A biomarker is “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” [Atkinson2001]. Biomarkers may be measured from a wide variety of sources, such as tissue samples, cell plating, and imaging. The latter are often referred to as imaging biomarkers [OConnor2016]. Imaging biomarkers consist of both qualitative biomarkers, which require expert interpretation, and quantitative biomarkers which are based on mathematical definitions. Calculation of quantitative imaging biomarkers can be automated, which enables high-throughput analyses. We refer to such (high-throughput) quantitative biomarkers as image biomarkers to differentiate them from qualitative imaging biomarkers. Image biomarkers characterise the contents of (regions of) an image, such as volume or mean intensity. Because of the historically close relationship with the computer vision field, image biomarkers are also referred to as image features. The term features, instead of biomarkers, will be used throughout the remainder of the reference manual, as the contents are generally applicable and not limited to life sciences and medicine only.
This work focuses specifically on the (high-throughput) extraction of image biomarkers from acquired, reconstructed and stored imaging. High-throughput quantitative image analysis (radiomics) has shown considerable growth in e.g. cancer research [Lambin2017], but the scarceness of consensus guidelines and definitions has led to it being described as a “wild frontier” [Caicedo2017]. This reference manual therefore presents an effort to chart a course through part of this frontier by presenting consensus-based recommendations, guidelines, definitions and reference values for image biomarkers and defining a general radiomics image processing scheme. We hope use of this manual will improve reproducibility of radiomic studies.
We opted for a specific focus on the computation of image biomarkers from acquired imaging. Thus, validation of imaging biomarkers, either viewed in a broader framework such as the one presented by [OConnor2016], or within smaller-scope settings such as those presented by [Caicedo2017] and by [Lambin2017], falls beyond the scope of this work. Notably, the issue of harmonising and standardising (medical) image acquisition and reconstruction is being addressed in a more comprehensive manner by groups such as the Quantitative Imaging Biomarker Alliance [Sullivan2015][Mulshine2015], the Quantitative Imaging Network [Clarke2014][nordstrom2016quantitative], and task groups and committees of the American Association of Physicists in Medicine, the European Association for Nuclear Medicine [Boellaard2015], the European Society of Radiology (ESR) [EuropeanSocietyofRadiologyESR2013], and the European Organisation for Research and Treatment of Cancer (EORTC) [Waterton2012][OConnor2016], among others. Where overlap does exists, the reference manual refers to existing recommendations and guidelines.
This reference manual is divided into several chapters that describe processing of acquired and reconstructed (medical) imaging for high-throughput computation of image biomarkers (Chapter 2: Image processing); that define a diverse set of image biomarkers (Chapter 3: Image features); that describe guidelines for reporting on radiomic studies and provide nomenclature for image biomarkers (Chapter 4: Radiomics reporting guidelines and nomenclature); and that describe the data sets and image processing configurations used to find reference values for image biomarkers (Chapter 5: Reference data sets).