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Extraction and classification of structured data from unstructured hepatobiliary pathology reports using large

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Summary
This summary is machine-generated.

Large language models (LLMs) accurately extract key pathology elements for cancer research, significantly reducing manual curation time. This advancement promises to accelerate crucial cancer research by simplifying data extraction from pathology reports.

Keywords:
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Area of Science:

  • Oncology
  • Pathology Informatics
  • Artificial Intelligence in Medicine

Background:

  • Structured reporting in pathology is crucial but not universally adopted.
  • Manual extraction of essential pathology elements for research is time-consuming and costly.
  • Large language models (LLMs) offer a potential solution for automated data extraction.

Purpose of the Study:

  • To evaluate the accuracy and feasibility of using LLMs for extracting essential pathology elements for cancer research.
  • To compare LLM performance against a traditional rules-based (REGEX) approach.

Main Methods:

  • Retrospective analysis of 88 pathology reports for patients with suspected hepatocellular carcinoma.
  • Utilized Generative Pre-trained Transformer (GPT) 3.5 turbo and GPT-4 for element extraction.
  • Compared LLM accuracy with a regular expressions (REGEX) based extraction method.

Main Results:

  • Both LLMs and REGEX demonstrated high accuracy in extracting research elements from pathology reports.
  • Average accuracy ranged from 84.1% to 94.8% for the evaluated methods.

Conclusions:

  • LLMs show significant potential to streamline the extraction of research elements from pathology reports.
  • Automated extraction using LLMs can accelerate the pace of cancer research.
  • This technology can reduce the manual effort and cost associated with pathology data curation.