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As the reader might know, Imaging Biomarkers are parameters obtained objectively from medical images, which represent and quantify a tissue property (structural, functional or biological) extracted after applying computational models to images of specific medical imaging modalities like computed tomography (CT), magnetic resonance imaging (MR), conventional radiography (CR) and so on.

One of the main challenges for Imaging Biomarkers integration in clinics and in clinical trials is the need for standardized image acquisition protocols  and  pipelines for the analysis of imaging biomarkers, including the processing, analysis and reporting of the results.

As specified in this publication from QIBA (Quantitative Imaging Biomarkers Alliance) team: “Most Biomarkers require a computation algorithm, which may be simple or highly complex. While there is a rich history of the development of biomarker techniques, there has been comparatively little attention paid to the evaluation and comparison of the algorithms used to produce the biomarker results. Estimation errors in algorithm output can arise from several sources during both image formation and the algorithmic estimation of the biomarker. These errors combine (additively or non-additively) with the inherent underlying biological variation of the biomarker. Studies are thus needed to evaluate the imaging biomarker assay with respect to bias, and precision. A recurring issue is the lack of reported estimation errors associated with the output of the biomarker. Another challenge is the inappropriate choice of biomarker metrics and/or parametric statistics. For example, tumor volume doubling time is sometimes used in studies as a biomarker. However, it may not be appropriate to use the mean as the parametric statistic for an inverted, non-normal, measurement space. Since a zero growth rate corresponds to a doubling time of infinity, it is easy to see that parametric statistics based on tumor volume doubling time (e.g. mean doubling time) may be skewed and/or not properly representative of the population. In addition, there may be discordance between what might be a superior metric statistically and what is clinically acceptable or considered clinically relevant. For example, a more precise measuring method will typically better predict the medical condition, but only until the measurement precision exceeds normal biological variation; further improvement in precision will offer no significant improvement in efficacy. Finally, when potentially improved algorithms are developed, data from previous studies are often not in a form that allows new algorithms to be tested against the original data.

A metrology-by-design strategy for imaging biomarkers design and implementation has still not been conceived, however, there is a clear need to fill this gap, since medical images are suffering a paradigm shift from being qualitatively interpreted by radiologists to become a powerful measurement instrument, that can provide a non-negligible and highly relevant information in many pathological processes.

Following the experience of our group and QUIBIM, with a model of success for the integration of quantitative image analysis in clinics, we published in 2012 our methodology for the integration of imaging biomarkers in clinics that was later supported by the European Society of Radiology in 2013, through the publication “ESR position on the stepwise development of imaging biomarkers” in Insights into Imaging. This paper was the former work of the ESR supporting the creation of the European Imaging Biomarkers Alliance (EIBALL) in 2015.

Now, several actions should be undertaken to allow for the implementation of biomarkers in clinical use and the evaluation of therapy response:

  • To determine the most significant sources of uncertainty for imaging biomarkers.
  • To establish a standard methodology for definition of the method and the protocol for image acquisition (Signal-to-noise ratio, contrast-to-noise ratio) for traceable measurements.
  • To harmonise calibration routes and methods for imaging biomarkers in European metrology institutes. This objective includes knowledge exchange and transfer and comparison measurements.
  • To provide metrology input and pre-normative research to the evolution of International (ISO, EN) standards concerning imaging biomarkers.
  • To engage with image device manufacturers to facilitate the take up of the technology and measurement systems developed, to support the development of new, innovative biomarkers, thereby enhancing the competitiveness of EU industry.

While researchers are dealing with these issues, there are specific entities that have been always in charge of supervision, validation and certification of measures: the National Metrology Institutes. The umbrella covering all National Metrology Institutes in Europe is EURAMET. Therefore, we should start to think in incorporating this metrology-by-design strategy in the process of creation, implementation and validation of imaging biomarkers. In QUIBIM we have already started this strategy, following QIBA recommendations and implementing new algorithms in a modular basis, identifying the sources of uncertainty in all the measurements that we perform with imaging biomarkers.

by Angel Alberich-Bayarri (@aalberich).

CEO & Founder of QUIBIM

Ángel Alberich Bayarri