Personalized prediction of breast cancer candidates for Anti-HER2 therapy using F-FDG PET/CT parameters and machine learning: a dual-center study.

Journal: Frontiers in oncology
Published Date: Jan 1, 2025

Abstract

BACKGROUND: Accurately evaluating human epidermal growth factor receptor (HER2) expression status in breast cancer enables clinicians to develop individualized treatment plans and improve patient prognosis. The purpose of this study was to assess the performance of a machine learning (ML) model that was developed using F-FDG PET/CT parameters and clinicopathological features in distinguishing different levels of HER2 expression in breast cancer.

Authors

  • Zhenguo Sun
    Key Laboratory of Eco-textiles of Ministry of Education, Jiangnan University, Wuxi 214122, China.
  • Jianxiong Gao
    Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.
  • Wenji Yu
    Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.
  • Xiaoshuai Yuan
    Department of Nuclear Medicine, The First People's Hospital of Lianyungang/The First Affiliated Hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, China.
  • Peng Du
    Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
  • Peng Chen
  • Yuetao Wang
    Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.

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