A prognostic model for thermal ablation of benign thyroid nodules based on interpretable machine learning.

Journal: Frontiers in endocrinology
PMID: 39224123

Abstract

INTRODUCTION: The detection rate of benign thyroid nodules is increasing every year, with some affected patients experiencing symptoms. Ultrasound-guided thermal ablation can reduce the volume of nodules to alleviate symptoms. As the degree and speed of lesion absorption vary greatly between individuals, an effective model to predict curative effect after ablation is lacking. This study aims to predict the efficacy of ultrasound-guided thermal ablation for benign thyroid nodules using machine learning and explain the characteristics affecting the nodule volume reduction ratio (VRR).

Authors

  • Zuolin Li
    Department of Ultrasound, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China.
  • Wei Nie
    Radiation Oncology Division, Inova Schar Cancer Institute, Fairfax, VA, United States of America.
  • Qingfa Liu
    School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China.
  • Min Lin
  • Xiaolian Li
    Department of Ultrasound, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China.
  • Jiantang Zhang
    Department of Ultrasound, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China.
  • Tengfu Liu
    Department of Ultrasound, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China.
  • Yongluo Deng
    Department of Ultrasound, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China.
  • Shuiping Li
    Department of Ultrasound, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China.

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