Skip to main content
SpringerLink
Log in

Imaging Biomarker Ontology (IBO): A Biomedical Ontology to Annotate and Share Imaging Biomarker Data

  • Original Article
  • Published:
Journal on Data Semantics

Abstract

Imaging biomarkers refer to radiological measurements that characterize biological processes of imaged subjects and help clinicians particularly in the assessment of therapeutic responses and the early prediction of pathologies. Several imaging features (size of a lesion, volume of a tumor, blood perfusion in a specific anatomical region, anisotropic water diffusion in a particular tissue region, etc.) are quantified and reported in the clinical practice. The growth of the number of research studies addressing imaging biomarkers and the increasing use of these measurements in the radiological routine necessitates the use of semantic research tools. The use of semantic technologies will enable to efficiently retrieve imaging-related data and to enhance the interoperability in the biomedical field. While many efforts have been conducted regarding the definition of a standardized vocabulary to support the sharing of the imaging biomarker knowledge, the definition of the term “imaging biomarker” stills inconsistent. In this paper, we introduce our motivation for semantically describing this concept and we outline shortcomings of the state-of-the-art methods. Here, we propose a semantic representation of the imaging biomarker concept that is based on the articulation of its three main semantic axes, namely the measured quality, the measurement tool and the decision tool. The developed ontology is called the Imaging Biomarker Ontology (IBO) and uses existing biomedical ontologies. A preliminary use case is studied to illustrate the utility of IBO in annotating quantitative and qualitative imaging data from the TCGA (The Cancer Genome Atlas) collection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. https://web.stanford.edu/group/qil/cgi-bin/mediawiki/index.php/AIM-API.

  2. https://loinc.org/international/.

  3. http://wwww.who.int/classifications/icd/en/.

  4. http://purl.bioontology.org/ontology/ONL-DP.

  5. http://ontofox.hegroup.org/.

  6. http://www.W3.org/TR/owl2-syntax/.

  7. https://medicis.univrennes1.fr/_media/members/bernard.gibaud/ibo-final-version.zip?id=members%3Abernard.g-ibaud%3Aindex_cache=cache.

  8. https://www.w3.org/2001/sw/wiki/Corese.

  9. https://wiki.cancerimagingarchive.net/download/attach/18514300/JainPoisson2014_Radiology_Dataset.xlsx?.

References

  1. Abramson RG, Yankeelov TE (2014) Imaging biomarkers and surrogate endpoints in oncology clinical trials. In: Luna A, Vilanova JC, Hygino Da Cruz Jr LC, Rossi SE Functional imaging in oncology. Springer, Berlin, pp 29–42

    Google Scholar 

  2. Asslaber M, Zatloukal K (2007) Biobanks: transnational, European and global networks. Brief Funct Genom Proteom 6(3):193–201

    Article  Google Scholar 

  3. Ball JR, Micheel CM et al (2010) Evaluation of biomarkers and surrogate endpoints in chronic disease. National Academies Press, Washington

    Google Scholar 

  4. Bosmans JML, Peremans L, Menni M, De Schepper AM, Duyck PO, Parizel PM (2012) Structured reporting: if, why, when, how and at what expense? Results of a focus group meeting of radiology professionals from eight countries. Insights Imaging 3(3):295–302

    Article  Google Scholar 

  5. Brinkman RR, Courtot M, Derom D, Fostel J, He Y, Lord PW, Malone J, Parkinson HE, Peters B, Rocca-Serra P et al (2010) Modeling biomedical experimental processes with OBI. J Biomed Semant 1(S–1):S7

    Article  Google Scholar 

  6. Brochhausen M, Zheng J, Birtwell D, Williams H, Masci AM, Ellis HJ, Stoeckert CJ (2016) OBIB-a novel ontology for biobanking. J Biomed Semant 7(1):23

    Article  Google Scholar 

  7. Brunnbauer M (2013) Dicom metadata as rdf. In: GI-Jahrestagung, pp 1796–1804

  8. Buckler AJ, Bresolin L, Dunnick NR, Sullivan DC (2011) A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging. Radiology 258(3):906–914

    Article  Google Scholar 

  9. Buckler AJ, Paik D, Ouellette M, Danagoulian J, Wernsing G, Suzek BE (2013b) A novel knowledge representation framework for the statistical validation of quantitative imaging biomarkers. J Digit Imaging 26(4):614–629

    Article  Google Scholar 

  10. Buckler AJ, Ouellette M, Danagoulian J, Wernsing G, Liu TT, Savig E, Suzek BE, Paik D, Rubin DL (2013a) Quantitative imaging biomarker ontology (QIBO) for knowledge representation of biomedical imaging biomarkers. J Digit Imaging 26(4):630–641

    Article  Google Scholar 

  11. Ceusters W (2012) An information artifact ontology perspective on data collections and associated representational artifacts. In: MIE, pp 68–72

  12. Ceusters W, Smith B (2015) Biomarkers in the ontology for general medical science. In: Cornet R (ed) Digital healthcare empowering Europeans, vol 512, no 8, pp 155–159

  13. Channin DS, Mongkolwat P, Kleper V, Sepukar K, Rubin DL (2010) The caBIG annotation and image markup project. J Digit Imaging 23(2):217–225

    Article  Google Scholar 

  14. Clunie DA (2007) Encoding of oncology clinical trial read results in DICOM SR. http://qibawiki.rsna.org/images/f/fc/ClinicalTrialResults_20111123_QIBA.doc. Accessed 23 Nov 2011

  15. Clunie DA (2000) DICOM structured reporting. PixelMed Publishing, Bangor

    Google Scholar 

  16. Degtyarenko K, De Matos P, Ennis M, Hastings J, Zbinden M, McNaught A, Alcántara R, Darsow M, Guedj Ml, Ashburner M (2008) ChEBI: a database and ontology for chemical entities of biological interest. Nucl Acids Res 36(suppl 1):D344–D350

    Google Scholar 

  17. Diehn M, Nardini C, Wang DS, McGovern S, Jayaraman M, Liang YU, Aldape K, Cha S, Kuo MD (2008) Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci 105(13):5213–5218

    Article  Google Scholar 

  18. Eisenhauer EA, Bogaerts J, Therasse P, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247

    Article  Google Scholar 

  19. ESR (2010) White paper on imaging biomarkers. Insights Imaging 1(2):42–45

  20. Gene Ontology Consortium et al (2008) The gene ontology project in 2008. Nucl Acids Res 36(suppl 1):D440–D444

  21. Gkoutos GV, Schofield PN, Hoehndorf R (2012) The units ontology: a tool for integrating units of measurement in science. Database. https://doi.org/10.1093/database/bas033

    Article  Google Scholar 

  22. Hewitt R, Watson P (2013) Defining biobank. Biopreserv Biobank 11(5):309–315

    Article  Google Scholar 

  23. Horridge M, Tudorache T, Nuylas C, Vendetti J, Noy NF, Musen MA (2014) Webprotégé: a collaborative web-based platform for editing biomedical ontologies. Bioinformatics 30(16):2384–2385

    Article  Google Scholar 

  24. Jaffer FA, Weissleder R (2005) Molecular imaging in the clinical arena. Jama 293(7):855–862

    Article  Google Scholar 

  25. Jain R, Poisson LM, Gutman D, Scarpace L, Hwang SN, Holder CA, Wintermark M, Rao A, Colen RR, Kirby J et al (2014) Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. Radiology 272(2):484–493

    Article  Google Scholar 

  26. Kelloff GJ, Bast RC, Coffey DS, DAmico AV, Kerbel RS, Park JW, Ruddon RW, Rustin GJS, Schilsky RL, Sigman CC et al (2004) Biomarkers, surrogate endpoints, and the acceleration of drug development for cancer prevention and treatment an update prologue. Clin Cancer Res 10(11):3881–3884

    Article  Google Scholar 

  27. Kessler LG, Barnhart HX, Buckler AJ, Choudhury KR, Kondratovich MV, Toledano A, Guimaraes AR, Filice R, Zhang Z, Sullivan DC (2015) The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat Methods Med Res 24(1):9–26

    Article  MathSciNet  Google Scholar 

  28. Malone J, Holloway E, Adamusiak T, Kapushesky M, Zheng J, Kolesnikov N, Zhukova A, Brazma A, Parkinson H (2010) Modeling sample variables with an experimental factor ontology. Bioinformatics 26(8):1112–1118

    Article  Google Scholar 

  29. Mungall C, Gkoutos G, Washington N, Lewis S (2007) Representing phenotypes in OWL. In: Golbreich C, Kalyanpur A, Parsia B (eds) Proceedings of the OWLED 2007 workshop on OWL: experience and directions, Innsbruck, Austria

  30. O’Connor JPB, Jackson A, Asselin MC, Buckley DL, Parker GJM, Jayson GC (2008) Quantitative imaging biomarkers in the clinical development of targeted therapeutics: current and future perspectives. Lancet Oncol 9(8):766–776

    Article  Google Scholar 

  31. Ofoghi B, Campos GHL, Verspoor K, Sanchez FJM (2014) BiomRKRS: a biomarker retrieval and knowledge reasoning system. In: Proceedings of the seventh Australasian workshop on health informatics and knowledge management, vol 153. Australian Computer Society, Inc, pp 31–39

  32. Rosse C, José LV, Mejino JR (2003) A reference ontology for biomedical informatics: the foundational model of anatomy. J Biomed Inform 36(6):478–500

    Article  Google Scholar 

  33. RSNA. (2007) Quantitative imaging biomarkers alliance. https://www.rsna.org/QIBA.aspx. Accessed 18 July 2018

  34. Rubin DL, Rodriguez C, Shah P, Beaulieu CF (2008) iPad: semantic annotation and markup of radiological images. In: AMIA

  35. Scheuermann RH, Ceusters W, Smith B (2009) Toward an ontological treatment of disease and diagnosis. Summit Transl Bioinform 2009:116

    Google Scholar 

  36. Schriml L (2016) Human disease ontology. https://nciphub.org/resources/1838

  37. TCGA-Research-Group (2013) Wiki for the Vasari feature set. Vasari research project. https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project. Accessed 13 Feb 2016

  38. TCGA-Research-Group (2014) The Cancer Genome Atlas glioblastoma multiforme data collection. https://wiki.cancerimagingarchive.net/display/Public/TCGA-GBM. Accessed 13 Feb 2016

  39. Wagner JA, Williams SA, Webster CJ (2007) Biomarkers and surrogate end points for fit-for-purpose development and regulatory evaluation of new drugs. Clin Pharmacol Ther 81(1):104–107

    Article  Google Scholar 

  40. Xiang Z, Courtot M, Brinkman RR, Ruttenberg A, He Y (2010) Ontofox: web-based support for ontology reuse. BMC Res Notes 3(1):175

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bcom Institute of Research and Technologies, Rennes, France

    Emna Amdouni & Bernard Gibaud

  2. LTSI Inserm 1099, Université de Rennes 1, Rennes, France

    Emna Amdouni & Bernard Gibaud

Authors
  1. Emna Amdouni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bernard Gibaud
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emna Amdouni.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amdouni, E., Gibaud, B. Imaging Biomarker Ontology (IBO): A Biomedical Ontology to Annotate and Share Imaging Biomarker Data. J Data Semant 7, 223–236 (2018). https://doi.org/10.1007/s13740-018-0093-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13740-018-0093-3

Keywords

Search

Navigation