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

Name: A mathematical descriptor of tumor mesoscopic-structure from computed tomography images annotates prognostic and molecular phenotypes of epithelial ovarian cancer
Creator: Haonan Lu
Published: 2018-12-15T19:22:33.489Z
Keywords: Proteomics, Biomedical Imaging

Lu, Haonan; Arshad, Mubarik; Thornton, Andrew; Avesani, Giacomo; Cunnea, Paula; Curry, Ed; Kanavati, Fahdi; Liang, Jack; Nixon, Katherine; Williams, Sophie; Hassan, Mona; Bowtell, David; Gabra, Hani; Fotopoulou, Christina; Rockall, Andrea; Aboagye, Eric

doi:10.17632/4c5znk5m8t.2

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

Published: 15 December 2018| Version 2 | DOI: 10.17632/4c5znk5m8t.2

Contributors:

, Mubarik Arshad, Andrew Thornton, Giacomo Avesani, Paula Cunnea, Ed Curry, Fahdi Kanavati, Jack Liang, Katherine Nixon, Sophie Williams, Mona Hassan, David Bowtell, Hani Gabra, Christina Fotopoulou, Andrea Rockall,

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The clinical, radiomics and proteomics data to reproduce the key findings in 'A mathematical descriptor of tumor mesoscopic-structure from computed tomography images annotates prognostic and molecular phenotypes of epithelial ovarian cancer'. Radiomics data for both the HH and the TCGA cohort are scaled and centered; RPV and eRPV (Affymentrix U133) are included in the clinical dataframes; Normalised, log2 transformed and median centered proteomics data are provided from reverse phase protein array (RPPA).

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

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