ArticleJCO clinical cancer informatics2024
Path-BigBird: An AI-Driven Transformer Approach to Classification of Cancer Pathology Reports.
Article in JCO clinical cancer informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Large language models for cancer registry abstraction: a real-world evaluation across models, variables, and cancer types.medRxiv : the preprint server for health sciences · 2026Article
- OncoPT: long-context transformer models for in hospital tumor phenotype extraction from pathology reports.NPJ digital medicine · 2026Article
- Mitigating Algorithmic Bias in Cancer Site Classification Models.JCO clinical cancer informatics · 2026Article
- Leveraging sequence-to-sequence models for semantic annotation of Dutch pathology reports.Journal of pathology informatics · 2026Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- Article
- Evaluating algorithmic bias on biomarker classification of breast cancer pathology reports.JAMIA open · 2025Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- Classifying Tumor Reportability Status From Unstructured Electronic Pathology Reports Using Language Models in a Population-Based Cancer Registry Setting.JCO clinical cancer informatics · 2024Article
- Transforming breast cancer management with real-world data and artificial intelligence.ESMO real world data and digital oncology · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
purposeSurgical pathology reports are critical for cancer diagnosis and management. To accurately extract information about tumor characteristics from pathology reports in near real time, we explore the impact of using domain-specific transformer models that understand cancer pathology reports.
methodsWe built a pathology transformer model, Path-BigBird, by using 2.7 million pathology reports from six SEER cancer registries. We then compare different variations of Path-BigBird with two less computationally intensive methods: Hierarchical Self-Attention Network (HiSAN) classification model and an off-the-shelf clinical transformer model (Clinical BigBird). We use five pathology information extraction tasks for evaluation: site, subsite, laterality, histology, and behavior. Model performance is evaluated by using macro and micro
resultsWe found that Path-BigBird and Clinical BigBird outperformed the HiSAN in all tasks. Clinical BigBird performed better on the
conclusionThe Path-BigBird pathology transformer model improves automated information extraction from pathology reports. Although Path-BigBird outperforms Clinical BigBird and HiSAN, these less computationally expensive models still have utility when resources are constrained.
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