Fully automated 3D machine learning model for HPV status characterization in oropharyngeal squamous cell carcinomas based on CT images.

Journal: American journal of otolaryngology
PMID: 38703612

Abstract

BACKGROUND: Human papillomavirus (HPV) status plays a major role in predicting oropharyngeal squamous cell carcinoma (OPSCC) survival. This study assesses the accuracy of a fully automated 3D convolutional neural network (CNN) in predicting HPV status using CT images.

Authors

  • Edwin Qiu
    Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States of America.
  • Maryam Vejdani-Jahromi
    Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Artem Kaliaev
    Department of Radiology, Boston Medical Center, Boston, Massachusetts.
  • Sherwin Fazelpour
    Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States of America.
  • Deniz Goodman
    Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA.
  • Inseon Ryoo
    Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea. isryoo@gmail.com.
  • V Carlota Andreu-Arasa
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, FGH Building, 3rd Floor, 820 Harrison Avenue, Boston, MA, 02118, USA.
  • Noriyuki Fujima
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, FGH Building, 3rd Floor, 820 Harrison Avenue, Boston, MA, 02118, USA.
  • Karen Buch
    Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.
  • Osamu Sakai
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, FGH Building, 3rd Floor, 820 Harrison Avenue, Boston, MA, 02118, USA. Osamu.Sakai@bmc.org.

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