radiology, Box 218, Cambridge Biomedical Campus, Cambridge, United Kingdom
^g Department of Radiology, Montpellier Cancer Institute, INSERM, U1194, University of Montpellier, 34295 Montpellier, France

^⁎Corresponding author.

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Abstract

Tumor heterogeneity in ovarian cancer has been reported at the histological and genetic levels and is associated with adverse clinical outcomes. Tumor evaluation using standard computed tomography or magnetic resonance imaging techniques does not account for the intra- or inter-tumoral heterogeneity in advanced ovarian cancer with peritoneal carcinomatosis. As such, computational approaches in assessing tumor heterogeneity have been proposed using radiomics and radiogenomics in order to analyze the whole tumor heterogeneity as opposed to single biopsy sampling. As part of radiomics, texture analysis, which includes the extraction of multiple data from images has been proposed recently to evaluate advanced ovarian tumor heterogeneity. In this short review, we explain the basics of radiomics, how to perform texture analysis, and its applications to ovarian cancer imaging.

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Keywords : Ovarian cancer, Radiomics, Texture analysis, Genomics, Advanced imaging