Academic radiology is poised to play an important role in the development and implementation of quantitative imaging (QI) tools. This article, drafted by the Association of University Radiologists Radiology Research Alliance Quantitative Imaging Task Force, reviews current issues in QI biomarker research. We discuss motivations for advancing QI, define key terms, present a framework for QI biomarker research, and outline challenges in QI biomarker development. We conclude by describing where QI research and development is currently taking place and discussing the paramount role of academic radiology in this rapidly evolving field.
Medical imaging has evolved dramatically since the first roentgenogram nearly 125 years ago . Modern techniques including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) now provide an unprecedented level of spatial detail and functional information . As medical imaging has progressed, older analog techniques have been steadily replaced with newer digital methods of image acquisition, processing, archiving, and display. This evolution has occurred in parallel with advancements in our understanding of the molecular underpinnings of disease and the rise of a more statistical and evidence-based approach to diagnosis and treatment. Medical imaging is now poised to leverage quantitative techniques in support of a wide range of clinical and research goals .
In a broad sense, quantitative imaging (QI) refers to the extraction and use of numerical/statistical features from medical images (see Box 1 for definitions of key terms). As a research field, QI includes the development, standardization, optimization, and application of anatomic, functional, and molecular imaging acquisition protocols, data analyses, display methods, and reporting structures, as well as the validation of QI results against relevant biological and clinical data . The QI concept is closely tied to that of a biomarker, defined as a characteristic that is objectively measured and evaluated as an indicator of a normal biological process, pathologic process, or response to a therapeutic intervention . A QI biomarker is therefore an objectively measured characteristic, derived from a medical image, that can be correlated with anatomically and physiologically relevant parameters including disease presence, disease severity, disease characterization (particularly at a molecular level), predicted disease course (both with and without treatment), and treatment response. The Quantitative Imaging Biomarkers Alliance (QIBA), organized by the Radiological Society of North America, has formally defined a QI biomarker as “an objective characteristic derived from an in vivo image measured on a ratio or interval scale as indicators [ sic ] of normal biological processes, pathogenic processes, or a response to a therapeutic intervention.” The emphasis of this definition on a ratio or interval variables would imply that tumor volumes or PET standardized uptake values would be considered QI biomarkers, because the difference or ratio between two values is meaningful, whereas ordinal variables such as Breast Imaging Reporting and Data System assessment categories would not. This strict definition is meant to guide QI research toward biomarkers that may be assessed and compared using robust statistical calculations including frequency distributions, medians, means, standard deviations, and standard errors of the mean .
Box 1:
Definitions related to quantitative imaging biomarker development
Analytical validation —Demonstration of the accuracy, precision, and feasibility of biomarker measurement
Biomarker —A characteristic that is objectively measured and evaluated as an indicator of a normal biological process, a pathogenic process, or a response to a therapeutic intervention
Predictive biomarker —A biomarker intended to forecast disease course in the presence of a specific treatment
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Motivations for QI biomarker development
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Challenges in QI biomarker development
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Analytical validation
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Qualification
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Utilization
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Where QI research is taking place
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The role of academic radiology in QI biomarker research
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Conclusions
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Acknowledgments
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