Open LLM-based actionable incidental finding extraction from [18F]fluorodeoxyglucose PET-CT radiology reports
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![Quantification of Atherosclerotic Plaque Activity and Vascular Inflammation using [18-F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG-PET/CT)](https://visualize.jove.com/wp-content/cache/webp/b8c229072d4d1c7083684b4991385327.webp)
View abstract on PubMed
Summary
This summary is machine-generated.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.
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.
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