Random forests to predict tumor recurrence following cervical cancer therapy using pre- and per-treatment 18F-FDG PET parameters

Geoffrey Roman-Jimenez; Oscar Acosta; Julie Leseur; Anne Devillers; Henri Der Sarkissian; Lina Guzman; Eloi'se Grossiord; J.D. Ospina; Renaud de Crevoisier

doi:https://doi.org/10.1109/embc.2016.7591224

doi.org/10.1109/embc.2016.7591224

Random forests to predict tumor recurrence following cervical cancer therapy using pre- and per-treatment 18F-FDG PET parameters

Geoffrey Roman-Jimenez

1

,

Oscar Acosta

22

,

..., Renaud de Crevoisier

33

Published: Aug 1, 2016

Abstract

The ability to predict tumor recurrence after chemoradiotherapy of locally advanced cervical cancer is a crucial clinical issue to intensify the treatment of the most high-risk patients. The objective of this study was to investigate tumor metabolism characteristics extracted from pre- and per-treatment 18F-FDG PET images to predict 3-year overall recurrence (OR). A total of 53 locally advanced cervical cancer patients underwent pre- and...

Paper Fields

Paper Details

Title

Random forests to predict tumor recurrence following cervical cancer therapy using pre- and per-treatment 18F-FDG PET parameters

DOI

doi.org/10.1109/embc.2016.7591224

Published Date

Aug 1, 2016

Citation AnalysisPro

You’ll need to upgrade your plan to Pro

Looking to understand the true influence of a researcher’s work across journals & affiliations?

Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.

Learn more

Notes

History