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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Nature Publishing Group
2019-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-08718-9 |
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doaj-7fff94167bb54d9d9ccc2351fa73055a2021-05-11T11:42:06ZengNature Publishing GroupNature Communications2041-17232019-02-0110111110.1038/s41467-019-08718-9A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancerHaonan Lu0Mubarik Arshad1Andrew Thornton2Giacomo Avesani3Paula Cunnea4Ed Curry5Fahdi Kanavati6Jack Liang7Katherine Nixon8Sophie T. Williams9Mona Ali Hassan10David D. L. Bowtell11Hani Gabra12Christina Fotopoulou13Andrea Rockall14Eric O. Aboagye15Ovarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonCancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonOvarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonCancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonOvarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonOvarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonCancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonCancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonOvarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonOvarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonOvarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonPeter MacCallum Cancer CentreOvarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonOvarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonCancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonCancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College LondonRadiomics—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.https://doi.org/10.1038/s41467-019-08718-9 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| 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 |
| spellingShingle |
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 Nature Communications |
| 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 Publishing Group |
| series |
Nature Communications |
| issn |
2041-1723 |
| publishDate |
2019-02-01 |
| 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. |
| url |
https://doi.org/10.1038/s41467-019-08718-9 |
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