ArticleNature communications2024
Generalizable and automated classification of TNM stage from pathology reports with external validation.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- Adapting transformer-based encoder models for automated billing code assignment of current procedural terminology from pathology reports in multi-institutional settings.Journal of pathology informatics · 2026Article
- Pathology-derived clinical micro-architectural diagnostics of tumour-microbiome interactions in colorectal cancer.Journal of translational medicine · 2026Article
- Comparative Evaluation of Pretrained Large Language Models for Suicide Risk Prediction from Clinical Notes in U.S. Veterans.medRxiv : the preprint server for health sciences · 2026Article
- Cancer staging data collection using rules-based natural language processing for entity extraction from pathology notifications: the WA cancer staging project.BMC medical informatics and decision making · 2026Article
- OncoPT: long-context transformer models for in hospital tumor phenotype extraction from pathology reports.NPJ digital medicine · 2026Article
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- Application of a Natural Language Processing Framework for Data Extraction From Pathology Reports Across Multiple Cancer Types.Journal of Korean medical science · 2026Article
- Integrating Large Language Models with Deep Learning for Breast Cancer Treatment Decision Support.Diagnostics (Basel, Switzerland) · 2026Article
- Casting wider than U-Nets: evaluating the role of novel machine learning applications in radiation therapy.BJR artificial intelligence · 2026Review
- Enabling Just-in-Time Clinical Oncology Analysis With Large Language Models: Feasibility and Validation Study Using Unstructured Synthetic Data.JMIR medical informatics · 2025Article
- Real-world application of large language models for automated TNM staging using unstructured gynecologic oncology reports.NPJ precision oncology · 2025Article
- Development of a machine learning model for automatic data extraction from breast cancer pathology reports.Scientific reports · 2025Article
- Towards scalable and cross-lingual specialist language models for oncology.Scientific reports · 2025Article
- Cancer type, stage and prognosis assessment from pathology reports using LLMs.Scientific reports · 2025Article
- Machine-Learning Approach to Identify Organic Functional Groups from FT-IR and NMR Spectral Data.ACS omega · 2025Article
- Article
- Applications of Large Language Models in Pathology.Bioengineering (Basel, Switzerland) · 2024Review
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5 authors.
Funding
Abstract
Cancer staging is an essential clinical attribute informing patient prognosis and clinical trial eligibility. However, it is not routinely recorded in structured electronic health records. Here, we present BB-TEN: Big Bird - TNM staging Extracted from Notes, a generalizable method for the automated classification of TNM stage directly from pathology report text. We train a BERT-based model using publicly available pathology reports across approximately 7000 patients and 23 cancer types. We explore the use of different model types, with differing input sizes, parameters, and model architectures. Our final model goes beyond term-extraction, inferring TNM stage from context when it is not included in the report text explicitly. As external validation, we test our model on almost 8000 pathology reports from Columbia University Medical Center, finding that our trained model achieved an AU-ROC of 0.815-0.942. This suggests that our model can be applied broadly to other institutions without additional institution-specific fine-tuning.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.