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Related Experiment Video

Updated: Jun 21, 2025

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A BERT-GNN Approach for Metastatic Breast Cancer Prediction Using Histopathology Reports.

Abdullah Basaad1, Shadi Basurra1, Edlira Vakaj1

  • 1School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK.

Diagnostics (Basel, Switzerland)
|July 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel BERT-GNN model for non-invasive metastatic breast cancer (MBC) prediction using histopathology reports. The model achieves high accuracy in identifying MBC patients, offering a promising tool for improved cancer diagnostics.

Keywords:
BERTGNNLLMMBCXAIextra trees classifiernode classificationrandom forest classifierunivariate selection

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Area of Science:

  • Oncology
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Metastatic breast cancer (MBC) is a significant cause of cancer mortality in women.
  • Accurate and early identification of MBC is crucial for effective treatment and patient outcomes.
  • Current diagnostic methods may have limitations in detecting cancer metastases non-invasively.

Purpose of the Study:

  • To develop and evaluate an innovative non-invasive classification model for predicting metastatic breast cancer (MBC).
  • To leverage the power of large language models (LLMs) and graph neural networks (GNNs) for MBC patient identification.
  • To explore the utility of histopathology reports in predicting MBC using advanced AI techniques.

Main Methods:

  • A novel BERT-GNN approach (BG-MBC) was developed, integrating graph information derived from BERT embeddings of histopathology reports.
  • Nodes were constructed from patient medical records, and BERT embeddings vectorized word representations to capture semantic information.
  • Feature selection methods including univariate selection, extra trees classifier, and Shapley values identified the top 30 crucial features from 676 embeddings.

Main Results:

  • The BG-MBC model demonstrated outstanding predictive performance with a detection rate of 0.98 and an Area Under the Curve (AUC) of 0.98.
  • The model effectively utilized attention scores from the LLM to capture pertinent features from histopathology reports for classification.
  • The study identified the most impactful features contributing to MBC prediction.

Conclusions:

  • The developed BERT-GNN model (BG-MBC) shows significant promise as a non-invasive tool for accurate metastatic breast cancer prediction.
  • Integrating LLMs and GNNs with histopathology report analysis offers a powerful approach for improving cancer diagnostics.
  • Further research is warranted to validate these findings and explore clinical applications of this innovative model.