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Updated: Jul 2, 2026
07:50
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
Coding free text radiology reports using the Cancer Text Information Extraction System (caTIES)
David Carrell1, Diana Miglioretti, Rebecca Smith-Bindman
1Group Health Cooperative, Center for Health Studies, Seattle, WA, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|August 13, 2008
Summary
The Cancer Text Information Extraction System (caTIES) moderately identified ovarian cancer cases in radiology reports, showing promise for automated case ascertainment in women's health research.
Area of Science:
- Oncology
- Radiology Informatics
- Natural Language Processing
Background:
- Accurate case ascertainment is crucial for ovarian cancer research.
- Manual review of radiology reports is time-consuming and resource-intensive.
Purpose of the Study:
- To evaluate the effectiveness of the Cancer Text Information Extraction System (caTIES) for automated case ascertainment of ovarian cancer from radiology reports.
- To assess the sensitivity and specificity of caTIES in identifying cancer cases and controls.
Main Methods:
- Utilized an unmodified deployment of the publicly-available caTIES tool.
- Coded 700 radiology reports from 373 women.
- Evaluated caTIES performance on a random sample of cancer cases and controls.
Main Results:
- caTIES achieved moderate success in case ascertainment.
- Identified 9 out of 11 cancer cases (82% sensitivity).
- Correctly identified 95 out of 100 controls (95% specificity).
Conclusions:
- caTIES demonstrates potential for efficient ovarian cancer case ascertainment.
- Further development is underway to enhance caTIES for clinical risk assessment and ovarian cancer classification.
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