Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma.

Journal: Oral oncology
Published Date: Mar 16, 2019

Abstract

OBJECTIVES: To develop and validate an algorithm to predict occult nodal metastasis in clinically node negative oral cavity squamous cell carcinoma (OCSCC) using machine learning. To compare algorithm performance to a model based on tumor depth of invasion (DOI).

Authors

  • AndrĂ©s M Bur
    1 Department of Otolaryngology-Head and Neck Surgery, School of Medicine, University of Kansas, Kansas City, Kansas, USA.
  • Andrew Holcomb
    Department of Otolaryngology - Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA.
  • Sara Goodwin
    Department of Otolaryngology - Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA.
  • Janet Woodroof
    Department of Pathology and Laboratory Medicine, University of Kansas School of Medicine, 3901 Rainbow Boulevard, Kansas City, KS, USA.
  • Omar Karadaghy
    Department of Otolaryngology - Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA.
  • Yelizaveta Shnayder
    Department of Otolaryngology - Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA.
  • Kiran Kakarala
    Department of Otolaryngology - Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA.
  • Jason Brant
    Department of Otolaryngology - Head and Neck Surgery, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, USA.
  • Matthew Shew
    1 Department of Otolaryngology-Head and Neck Surgery, School of Medicine, University of Kansas, Kansas City, Kansas, USA.

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