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

TitleA mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer
Authors
Issue Date2019
Citation
Nature Communications, 2019, v. 10, n. 1, article no. 764 How to Cite?
AbstractThe 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.
Persistent Identifierhttp://hdl.handle.net/10722/341485
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLu, Haonan-
dc.contributor.authorArshad, Mubarik-
dc.contributor.authorThornton, Andrew-
dc.contributor.authorAvesani, Giacomo-
dc.contributor.authorCunnea, Paula-
dc.contributor.authorCurry, Ed-
dc.contributor.authorKanavati, Fahdi-
dc.contributor.authorLiang, Jack-
dc.contributor.authorNixon, Katherine-
dc.contributor.authorWilliams, Sophie T.-
dc.contributor.authorHassan, Mona Ali-
dc.contributor.authorBowtell, David D.L.-
dc.contributor.authorGabra, Hani-
dc.contributor.authorFotopoulou, Christina-
dc.contributor.authorRockall, Andrea-
dc.contributor.authorAboagye, Eric O.-
dc.date.accessioned2024-03-13T08:43:10Z-
dc.date.available2024-03-13T08:43:10Z-
dc.date.issued2019-
dc.identifier.citationNature Communications, 2019, v. 10, n. 1, article no. 764-
dc.identifier.urihttp://hdl.handle.net/10722/341485-
dc.description.abstractThe 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.-
dc.languageeng-
dc.relation.ispartofNature Communications-
dc.titleA mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1038/s41467-019-08718-9-
dc.identifier.pmid30770825-
dc.identifier.scopuseid_2-s2.0-85061583839-
dc.identifier.volume10-
dc.identifier.issue1-
dc.identifier.spagearticle no. 764-
dc.identifier.epagearticle no. 764-
dc.identifier.eissn2041-1723-
dc.identifier.isiWOS:000458754700007-

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