Data Extraction and Curation from Radiology Reports for Pancreatic Cyst Surveillance Using Large Language Models.

Journal: Journal of the American College of Surgeons
Published Date: Jul 10, 2025

Abstract

INTRODUCTION: Manual curation of radiographic features in pancreatic cyst registries for data abstraction and longitudinal evaluation is time consuming and limits widespread implementation. We examined the feasibility and accuracy of using large language models (LLMs) to extract clinical variables from radiology reports.

Authors

  • Ankur P Choubey
    Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Emanuel Eguia
    Department of Surgery, Loyola University Medical Center, Maywood, IL, USA.
  • Alexander Hollingsworth
    Digital Informatics & Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Subrata Chatterjee
    Department of Artificial Intelligence & Machine Learning, DigITs, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Michael I D'Angelica
    Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY.
  • William R Jarnagin
    Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY.
  • Alice C Wei
    Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
  • Mark A Schattner
    Department of Gastroenterology, Hepatology, and Nutrition, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Richard K G Do
    Department of Radiology, Body Imaging Service, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA. dok@mskcc.org.
  • Kevin C Soares
    Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals