Methods and challenges in quantitative imaging biomarker development

Abstract

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.

Keywords: Radiology; biomarker development; quantitative imaging.

Fig. 1

Dynamic contrast-enhanced MRI (DCE-MRI) as a quantitative imaging (QI) technique for assessing breast cancer response to neoadjuvant therapy (color overlay = tumor). The top row illustrates an early reduction in the quantitative DCE-MRI parameter K^trans in a patient who had a documented pathological complete response (pCR) at surgery (A: prior to therapy, B: after one cycle of neoadjuvant therapy, C: at the conclusion of neoadjuvant therapy). The bottom row illustrates an early increase in K^trans in a patient who had residual disease (non-pCR) at surgery (D: prior to therapy, E: after one cycle of neoadjuvant therapy, F: at the conclusion of neoadjuvant therapy. (Image courtesy of Lisa Li, Ph.D., Vanderbilt University)

Fig. 2

Repeatability of apparent diffusion coefficient (ADC) measurements from diffusion-weighted MRI (DW-MRI) in a breast cancer patient (color overlay = tumor). Panel A shows the distributions of the ADC values from the tumor obtained on two separate scans within one week of each other. Panels B (visit 1) and C (visit 2) show the spatial variations at the voxel level. The mean with 95% confidence intervals for the two visits were 1.06 +/− 0.01 mm²/ms and 1.03 +/− 0.01 mm²/ms, respectively. The lack of overlap in the confidence intervals, despite the apparent similarity in the histograms, illustrates the importance of analytical validation studies to establish ranges of measurement error before deploying quantitative techniques to interrogate changes in underlying biology. (Image courtesy of Lori Arlinghaus, Ph.D, Vanderbilt University.)

Fig. 3

Volume-rendered CT of the abdomen and pelvis with overlaid 3-D surface rendering of the spleen, segmented by a fully automated multi-atlas content labeling algorithm. This technology is under investigation as a means of efficiently and accurately extracting spleen volume data for biomarker analyses. (Image courtesy of Zhoubing Xu, Ph.D. graduate student, Vanderbilt University.)