A Robust BERT-Based Deep Learning Model for Automated Cancer Type Extraction from Unstructured Pathology Reports

• URL: http://arxiv.org/abs/2508.15149v1
• Date: Thu, 21 Aug 2025 01:12:39 GMT
• Title: A Robust BERT-Based Deep Learning Model for Automated Cancer Type Extraction from Unstructured Pathology Reports
• Authors: Minh Tran, Jeffery C. Chan, Min Li Huang, Maya Kansara, John P. Grady, Christine E. Napier, Subotheni Thavaneswaran, Mandy L. Ballinger, David M. Thomas, Frank P. Lin,
• Abstract summary: Fine-tuning domain-specific models for precision tasks in oncology may pave the way for more efficient and accurate clinical information extraction.<n>This model significantly outperformed the baseline model and a Large Language Model, Mistral 7B, achieving FBertscore 0.98 and overall exact match of 80.61%.
• Score: 1.2546979106262524
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The accurate extraction of clinical information from electronic medical records is particularly critical to clinical research but require much trained expertise and manual labor. In this study we developed a robust system for automated extraction of the specific cancer types for the purpose of supporting precision oncology research. from pathology reports using a fine-tuned RoBERTa model. This model significantly outperformed the baseline model and a Large Language Model, Mistral 7B, achieving F1_Bertscore 0.98 and overall exact match of 80.61%. This fine-tuning approach demonstrates the potential for scalability that can integrate seamlessly into the molecular tumour board process. Fine-tuning domain-specific models for precision tasks in oncology, may pave the way for more efficient and accurate clinical information extraction.

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