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Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal

Yan Ding1, Fan Yang2, Mengxue Han1

  • 1Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China.

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Summary

A novel deep learning model integrating clinicopathological data and digital pathology images accurately predicts breast cancer lymph node metastasis. This multi-modal and multi-instance (MMMI) approach shows superior performance, especially for triple-negative breast cancer.

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Lymph node metastasis is a critical prognostic factor in breast cancer.
  • Accurate prediction of lymph node status is essential for treatment planning.
  • Current prediction methods have limitations in accuracy and scope.

Purpose of the Study:

  • To develop and validate a deep learning model for predicting breast cancer lymph node metastasis.
  • To integrate clinicopathological data with digital whole slide images (WSI) for enhanced prediction.
  • To compare the performance of the integrated model against models using individual data types.

Main Methods:

  • Collected data from 3891 breast cancer patients across multiple medical centers.
  • Developed a multi-modal and multi-instance (MMMI) deep learning model.
  • Integrated clinicopathological features and WSI features for prediction.
  • Validated the model's performance using Area Under the Curve (AUC) for various lymph node metastasis classifications.

Main Results:

  • The MMMI model achieved the highest AUC for predicting lymph node metastasis (0.809) compared to clinicopathological features (0.770) and WSI alone (0.709).
  • MMMI demonstrated superior accuracy across all four lymph node status classifications (no metastasis, ITCs, micrometastasis, macrometastasis).
  • The model showed enhanced prediction accuracy for triple-negative breast cancer (TNBC) and validated well on external datasets.

Conclusions:

  • The developed MMMI deep learning model offers a highly accurate approach for predicting breast cancer lymph node metastasis.
  • Integrating multi-modal data significantly improves prediction performance over single-modality models.
  • This AI-driven tool has the potential to aid in clinical decision-making for breast cancer management.