Head and neck cancer is one of the most common cancers worldwide. MR imaging–based diffusion and perfusion techniques enable the noninvasive assessment of tumor biology and physiology, which supplement information obtained from standard structural scans. Diffusion and perfusion MR imaging techniques provide novel biomarkers that can aid monitoring in pretreatment, during treatment, and posttreatment stages to improve patient selection for therapeutic strategies; provide evidence for change of therapy regime; and evaluate treatment response. This review discusses pertinent aspects of the role of diffusion and perfusion MR imaging and computational analysis methods in studying head and neck cancer.
Key points
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Functional MR imaging techniques (diffusion and perfusion MR imaging) allow for quantifying tumor characteristics related to tumor physiology and biology.
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Compared with anatomic imaging techniques, functional MR imaging techniques have shown their added value in head and neck tumor detection, characterization, staging, treatment response monitoring, and prediction.
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Dedicated MR imaging hardware and software and knowledgeable personnel are essential to obtain reliable data and to translate to the head and neck clinic.
Discussion of problem/clinical presentation
Head and neck (HN) cancer is one of the major types of cancer, affecting 50,000 new patients in the United States every year. HN cancers typically originate from the mucosal epithelia of the oral cavity, pharynx, and larynx and can be linked to alcohol consumption and tobacco smoking. For early HN cancers, encouraging locoregional control can be reached through radiation or surgery treatment. However, for advanced HN cancer, the odds are less favorable, as with standard therapy, only 60% of patients will survive 5 years. HN cancers frequently metastasize to (cervical) lymph nodes before they penetrate distant organs such as the lungs. In spite of recent advances in surgical and oncologic treatments, the overall survival rate of patients with HN cancer has unfortunately not improved much over recent years. Important causes for unfavorable outcome in advanced HN cancer can be a delayed diagnosis (followed by loco regional failure) and a tardy salvage treatment at the recurrence of the disease. A priori predictors of outcome and predictive biomarkers of treatment response are desperately needed to advance patient care and individualized treatment. For example, noninvasive imaging biomarkers could have an important role in the clinical decision-making process, thereby allowing oncologists to use interventions with alternative therapy strategies. Imaging has several benefits as a method for improving the tumor treatment evaluation, as it can sample the entire tumor noninvasively and can be repeated longitudinally to monitor changes at regular intervals.
Functional MR imaging might provide the ideal tools yielding such noninvasive markers. This review focuses on the promises of diffusion- and perfusion-weighted MR imaging techniques in HN cancer. Diffusion-weighted (DW) MR imaging can quantify and map the diffusion of molecules (typically water), in biological tissues, whereas perfusion-weighted MR imaging can assess the passage of blood through vessels through tissue. Both MR imaging techniques have a rich history that extends decades, and the MR imaging tools available to assess the associated processes are currently very mature, providing excellent opportunities to study both diffusion and perfusion in HN cancer. Although some might consider magnetic resonance (MR) spectroscopy also to be a functional MR imaging technique, it falls beyond the scope of this review, and we refer the reader to an excellent review by Razek and Poptani.
Diffusion
DW imaging (DWI) is an MR technique that allows the measurement of water self-diffusivity. Because freedom of motion of water molecules is hindered by interactions with other molecules and cellular barriers, water molecule diffusion abnormalities can reflect changes of tissue organization at the cellular level (eg, increase of extracellular space owing to cell death). These microstructural changes affect the (hindered) motion of water molecules and consequently alter the water diffusion properties and thus the MR signal. Apart from deriving a measure for the average extent of molecular motion that is affected by cellular organization and integrity (apparent diffusion coefficient [ADC]), it is also possible using diffusion tensor imaging (in which diffusion is measured in several directions) to measure the preferred direction of molecular motion, which provides information on the degree of alignment of cellular structures and their structural integrity (fractional anisotropy). Recently, also DWI techniques have entered the HN cancer clinic, in which images are acquired with multiple b values, yielding techniques such as intravoxel incoherent imaging (IVIM) or diffusion kurtosis imaging, techniques that aim to provide information that extends diffusion of water, such as perfusion (for IVIM) or non-Gaussian diffusion behavior (for diffusion kurtosis imaging).
Perfusion
Perfusion is physiologically defined as the steady-state delivery of blood to tissue. Two major approaches exist to assess perfusion with MR imaging. The first is the application of an exogenous contrast agent (usually gadolinium based), exploiting the susceptibility effects or relaxivity effects of the contrast agents on the signal, respectively, dynamic susceptibility contrast–enhanced MR perfusion or dynamic contrast–enhanced (DCE) MR perfusion. The second application involves the use of an endogenous contrast agent, namely, magnetically labeled arterial blood water, as a diffusible flow tracer in arterial spin labeling (ASL) MR perfusion. DCE and, to a lesser extend ASL, are currently being used to study HN cancer.
Outline
This review summarizes recent literature and provides an overview of the various studies in which diffusion- or perfusion-based MR imaging studies are applied to HN cancer. This review provides an overview of commonly used acquisition protocols and postprocessing methods, advanced data analysis, imaging findings regarding tumor characterization and differentiation, tumor risk stratification and staging, monitoring, and prediction of treatment response. Subsequently, limitations are highlighted followed by a conclusion with recommendations for future research.
Discussion of problem/clinical presentation
Head and neck (HN) cancer is one of the major types of cancer, affecting 50,000 new patients in the United States every year. HN cancers typically originate from the mucosal epithelia of the oral cavity, pharynx, and larynx and can be linked to alcohol consumption and tobacco smoking. For early HN cancers, encouraging locoregional control can be reached through radiation or surgery treatment. However, for advanced HN cancer, the odds are less favorable, as with standard therapy, only 60% of patients will survive 5 years. HN cancers frequently metastasize to (cervical) lymph nodes before they penetrate distant organs such as the lungs. In spite of recent advances in surgical and oncologic treatments, the overall survival rate of patients with HN cancer has unfortunately not improved much over recent years. Important causes for unfavorable outcome in advanced HN cancer can be a delayed diagnosis (followed by loco regional failure) and a tardy salvage treatment at the recurrence of the disease. A priori predictors of outcome and predictive biomarkers of treatment response are desperately needed to advance patient care and individualized treatment. For example, noninvasive imaging biomarkers could have an important role in the clinical decision-making process, thereby allowing oncologists to use interventions with alternative therapy strategies. Imaging has several benefits as a method for improving the tumor treatment evaluation, as it can sample the entire tumor noninvasively and can be repeated longitudinally to monitor changes at regular intervals.
Functional MR imaging might provide the ideal tools yielding such noninvasive markers. This review focuses on the promises of diffusion- and perfusion-weighted MR imaging techniques in HN cancer. Diffusion-weighted (DW) MR imaging can quantify and map the diffusion of molecules (typically water), in biological tissues, whereas perfusion-weighted MR imaging can assess the passage of blood through vessels through tissue. Both MR imaging techniques have a rich history that extends decades, and the MR imaging tools available to assess the associated processes are currently very mature, providing excellent opportunities to study both diffusion and perfusion in HN cancer. Although some might consider magnetic resonance (MR) spectroscopy also to be a functional MR imaging technique, it falls beyond the scope of this review, and we refer the reader to an excellent review by Razek and Poptani.
Diffusion
DW imaging (DWI) is an MR technique that allows the measurement of water self-diffusivity. Because freedom of motion of water molecules is hindered by interactions with other molecules and cellular barriers, water molecule diffusion abnormalities can reflect changes of tissue organization at the cellular level (eg, increase of extracellular space owing to cell death). These microstructural changes affect the (hindered) motion of water molecules and consequently alter the water diffusion properties and thus the MR signal. Apart from deriving a measure for the average extent of molecular motion that is affected by cellular organization and integrity (apparent diffusion coefficient [ADC]), it is also possible using diffusion tensor imaging (in which diffusion is measured in several directions) to measure the preferred direction of molecular motion, which provides information on the degree of alignment of cellular structures and their structural integrity (fractional anisotropy). Recently, also DWI techniques have entered the HN cancer clinic, in which images are acquired with multiple b values, yielding techniques such as intravoxel incoherent imaging (IVIM) or diffusion kurtosis imaging, techniques that aim to provide information that extends diffusion of water, such as perfusion (for IVIM) or non-Gaussian diffusion behavior (for diffusion kurtosis imaging).
Perfusion
Perfusion is physiologically defined as the steady-state delivery of blood to tissue. Two major approaches exist to assess perfusion with MR imaging. The first is the application of an exogenous contrast agent (usually gadolinium based), exploiting the susceptibility effects or relaxivity effects of the contrast agents on the signal, respectively, dynamic susceptibility contrast–enhanced MR perfusion or dynamic contrast–enhanced (DCE) MR perfusion. The second application involves the use of an endogenous contrast agent, namely, magnetically labeled arterial blood water, as a diffusible flow tracer in arterial spin labeling (ASL) MR perfusion. DCE and, to a lesser extend ASL, are currently being used to study HN cancer.
Outline
This review summarizes recent literature and provides an overview of the various studies in which diffusion- or perfusion-based MR imaging studies are applied to HN cancer. This review provides an overview of commonly used acquisition protocols and postprocessing methods, advanced data analysis, imaging findings regarding tumor characterization and differentiation, tumor risk stratification and staging, monitoring, and prediction of treatment response. Subsequently, limitations are highlighted followed by a conclusion with recommendations for future research.
Imaging protocols
Diffusion-Weighted MR Imaging
Data acquisition
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MR imaging scanner and coil: DW MR imaging studies for HN cancers are commonly carried out on 1.5-T or 3-T MR imaging scanners using dedicated neurovascular phase array coils.
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Pulse sequence: Clinical DW MR imaging is most commonly performed using single-shot spin-echo echo planar imaging (EPI), axial free breathing.
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Acquisition parameters ( Table 1 ): Protocol optimization is a prerequisite for obtaining optimum signal-to-noise ratio in DW images. The number of b values for mono exponential modeling of the data are 2 to 3 and the b values are greater than100 s/mm 2 ; (usually between 500 and 1200 s/mm 2 ), whereas the number of b values increases up to 10 or more (usually between 0 to 1500 s/mm 2 ) including both the high and low b values for biexponential modeling of the data ; slice thickness, 5 to 8 mm, gap thickness, 0 mm, field of view, 200 to 380 mm ; acquired matrix, 128 x 128 or higher ; number of averages, 2 to 4; parallel imaging (SENSE or ASSET), factor, 2; echo time (TE, ideal/target), minimum TE; acceptable, less than 110 milliseconds; repetition time (TR), 2 to 4 seconds; receiver bandwidth, greater than 1000 Hz/voxel.
Table 1
Typical acquisition parameters
Contrast
Sequence
TE/TR (ms)
Slice Thickness (mm)
FOV (mm)
Matrix
Extras
DW MR imaging
Single-shot spin-echo EPI
<110/2000–4000
5–8
200–380
≥128×128
>2 b values, 0–1500 s/mm 2
DCE MR imaging
3-D spoiled gradient echo
∼1.4/5.3
5–8
∼220
≥128×128
Gd-DTPA bolus 0.1 mmol/kg at 2 ml/s, followed by a 20-mL saline flush
Temporal resolution: 3–6 s; ∼50 phases
ASL
Multishot spin-echo echo-planar, pCASL
∼20/4000
5
∼230
∼80×80
Labeling duration: 1650 ms; postlabel delay: 1280 ms; 2 shots; labeling just under the bifurcation
Diffusion-weighted MR imaging data processing
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Region of interest analysis: The regions of interest (ROIs) are usually drawn on the DW MR images by an experienced neuroradiologist based on the radiologic and clinical information. The ROI encompasses the entire tumor and node of interest.
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Quantitative methods: Mono- and biexponential models are usually used for quantifying diffusion either based on voxel by voxel or the ROI. For monoexponential models, ADC value can be quantified using S/S0 = exp(-b × ADC), where S and S0 are the signal intensities with and without diffusion weighting, respectively, and b is the gradient factor (b value, seconds per millimeter squared). For biexponential models, metrics related to intravoxel incoherent motions can be calculated using
or
where f is the vascular volume fraction or perfusion factor, D is the pure diffusion coefficient (millimeter squared per second), D ∗ is the pseudodiffusion coefficient (millimeter squared per second) associated with blood velocity and capillary geometry, and K is the diffusion kurtosis coefficient. Noise floor rectification schemes are commonly used in the above diffusion quantifications.
Dynamic Contrast–Enhanced MR Imaging Data Acquisition
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MR imaging scanner and coil: DCE MR imaging studies for HN cancers are commonly being carried out on 1.5-T or 3-T MR imaging scanners using dedicated neurovascular phase array coils.
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Contrast agent: The most commonly used contrast agent is paramagnetic gadolinium chelates, such as Gd-DTPA (gadopentetic diethylenetriamine pentaacetic acid) (Magnevist; Berlex Laboratories, Wayne, NJ). The bolus of contrast agent is typically delivered at 0.1 mmol/kg body weight at 2 mL/s followed by a 20-mL saline flush with a flow rate of 2 mL/s using an MR-compatible, programmable power injector (eg, Spectris; Medrad, Indianola, PA).
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Pulse sequence: Most of the DCE MR imaging data acquisition is performed using a fast 2-dimensional (D) or 3-D gradient-echo sequence because of its high T1 sensitivity and rapid image acquisition. A 3-D spoiled gradient-echo sequence is more widely applied than 2-D spoiled gradient-echo because of its ability to achieve higher spatial resolution and signal-to-noise ratio.
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Acquisition parameters (see Table 1 ): The acquisition parameters can be tailored depending on whether the study design needs higher spatial resolution or higher temporal resolution. Approximate typical parameters on gradient-echo MR scanners are field of view (FOV), 22 cm; TR, 5.3 milliseconds; TE, 1.4 milliseconds; temporal resolution, 4 seconds; phases, 50; number of excitations (NEX), 1. Temporal resolution ranges from 3 to 6 seconds and acquisition time is generally in the range of 2 to 10 minutes.
Dynamic contrast–enhanced MR imaging data processing
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Radiofrequency (RF) field inhomogeneity correction: RF field nonuniformities often cause inhomogeneity in image profile. Image correction methods, such as an edge-completed low pass filter algorithm, can be used to correct this kind of image artifacts. Additionally, this inhomogeneity can result in deviation of the flip angles from nominal values when using gradient-echo sequences for data acquisition. This flip angle deviation has a great impact on the calculation of native T1 relaxation time values, further influencing the accuracies of the estimated pharmacokinetic parameters. A double-echo method can be used to correct this artifact.
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Motion artifact correction: DCE MR images in the HN region suffer from motion artifacts caused by the voluntary and involuntary motions of patients. The motions can cause in-plane and through-plane image artifacts. Image registration methods are commonly used to correct the through-plane image artifacts by realigning the DCE MR imaging time series image itself or coregistering DCE MR images with other image modalities, such as T1- or T2-weighted images. Rigid body alignments are more readily performed than nonrigid deformations.
Dynamic contrast–enhanced MR imaging data quantification
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Semiquantitative methods: Semiquantitative methods classify the signal intensity time curve of DCE MR imaging into different patterns or provide some simple summary descriptors about the curve. For curve pattern classification, the initial enhancement (1–2 min) of the curve is usually described as fast, medium, and slow uptake. The late enhancement (>2 min) of the curve is often classified as persistent, plateau, and washout. Normal tissues and tumor tissues with different degrees of malignancy could show different curve patterns. This feature can be used for tumor detection and tumor differentiation. For curve summary description, several summary parameters, such as maximum contrast index (CI), time to reach maximum CI, maximum slope, washout slope, area under the curve (AUC) at a specific time (eg, AUC90 means the area under the curve 90 seconds after contrast injection), are used.
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Pharmacokinetic modeling methods: Pharmacokinetic modeling methods provide characteristics of tumor microvasculature (related to endothelial permeability, the size of extracellular extravascular space [EES], and the size of intravascular space) by modeling tumor contrast kinetics into separate compartments and establishing the transport equation of the contrast agent. Commonly used models are the Tofts model, extended T model, shutter speed model, and the 2-compartment exchange model. Among these models, the Tofts model is used the most. From the Tofts model, kinetic parameters such as K trans (volume transfer rate between vascular space and EES, min −1 ) and v e (volume fraction of the EES) can be characterized on a basis of tumor ROI or voxel by voxel. For these models, accurate estimation of the arterial input function (AIF) is required, and when this is not possible in individual cases, also averaged population-based AIF functions can be used.
Arterial Spin Labeling MR Imaging Data Acquisition
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MR imaging scanner and coil: ASL MR imaging studies for HN cancers have been reported using 3-T MR imaging scanners using dedicated neurovascular phase array coils.
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Pulse sequence: ASL can be acquired with a sequence using echo-planar MR imaging signal targeted by alternating RF pulses (EPI STAR). Magnetic labeling of in-flowing arterial blood can be achieved using section-selective 180° RF pulses in labeling slab. After the labeling, a Look-Locker readout of gradient-echo EPI with an excitation pulse of 30° can be used for image acquisition. Additionally, control images without labeling need to be acquired. Also, pCASL (Pseudocontinuous arterial spin labeling) techniques have been reported. The acquisition of pCASL can be performed by using multishot spin-echo echo-planar imaging to obtain control and labeled images. The labeling slab can be placed just under the bifurcation of the internal and external carotid arteries.
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Acquisition parameters (see Table 1 ): Typical parameters on 3-T Philips MR scanners for EPI STAR are TR, 3000 milliseconds; TE, 24 milliseconds; FOV, 230 × 230 mm; matrix, 80 x 80; slice thickness, 10 mm; interslice gap, 30%; NEX 30. Label slab is 58.5 mm thick located 20 mm proximal to the imaging section. For pCASL, parameters are labeling duration, 1650 milliseconds; postlabel delay, 1280 milliseconds; TR, 3619 milliseconds; TE, 18 milliseconds; flip angle, 90°; number of shots, 2; FOV, 230 × 230 mm; matrix, 80 x 80; slice thickness, 5 mm; number of slices, 15; acceleration factor for parallel imaging, 2.
Arterial spin labeling data quantification
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Tumor blood flow (TBF) can be calculated using image processing software such as MatLab (MathWorks, Natick, MA). TBF can be calculated from analysis of magnetization difference obtained by subtracting the labeled images from the ASL control images. TBF maps can be created on a pixel-by-pixel basis.
Advanced data analysis
In addition to using perfusion- or diffusion-based MR imaging contrasts for a better evaluation of HN cancer, on the data analysis side, improvements in the applicability of MR images in HN cancer are developing. Most of these techniques do not need specific MR imaging contrasts as input, because, in principle, they work on any quantitative map. For example, the parametric response map approach is a voxel-based approach that allows segmentation of a tumor volume based on regional intratumoral changes in the MR signal. It is ideally suited to accurately follow treatment-induced changes in tumors on a voxel-by-voxel basis. Another analysis method allows for accurate assessment of tumor heterogeneity. HN cancer can be very heterogeneous in nature, as the tumor vascular system is typically chaotic and poorly organized, and tumor heterogeneity itself is a well-recognized feature that is associated with tumor malignancy. In particular, tumor heterogeneity in the blood supply may prevent therapeutic efficacy and result in treatment resistance. Therefore, tumor heterogeneity may play an important role in assessing tumor malignancy and predicting treatment response. Most studies typically use summarizing characteristics, such as mean, median, or standard deviation of voxelwise measures, to describe the nature of the whole tumor volume. However, these commonly used measures do not necessarily reflect the marked morphologic heterogeneity in nodal metastases of HN cancer. Image texture analysis may be an ideal candidate to assess tumor tissue heterogeneity in a reliable manner. In texture analysis, an algorithm that assesses spatial intensity coherence is applied to an image yielding several textural features (reflecting heterogeneity), independent of the image’s mean and variance. The gray-level co-occurrence matrix, or gray-level spatial dependence matrix, is one of the most important algorithms used for texture analysis.
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