Feasibility of extracting critical diagnostic imaging report findings following percutaneous liver ablation with a large language model

Alexander Shieh; Iwan Paolucci; Jessica Albuquerque; Kristy Brock; Bruno Odisio

doi:10.1117/12.3008791

2 April 2024 Feasibility of extracting critical diagnostic imaging report findings following percutaneous liver ablation with a large language model

Alexander Shieh, Iwan Paolucci, Jessica Albuquerque, Kristy Brock, Bruno Odisio

Author Affiliations +

Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 1293104 (2024) https://doi.org/10.1117/12.3008791
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Percutaneous liver ablation is a minimally invasive procedure to treat liver tumors. Postablation images are highly significant as they distinguish normal post-procedure changes from abnormalities, preventing unnecessary retreatment and confirming procedural quality. However, the cancer surveillance imaging reports after the procedure can be numerous and challenging to read. Moreover, annotated data is limited in this setting. In this study we used the cutting-edge large language model Llama 2 to automatically extract critical findings from real-world diagnostic imaging reports without the need of training a new information extraction model. This could potentially automate part of the outcome research and registry construction process, as well as decrease the number of studies needed to review for research purposes. A dataset of 87 full-text reports from 13 patients who underwent percutaneous thermal ablation for pancreatic liver metastases were used to benchmark the capability of Llama 2 for cancer progression finding extraction and classification. We asked Llama 2 to determine whether there is cancer progression within the given report and then classify progression findings into Local Tumor Progression (LTP), Intrahepatic Progression (IHP) and Extrahepatic Progression (EHP). Llama 2 achieved decent performance for detecting progression at study level. The precision is 0.91 and recall is 0.96, with specificity 0.84. However, the classification of progression into LTP, IHP and EHP still needs to be improved.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Alexander Shieh, Iwan Paolucci, Jessica Albuquerque, Kristy Brock, and Bruno Odisio "Feasibility of extracting critical diagnostic imaging report findings following percutaneous liver ablation with a large language model", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 1293104 (2 April 2024); https://doi.org/10.1117/12.3008791

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