Applying a multi-task and multi-instance framework to predict axillary lymph node metastases in breast cancer.

Journal: NPJ precision oncology
Published Date: Jun 18, 2025

Abstract

Deep learning (DL) models have shown promise in predicting axillary lymph node (ALN) status. However, most existing DL models were classification-only models and did not consider the practical application scenarios of multi-view joint prediction. Here, we propose a Multi-Task Learning (MTL) and Multi-Instance Learning (MIL) framework that simulates the real-world clinical diagnostic scenario for ALN status prediction in breast cancer. Ultrasound images of the primary tumor and ALN (if available) regions were collected, each annotated with a segmentation label. The model was trained on a training cohort and tested on both internal and external test cohorts. The proposed two-stage DL framework using one of the Transformer models, Segformer, as the network backbone, exhibits the top-performing model. It achieved an AUC of 0.832, a sensitivity of 0.815, and a specificity of 0.854 in the internal test cohort. In the external cohort, this model attained an AUC of 0.918, a sensitivity of 0.851 and a specificity of 0.957. The Class Activation Mapping method demonstrated that the DL model correctly identified the characteristic areas of metastasis within the primary tumor and ALN regions. This framework may serve as an effective second reader to assist clinicians in ALN status assessment.

Authors

  • Yizhi Li
    Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, 410000, China.
  • Zonglin Chen
    Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, 410000, China.
  • Ziyuan Ding
    School of Computer Science, Central South University, Changsha, 410006, China.
  • Danyang Mei
    Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, 410000, China.
  • Zhenzhen Liu
    Department of Functional Science, School of Medicine, Yangtze University, No.1 Nanhuan Road, Jingzhou City 434100, China.
  • Jia Wang
    Institute of Special Animal and Plant Sciences, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
  • Kui Tang
    Department of Ultrasound Diagnosis, The Second Xiangya Hospital of Central South University, Changsha, 410000, China.
  • Wenjun Yi
    Department of General Surgery, The Second Xiangya Hospital of Central South University, No. 139, Renmin Central Road, Changsha, 410011, P.R. China. yiwenjun@csu.edu.cn.
  • Yan Xu
    Department of Nephrology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.
  • Yixiong Liang
    School of Information Science and Engineering, Central South University, Changsha, China; Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China. Electronic address: yxliang@csu.edu.cn.
  • Yan Cheng
    The First Clinical Medical College of Shaanxi University of Chinese Medicine, Xianyang, China.

Keywords

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