Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials

Abstract

Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion-weighted imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101-e121.

Keywords: DCE; DWI; MRI; quantitative imaging biomarkers.

Figure 1:

The %RC is the cut-point where a change in the biomarker measurements is considered a real change, not merely a measurement error, with 95% confidence. The graph illustrates how this cut-point increases with the within-subject CV (wCV). When the wCV is small (i.e., high precision), very small changes in the biomarker can be detected. Whereas when the wCV is large (i.e., low precision), large changes in the biomarker are needed before one can be confident that a real change has occurred.

Figure 2:

Box-and-whisker plot demonstrating apparent diffusion coefficient (ADC) repeatability of water for multi-site results at 3T magnetic resonance imaging scanners using the ice water phantom. Note: Inset is the ADC map of the phantom. (Images compliments of authors from sites 1, 2, and 3: Memorial Sloan Kettering Cancer Center, Columbia University Medical Center, and University of Michigan.

Figure 3:

Repeatability results obtained using the National Institute of Standards andTechnology/Radiological Society of North America Quantitative Imaging Biomarkers Alliance diffusion-weighted magnetic resonance imaging phantom containing vials with varying concentrations of polyvinylpyrrolidone (0–50%) to generate physiologically relevant apparent diffusion coefficient (ADC) values at different vial positions (c = central; o = outer; I = inner). The phantom and ADC image are shown as inserts in the graph. Graph showing ADC (mean±sd) values for each vial in 4 experiments performed at (A) site 1, (B) site 2, and (C) at site 3. (Images compliments of author from sites 1, 2, and 3: Memorial Sloan Kettering Cancer Center; Columbia University Medical Center, and University of Michigan.

Figure 4:

(A) The Quantitative Imaging Biomarkers Alliance dynamic contrast-enhanced phantom layout with 32 spheres, with different concentrations of NiCl2 solutions for varying T1 relaxation rates (R1). (B) T1-weighted magnetic resonance image of the phantom showing the 32 spheres, and (C) R1 values of the 8-vascular input function mimicking inserts compared with National Institute of Standards and Technology theoretical R1 values. (D) R1 values for the 24 tissue-mimicking inserts. (Images compliments of Edward Jackson, University of Wisconsin-Madison).

Figure 5:

Portable perfusion phantom and its repeatability measurement. (A) Photograph of a portable perfusion phantom, and (B) contrast enhancement curves of three phantoms placed in a 3T magnetic resonance imaging scanner (temporal resolution = 2.9 s). Repeatability determined by the intraclass correlation coefficient is larger than 0.99. (Images compliments of Harrison Kim, University of Alabama at Birmingham).

Figure 6:

Representative pre-treatment magnetic resonance images of a patient with grade 4 brain tumor (65 years, female). (A) T2-weighted image. (B) Apparent diffusion coefficient × 10⁻³ (mm²/s) map generated using 3 b-values (b = 0, 100, 1000 s/mm²). (C) K^trans (min⁻¹) map generated from dynamic contrast enhanced data with insert of T1-weighted gadolinium contrast image. (Images compliments of Thomas Chenevert, University of Michigan).

Figure 7:

Representative pretreatment magnetic resonance images of a patient with prostate cancer—Gleason Score 4+3 (66 years, male). (A) T2-weighted image, (B) apparent diffusion coefficient × 10⁻³ (mm²/s) map generated using 2 b-values (i.e., b = 0, 600 s/mm²), and (C) K^trans (min⁻¹) map generated from dynamic contrast-enhanced data. (Images compliments of Susan M. Noworolski, University of California San Francisco).

Figure 8:

Representative magnetic resonance images from a breast cancer patient (34 years, female) with grade II invasive ductal carcinoma (IDC) in the right breast. (A) T2-weighted image with fat saturation, (B) color K^trans (min⁻¹) map of the tumor overlaid on T1-weighted dynamic contrast-enhanced image with fat saturation, and (C) Representative apparent diffusion coefficient × 10⁻³ (mm²/s) (ADC) map from a breast cancer patient (37 years, female) with grade II IDC in the right breast. Composite ADC map was generated from diffusion-weighted imaging images with b = 0 and 800 s/mm² showing decreased ADC in tumor (arrow). (Images compliments of Wei Huang, Oregon Health & Science University).

Figure 9:

Representative magnetic resonance images from a recurrent hepatocellular carcinoma patient (57 years, male) acquired on a 3T scanner. Dynamic contrast-enhanced magnetic resonance imaging scan showing (A) enhancing tumor and (B) contrast enhancement time course. (C) The gadolinium concentration time course and extended Tofts model fit and (D) Composite apparent diffusion coefficient map generated from diffusion-weighted imaging images, with b = 0, 600 s/mm² from same patient. (Images compliments of Sachin Jambawalikar, Columbia University Medical Center)

Figure 10:

Representative pretreatment magnetic resonance images of head and neck cancer patient (52 years, male). (A) T2-weighted image, (B) apparent diffusion coefficient ×10⁻³ (mm²/s) map overlaid on diffusion-weighted (b = 0 s/mm²) images generated using 10 b-values (0, 20, 50, 80, 200, 300, 500, 800, 1500, and 2000 s/mm²), (C) K^trans (min⁻¹) map overlaid on pre-contrast T1-weighted image. (Images compliments of Amita Shukla-Dave, Memorial Sloan Kettering Cancer Center).