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Nat Commun. 2019 Feb 15;10(1):764. doi: 10.1038/s41467-019-08718-9.

A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer.

Author information

1
Ovarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK.
2
Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK.
3
Peter MacCallum Cancer Centre, Melbourne, 3010, VIC, Australia.
4
Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, 3010, VIC, Australia.
5
Early Clinical Development, iMED Biotech Unit, AstraZeneca, Cambridge, SG8 6HB, UK.
6
Department of Radiology, Imperial College Healthcare NHS Trust, London, W12 0HS, UK.
7
Department of Radiology, The Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK.
8
Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK. eric.aboagye@imperial.ac.uk.

Abstract

The five-year survival rate of epithelial ovarian cancer (EOC) is approximately 35-40% despite maximal treatment efforts, highlighting a need for stratification biomarkers for personalized treatment. Here we extract 657 quantitative mathematical descriptors from the preoperative CT images of 364 EOC patients at their initial presentation. Using machine learning, we derive a non-invasive summary-statistic of the primary ovarian tumor based on 4 descriptors, which we name "Radiomic Prognostic Vector" (RPV). RPV reliably identifies the 5% of patients with median overall survival less than 2 years, significantly improves established prognostic methods, and is validated in two independent, multi-center cohorts. Furthermore, genetic, transcriptomic and proteomic analysis from two independent datasets elucidate that stromal phenotype and DNA damage response pathways are activated in RPV-stratified tumors. RPV and its associated analysis platform could be exploited to guide personalized therapy of EOC and is potentially transferrable to other cancer types.

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