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Radiation Oncology Terminology Linker: A Step Towards a Linked Data Knowledge Base
Tim Lustberg1, Johan van Soest1, Peter Fick1
1Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
Studies in Health Technology and Informatics
|April 22, 2018
Summary
This study introduces a scalable REST service using Semantic Web technology to standardize organ and tumor delineation names in radiation oncology. This overcomes challenges in large-scale image feature extraction, improving data usability.
Area of Science:
- Medical Imaging
- Semantic Web Technologies
- Radiation Oncology
Background:
- Image feature extraction in radiation oncology relies on clinical staff delineations.
- Delineation names are free-text DICOM metadata, causing undefined information and hindering large-scale efforts.
Purpose of the Study:
- To develop a scalable solution for standardizing radiation oncology image feature extraction.
- To overcome challenges posed by inconsistent naming conventions in clinical data.
Main Methods:
- Implemented a REST service leveraging Semantic Web technology.
- Converted undefined DICOM metadata into linked data.
- Utilized open-source software for image feature computation.
Main Results:
- Successfully created a scalable solution for standardizing delineation names.
- Demonstrated the conversion of free-text metadata to structured linked data.
- Enabled efficient computation of radiation oncology image features.
Conclusions:
- The proposed REST service effectively addresses naming convention challenges in radiation oncology.
- Semantic Web technology provides a scalable approach for data standardization.
- This work facilitates large-scale image feature extraction in clinical research.
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