Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis.

Journal: Annals of medicine
Published Date: Jul 20, 2025

Abstract

INTRODUCTION: Accurate breast cancer risk prediction is essential for early detection and personalized prevention strategies. While traditional models, such as Gail and Tyrer-Cuzick, are widely utilized, machine learning-based approaches may offer enhanced predictive performance. This systematic review and meta-analysis compare the accuracy of traditional statistical models and machine learning models in breast cancer risk prediction.

Authors

  • Liyuan Liu
    State Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.
  • Peng Zhou
    School of International Studies, Zhejiang University, Hangzhou, China.
  • Lijuan Hou
    Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine Shandong University, Jinan, China.
  • Chunyu Kao
    Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, Shandong 250100, China.
  • Ziyu Zhang
    Radiography and Medical Imaging, Monash University, Clayton, Victoria, Australia. Electronic address: zhangc.ziyu@gmail.com.
  • Di Wang
    Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Lixiang Yu
    Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China.
  • Fei Wang
    Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.
  • Yongjiu Wang
    Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine Shandong University, Jinan, China.
  • Zhigang Yu
    Department of Biomedical Engineering, ShenZhen University, ShenZhen, 518000, China.

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