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

ABSTRACT:

Introduction

We developed an open, large language model (LLM)-based pipeline to extract actionable incidental findings (AIFs) from [¹⁸F]fluorodeoxyglucose positron emission tomography-computed tomography ([¹⁸F]FDG PET-CT) reports. This imaging modality often uncovers AIFs, which can affect a patient's treatment. The pipeline classifies reports for the presence of AIFs, extracts the relevant sentences, and stores the results in structured JavaScript Object Notation format, enabling use in both short- and long-term applications.

Methods

Training, validation, and test datasets of 1,999, 248, and 250 lung cancer [¹⁸F]FDG PET-CT reports, respectively, were annotated by a nuclear medicine physician. An external test dataset of 460 reports was annot

SUBMITTER: Barlow SH 

PROVIDER: S-EPMC12660291 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature