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

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

Radiomics—the quantification of features within tumor images—has shown prognostic potential in cancer. Here, the authors use a machine learning approach to develop a radiomic-based small set of descriptors to predict ovarian cancer patient survival based on CT scans acquired pre-operatively in 364 p...

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Autores principales: Haonan Lu, Mubarik Arshad, Andrew Thornton, Giacomo Avesani, Paula Cunnea, Ed Curry, Fahdi Kanavati, Jack Liang, Katherine Nixon, Sophie T. Williams, Mona Ali Hassan, David D. L. Bowtell, Hani Gabra, Christina Fotopoulou, Andrea Rockall, Eric O. Aboagye
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Lenguaje:EN
Publicado: Nature Portfolio 2019
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Acceso en línea:https://doaj.org/article/7fff94167bb54d9d9ccc2351fa73055a
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spelling oai:doaj.org-article:7fff94167bb54d9d9ccc2351fa73055a2021-12-02T16:57:51ZA mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer10.1038/s41467-019-08718-92041-1723https://doaj.org/article/7fff94167bb54d9d9ccc2351fa73055a2019-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-08718-9https://doaj.org/toc/2041-1723Radiomics—the quantification of features within tumor images—has shown prognostic potential in cancer. Here, the authors use a machine learning approach to develop a radiomic-based small set of descriptors to predict ovarian cancer patient survival based on CT scans acquired pre-operatively in 364 patients.Haonan LuMubarik ArshadAndrew ThorntonGiacomo AvesaniPaula CunneaEd CurryFahdi KanavatiJack LiangKatherine NixonSophie T. WilliamsMona Ali HassanDavid D. L. BowtellHani GabraChristina FotopoulouAndrea RockallEric O. AboagyeNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-11 (2019)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Haonan Lu
Mubarik Arshad
Andrew Thornton
Giacomo Avesani
Paula Cunnea
Ed Curry
Fahdi Kanavati
Jack Liang
Katherine Nixon
Sophie T. Williams
Mona Ali Hassan
David D. L. Bowtell
Hani Gabra
Christina Fotopoulou
Andrea Rockall
Eric O. Aboagye
A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer
description Radiomics—the quantification of features within tumor images—has shown prognostic potential in cancer. Here, the authors use a machine learning approach to develop a radiomic-based small set of descriptors to predict ovarian cancer patient survival based on CT scans acquired pre-operatively in 364 patients.
format article
author Haonan Lu
Mubarik Arshad
Andrew Thornton
Giacomo Avesani
Paula Cunnea
Ed Curry
Fahdi Kanavati
Jack Liang
Katherine Nixon
Sophie T. Williams
Mona Ali Hassan
David D. L. Bowtell
Hani Gabra
Christina Fotopoulou
Andrea Rockall
Eric O. Aboagye
author_facet Haonan Lu
Mubarik Arshad
Andrew Thornton
Giacomo Avesani
Paula Cunnea
Ed Curry
Fahdi Kanavati
Jack Liang
Katherine Nixon
Sophie T. Williams
Mona Ali Hassan
David D. L. Bowtell
Hani Gabra
Christina Fotopoulou
Andrea Rockall
Eric O. Aboagye
author_sort Haonan Lu
title A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer
title_short A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer
title_full A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer
title_fullStr A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer
title_full_unstemmed A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer
title_sort mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer
publisher Nature Portfolio
publishDate 2019
url https://doaj.org/article/7fff94167bb54d9d9ccc2351fa73055a
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