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Related Experiment Video

Updated: Sep 27, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Automated Identification and Measurement Extraction of Pancreatic Cystic Lesions from Free-Text Radiology Reports

Rikiya Yamashita1, Kristen Bird1, Philip Yue-Cheng Cheung1

  • 1Departments of Biomedical Data Science (R.Y., D.L.R.) and Radiology (K.B., P.Y.C.C., J.H.D., M.N.F., D.G., L.N.M., A.S., A.L.W., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.

Radiology. Artificial Intelligence
|April 8, 2022
PubMed
Summary

A new natural language processing (NLP) system accurately identifies pancreatic cystic lesions (PCLs) and extracts their measurements from radiology reports. This informatics tool aids in studying PCLs and related risks.

Keywords:
Abdomen/GIComputer Applications-General (Informatics)CystsInformaticsNamed Entity RecognitionPancreas

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Area of Science:

  • Radiology Informatics
  • Medical Imaging Analysis
  • Natural Language Processing (NLP)

Background:

  • Pancreatic cystic lesions (PCLs) require accurate identification and measurement for clinical management.
  • Automated analysis of radiology reports can improve efficiency and data extraction.

Purpose of the Study:

  • To develop and evaluate an NLP-based system for identifying patients with PCLs.
  • To extract PCL measurements automatically from historical CT and MRI reports.

Main Methods:

  • A retrospective study utilizing a large dataset of free-text radiology reports (1991-2019).
  • Development of a PCL identification model using rule-based information extraction.
  • Implementation of a question answering system for measurement extraction.
  • Performance evaluation against radiologist annotations.

Main Results:

  • The NLP model achieved near-perfect interobserver agreement (Fleiss κ = 0.951) with radiologists.
  • High accuracy in PCL identification (98.2% true-positive rate, 3.0% false-positive rate).
  • Accurate measurement extraction with an overall accuracy of 0.958 and concordance correlation coefficient of 0.874.

Conclusions:

  • An NLP system effectively identifies PCLs and extracts measurements from radiology reports.
  • This automated approach has potential for studying PCL natural history and risks.
  • The methodology is adaptable for other clinical use cases in medical informatics.