Optimizing thyroid AUS nodules malignancy prediction: a comprehensive study of logistic regression and machine learning models.

Journal: Frontiers in endocrinology
PMID: 39568807

Abstract

BACKGROUND: The accurate diagnosis of thyroid nodules with indeterminate cytology, particularly in the atypia of undetermined significance (AUS) category, remains challenging. This study aims to predict the risk of malignancy in AUS nodules by comparing two machine learning (ML) and three conventional logistic regression (LR) models.

Authors

  • Yuan Cao
    Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yixian Yang
    Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Yunchao Chen
    Department of Ultrasound, Zhongshan Hospital (Xiamen Branch), Fudan University, Xiamen, Fujian, China.
  • Mengqi Luan
    Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yan Hu
    Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • Lu Zhang
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States.
  • Weiwei Zhan
    Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China.
  • Wei Zhou
    Department of Eye Function Laboratory, Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China.

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