Leveraging Large Language Models for Structured Information Extraction from Pathology Reports

Jeya Balaji Balasubramanian, Daniel Adams, Ioannis Roxanis,Amy Berrington de Gonzalez, Penny Coulson,Jonas S. Almeida,Montserrat García-Closas

CoRR（2025）

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Abstract

Background: Structured information extraction from unstructured histopathology reports facilitates data accessibility for clinical research. Manual extraction by experts is time-consuming and expensive, limiting scalability. Large language models (LLMs) offer efficient automated extraction through zero-shot prompting, requiring only natural language instructions without labeled data or training. We evaluate LLMs' accuracy in extracting structured information from breast cancer histopathology reports, compared to manual extraction by a trained human annotator. Methods: We developed the Medical Report Information Extractor, a web application leveraging LLMs for automated extraction. We developed a gold standard extraction dataset to evaluate the human annotator alongside five LLMs including GPT-4o, a leading proprietary model, and the Llama 3 model family, which allows self-hosting for data privacy. Our assessment involved 111 histopathology reports from the Breast Cancer Now (BCN) Generations Study, extracting 51 pathology features specified in the study's data dictionary. Results: Evaluation against the gold standard dataset showed that both Llama 3.1 405B (94.7 comparable to the human annotator (95.4 respectively). While Llama 3.1 70B (91.6 <0.001), its reduced computational requirements make it a viable option for self-hosting. Conclusion: We developed an open-source tool for structured information extraction that can be customized by non-programmers using natural language. Its modular design enables reuse for various extraction tasks, producing standardized, structured data from unstructured text reports to facilitate analytics through improved accessibility and interoperability.

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【要点】：论文提出了一种利用大型语言模型（LLM）从病理报告中自动提取结构化信息的方法，并与人工标注进行了对比，显示出高效性，创新性地实现了无需标注数据或训练的零样本提示提取。

【方法】：研究者开发了Medical Report Information Extractor这一基于LLM的自动化提取工具，并创建了一个金标准提取数据集来评估人工标注者与五个LLM的性能。

【实验】：实验使用了来自Breast Cancer Now (BCN) Generations Study的111份病理报告，提取了51个病理特征，实验结果表明Llama 3.1 405B模型的提取准确度与人工标注相当，而Llama 3.1 70B模型尽管准确度稍低，但其较低的计算要求使其成为可行的自托管选项。

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