Prediction of the treatment outcome using machine learning with FDG-PET image-based multiparametric approach in patients with oral cavity squamous cell carcinoma.

Journal: Clinical radiology
Published Date: Apr 30, 2021

Abstract

AIM: To investigate the value of machine learning-based multiparametric analysis using 2-[F]-fluoro-2-deoxy-d-glucose positron-emission tomography (FDG-PET) images to predict treatment outcome in patients with oral cavity squamous cell carcinoma (OCSCC).

Authors

  • N Fujima
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, USA; Research Center for Cooperative Projects, Hokkaido University Graduate School of Medicine, Japan.
  • V C Andreu-Arasa
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, USA.
  • S K Meibom
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, USA.
  • G A Mercier
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, USA.
  • A R Salama
    Department of Otolaryngology - Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, USA; Department of Oral & Maxillofacial Surgery, Boston Medical Center, Boston University Henry M. Goldman School of Dental Medicine, USA.
  • M T Truong
    Department of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, USA.
  • O Sakai
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, USA; Department of Otolaryngology - Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, USA; Department of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, USA. Electronic address: Osamu.Sakai@bmc.org.

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