Nature Communications (Feb 2019)

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,
  • Eric O. Aboagye

DOI
https://doi.org/10.1038/s41467-019-08718-9
Journal volume & issue
Vol. 10, no. 1
pp. 1 – 11

Abstract

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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.