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Molecular Classification of Breast Cancer Using Weakly Supervised Learning.

Wooyoung Jang1, Jonghyun Lee2,3, Kyong Hwa Park4

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Weakly supervised learning using whole-slide images shows promise for classifying breast cancer molecular subtypes. This artificial intelligence approach aids in diagnosis and can reduce workload for pathologists.

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Area of Science:

  • Digital pathology and computational biology.
  • Machine learning applications in oncology.

Background:

  • Accurate molecular classification of breast cancer is vital for treatment selection.
  • Digital pathology and weakly supervised learning (WSL) offer efficient alternatives to manual annotation for deep learning models.

Purpose of the Study:

  • To classify breast cancer molecular subtypes using WSL on whole-slide images.
  • To develop and evaluate an artificial intelligence (AI) model for this classification task.

Main Methods:

  • Utilized two whole-slide image datasets: Korea University Guro Hospital (KG) and The Cancer Genomic Atlas (TCGA).
  • Employed an attention-based heatmap for visualizing model predictions and analyzing histomorphological features.
  • Merged datasets to address subtype imbalance and improve model performance.

Main Results:

  • The combined KG+TCGA model achieved an area under the receiver operating characteristics curve of 0.749.
  • Attentive patches identified by the AI model correlated with known histomorphological features (e.g., high-grade nuclei in triple-negative, collagen fibers in luminal A).
  • WSL approach demonstrated promising performance despite data imbalance challenges.

Conclusions:

  • AI models utilizing WSL on whole-slide images show potential for breast cancer molecular subtyping.
  • Visual interpretability of AI predictions through heatmaps enhances understanding of model behavior.
  • AI can serve as a valuable screening tool, potentially reducing costs and pathologist workload.