Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [18F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer

Volume: 23, Issue: 5, Pages: 756 - 765
Published: Mar 24, 2021
Abstract
To examine the prognostic significance of pretreatment 2-deoxy-2-[18F]fluoro-d-glucose ([18F]-FDG) positron emission tomography (PET)-based radiomic features using a machine learning approach in patients with endometrial cancers. Included in this retrospective study were 53 patients with endometrial cancers who underwent [18F]-FDG PET/X-ray computed tomography (CT) before treatment. Since two different PET scanners were used, post-reconstruction...
Paper Details
Title
Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [18F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer
Published Date
Mar 24, 2021
Volume
23
Issue
5
Pages
756 - 765
Citation AnalysisPro
  • 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.