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This study introduces a novel deep learning framework for predicting breast cancer axillary lymph node status using multi-view imaging. The advanced model shows high accuracy, aiding clinicians in diagnosis.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Oncology
  • Breast Cancer Diagnostics

Background:

  • Deep learning models show potential for predicting axillary lymph node (ALN) status.
  • Existing models often lack multi-view joint prediction capabilities for real-world clinical scenarios.

Purpose of the Study:

  • To develop and evaluate a Multi-Task Learning (MTL) and Multi-Instance Learning (MIL) deep learning framework for ALN status prediction in breast cancer.
  • To simulate clinical diagnostic scenarios using ultrasound images of primary tumors and ALN regions.

Main Methods:

  • A two-stage deep learning framework utilizing the Segformer Transformer model was proposed.
  • The model was trained on ultrasound images with segmentation labels and validated on internal and external test cohorts.
  • Class Activation Mapping was employed to visualize model predictions.

Main Results:

  • The framework achieved an AUC of 0.832 (sensitivity 0.815, specificity 0.854) on the internal test cohort.
  • On the external test cohort, the model attained an AUC of 0.918 (sensitivity 0.851, specificity 0.957).
  • Class Activation Mapping confirmed accurate identification of metastatic areas in tumor and ALN regions.

Conclusions:

  • The proposed deep learning framework demonstrates strong performance in predicting ALN status in breast cancer.
  • This model can serve as an effective tool to assist clinicians in ALN status assessment, potentially improving diagnostic accuracy.