Multi-task learning for joint prediction of breast cancer histological indicators in dynamic contrast-enhanced magnetic resonance imaging.

Journal: Computer methods and programs in biomedicine
Published Date: May 6, 2025

Abstract

OBJECTIVES: Achieving efficient analysis of multiple pathological indicators has great significance for breast cancer prognosis and therapeutic decision-making. In this study, we aim to explore a deep multi-task learning (MTL) framework for collaborative prediction of histological grade and proliferation marker (Ki-67) status in breast cancer using multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Authors

  • Rong Sun
    Hefei National Laboratory for Physical Sciences at the Microscale, Hefei, Anhui, P. R. China.
  • Xiujuan Li
    School of Electrical Engineering, Guangxi University, Nanning 530004, China. Electronic address: l781453379@163.com.
  • Baosan Han
    Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
  • Yuanzhong Xie
    Medical Imaging Center, Taian Central Hospital, Taian, Shandong, China. xie01088@126.com.
  • Shengdong Nie
    School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, 516 Jun Gong Road, Shanghai, 200093, China.

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