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Open LLM-based actionable incidental finding extraction from [18F]fluorodeoxyglucose PET-CT radiology reports.

Stephen H Barlow1, Sugama Chicklore1,2, Yulan He3,4,5

  • 1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.

Frontiers in Digital Health
|December 1, 2025
PubMed
Summary

A new pipeline using a large language model (LLM) effectively extracts actionable incidental findings (AIFs) from [18F]FDG PET-CT reports. This AI tool aids in clinical alerts and managing patient comorbidities.

Keywords:
artificial intelligencediagnostic imagingincidental findingsnatural language processingpositron emission tomography-computed tomography

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Natural Language Processing

Background:

  • [18F]fluorodeoxyglucose positron emission tomography-computed tomography ([18F]FDG PET-CT) reports frequently contain actionable incidental findings (AIFs) that impact patient management.
  • Automated extraction of AIFs from these reports is crucial for timely clinical intervention.
  • Current methods for AIF identification are often manual and time-consuming.

Purpose of the Study:

  • To develop and evaluate an open, large language model (LLM)-based pipeline for extracting AIFs from [18F]FDG PET-CT reports.
  • To enable both short-term clinical applications (e.g., alerts) and long-term research using structured AIF data.
  • To assess the performance of the LLM pipeline on both internal and external datasets.

Main Methods:

  • A pipeline was created using an LLM fine-tuned with QLoRA and chain-of-thought (CoT) prompting on annotated [18F]FDG PET-CT reports.
  • The pipeline classifies reports for AIFs, extracts relevant sentences, and stores findings in JSON format.
  • Performance was evaluated using F1 scores on internal and external test datasets, with both quantitative and qualitative analyses.

Main Results:

  • The pipeline achieved high document-level F1 scores (0.917 on internal, 0.79 on external test datasets).
  • Sentence-level F1 scores were 0.754 (internal) and 0.522 (external), with qualitative analysis indicating practical utility exceeding quantitative scores.
  • Llama-3.1-8B Instruct, adapted with QLoRA and CoT prompting, demonstrated optimal performance and efficiency.

Conclusions:

  • A QLoRA-adapted LLM with CoT prompting can reliably extract AIF information from PET-CT reports.
  • The developed pipeline offers significant potential for clinical decision support, including alerts and reminders.
  • The model's effectiveness in extracting AIFs can aid in managing patient comorbidities and improving healthcare outcomes.