Skip to main content
  • Oral presentation
  • Open access
  • Published:

Translating imaging biomarkers into clinical practice

Biomarkers are ‘objectively measured and evaluated indicators of normal biological processes, pathogenic processes or pharmacologic responses to a therapeutic intervention’ that identify increased or decreased risk of patient benefit or harm [1]. Imaging biomarkers (IB) are integral to cancer healthcare and research. In oncology, patient management relies heavily on using ordered categorical IBs to stage patients (e.g. assignation of T, N and M status) and to monitor therapeutic efficacy (e.g. objective response, measured by RECIST 1.1 or equivalent criteria) [2, 3]. IBs are also used to measure toxicity in cancer patients. For example, SPECT quantification of cardiac ejection fraction is an important biomarker of drug-induced cardiotoxicity [4].

The role of IBs in oncology continues to increase in the era of personalised medicine. Every year thousands of imaging studies develop IBs and test their role as putative prognostic, predictive, monitoring and radiation planning biomarkers - both for use in healthcare and in clinical trials of novel drug or radiotherapy treatments [5]. Some IBs modify existing metrics. For example, basing response criteria largely on 18F-FDG PET signal changes rather than size changes (as in PERCIST v RECIST) may stratify patients differently but still uses the same conceptual biomarker, namely objective response [6].

In distinction, many other IBs derive parameters that measure novel aspects of tumour molecular biology, pathophysiology or structural morphology. These IBs are usually designed to quantify an unmet clinical need, such as the hallmarks of cancer that are targets for drug development. Examples include optical imaging of deoxy-Hb and oxy-Hb ratios as a biomarker of hypoxia; measuring 13C-bicarbonate/CO2 ratios through dynamic nuclear polarisation to map tumour pH; measuring changes in glucose metabolism through quantifying percentage reduction in 18F-FDG PET SUVmax ; measuring changes in vascular function through quantifying percentage reduction in Ktrans; or measuring tumour heterogeneity by texture, fractal or other feature-based analyses [79].

Unfortunately, translation of new IBs has been disappointing. Quantitative IBs in particular have been slow to cross the translational gaps to become useful decision making tools in drug development (pharmacodynamic or PD IB) or in altering healthcare (as companion diagnostics, or for screening, response, monitoring or outcome). The key reason that IBs have failed to make substantial impact is the lack of clear roadmap for IB validation and qualification. IBs have several important differences from the more familiar biospecimen-derived biomarkers and require a different validation roadmap tailored to the strengths and limitations of IB. Recognising this need, Cancer Research UK and the European Organization for Research and Treatment of Cancer (EORTC) have sponsored an international consensus effort to devise a roadmap and produce key recommendations for the design, performance, governance and publication of future IB studies [10].

This talk aims to:

  1. 1.

    Challenge delegates in their understanding of what constitutes an IB

  2. 2.

    Introduce current thinking around how IBs should be validated and qualified (the ‘imaging biomarker roadmap for use in cancer studies’)

  3. 3.

    Provide a range of examples that highlight the successes and failures of many popular and emerging IBs


  1. Biomarkers Definitions Working Group: Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001, 69: 89-95.

    Article  Google Scholar 

  2. Edge SB, Byrd DR, Compton CC, Fritz AG, Greene FL, Trotti A: AJCC Cancer Staging Handbook. 2010, New York: Springer, 5th edition

    Google Scholar 

  3. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009, 45: 228-247. 10.1016/j.ejca.2008.10.026.

    Article  PubMed  CAS  Google Scholar 

  4. Plana JC, Galderisi M, Barac A, Ewer MS, Ky B, Scherrer-Crosbie M, et al: Expert consensus for multimodality imaging evaluation of adult patients during and after cancer therapy: a report from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2014, 27: 911-939. 10.1016/j.echo.2014.07.012.

    Article  PubMed  Google Scholar 

  5. Waterton JC, Pylkkanen L: Qualification of Imaging Biomarkers for Oncology Drug Development. Eur J Cancer. 2012, 48: 409-415. 10.1016/j.ejca.2011.11.037.

    Article  PubMed  CAS  Google Scholar 

  6. Wahl RL, Jacene H, Kasamon Y, Lodge MA: From RECIST to PERCIST: Evolving Considerations for PET response criteria in solid tumors. J Nucl Med. 2009, 50 (Suppl 1): 122S-150S.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  7. Gallagher FA, Kettunen MI, Day SE, Hu DE, Ardenkjaer-Larsen JH, Zandt R, et al: Magnetic resonance imaging of pH in vivo using hyperpolarized 13C-labelled bicarbonate. Nature. 2008, 453: 940-943. 10.1038/nature07017.

    Article  PubMed  CAS  Google Scholar 

  8. O'Connor JP, Jackson A, Parker GJ, Roberts C, Jayson GC: Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies. Nat Rev Clin Oncol. 2012, 9: 167-177. 10.1038/nrclinonc.2012.2.

    Article  PubMed  Google Scholar 

  9. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Cavalho S, et al: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014, 5: 4006-

    PubMed  CAS  PubMed Central  Google Scholar 

  10. (accessed 1st July 2015)

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to James PB O'Connor.

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

O'Connor, J.P. Translating imaging biomarkers into clinical practice. Cancer Imaging 15 (Suppl 1), O9 (2015).

Download citation

  • Published:

  • DOI: