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Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
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Technical Note: Ontology-guided radiomics analysis workflow (O-RAW).

Zhenwei Shi1, Alberto Traverso1, Johan van Soest1

  • 1Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Development Biology, Maastricht University Medical Centre+, Maastricht, 6229 ET, The Netherlands.

Medical Physics
|October 4, 2019
PubMed
Summary
This summary is machine-generated.

The open-source ontology-guided radiomics analysis workflow (O-RAW) standardizes radiomics analysis and publishes features as FAIR data. This enhances reproducibility and clinical adoption of radiomics research.

Keywords:
FAIR dataontologyradiomicssemantic websoftware

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Area of Science:

  • Medical Imaging Analysis
  • Radiomics Research
  • Ontology Engineering

Background:

  • Radiomics enables tumor phenotype quantification and treatment response prediction from medical images.
  • Current radiomics research faces challenges in standardization, feature lexicon, and detailed extraction parameters, hindering multicenter validation.
  • Barriers impede the application of radiomics in clinical practice due to inconsistent methodologies.

Purpose of the Study:

  • To introduce an open-source ontology-guided radiomics analysis workflow (O-RAW).
  • To address challenges in radiomics standardization and clinical adoption.
  • To enable FAIR (findable, accessible, interoperable, reusable) data principles for radiomics features.

Main Methods:

  • O-RAW is a Python-based workflow utilizing PyRadiomics Extension and PyRadiomics.
  • It processes DICOM-RT inputs to extract radiomic features from volumes of interest (VOIs).
  • Features are published as W3C-compliant Semantic Web triple stores with ontology-derived meta-labels, compatible with SPARQL endpoints.

Main Results:

  • O-RAW demonstrated efficient execution across diverse datasets (CT, PET, MR) with varying modalities.
  • Feature extraction and conversion to Resource Description Framework (RDF) objects were completed within minutes per dataset.
  • A use case successfully published radiomics results as FAIR data, enabling querying of features and calculation details via SPARQL.

Conclusions:

  • O-RAW facilitates FAIR radiomics analysis by publishing DICOM-RT-derived features as semantic web triples.
  • The workflow's practicality and flexibility are expected to accelerate radiomics research and clinical translation.
  • Standardization and interoperability are key to advancing radiomics applications.