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

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Title: A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic and molecular-phenotypes of epithelial ovarian cancer
Authors: Lu, H
Arshad, M
Thornton, A
Avesani, G
Cunnea, P
Curry, E
Kanavati, F
Nixon, K
Williams, ST
Ali Hassan, M
Bowtell, DDL
Gabra, H
Fotopoulou, C
Rockall, A
Aboagye, E
Item Type: Journal Article
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.
Issue Date: 15-Feb-2019
Date of Acceptance: 24-Jan-2019
URI: http://hdl.handle.net/10044/1/67260
DOI: https://dx.doi.org/10.1038/s41467-019-08718-9
ISSN: 2041-1723
Publisher: Nature Research (part of Springer Nature)
Journal / Book Title: Nature Communications
Volume: 10
Copyright Statement: © 2019 The Author(s). Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Sponsor/Funder: Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
Funder's Grant Number: RDC04 79560
RDC04 79560
RDC04
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
RELEVANT GENE SIGNATURES
TEXTURAL FEATURES
GENOMIC ANALYSES
EXPRESSION
GRADE
STROMA
RESISTANCE
SURVIVAL
CHEMORESISTANCE
RECOMMENDATIONS
MD Multidisciplinary
Publication Status: Published
Article Number: 764 (2019)
Appears in Collections:Division of Surgery
Division of Cancer
Faculty of Medicine



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