Tumour response prediction by diffusion-weighted MR imaging: Ready for clinical use?
Highlights
► DWI shows potential for response prediction and monitoring. ► Some studies show that low pretreatment ADC is related to response to therapy. ► An increase in ADC during/after treatment is related with response in most studies. ► Validation of DWI is hampered by the lack of reproducibility and standardisation.
Introduction
With the increased use of targeted therapies in patients with advanced cancer, it has become clear that the standard anatomical methods of response evaluation (i.e. assessment of lesion size on CT scans) are of limited value to assess the efficacy of these new treatment modalities. Targeted therapies induce necrosis and cavitation, which means that effective treatment does not necessarily result in a reduction of tumour diameter. This has increased the interest in applications of functional imaging techniques for response prediction, such as dynamic contrast enhanced MRI (DCE-MRI) [1]. DCE-MRI is a non-invasive imaging technique that can be used to measure properties of tissue microvasculature. It is widely explored in research and a favoured technique to evaluate tumours with respect to their state of microcirculation [2]. In the last decade, diffusion weighted imaging (DWI), another functional MR imaging modality, has been the subject of research with promising results. Preclinical studies comparing DCE-MRI and DWI suggest that both perform well in early response monitoring after anti-vascular therapy. Both imaging modalities showed changes in their parameters hours to days after treatment [3], [4], [5], [6]. In this review, we will focus on clinical studies to assess the potential of DWI for predicting response to cancer therapy and discuss the additional value of DWI compared to functional imaging with DCE-MRI.
As some technical background is indispensable for understanding the study results, we will start with a description of the basic principles of DWI.
Although MR methods for measuring molecular diffusion were already developed in the 1960s [7], only in the last decade has measurement of water diffusion received increased attention in biomedical studies [8], [9], [10], [11]. Diffusion results from random thermal motion of molecules, known as Brownian motion. It can be restricted by cell structures, such as cell membranes. Therefore, measurement of water diffusion may reflect cellularity. The diffusion of water at this microscopic level can be quantitatively measured with MRI. In DWI, the rate of water diffusion within tissues is measured by movement probing field gradients, that de- and refocus the signal of the water molecules. The dephasing gradient leads to a phase shift of water proton spins that is compensated by a rephasing gradient if a water molecule remains at the same location. However, if the water molecule has moved to another position between these two gradients, this leads to incomplete re-phasing (Fig. 1). Therefore, all moving water molecules result in a certain loss of the water signal. The greater the movement of the water molecules, the lower the signal will be, so the magnitude of this loss reflects the mobility of water.
By using different gradient durations and amplitudes, combined in b-values, the rate of the diffusion of microscopic water within tissues can be measured. The b-value is proportional to the square of the gradient strength (G) and the diffusion time interval (Δ): b ∼ G2Δ. A diffusion coefficient is calculated using the equation: ADC = −bln(SB/S0), in which the signal intensity with a certain b-value (SB) is divided by the signal intensity when the b-value is zero (S0) [12]. The calculated diffusion coefficient is called apparent diffusion coefficient (ADC). The word “apparent” is added because other factors than random diffusion may influence the mobility of water. The ADC is an average of the water mobility in all directions (if the experiment takes all 3 spatial axes into account) and is therefore influenced by the presence of structures such as cellular membranes and the extracellular matrix: a relatively high cellularity will give a relatively low ADC value (Fig. 2, Fig. 3). Obviously, diffusion weighted imaging is also sensitive to other types of motion, such as perfusion, cardiac and respiratory motion. Diffusion weighted images can be evaluated by visual inspection: however, the signal intensity in the image does not only reflect diffusion but also the T2 value of the tissue. This so-called T2 shine-through effect is countered by using an apparent diffusion coefficient map with high b-values.
Most early DWI research was performed in the brain, because of little tissue movement, in addition to other advantages such as good magnetic field homogeneity and high signal to noise ratios [8]. Due to stronger cardiac and respiratory motion, DWI of the abdomen and especially of the liver, is much more difficult to perform adequately.
Section snippets
Role of DWI in tissue characterisation
In principal, DWI could characterise specific tissue properties, rendering invasive biopsies unnecessary if a highly negative predictive value could be reached. This could be particularly useful in lesions that are difficult to access and in patients that are at risk for complications of a biopsy procedure. Since ADC-values from DWI measurements reflect cellularity, the mean ADC-value could be a tool to distinguish benign from malignant lesions, and to differentiate between different grades of
Role of DWI in response prediction: pretreatment ADC
Necrotic areas in tumours are often surrounded by hypoxic, but viable cells. This is of clinical importance, since hypoxic tumours are less sensitive to ionising radiation [34], are more prone to aggressive behaviour and may be less sensitive to cytotoxic agents [35]. Therefore, it may be hypothesised that patients with necrotic areas in their tumours, and thus high pretreatment ADCs, would have a worse treatment outcome. Several studies have indeed shown a relation between pretreatment ADC and
Role of DWI in response evaluation
Supposing that systemic anti-tumour treatment decreases tumour cellularity, treatment should increase ADC-values (Fig. 4). Decreases in tumour cellularity will ultimately lead to reduction in tumour size. This reduction in tumour size can be expected after 2–3 cycles of systemic treatment, which usually is between 6 and 12 weeks after start of treatment. This has become the conventional time of response evaluation. First we will discuss the role of ADC measurements at the conventional time of
Comparison of DWI and DCE-MRI
DCE-MRI appears to be a favoured functional imaging technique in many studies. In DCE-MRI, the tissue uptake of an MR contrast agent is monitored over time. Essentially, DCE-MRI is sensitive to a combination of vascular flow, volume and permeability. In tumour tissue this combination may have acquired typical properties due to angiogenesis. Therefore, these may be used as biomarkers to identify tumour tissue and to assess tumour response [7], [60], [61], [62]. The dynamic uptake of a contrast
Conclusions
DWI may be regarded as a promising imaging tool for the prediction of response to treatment. Based on the results thus far, in the clinic DWI seems to perform as well as DCE-MRI and it may be suitable for a broader range of therapies. In fact, DWI can be used both in the evaluation of conventional cytotoxic treatment and anti-vascular therapy.
However, an important question is whether this new imaging technology is ready to be implemented in clinical practice. DWI adds to conventional imaging,
Reviewers
Allison T. Stopeck, M.D. Associate Professor of Medicine, University of Arizona, Arizona Cancer Center, Tucson, AZ, United States.
Marty Pagel, Ph.D., Assistant Professor, The University of Arizona, Biomedical Engineering and Chemistry, Arizona Cancer Center, 1515 North Campbell Ave., Tucson, AZ 85724, United States.
Conflict of interest statement
None of the authors have a conflict of interest (either financial or otherwise) to declare.
Acknowledgement
Supported by a grant from the Dutch Cancer Society; (KWF), no. KUN 2008-4098.
Linda Heijmen, M.D. is a PhD student. The main research activities focus on multimodality imaging (including diffusion weighted imaging, DCE-MRI, spectroscopy and FDG-PET) for response prediction and monitoring in advanced stage colorectal cancer.
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2017, Advanced Drug Delivery ReviewsCitation Excerpt :Global standardization of these factors is therefore essential to enable comparisons and validation of data from FDG-PET imaging across institutions [174–178]. Multiple studies have demonstrated that diffusion-weighted MRI parameters, such as the apparent diffusion coefficient (ADC), which quantifies water mobility in tissues, can accurately predict response and survival in multiple tumor sites [179–182]. However, issues concerning reproducibility of ADC measurements – which can be attributed to the dearth of standardization of instrumentation among vendors and of internationally recognized calibration protocols – remain a challenge for these kinds of investigations [183].
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2016, Critical Reviews in Oncology/HematologyCitation Excerpt :Conversely, highly cellular tissues such as tumors, which typically have higher water and protein content, will appear persistently bright for low signal attenuation on high b value images (Koh and Collins, 2007; Padhani et al., 2009, 2011; Koh et al., 2012; Nguyen et al., 2014) (Figs. 9A and 12A ). Moreover, in the therapy assessment setting for evaluating treatment response of tumors, changes in the extent and in signal intensity may be used to indicate the success of treatments by comparing serial studies over time (Koh and Collins, 2007; Padhani et al., 2009, 2011; Koh et al., 2012; Nguyen et al., 2014; Heijmen et al., 2012; Marcus et al., 2009; Gwyther, 1999) (Fig. 12). Thus, visual assessment of the relative tissue signal attenuation at images with progressively higher b values is being applied for tumor detection, tumor characterization, and evaluation of treatment response in oncologic patients (Koh and Collins, 2007; Padhani et al., 2009, 2011; Koh et al., 2012; Lichy et al., 2007; Gümüştaş et al., 2011; Nguyen et al., 2014; Blackledge et al., 2014; Priola et al., 2015c; Heijmen et al., 2012; Marcus et al., 2009; Gwyther, 1999).
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Linda Heijmen, M.D. is a PhD student. The main research activities focus on multimodality imaging (including diffusion weighted imaging, DCE-MRI, spectroscopy and FDG-PET) for response prediction and monitoring in advanced stage colorectal cancer.
Hanneke W.M. van Laarhoven, M.D; PhD is a oncologist with a main focus on breast and colorectal cancer. Her PhD thesis was written on imaging modalities in colorectal cancer.