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

Basaad, Abdullah and Basurra, Shadi and Vakaj, Edlira and Eldaly, Ahmed Karam and Abdelsamea, Mohammed M. (2024) A BERT-GNN Approach for Metastatic Breast Cancer Prediction Using Histopathology Reports. Diagnostics, 14 (13). p. 1365. ISSN 2075-4418

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Abstract

Metastatic breast cancer (MBC) continues to be a leading cause of cancer-related deaths among women. This work introduces an innovative non-invasive breast cancer classification model designed to improve the identification of cancer metastases. While this study marks the initial exploration into predicting MBC, additional investigations are essential to validate the occurrence of MBC. Our approach combines the strengths of large language models (LLMs), specifically the bidirectional encoder representations from transformers (BERT) model, with the powerful capabilities of graph neural networks (GNNs) to predict MBC patients based on their histopathology reports. This paper introduces a BERT-GNN approach for metastatic breast cancer prediction (BG-MBC) that integrates graph information derived from the BERT model. In this model, nodes are constructed from patient medical records, while BERT embeddings are employed to vectorise representations of the words in histopathology reports, thereby capturing semantic information crucial for classification by employing three distinct approaches (namely univariate selection, extra trees classifier for feature importance, and Shapley values to identify the features that have the most significant impact). Identifying the most crucial 30 features out of 676 generated as embeddings during model training, our model further enhances its predictive capabilities. The BG-MBC model achieves outstanding accuracy, with a detection rate of 0.98 and an area under curve (AUC) of 0.98, in identifying MBC patients. This remarkable performance is credited to the model’s utilisation of attention scores generated by the LLM from histopathology reports, effectively capturing pertinent features for classification.

Item Type:	Article
Identification Number:	10.3390/diagnostics14131365
Dates:	Date Event 25 June 2024 Accepted 27 June 2024 Published Online
Uncontrolled Keywords:	LLM, GNN, XAI, node classification, MBC, extra trees classifier, univariate selection, random forest classifier, BERT
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	05 Nov 2026 14:38
Last Modified:	05 Nov 2026 14:38
URI:	https://www.open-access.bcu.ac.uk/id/eprint/17293

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