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Artificial intelligence modelling in grading breast phyllodes tumours.

Jessica Ee Ting Koong1, Abubakr Shafique2, Nur Diyana Md Nasir3

  • 1Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

Histopathology
|March 19, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in grading breast phyllodes tumors (PT). This study used AI to analyze histological features, achieving 67% accuracy in classifying tumor grades, offering a potential diagnostic aid.

Keywords:
artificial intelligencebreast phyllodes tumoursdiagnostic aidgradingwhole slide images

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Area of Science:

  • Digital pathology
  • Computational pathology
  • Artificial intelligence in medicine

Background:

  • Breast phyllodes tumors (PT) are rare biphasic neoplasms with benign, borderline, and malignant classifications.
  • Accurate grading of PTs is challenging due to complex histological parameters.
  • Investigating artificial intelligence (AI) as a diagnostic aid for PT grading is crucial.

Purpose of the Study:

  • To explore the potential of AI in stratifying breast phyllodes tumor grades.
  • To assess AI's ability to classify PTs based on histological similarities.
  • To evaluate AI as a tool for improving diagnostic accuracy in PT grading.

Main Methods:

  • Utilized 15 PT whole slide images (WSIs) across benign, borderline, and malignant categories.
  • Employed the Yottixel framework for WSI processing and KimiaNet for feature extraction.
  • Compared WSI barcodes at various patch sizes to identify histological similarities for grading.

Main Results:

  • Achieved a maximum accuracy of 67% for PT grade stratification.
  • Optimal performance was observed with a 3000×3000 patch size using majority voting (n=4).
  • Demonstrated AI's capability in identifying relevant histological features for tumor grading.

Conclusions:

  • AI shows potential for grading breast phyllodes tumors through histological feature matching.
  • This study serves as a proof of concept for AI-driven PT grade stratification.
  • Further refinement could lead to routine clinical application of AI in PT diagnosis.