Machine learning model for diagnosing salivary gland adenoid cystic carcinoma based on clinical and ultrasound features.

Journal: Insights into imaging
Published Date: May 8, 2025

Abstract

OBJECTIVE: To develop and validate machine learning (ML) models for diagnosing salivary gland adenoid cystic carcinoma (ACC) in the salivary glands based on clinical and ultrasound features.

Authors

  • Huan-Zhong Su
    Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China. suhz666@163.com.
  • Zhi-Yong Li
    Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China.
  • Long-Cheng Hong
    Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
  • Yu-Hui Wu
    Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
  • Feng Zhang
    Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China; Key Laboratory of Food Quality and Safety for State Market Regulation, Beijing 100176, China. Electronic address: fengzhang@126.com.
  • Zuo-Bing Zhang
    Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
  • Xiao-Dong Zhang
    Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China. zxdon11@163.com.

Keywords

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