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

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 and Eric O. Aboagye ()
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Haonan Lu: Imperial College London
Mubarik Arshad: Imperial College London
Andrew Thornton: Imperial College London
Giacomo Avesani: Imperial College London
Paula Cunnea: Imperial College London
Ed Curry: Imperial College London
Fahdi Kanavati: Imperial College London
Jack Liang: Imperial College London
Katherine Nixon: Imperial College London
Sophie T. Williams: Imperial College London
Mona Ali Hassan: Imperial College London
David D. L. Bowtell: Peter MacCallum Cancer Centre
Hani Gabra: Imperial College London
Christina Fotopoulou: Imperial College London
Andrea Rockall: Imperial College London
Eric O. Aboagye: Imperial College London

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: 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: 2019
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DOI: 10.1038/s41467-019-08718-9

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