A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic and molecular-phenotypes of epithelial ovarian cancer
File(s)
Published version
Author(s)
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.
Date Issued
2019-02-15
Date Acceptance
2019-01-24
Citation
Nature Communications, 2019, 10 (1)
ISSN
2041-1723
Publisher
Nature Research (part of Springer Nature)
Journal / Book Title
Nature Communications
Volume
10
Issue
1
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/.
License URL
Sponsor
Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
Cancer Research UK
National Institute for Health Research
Medical Research Council (MRC)
Grant Number
RDC04 79560
RDC04 79560
RDC04
16584
HTA/14/31/04
MR/N020782/1
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
RELEVANT GENE SIGNATURES
TEXTURAL FEATURES
GENOMIC ANALYSES
EXPRESSION
GRADE
STROMA
RESISTANCE
SURVIVAL
CHEMORESISTANCE
RECOMMENDATIONS
DNA Damage
Female
Humans
Machine Learning
Ovarian Neoplasms
Proteomics
Tomography, X-Ray Computed
Humans
Ovarian Neoplasms
DNA Damage
Tomography, X-Ray Computed
Proteomics
Female
Machine Learning
Publication Status
Published
Article Number
ARTN 764