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|title=None
|pdfUrl=https://ceur-ws.org/Vol-2849/paper-22.pdf
|volume=Vol-2849
|dblpUrl=https://dblp.org/rec/conf/swat4ls/TraversoVSWMD19
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==None==
https://ceur-ws.org/Vol-2849/paper-22.pdf
Publishing linked and FAIR-compliant radiomics data in
radiation oncology via ontologies and Semantic Web
techniques
A. Traverso1, M. Vallières2, J. van Soest1, L. Wee1, O. Morin3, A.
Dekker1
1Department of Radiation Oncology (MAASTRO), GROW – School for Oncology
and Development Biology, Maastricht University Medical Center, Maastricht, the Netherlands
2Medical Physics Unit, McGill University, Montreal, Québec, Canada
3Department of Radiation Oncology, University California San Francisco, San Francisco, Cali-
fornia, USA
Keywords: Ontologies, Radiation Oncology, Radiomics, Imaging.
1 Introduction
Medical images potentially embed much more information (‘features’) than can be
exploited via visual inspection. Radiomics, the automated extraction of informative
quantitative imaging features from patients’ scans, could provide additional
knowledge besides clinical prognostic factors for decision support systems in radia-
tion oncology[1]. However, several limitations exist: no consensus on radiomics fea-
tures’ standardization, strong feature dependencies on how images are acquired and
on settings (e.g. digital image pre-processing) defined for computations, poor quality
of reporting and lack of transparency[2]. The IBSI (Image Biomarker Standardization
Initiative) is a worldwide effort aiming at the standardization of radiomics computa-
tions[3]. One of the pillars of the IBSI workbook is that simply recording and compar-
ing raw features values is not enough. Storing metadata associated with features com-
putation, as well as the possibility to overcome differences in nomenclature between
different computational packages to guarantee their interoperability and reproducibil-
ity in multi-center studies is needed. Also, radiomics data and metadata should be
connected to corresponding clinical data (linked data) as input for AI algorithms. In
this study, we present a proof-of-concept study using our newly developed radiomics
ontology, combined with Semantic Web technologies, as instrument for enabling
interoperability of radiomics data following FAIR principles: a) Findable→ associat-
ed radiomics studies data and metadata have unique identifiers as per the Radiomics
Ontology (RO); Accessible→ metadata and data for a radiomics experiment are per-
manently stored in repository (e.g. SPARQL endpoint); Interoperable→ via universal
concepts defined in the RO full experiment results and methods can be retrieved;
Reusable→ data and metadata can be re-used to re-produce the study.
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2
2 Material and Methods
We developed a the radiomics ontology (RO)[4]: 458 classes and 76 predicates cover-
ing the whole spectrum of the workflow of radiomics computation, fully compliant to
the IBSI guidelines. To test the RO, two institutions used two different open source
radiomics packages in a blind fashion to extract radiomics from a publicly available
dataset of CT scans of lung cancer patient. Each institution converted features and
associated metadata of their experiments to RDF triples and uploaded to a SPARQL
endpoint.
3 Results
Each of the users could independently query all the features generated from the other
institution, without having any prior knowledge of the original labels used to store
features and associated computational details. Using SPARQL queries, we could for
example extract properties of the software used for computations from the other insti-
tution. Finally, radiomics features were linked to corresponding clinical data. To help
the users with familiarize with this experiment, the full proof of concept is available at
https://github.com/albytrav/RadiomicsOntologyIBSI.
4. Conclusion
Ontologies and Sematic Web technologies allows the integration of radiomics with
multi-source clinical data for biomarker discoveries. The Radiomics Ontology could
speed up harmonization, standardization transparency of radiomics studies.
References
[1] R. J. Gillies, P. E. Kinahan, and H. Hricak, ‘Radiomics: Images Are More than
Pictures, They Are Data’, Radiology, vol. 278, no. 2, pp. 563–577, Feb. 2016.
[2] A. Traverso, L. Wee, A. Dekker, and R. Gillies, ‘Repeatability and Reproducibil-
ity of Radiomic Features: A Systematic Review’, International Journal of Radia-
tion Oncology*Biology*Physics, vol. 102, no. 4, pp. 1143–1158, Nov. 2018.
[3] A. Zwanenburg, S. Leger, M. Vallières, S. Löck, and for the I. B. S. Initiative,
‘Image biomarker standardisation initiative’, arXiv:1612.07003 [cs], Dec. 2016.
[4] https://bioportal.bioontology.org/ontologies/RO