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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Automated extraction of Biomarker information from pathology reports
Jeongeun Lee1, Hyun-Je Song2, Eunsil Yoon3
1Interdisciplinary Program for Bioengineering, Graduate School, Seoul National Universty, Seoul, Republic of Korea.
BMC Medical Informatics and Decision Making
|May 23, 2018
Summary
This study developed an automated system to extract biomarker profiles from pathology reports, overcoming data limitations. The system efficiently parses immunohistochemistry reports, yielding comprehensive biomarker data for clinical applications.
Area of Science:
- Biomedical Informatics
- Computational Pathology
Background:
- Pathology reports are unstructured free text, hindering efficient data extraction.
- Automated systems are needed to gather biomarker profiles from clinical pathology data.
Purpose of the Study:
- To design an automated system for extracting biomarker profiles from pathology reports.
- To develop a novel data model for representing biomarker knowledge.
Main Methods:
- Developed a data model for biomarker knowledge representation.
- Implemented an automated system to parse immunohistochemistry (IHC) and surgical pathology reports.
- Defined "slide paragraph" as a unit for IHC findings.
- Utilized context-free grammar and tree-like structures for parsing.
Main Results:
- Achieved high F-scores (0.999 for biomarker name, 0.998 for test results) in parsing IHC reports.
- Extracted information on 221 unique biomarkers, exceeding literature-based profiles.
- Successfully associated biomarker profiles with pathology findings across reports.
- Resolved term variations using dictionary look-up and text similarity.
Conclusions:
- The proposed approach effectively extracts biomarker data from clinical pathology reports.
- The system handles real-world data challenges like errors and inconsistent formatting.
- This method enhances biomarker profile representation and data accessibility.
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