Imperial College London
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DrPaulaCunnea

Faculty of Medicine, Department of Surgery & Cancer

Advanced Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 1548p.cunnea

 
 
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Location

 

Institute of Reproductive and Developmental BiologyHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Lu:2019:10.1038/s41467-019-08718-9,
author = {Lu, H and Arshad, M and Thornton, A and Avesani, G and Cunnea, P and Curry, E and Kanavati, F and Nixon, K and Williams, ST and Ali, Hassan M and Bowtell, DDL and Gabra, H and Fotopoulou, C and Rockall, A and Aboagye, E},
doi = {10.1038/s41467-019-08718-9},
journal = {Nature Communications},
title = {A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic and molecular-phenotypes of epithelial ovarian cancer},
url = {http://dx.doi.org/10.1038/s41467-019-08718-9},
volume = {10},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Lu,H
AU  - Arshad,M
AU  - Thornton,A
AU  - Avesani,G
AU  - Cunnea,P
AU  - Curry,E
AU  - Kanavati,F
AU  - Nixon,K
AU  - Williams,ST
AU  - Ali,Hassan M
AU  - Bowtell,DDL
AU  - Gabra,H
AU  - Fotopoulou,C
AU  - Rockall,A
AU  - Aboagye,E
DO  - 10.1038/s41467-019-08718-9
PY  - 2019///
SN  - 2041-1723
TI  - A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic and molecular-phenotypes of epithelial ovarian cancer
T2  - Nature Communications
UR  - http://dx.doi.org/10.1038/s41467-019-08718-9
UR  - http://hdl.handle.net/10044/1/67260
VL  - 10
ER  -
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