Exploring personalized neoadjuvant therapy selection strategies in breast cancer: an explainable multi-modal response model.

Journal: EClinicalMedicine
Published Date: Jul 17, 2025

Abstract

BACKGROUND: Neoadjuvant therapy (NAT) regimens for breast cancer are generally determined according to cancer stage and molecular subtypes without fully considering the inter-patient variability, which may lead to inefficiency or overtreatment. Artificial intelligence (AI) may support personalized regimen recommendations by learning the synergistic relationship between pre-NAT individual-patient data, regimens, and corresponding short- or long-term therapy responses.

Authors

  • Luyi Han
    College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, 610065, China.
  • Tianyu Zhang
    State Key Laboratory of Respiratory Disease, Joint School of Life Sciences, Guangzhou Chest Hospital, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, China.
  • Anna D'Angelo
    Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario 'A. Gemelli' IRCCS, Rome, Italy.
  • Anna van der Voort
    Department of Internal Medicine, Dijklander Hospital, Hoorn, the Netherlands.
  • Katja Pinker-Domenig
    Division of Breast Imaging, Department of Radiology, Columbia University, Vagelos College of Physicians and Surgeons, New York, USA.
  • Marleen Kok
    Department of Tumor Biology and Immunology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
  • Gabe Sonke
    Department of Medical Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam, 1066 CX, the Netherlands.
  • Yuan Gao
    Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou Zhejiang Province, China.
  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Chunyao Lu
    Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Geert Grooteplein 10, Nijmegen, 6525 GA, the Netherlands.
  • Xinglong Liang
    Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Geert Grooteplein 10, Nijmegen, 6525 GA, the Netherlands.
  • Jonas Teuwen
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
  • Tao Tan
    Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.
  • Ritse Mann
    Diagnostic Image Analysis Group, Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.

Keywords

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