Machine learning based on magnetic resonance imaging and clinical parameters helps predict mesenchymal-epithelial transition factor expression in oral tongue squamous cell carcinoma: a pilot study.

Allergy & Immunology Dermatology Hematology Hospital-Based Medicine Oncology/Hematology
Journal: Oral surgery, oral medicine, oral pathology and oral radiology
Published Date: Dec 27, 2023

Abstract

OBJECTIVES: This study aimed to develop machine learning models to predict phosphorylated mesenchymal-epithelial transition factor (p-MET) expression in oral tongue squamous cell carcinoma (OTSCC) using magnetic resonance imaging (MRI)-derived texture features and clinical features.

Authors

  • Gongxin Yang
    Department of Radiology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Zebin Xiao
    Department of Biomedical Sciences, University of Pennsylvania, Pennsylvania, PA, USA.
  • Jiliang Ren
    Department of Radiology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • RongHui Xia
    Department of Radiology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Yingwei Wu
    Department of Radiology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Ying Yuan
    Department of Radiology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. Electronic address: [email protected].
  • Xiaofeng Tao
    Department of Radiology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. Electronic address: [email protected].

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