Machine learning in personalized laryngeal cancer management: insights into clinical characteristics, therapeutic options, and survival predictions.

Journal: European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PMID: 39714621

Abstract

PURPOSE: Over the last 40 years, there has been an unusual trend where, even though there are more varied treatments, survival rates have not improved much. Our study used survival analysis and machine learning (ML) to investigate this odd situation and to improve prediction methods for treating non-metastatic LSCC.

Authors

  • Sakhr Alshwayyat
    Faculty of Medicine, Jordan University of Science & Technology, Irbid, Jordan.
  • Tamara Feras Kamal
    Faculty of Medicine, Jordan University of Science & Technology, Irbid, Jordan.
  • Tala Abdulsalam Alshwayyat
    Faculty of Medicine, Jordan University of Science & Technology, Irbid, Jordan.
  • Mustafa Alshwayyat
    Faculty of Medicine, Jordan University of Science & Technology, Irbid, Jordan.
  • Hamdah Hanifa
    Faculty of Medicine, University of Kalamoon, Al-Nabk, Syria.
  • Ramez M Odat
    Faculty of Medicine, Jordan University of Science & Technology, Irbid, Jordan.
  • Miassar Rawashdeh
    Division of Otolaryngology, Department of Special Surgery, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
  • Alia Alawneh
    Internal Medicine Department, Palliative Medicine, Jordan University of Science and Technology, Irbid, Jordan.
  • Kholoud Qassem
    King Hussein Cancer Center, Medical Oncology Department, Amman, Jordan.

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