Hi,I am a master’s student, majoring in medical physicsI need help to make a presentation based on the attached research (summarize the attached research in a presentation). Also, notes to read with appropriate slidesPaper title is “Imaging biomarkers in oncology: Basics and application to MRI”.The presentation is no more than 8 minutes and a maximum of 12 slides.Can someone help me?Thank youCME ARTICLE
Imaging Biomarkers in Oncology: Basics
and Application to MRI
Isabel Dregely, PhD
,1 Davide Prezzi, FRCR,2,3 Christian Kelly-Morland, FRCR,2,3
Elisa Roccia, MRes,1 Radhouene Neji, PhD,1,4 and Vicky Goh, FRCR
CME Information: Imaging Biomarkers in Oncology: Basics and
Application to Magnetic Resonance Imaging (MRI)
If you wish to receive credit for this activity, please refer to the website:
www.wileyhealthlearning.com
2,3
*
Shreyas Vasanawala, MD, PhD, discloses research support from GE Healthcare, and founder’s equity in Arterys.
Eric Chang, MD, Feng Feng, MD, and Bruno Madore, PhD; no conflicts of
interest or financial relationships relevant to this article were reported.
Authors:
Educational Objectives
Upon completion of this educational activity, participants will be better able
to:
• Name and describe established and emerging magnetic resonance imaging
biomarkers for diagnosis, prognosis and response assessment in cancer
imaging
• Describe the key principles for imaging biomarker development
Isabel Dregely, Davide Prezzi, Christian Kelly-Morland, Elisa Roccia, Radhouene
Neji, and Vicky Goh; no conflicts of interest or financial relationships relevant to
this article were reported.
This activity underwent peer review in line with the standards of editorial
integrity and publication ethics. Conflicts of interest have been identified and
resolved in accordance with John Wiley and Sons, Inc.’s Policy on Activity
Disclosure and Conflict of Interest.
Accreditation
Activity Disclosures
No commercial support has been accepted related to the development or publication of this activity.
Faculty Disclosures:
Editor-in-Chief: Mark E. Schweitzer, MD, discloses consultant fees from
MCRA and MMI.
CME Editor: Mustafa R. Bashir, MD, discloses research support from GE
Healthcare, Madrigal Pharmaceuticals, NGM Biopharmaceuticals, Siemens
Healthcare and Taiwan J Pharma, and consultant fees from RadMD.
CME Committee:
Bonnie Joe, MD, PhD, discloses author royalties from UpToDate.
Tim Leiner, MD, PhD, discloses research grants from Bayer Healthcare and
Philips Healthcare.
John Wiley and Sons, Inc. is accredited by the Accreditation Council for
Continuing Medical Education to provide continuing medical education for
physicians.
John Wiley and Sons, Inc. designates this journal-based CME activity for a
maximum of 1.0 AMA PRA Category 1 CreditTM. Physicians should only
claim credit commensurate with the extent of their participation in the
activity.
For information on applicability and acceptance of continuing medical education credit for this activity, please consult your professional licensing board.
This activity is designed to be completed within 1 hour. To successfully earn
credit, participants must complete the activity during the valid credit period,
which is up to two years from initial publication. Additionally, up to 3
attempts and a score of 70% or better is needed to pass the post test.
View this article online at wileyonlinelibrary.com. DOI: 10.1002/jmri.26058
Received Feb 5, 2018, Accepted for publication Mar 26, 2018.
View this article online at wileyonlinelibrary.com. DOI: 10.1002/jmri.26058
*Address reprint requests to: V.G., Department of Radiology, Level 1, Lambeth Wing, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH,
Received Feb 5,UK.
2018,
Accepted
for publication Mar 26, 2018.
E-mail:
vicky.goh@kcl.ac.uk
1
*Address
reprint
requestsEngineering,
to: V.G., Department
of Radiology,
Level 1, Lambeth
Wing,
St Thomas’
Westminster
BridgeHospital,
Road, London
SE1UK;
7EH,
From the
Biomedical
School of Biomedical
Engineering
& Imaging
Sciences,
King’sHospital,
Health Partners,
St Thomas’
London,
UK.
E-mail: King’s
vicky.goh@kcl.ac.uk
Cancer Imaging, School of Biomedical Engineering & Imaging
Sciences,
College London, King’s Health Partners, St Thomas’ Hospital, London, UK;
3
Radiology, Guy’s & St Thomas’ NHS Foundation Trust, London, UK; and 4MR Research Collaborations, Siemens Healthcare, Frimley, UK.
From the 1Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s Health Partners, St Thomas’ Hospital, London, UK;
2
Cancer
School
ofarticle
Biomedical
Imaging
Sciences,
King’s
College
London,
King’s
Health
Partners,
St Thomas’
Hospital, London,
ThisImaging,
is an open
access
under Engineering
the terms of &
the
Creative
Commons
Attribution
License,
which
permits
use,
distribution
and reproduction
in any UK;
3
4
Radiology, Guy’s & St Thomas’ NHS Foundation
London,
UK; andwork
MR isResearch
Siemens Healthcare, Frimley, UK.
medium,Trust,
provided
the original
properlyCollaborations,
cited.
2
C 2018 International Society for Magnetic Resonance in Medicine
V
13
© 2018 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc.
on behalf of International Society for Magnetic Resonance in Medicine.
13
Journal of Magnetic Resonance Imaging
Cancer remains a global killer alongside cardiovascular disease. A better understanding of cancer biology has transformed its management with an increasing emphasis on a personalized approach, so-called “precision cancer medicine.” Imaging has a key role to play in the management of cancer patients. Imaging biomarkers that objectively inform
on tumor biology, the tumor environment, and tumor changes in response to an intervention complement genomic and
molecular diagnostics. In this review we describe the key principles for imaging biomarker development and discuss the
current status with respect to magnetic resonance imaging (MRI).
Level of Evidence: 5
Technical Efficacy: Stage 5
J. MAGN. RESON. IMAGING 2018;48:13–26.
C
ancer affects 14.1 million new patients yearly and is
the second most common killer disease worldwide.1
Clinicians have long recognized that cancer represents a very
heterogeneous disease. Patients with the same clinical presentation, tumor type, and stage may respond very differently to the same therapies and have different oncological
outcomes. A better understanding of the extent of the genomic and molecular heterogeneity within cancers, as demonstrated in renal cell cancer,2 has led to a refocusing of
clinical management in recent years from a global to a more
targeted approach.3 Currently, cancer therapies aim to be
personalized to the patient’s cancer, either to cure where
there is limited disease, or to extend progression-free survival (PFS) where disease is advanced, yet maintaining a
good quality of life, so-called “precision cancer medicine.”
The US Food and Administration (FDA) approval of
bevacizumab in 2004 for first-line metastatic colorectal cancer, after a Phase III trial demonstrated an improvement in
median PFS of 4 months,4 has paved the way for an
increasing number of licensed molecular targeted therapies.
These include targeted HER-2 (human epidermal growth
factor receptor 2) therapy (trastuzumab) for HER-2 overexpressing breast cancer and gastric/gastroesophageal cancer;
targeted EGFR (epidermal growth factor receptor) therapy
(cetuximab) for RAS wildtype colorectal cancer; targeted
EGFR therapy (gefitinib or erlotinib) for EGFR mutated
nonsmall-cell lung cancer; crizotinib for ALK (anaplastic
lymphoma kinase) gene rearrangement nonsmall-cell lung
cancer (present in 5% of adenocarcinomas); and multikinase inhibitors (pazopanib, sorafenib, sunitinib) or mammalian target of rapamycin (mTOR) inhibitors (everolimus) for
advanced renal cell cancer.
Trials of these therapies have highlighted the need for
better diagnostics to support patient stratification for therapy as well as a rethink of how we gather evidence for novel
therapeutics that may only work for a subgroup of patients.
There has been burgeoning development of precision diagnostics as a consequence. For single agents targeted to
clearly defined genetic “driver” alterations, companion diagnostics improve the selection of patients for therapy, eg,
HER-2 expression to guide trastuzumab therapy and O6methylguanine-DNA-methyltransferase (MGMT) methylation to guide temozolomide therapy. There has also been
increasing interest in genomic analysis to guide therapy with
14
the move from single to multiagent regimens and also to
improve prognostication, eg, oncotype DX in breast cancer
that predicts the likelihood of recurrence from a 21-gene
signature as well as the likelihood of response to
chemotherapy.
While the advantages of genomic analysis and molecular analysis to improve patient stratification and to assist
drug development is clear, in practice there have been continuing challenges to implementation. Some putative biomarkers may be invalid, as shown with EGFR expression
for cetuximab.5 Cancers are also temporally and spatially
heterogeneous, ie, a biopsy or assay may only reflect a
moment in time, or one of a number of lesions. This plasticity has been a reason for mixed responses to therapies and
the development of therapy resistance during previously
effective targeted therapy.6 There may also be issues such as
suboptimal methodology, challenging assays, validation, regulatory issues, and governance or cost that are a challenge
for multicenter clinical trials.
Imaging still has an important role to play in personalized cancer medicine.7 Imaging is performed widely for the
detection and characterization of cancer, for staging, for
monitoring therapy, for detecting disease recurrence, or surveillance; imaging biomarkers hold great potential for optimizing patient care. The role of magnetic resonance
imaging (MRI) has evolved within oncological practice in
recent years. Previously reserved as an adjunctive problemsolving tool, the primary use of MRI has increased, such
that MRI is now the primary imaging assessment tool for
many cancers and plays an important part in management
decisions. It is the initial imaging modality for diagnosing
prostate cancer and myeloma; for staging rectal, cervical,
and endometrial cancer; and for response assessment in
hepatocellular cancer. In this review we will describe what
constitutes an imaging biomarker, the principles of imaging
biomarker development, and the current status of imaging
biomarkers with respect to MRI.
What Constitutes a Biomarker?
The term “biomarker” refers to a characteristic that is measured objectively, as an indicator of normal biological processes, pathological changes, or response to an intervention.8
It includes molecular, histologic, radiographic, or physiologic characteristics. In terms of imaging, this may include
Volume 48, No. 1
Dregely et al.: MRI Biomarkers in Oncology
FIGURE 1: Schema highlighting steps taken in developing a potential imaging biomarker
anatomical, functional, and molecular characteristics.7 The
advantages of imaging are its versatility, its widespread use,
its relatively noninvasive nature (facilitating whole body
imaging as well as longitudinal studies in individuals, thus
capturing spatial and temporal heterogeneity), and its inherently quantitative nature. Imaging biomarkers may reflect a
general cancer hallmark, eg, proliferation, metabolism,
angiogenesis, apoptosis; specific molecular interactions; or
agnostic features.9 Imaging biomarkers in cancer patients
include biomarkers for detection (the identification of disease), prediction (the prediction of risk of disease or therapeutic outcome), prognostication (the prediction of
oncological outcome), and response assessment (the evaluation of change with therapy). A number of imaging biomarkers are well established in clinical practice. Examples
include staging with the American Joint Committee on
Cancer (AJCC) TNM (tumor, node, metastasis) staging system (a prognostic biomarker) and objective response assessment by RECIST (Response Evaluation in Solid Tumors)10
in clinical trials (a response biomarker).
In the initial phase, including development, evaluation,
and validation, the aim is to ensure that the potential biomarker is robust and fit for purpose. Technical validation
includes assessment of accuracy, precision, repeatability, and
reproducibility across single and multiple centers; biological
and clinical validation ensure that the biomarkers are linked
to tumor biology, outcome variables, and thus of actual
value in guiding decision-making in patients. During this
phase, initial health economic analysis may also be undertaken to identify if there are cost barriers to implementation. Once the biomarker is established, it should be reliable
enough to be implemented in clinical trials to test research
hypotheses.
During the next phase, qualification of the biomarker
may also be undertaken in large prospective trials. Qualification aims to confirm that the biomarker is associated with
the clinical endpoint of interest and aims to demonstrate
cost effectiveness and health impact. This supportive evidence is key to the translation into clinical practice and
widespread utilization. Key recommendations have been
proposed in a recent consensus article.11
Imaging Biomarkers: From Discovery to
Clinical Practice
Advantages of MRI as an Imaging Biomarker
For new potential imaging biomarkers several steps, often in
parallel and complementary to each other, need to be
undertaken for translation into clinical practice. These can
be divided into the following phases following discovery:
development and evaluation, validation, implementation,
qualification, and utilization, essentially crossing two main
translational gaps, translation into patients and translation
into practice (Fig. 1).
Ideally, there are a number of characteristics an imaging biomarker should have (Table 1). MRI has many advantages,
including its superior soft-tissue contrast, high spatial resolution; its ability to obtain multiple contrasts in a single
examination; and its ability to assess physiology, eg, vascularization, oxygenation, and diffusion. Assessment of the
molecular environment is also achievable, albeit at a lower
sensitivity compared to positron emission tomography
July 2018
15
Journal of Magnetic Resonance Imaging
TABLE 1. Key Characteristics and Challenges for MRI Biomarkers
Characteristics
Challenges for MRI
Developments
Sensitive
Signal to noise ratio (SNR)
Contrast to noise ratio (CNR)
Spatial resolution
Artifacts
New sequences
Specific &
biologically
relevant
Targeted versus physiological or
morphological imaging
Evaluation of more targeted imaging, eg, receptor imaging,
targeted nanoparticles
Robust
Variance among imaging systems,
manufacturers & practice
Multivendor & multicenter involvement to
standardize data acquisition, reconstruction & analysis
Quantifiable & Variance among imaging systems,
reproducible
manufacturers & practice
Cost effective
Higher cost compared to computed
Reduction in scanner time with faster acquisitions
tomography (CT) or ultrasound (US)
(PET). A number of MRI biomarkers are already established
or well on their way to being established in clinical practice
for oncological assessments (Table 2). These include BIRADS (Breast Imaging Reporting and Data System),12 LIRADS (Liver Imaging Reporting and Data System),13,14
and PI-RADS (Prostate Imaging Reporting and Data System)15 for the diagnosis of breast, hepatocellular cancers,
and prostate, respectively, in addition to TNM staging and
RECIST response evaluation. Quantitative biomarkers that
have crossed the first translational gap and are being used to
test hypotheses in research studies and clinical trials include
vascular parameters such as initial area under the gadolinium curve (iAUGC) or transfer constant (Ktrans) from
dynamic gadolinium enhanced (DCE) contrast imaging and
apparent diffusion coefficient (ADC) from diffusionweighted MRI (Table 2).
Morphology-Based MRI Biomarkers
Current morphology-based cancer biomarkers utilize the
multiple contrasts and high spatial resolution of MRI. T2weighted and T1-weighted sequences are part of every cancer
protocol. T2-weighting highlights structures with a longer
T2 relaxation time. Thus, organs with a high water content,
eg, bladder, appear of high signal on T2-weighted imaging,
while cancers typically appear of intermediate signal. T2weighted image contrast is encoded by a long echo time
(TE) and long repetition time (TR). Typically, 2D imaging
is performed in axial, sagittal, and/or coronal planes using a
fast/turbo spin echo sequence. 3D imaging can be
16
Advanced acquisition and reconstruction to
exploit data redundancy
Single-sequence MRI to acquire several image
contrasts in a coregistered fashion, eg, MR fingerprinting
performed using a 3D T2w-TSE with optimized flip angle
evolution along the echo train (eg, Siemens SPACE, Philips
VISTA, GE CUBE). T1-weighting highlights structures with
a short T1, eg, fat, melanin. T1-weighted image contrast is
encoded by a short TE and short TR. T1w-MRI is acquired
with fast gradient echo sequences in 2D (Siemens FLASH,
Phillips FFE, GE GRE) or 3D (Siemens VIBE, Philips
THRIVE, GE Lava).
Diagnostic Biomarker
A key example of a recently established diagnostic biomarker
is PI-RADS in suspected prostate cancer, currently on version
2.0,15 utilizing multiparametric MRI. The PROMIS trial16,17
has recently published its findings confirming a role for multiparametric MRI in the diagnostic pathway of patients with
suspected prostate cancer. This enrolled 740 men, 576 of
whom underwent 1 5T multiparametric MRI followed by
both transrectal ultrasound (TRUS) biopsy and template
prostate mapping biopsy. On template prostate mapping
biopsy, 408 (71%) of 576 men had cancer with 230 (40%);
of 576 patients it was clinically significant. For clinically significant cancer, multiparametric MRI was more sensitive
(93%, 95% confidence interval [CI] 88–96%) than TRUS
biopsy (48%, 42–55%; P < 0 0001). Using multiparametric MRI to triage men might allow 27% of patients to avoid a primary biopsy and improve detection of clinically significant cancer. Using a structured reporting scheme such as PI-RADS standardizes practice, provides an objective score of the likelihood of disease, and helps direct targeted biopsy. Risk scores to assess the likelihood of clinically significant cancer are Volume 48, No. 1 Dregely et al.: MRI Biomarkers in Oncology TABLE 2. Established and Validated MRI Biomarkers in Clinical Use Biomarker Characteristic MRI sequence BI-RADS (Breast Imaging Reporting and Data System) PI-RADS (Prostate Imaging Reporting and Data System) LI-RADS (Liver Imaging Reporting and Data System) Lesion morphology T2-weighted, T1-weighted, diffusion weighted, postcontrast-enhanced imaging Curve shape Degree of vascularization Dynamic T1-weighted imaging following intravenous injection of gadolinium-based contrast agent Tumor morphology, presence of nodes, and metastases T2-weighted, T1-weighted imaging 6 diffusion weighted, postcontrast-enhanced imaging Change in tumor size T2-weighted imaging Apparent diffusion coefficient (ADC) Cellularity Diffusion-weighted imaging, at least 2 b-values Initial area under the gadolinium curve (iAUGC) Transfer constant (Ktrans) Perfusion Permeability Dynamic T1-weighted imaging following intravenous injection of gadolinium-based contrast agent Established biomarkers in clinical practice Detection & characterization Staging TNM stage Response RECIST (Response Evaluation Criteria In Solid Tumors) Validated biomarkers in clinical cancer research defined as PI-RADS 1: very low, PI-RADS 2: low, PI-RADS 3: intermediate, PI-RADS 4: high, to PI-RADS 5: very high. A meta-analysis has revealed overall high sensitivity and specificity of 0.74 and 0.88, respectively, for prostate cancer detection with PI-RADS.18,19 MRI is performed with a multiparametric acquisition of at least T2-weighted and diffusion- weighted sequences20 (Fig. 2). This combines high resolution, high soft-tissue contrast of T2-weighted imaging with the diffusion-weighted imaging sensitivity for cancer.21 Additional dynamic contrast-enhanced sequences provide information of wash-in and wash-out characteristics and may provide additional diagnostic value. A recent study has demonstrated FIGURE 2: Multiparametric prostate MRI demonstrates a left mid-gland PI-RADS 5 peripheral zone lesion extending beyond the prostate (a: T2-weighted, b: diffusion-weighted apparent diffusion coefficient map, c: arterial phase dynamic contrast-enhanced T1-weighted image). July 2018 17 Journal of Magnetic Resonance Imaging size change of specified measurable target lesions (>1 cm)
or nodes (>1.5 cm short axis) (Table 3). From a regulatory
perspective, RECIST remains the key response biomarker in
clinical trials and is used as a surrogate endpoint.
Validated MRI Biomarkers Requiring
Qualification
FIGURE 3: T2-weighted axial image demonstrates a T3N1 rectal
cancer extending beyond the rectal wall but not involving the
potential resection margin
an increase in the probability of cancer detection of 16%,
16%, and 9% for PI-RADS category 2, 3, and 4 lesions,
respectively, with DCE-MRI.22
Prognostic Biomarker: Staging
Staging is an important imaging biomarker for patient stratification. MRI is the primary staging modality for a number
of cancers including rectal cancer. In addition to TNMStage grouping, which provides an indication of relative 5year overall survival (Stage I [localized, T1/2], node negative: 95%; vs. Stage IV [metastatic, any T,N]: 11%), MRI
also has a predictive role in terms of likely involvement of
the resection margin and PFS23–25 (Fig. 3).
Response Biomarker: RECIST
RECIST criteria provide a standardized, objective assessment
of response to therapy in clinical trials.10 Classification of
response is divided into four categories (complete response,
partial response, stable disease, progressive disease) based on
Diffusion-Weighted MRI
ADC is a biomarker that has crossed the first translational
gap and is used to test research hypotheses in clinical trials.26 The biophysical basis of diffusion-weighted imaging is
the microscopic displacement of water molecules (Dx u 30
lm in Dt 5 50 msec) due to thermal Brownian motion. In
cancers the tumor environment restricts this motion, thus a
measurement of the effective displacement, the ADC, gives
important microscopic information. Tumor ADC from bvalues less than 1000 s/mm2 effectively provide a measure
of the extracellular space; although cell size, cell arrangements, cell density, integrity of cell membranes, glandular
structures, extracellular space viscosity, and tortuosity will
influence this measurement. Studies have correlated ADC
with histological grade in a number of cancers.27–30
The diffusion image contrast is encoded by using a
gradient pair (Stejskal-Tanner gradient26), which can be
either a bipolar gradient pair in gradient echo or the same
polarity in spin echo. This gradient causes a change in the
resonant Larmor frequency of a spin isochromat, leading to
the following phase accumulation u:
ðt
ðt
Dxdt 0 5 c
/5
0
~ðt 0 Þ ~
G
r ðt 0 Þdt 0
0
~ is the applied gradient waveform applied for a
where G
duration t, r~ is the spatial position of the spin
TABLE 3. Response Categorization Based on Changes in Target and Nontarget Lesions
RECIST
Categorization
Target lesions
Nontarget lesions
Complete response (CR)
Disappearance of all target lesions
(TL). All nodes 20% increase in the sum of TL diameters.
Absolute increase of at least 5 mm.
New lesions
Unequivocal progression of existing
nontarget lesions
New lesions
Target lesions: Up to 5 measured, 2 maximum per organ.
18
Volume 48, No. 1
Dregely et al.: MRI Biomarkers in Oncology
FIGURE 4: The T2 axial oblique image (a) of a rectal cancer, diffusion-weighted images with increasing b-weighting 0 (b), 100 (c),
500 (d), and 800 s/mm2 (e), and corresponding ADC0-800 map (f) is shown. Signal loss is demonstrated within the rectal cancer
with increasing b-weighting. The signal loss is greater for normal tissue than for the cancer.
isochromat, and c the gyromagnetic ratio. Thus, spins,
which move during the application of the gradient pair, will
not be properly rephased. This loss in phase coherence secondary to spatial displacement causes a reduction in the signal. For random spin diffusion motion in an image voxel,
this signal cancellation is related to the variance of the
Gaussian phase distribution and the product bD:
2
S5S0 e 2h/ i 5S0 e 2bD
Where S is the diffusion-weighted signal and S0 is the signal
without diffusion weighting.
Thus, the degree of attenuation depends on the dimensionless product of the diffusion coefficient D (in mm2/sec)
and the b-value (in sec/mm2). The b-value is used to control
the diffusion-weighted contrast with higher diffusion weighting at higher b-values. Typically, b-values of 0–1500 s/mm2
are applied in clinical practice and ADC is obtained from
monoexponential fitting of the signal loss (Fig. 4). In practice,
other factors contribute to signal loss including T2-relaxation
and bulk motion. In a given voxel, ADC will reflect the relative contribution of the different compartments.
A number of studies have evaluated ADC as a
response biomarker in a number of tumor types across different therapies in research studies including the multicenter
setting. These studies have shown that a common pattern is
an increase in ADCmean to a varying extent with different
therapies. This may occur within days of starting treatment;
a higher change in ADCmean is also associated with a pathological good response.31–39
July 2018
The variability of ADC in clinical studies has been
reported to be relatively low at 15%40 and in ice-water
phantom studies as low as 3%.41 Nevertheless, there are
considerations to be made in the trial setting42 and technical challenges to acquiring robust diffusion-weighted biomarkers and qualification as a biomarker.26 TR should be
sufficiently long to avoid underestimation of ADC due to
T1 saturation effect; TE should be minimized to achieve
better signal-to-noise ratio (SNR), to minimize motion and
susceptibility artifacts. Good fat suppression is required to
minimize ghosting artifacts; short tau inversion recovery
(STIR) may be preferred to spectral presaturation attenuated by inversion recovery (SPAIR) or chemical shift selective water-only excitation techniques, where a large field of
view is necessary at 1.5T, as STIR is less sensitive to B0
field inhomogeneities. Geometric distortion and susceptibility artifacts caused by eddy currents related to EPI may be
improved by shortening the echo train length, eg, through
adapting the receiver bandwidth to reduce the echo spacing,
use of parallel imaging, zoomed excitation, or readout segmented imaging.
Dynamic Contrast-Enhanced MRI
DCE MRI refers to the rapid acquisition of a time series of
T1w images before, during, and after intravenous administration of a gadolinium-based contrast agent. Gadolinium
contrast agents are small hydrophilic molecules with a short
circulation half-life, typically 50%), and baseline reproducibility
has been utilized in clinical trials on an individual basis in
order to be able to determine whether the measured change
is related to therapeutic effect. Accurate determination of
the arterial input function (AIF), which characterizes contrast agent arrival in a feeding blood vessel within the
tumor, remains a challenge to accurate quantification. As an
alternative to subject-specific direct measurement of AIF
(subject to flow artifacts, nonlinear effects of high contrast
agent concentrations, and partial volume effects),
population-based AIFs45 or reference tissue-based methods46
have been advocated. Accurate T1-mapping also remains a
challenge, as B1 inhomogeneity, particularly at 3T and
higher field strengths, limit the accuracy of T1-estimates
derived from the typically employed variable flip angle technique. Recent developments propose to include B11 for
T1-mapping.47 To overcome the challenge of achieving both
high spatial and temporal resolution for the DCE data
acquisition, advanced methods have been proposed, such as
combining parallel imaging, compressed sensing and nonCartesian sampling,48 view sharing,49 and motion
compensation.50
Emerging MRI Biomarkers
Further emerging quantitative biomarkers are undergoing
evaluation (Table 4), related to the following techniques:
intravoxel incoherent motion (IVIM), diffusion kurtosis
imaging, blood and tissue oxygenation level-dependent MRI
(BOLD/TOLD), MR elastography, and relaxometry imaging. There has also been growing interest in extracting additional agnostic features from standard and quantitative MRI
sequences, so-called radiomics.9
Pseudodiffusion and Intravoxel Incoherent Motion
Bulk water motion in capillaries can also cause phase dispersion in diffusion-weighted MRI.51,52 The loss in signal is
similar to that seen with true diffusion and more marked at
low b-values. Diffusion-weighted MRI always measures
both, but the relative contribution depends on the choice of
Volume 48, No. 1
Dregely et al.: MRI Biomarkers in Oncology
FIGURE 5: T2-weighted (a) and corresponding transfer constant maps (Ktrans, b) before and after three cycles of therapy with an
antiangiogenic and triplet chemotherapy. A decrease in tumor vascularization is noted following three cycles of therapy.
b-values. The contribution of true diffusion and perfusion
towards signal loss can be defined as follows:

SðbÞ5ð12fv Þe 2bD 1fv e 2bD
where S is the acquired diffusion-weighted signal, b represents
the b-value, fv represents the fractional volume of flowing
water molecules within capillaries; (12fv) is the fraction of
molecules undergoing true diffusion; D represents tissue
TABLE 4. Emerging Biomarkers Undergoing Validation in Research Studies
Emerging biomarkers
Measure/biological correlate
MRI sequence
f, D*
Pseudoperfusion
Multiple low b-value diffusion weighted imaging (intravoxel
incoherent motion, IVIM)
Kurtosis (Kapp)
Microstructural
complexity
Diffusion kurtosis imaging (DKI)
R2*
R1
DR2*
DR1
Relaxation rate
Oxygenation
Blood oxygenation level dependent imaging (BOLD)
Tissue oxygenation level dependent imaging (TOLD)
6 oxygen/carbogen challenge
Elasticity
Viscosity
Tissue mechanics and
viscoelastic parameters
Elastography: motion sensitive sequence to encode
shear wave propagation
Specific metabolites,
eg, Choline
Metabolite concentration
Spectroscopy
T1
T2
Relaxation time
Microenvironment
Multiecho relaxometry imaging
Texture features
Heterogeneity
Any
July 2018
21
Journal of Magnetic Resonance Imaging
diffusion coefficient and D* the pseudodiffusion coefficient. D*
the pseudodiffusion coefficient associated with blood flow is
about 10 3 1023 mm2/sec in the brain and 70 3 1023 mm2/
sec in the liver compared to D, which is 1 3 1023 mm2/sec.
Assessing fv and D* may be feasible for patients with
poor renal function, an allergy precluding intravenous
administration of contrast agent, or at high risk of developing nephrogenic systemic fibrosis.53
However, one of the issues highlighted to date is the
poor test–retest variability of f and D*,54 on the order of
>100% in some cancers, eg, rectal.55 There also appears some
contention as to technical/biological correlates: while some
studies have shown a relationship between IVIM and DCEMRI parameters,56–58 others have not in some cancers, eg,
hepatocellular carcinoma.59 One also has to be aware that
flow from glandular secretions, eg, pancreas, may be difficult
to separate from micro-capillary perfusion. A potential application is as a diagnostic biomarker, where current characterization may be a challenge, eg, pancreas.60,61
Apparent Diffusional Kurtosis
Diffusion kurtosis imaging characterizes non-Gaussian diffusion behavior at high b-values ranging from 1000–3000 sec/
mm2. A polynomial decay model is fitted to an acquisition
using at least three b-values to obtain Dapp and Kapp representing the heterogeneity of the cellular microstructure. The
diffusion signal Si for a given b-value bI is given by:
1
2
2
Si 5S0 e bI Dapp 16DbI D
app Kapp
where S0 is the signal without diffusion weighting, Kapp is
the apparent diffusional kurtosis, and Dapp is the diffusion
coefficient. Kapp reflects the signal curvature away from a
monoexponential fit. The rationale proposed for assessing
kurtosis is that it may better reflect the tumor intracellular
microstructure,62,63 although it will also be influenced by
extracellular properties. Higher kurtosis may be noted where
there are higher intracellular interfaces; for example,
increased nuclear-cytoplasmic ratio of tumor cells.64 Preliminary studies in prostate cancer have suggested potential as a
diagnostic biomarker,65 eg, to improve characterization
(grading),66,67 although not all studies have confirmed additional advantages over monoexponential ADC.68,69 Studies
have also suggested its potential as a response biomarker. A
study in hepatocellular carcinoma has suggested that Kapp
performs better than ADC in detecting viable disease
posttreatment.70
Tumor Elasticity and Viscosity
Magnetic resonance elastography (MRE) quantifies the viscoelastic properties of tissue by assessing its elastic response
to an applied force, similar to palpation in clinical practice.
The applied force consists of harmonic mechanical waves,
ranging typically between 20 and 80 Hz in frequency and
22
propagated into the human body by a vibrating transducer
applied to the body surface. The consequent tissue motion
is captured using rapid motion-sensitive MRI sequences.
Through mathematical inversion algorithms, the local shear
wave properties can be derived from the periodical variations in MRI signal; the local viscoelastic parameters (elasticity and viscosity) are then calculated using the complex
shear modulus equation.71 The underpinning experimental
observation for the application of MRE to cancer is that
malignancy increases stiffness through collagen deposition in
the extracellular matrix and raises interstitial pressure levels
from its abnormal vasculature.72 MRE has shown promising
potential for the characterization of focal lesions (benign vs.
malignant) in multiple organs, including the liver,73
breast,74 pancreas,75 and kidney.76 It may also serve as a
potential biomarker of treatment resistance.
Tumor Oxygenation
Tumor oxygenation may be measured indirectly by BOLD
and TOLD-MRI techniques. With BOLD MRI, endogenous
hemoglobin acts as a paramagnetic contrast agent that
increases the transverse relaxation rate (R2*) in blood and surrounding tissue. R2* is measured from multiple spoiled gradient recalled echo images with increasing echo times. R2* is
calculated from the gradient of a straight line fitted to a plot
of ln-signal intensity to TE. Higher R2* reflects higher deoxyhemoglobin levels and lower blood oxygenation. R2* may
have a role as a response biomarker. One study has shown that
R2* is inversely correlated to blood volume and increases in
breast cancer treated with two cycles of neoadjuvant chemotherapy with greater changes in patients with pathological
response.77 However, BOLD measurements will be affected
by the underlying tissue relaxivity and will be affected by
hemorrhage and susceptibility artifacts.
With TOLD MRI the longitudinal relaxation rate (R1)
is measured. R1 is sensitive to changes in the O2 dissolved in
blood plasma and interstitial fluid. When a hyperoxic gas is
inhaled, the excess oxygen dissolved will result in a higher R1
value. A positive change in R1 will identify areas with fully
saturated hemoglobin. Areas where there is no positive change
in R1 may reflect regions of hypoxia, particularly if perfusion
is present. Current approaches are focusing on the feasibility
of combining R2* and R1 measurement with oxygen challenge to assess tumor oxygenation.78
Quantitative MRI With or Without Exogenous
Contrast agents
In current clinical practice, a diagnosis based on MRI primarily relies on the qualitative assessment of images. In contrast,
quantitative measurements of tissue properties with or without endogenous contrast agents may provide more accurate
and reproducible information. Without the use of exogenous
contrast agents, relaxometry yields quantitative measurement
Volume 48, No. 1
Dregely et al.: MRI Biomarkers in Oncology
of intrinsic tissue relaxation times T1 and T2,79–82 T2*, proton
density. In addition, important molecular information about
tumor physiology and metabolism (“tumor microenvironment”) may be obtained from MR spectroscopy
(MRS),83–88 chemical exchange saturation transfer imaging
(CEST),89 and amide proton transfer (APT).90 Further, relaxometry with exogenous contrast agents enables imaging of
perfusion, using either gadolinium-based contrast agents91
and dynamic T1w (DCE), as discussed previously, or T2*w
MRI (dynamic susceptibility contrast-enhanced [DSC]).
Superparametric iron oxide (SPIO) nanoparticles in combination with T2w and T2*w MRI have been developed as imaging probes for targeted molecular MRI, cell tracking, and
drug delivery (“theranostics”).92–94 Alternatively, highly specific, background-free imaging can be achieved via nonproton
imaging using, eg, F-1995–97 or hyperpolarized agents C13.98,99 However, these require hardware modifications to be
able to image the nonproton frequencies.
Novel quantitative methods have also been proposed
to acquire several tissue properties at once.100,101 A method
termed “MR-fingerprinting” utilizes a (pseudo) randomized
acquisition sequence to encode a tissue-specific “Fingerprint”
into an MR time series signal.102 This has recently also
been adapted and applied to cancer imaging.103–105
Finally, to achieve its full potential, a key challenge of
multiparametric MRI is standardization across multiple platforms, which involves the use of phantoms and careful
review of implementation.106
Radiomics
Radiomics is an evolving area in medical imaging whereby a
large number of features are extracted and interpreted using
bioinformatic approaches.9,107 The underlying rationale for
radiomics lies in the supposed relationship between
extracted image parameters and tumor molecular phenotype
and/or genotype. It is known that genotypic heterogeneity
contributes to divergent tumor biological behavior, including poor treatment response and a more aggressive phenotype. Therefore, there is growing interest in using imaging
radiomic signatures either alone or in combination with
other clinical or -omics data, eg, radiogenomics, to improve
tumor phenotyping (prognostication), to allow tumor subregions with different biological characteristics that may contribute to treatment resistance to be identified/segmented
for therapies, and for the prediction and evaluation of therapies. Radiomic studies have used a number of techniques
including statistical methods (histogram; gray-level co-occurrence matrix [GLCM]; gray-level difference matrix
[GLDM], run length matrix [RLM], gray level size zone
matrix [GLSZM], and neighborhood gray tone difference
matrix [NGTDM]) with or without Gaussian or Wavelet
transformation; and fractal-based methods across different
sequences including T2-weighted, diffusion-weighted, and
July 2018
DCE sequences. Initial radiogenomic studies including MRI
have been performed in breast cancer108–110 renal cell carcinoma111 and glioma.112,113 Variable reproducibility has
been shown across different classes of features114 and further
validation work is still required for radiomic biomarkers.
Conclusion
Precision cancer medicine remains a desirable goal for
cancer care.
MRI offers many advantages as a diagnostic, prognostic,
predictive, or response biomarker in cancer given its capability of multiple contrast and multiparametric quantitative imaging.
A key challenge remains to improve the efficiency of biomarker translation from discovery to implementation.
Clinical translation for emerging biomarkers remains
slow.
To overcome issues regarding biomarker measurement
variability across devices and across manufacturers, phantoms for quality assurance, standardization of protocols
and availability of reference value databases has helped to
facilitate this, alongside networks and alliances including
the Quantitative Imaging Network (QIN) (http://imaging.cancer.gov/informatics/qin), the Quantitative Imaging
Biomarker Alliance (QIBA) (http://www.rsna.org/qiba/);
the Quantitative Imaging in Cancer: Connecting Cellular
Processes to Therapy (QuIC-ConCePT) (http://www.
quic-concept.eu/) consortium; and the American College
of Radiology Imaging Network (ACRIN).
With emerging machine-learning approaches, quantitative
MRI biomarkers will no doubt continue to expand to
meet new challenges in the personalized care of oncology
patients.
Acknowledgments
The authors acknowledge support from the Department of
Health via the National Institute for Health Research Comprehensive Biomedical Research Centre award to Guy’s & St
Thomas’ NHS Foundation Trust in partnership with King’s
College London and King’s College Hospital NHS Foundation Trust; from the King’s College London/University College London Comprehensive Cancer Imaging Centre funded
by Cancer Research UK and Engineering and Physical Sciences Research Council (EPSRC) in association with the
Medical Research Council and Department of Health
(C1519/A16463); and Wellcome EPSRC Centre for Medical Engineering at King’s College London (WT 203148/Z/
16/Z).
23
Journal of Magnetic Resonance Imaging
1. Globocan, 2012. www.globocan.iarc.fr
23. Beets-Tan RG, Beets GL, Vliegen RF, et al. Accuracy of magnetic resonance imaging in prediction of tumour-free resection margin in rectal
cancer surgery. Lancet 2001;357:497–504.
2. Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity
and branched evolution revealed by multiregion sequencing. N Engl J
Med 2012;366:883–892.
24. Diagnostic accuracy of preoperative magnetic resonance imaging in
predicting curative resection of rectal cancer: prospective observational study. BMJ 2006;333:779.
3. Schilsky RL. Personalized medicine in oncology: the future is now. Nat
Rev Drug Discov 2010;9:363–366.
25. Taylor FG, Quirke P, Heald RJ, et al. Preoperative magnetic resonance
imaging assessment of circumferential resection margin predicts
disease-free survival and local recurrence: 5-year follow-up results of
the MERCURY study. J Clin Oncol 2014;32:34–43.
References
4. Hurwitz H, Fehrenbacher L, Novotny W, et al. Bevacizumab plus irinotecan, fluorouracil, and leucovorin for metastatic colorectal cancer. N
Engl J Med 2004;350:2335–2342.
5. Chung KY, Shia J, Kemeny NE, et al. Cetuximab shows activity in colorectal cancer patients with tumors that do not express the epidermal
growth factor receptor by immunohistochemistry. J Clin Oncol 2005;
23:1803–1810.
6. Misale S, Yaeger R, Hobor S, et al. Emergence of KRAS mutations and
acquired resistance to anti-EGFR therapy in colorectal cancer. Nature
2012;486:532–536.
7. Medical imaging in personalised medicine: a white paper of the
research committee of the European Society of Radiology (ESR).
Insights Imaging 2015;6:141–155.
8. White paper on imaging biomarkers. Insights Imaging 2010;1:42–45.
9. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016;278:563–577.
10. Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation
criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J
Cancer 2009;45:228–247.
11. O’Connor JP, Aboagye EO, Adams JE, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 2017;14:169–86.
12. Mercado CL. BI-RADS update. Radiol Clin N Am 2014;52:481–487.
13. Tang A, Bashir MR, Corwin MT, et al. Evidence supporting LI-RADS
major features for CT- and MR imaging-based diagnosis of hepatocellular carcinoma: a systematic review. Radiology 2018;286:29–48.
14. Mitchell DG, Bruix J, Sherman M, Sirlin CB. LI-RADS (Liver Imaging
Reporting and Data System): summary, discussion, and consensus of
the LI-RADS Management Working Group and future directions. Hepatology 2015;61:1056–1065.
26. Padhani AR, Liu G, Koh DM, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 2009;11:102–125.
27. Nakanishi M, Chuma M, Hige S, et al. Relationship between diffusionweighted magnetic resonance imaging and histological tumor grading
of hepatocellular carcinoma. Ann Surg Oncol 2012;19:1302–1309.
28. Winfield JM, Orton MR, Collins DJ, et al. Separation of type and
grade in cervical tumours using non-mono-exponential models of
diffusion-weighted MRI. Eur Radiol 2017;27:627–636.
29. Hotker AM, Mazaheri Y, Aras O, et al. Assessment of prostate cancer
aggressiveness by use of the combination of quantitative DWI and
dynamic contrast-enhanced MRI. AJR Am J Roentgenol 2016;206:
756–763.
30. Kishimoto K, Tajima S, Maeda I, et al. Endometrial cancer: correlation
of apparent diffusion coefficient (ADC) with tumor cellularity and
tumor grade. Acta Radiol 2016;57:1021–1028.
31. Koh DM, Blackledge M, Collins DJ, et al. Reproducibility and changes
in the apparent diffusion coefficients of solid tumours treated with
combretastatin A4 phosphate and bevacizumab in a two-centre phase
I clinical trial. Eur Radiol 2009;19:2728–2738.
32. Lee EM, Hong YS, Kim KP, et al. Phase II study of preoperative chemoradiation with S-1 plus oxaliplatin in patients with locally advanced
rectal cancer. Cancer Sci 2013;104:111–115.
33. Sharma U, Danishad KK, Seenu V, Jagannathan NR. Longitudinal
study of the assessment by MRI and diffusion-weighted imaging of
tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. NMR Biomed 2009;22:104–113.
15. Weinreb JC, Barentsz JO, Choyke PL, et al. PI-RADS Prostate Imaging —
Reporting and Data System: 2015, Version 2. Eur Radiol 2016;69:
16–40.
34. Agarwal K, Sharma U, Sah RG, et al. Pre-operative assessment of
residual disease in locally advanced breast cancer patients: A sequential study by quantitative diffusion weighted MRI as a function of therapy. Magn Reson Imaging 2017;42:88–94.
16. El-Shater Bosaily A, Parker C, Brown LC, et al. PROMIS—Prostate MR
imaging study: A paired validating cohort study evaluating the role of
multiparametric MRI in men with clinical suspicion of prostate cancer.
Contemp Clin Trials 2015;42:26–40.
35. Cui Y, Zhang XP, Sun YS, Tang L, Shen L. Apparent diffusion coefficient: potential imaging biomarker for prediction and early detection
of response to chemotherapy in hepatic metastases. Radiology 2008;
248:894–900.
17. Ahmed HU, El-Shater Bosaily A, Brown LC, et al. Diagnostic accuracy
of multiparametric MRI and TRUS biopsy in prostate cancer (PROMIS):
a paired validating confirmatory study. Lancet 2017;389:815–822.
36. Reischauer C, Froehlich JM, Pless M, Binkert CA, Koh DM, Gutzeit A.
Early treatment response in non-small cell lung cancer patients using
diffusion-weighted imaging and functional diffusion maps—a feasibility
study. PLoS One 2014;9:e108052.
18. de Rooij M, Hamoen EH, Futterer JJ, Barentsz JO, Rovers MM. Accuracy of multiparametric MRI for prostate cancer detection: a metaanalysis. AJR Am J Roentgenol 2014;202:343–351.
19. Hamoen EHJ, de Rooij M, Witjes JA, Barentsz JO, Rovers MM. Use of
the Prostate Imaging Reporting and Data System (PI-RADS) for prostate cancer detection with multiparametric magnetic resonance imaging: a diagnostic meta-analysis. Eur Radiol 2015;67:1112–1121.
20. Kuhl CK, Bruhn R, Kramer N, Nebelung S, Heidenreich A, Schrading
S. Abbreviated biparametric prostate MR imaging in men with elevated prostate-specific antigen. Radiology 2017;285:493–505.
21. Wu LM, Xu JR, Ye YQ, Lu Q, Hu JN. The clinical value of diffusionweighted imaging in combination with T2-weighted imaging in diagnosing prostate carcinoma: a systematic review and meta-analysis.
AJR Am J Roentgenol 2012;199:103–110.
22. Greer MD, Shih JH, Lay N, et al. Validation of the dominant sequence
paradigm and role of dynamic contrast-enhanced imaging in PI-RADS
version 2. Radiology 2017;285:859–869.
24
37. Wong KH, Panek R, Welsh L, et al. The predictive value of early
assessment after 1 cycle of induction chemotherapy with 18F-FDG
PET/CT and diffusion-weighted MRI for response to radical chemoradiotherapy in head and neck squamous cell carcinoma. J Nucl Med
2016;57:1843–1850.
38. Harry VN, Semple SI, Gilbert FJ, Parkin DE. Diffusion-weighted magnetic resonance imaging in the early detection of response to chemoradiation in cervical cancer. Gynecol Oncol 2008;111:213–220.
39. Foltz WD, Wu A, Chung P, et al. Changes in apparent diffusion coefficient and T2 relaxation during radiotherapy for prostate cancer.
J Magn Reson Imaging 2013;37:909–916.
40. Jafar MM, Parsai A, Miquel ME. Diffusion-weighted magnetic resonance imaging in cancer: Reported apparent diffusion coefficients, invitro and in-vivo reproducibility. World J Radiol 2016;8:21–49.
41. Malyarenko D, Galban CJ, Londy FJ, et al. Multi-system repeatability
and reproducibility of apparent diffusion coefficient measurement
Volume 48, No. 1
Dregely et al.: MRI Biomarkers in Oncology
using an ice-water phantom. J Magn Reson Imaging 2013;37:1238–
1246.
42. deSouza NM, Winfield JM, Waterton JC, et al. Implementing
diffusion-weighted MRI for body imaging in prospective multicentre
trials: current considerations and future perspectives. Eur Radiol 2018;
28:1118–1131.
43. Tofts PS, Brix G, Buckley DL, et al. Estimating kinetic parameters from
dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer:
standardized quantities and symbols. J Magn Reson Imaging 1999;10:
223–232.
60. Concia M, Sprinkart AM, Penner AH, et al. Diffusion-weighted magnetic resonance imaging of the pancreas: diagnostic benefit from an
intravoxel incoherent motion model-based 3 b-value analysis. Invest
Radiol 2014;49:93–100.
61. Kang KM, Lee JM, Yoon JH, Kiefer B, Han JK, Choi BI. Intravoxel incoherent motion diffusion-weighted MR imaging for characterization of
focal pancreatic lesions. Radiology 2014;270:444–453.
62. Rosenkrantz AB, Padhani AR, Chenevert TL, et al. Body diffusion kurtosis imaging: Basic principles, applications, and considerations for
clinical practice. J Magn Reson Imaging 2015;42:1190–1202.
44. Sung YS, Park B, Choi Y, et al. Dynamic contrast-enhanced MRI for
oncology drug development. J Magn Reson Imaging 2016;44:251–
264.
63. Le Bihan D. Apparent diffusion coefficient and beyond: what diffusion
MR imaging can tell us about tissue structure. Radiology 2013;268:
318–322.
45. Parker GJ, Roberts C, Macdonald A, et al. Experimentally-derived
functional form for a population-averaged high-temporal-resolution
arterial input function for dynamic contrast-enhanced MRI. Magn
Reson Med 2006;56:993–1000.
64. Hectors SJ, Semaan S, Song C, et al. Advanced diffusion-weighted
imaging modeling for prostate cancer characterization: correlation
with quantitative histopathologic tumor tissue composition-A hypothesis-generating study. Radiology 2017:170904.
46. Kovar DA, Lewis M, Karczmar GS. A new method for imaging perfusion and contrast extraction fraction: input functions derived from reference tissues. J Magn Reson Imaging 1998;8:1126–1134.
65. Quentin M, Pentang G, Schimmoller L, et al. Feasibility of diffusional
kurtosis tensor imaging in prostate MRI for the assessment of prostate
cancer: preliminary results. Magn Reson Imaging 2014;32:880–885.
47. Sung K, Saranathan M, Daniel BL, Hargreaves BA. Simultaneous T1
and B11 mapping using reference region variable flip angle imaging.
Magn Reson Med 2013;70:954–961.
66. Wang Q, Li H, Yan X, et al. Histogram analysis of diffusion kurtosis
magnetic resonance imaging in differentiation of pathologic Gleason
grade of prostate cancer. Urol Oncol 2015;33:337.e15–24.
48. Rosenkrantz AB, Geppert C, Grimm R, et al. Dynamic contrastenhanced MRI of the prostate with high spatiotemporal resolution
using compressed sensing, parallel imaging, and continuous goldenangle radial sampling: Preliminary experience. J Magn Reson Imaging
2015;41:1365–1373.
67. Tamura C, Shinmoto H, Soga S, et al. Diffusion kurtosis imaging study
of prostate cancer: preliminary findings. J Magn Reson Imaging 2014;
40:723–729.
49. Levine E, Daniel B, Vasanawala S, Hargreaves B, Saranathan M. 3D
Cartesian MRI with compressed sensing and variable view sharing
using complementary poisson-disc sampling. Magn Reson Med 2016;
1785:1774–1785
50. Feng L, Axel L, Chandarana H, Block KT, Sodickson DK, Otazo R. XDGRASP: Golden-angle radial MRI with reconstruction of extra motionstate dimensions using compressed sensing. Magn Reson Med 2016:
75:775–778.
51. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, LavalJeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988;168:497–505.
52. Le Bihan D. Intravoxel incoherent motion imaging using steady-state
free precession. Magn Reson Med 1988;7:346–351.
53. Iima M, Le Bihan D. Clinical intravoxel incoherent motion and diffusion
MR imaging: past, present, and future. Radiology 2016;278:13–32.
54. Andreou A, Koh DM, Collins DJ, et al. Measurement reproducibility of
perfusion fraction and pseudodiffusion coefficient derived by intravoxel incoherent motion diffusion-weighted MR imaging in normal
liver and metastases. Eur Radiol 2013;23:428–434.
55. Sun H, Xu Y, Xu Q, Shi K, Wang W. Rectal cancer: Short-term reproducibility of intravoxel incoherent motion parameters in 3.0T magnetic
resonance imaging. Medicine 2017;96:e6866.
56. Lee EY, Hui ES, Chan KK, et al. Relationship between intravoxel incoherent motion diffusion-weighted MRI and dynamic contrast-enhanced
MRI in tissue perfusion of cervical cancers. J Magn Reson Imaging
2015;42:454–459.
57. Kim JH, Joo I, Kim TY, et al. Diffusion-related MRI parameters for
assessing early treatment response of liver metastases to cytotoxic
therapy in colorectal cancer. AJR Am J Roentgenol 2016;207:W26–32.
58. Woo S, Lee JM, Yoon JH, Joo I, Han JK, Choi BI. Intravoxel incoherent motion diffusion-weighted MR imaging of hepatocellular carcinoma: correlation with enhancement degree and histologic grade.
Radiology 2014;270:758–767.
59. Hectors SJ, Wagner M, Besa C, et al. Intravoxel incoherent motion
diffusion-weighted imaging of hepatocellular carcinoma: Is there a
correlation with flow and perfusion metrics obtained with dynamic
contrast-enhanced MRI? J Magn Reson Imaging 2016;44:856–864.
July 2018
68. Barrett T, McLean M, Priest AN, et al. Diagnostic evaluation of magnetization transfer and diffusion kurtosis imaging for prostate cancer
detection in a re-biopsy population. Eur Radiol 2017 [Epub ahead of
print].
69. Roethke MC, Kuder TA, Kuru TH, et al. Evaluation of diffusion kurtosis
imaging versus standard diffusion imaging for detection and grading
of peripheral zone prostate cancer. Invest Radiol 2015;50:483–489.
70. Goshima S, Kanematsu M, Noda Y, Kondo H, Watanabe H, Bae KT.
Diffusion kurtosis imaging to assess response to treatment in hypervascular hepatocellular carcinoma. AJR Am J Roentgenol 2015;204:
W543–549.
71. Muthupillai R, Lomas DJ, Rossman PJ, Greenleaf JF, Manduca A,
Ehman RL. Magnetic resonance elastography by direct visualization of
propagating acoustic strain waves. Science 1995;269:1854–1857.
72. Dvorak HF. Tumors: wounds that do not heal. Similarities between
tumor stroma generation and wound healing. N Engl J Med 1986;
315:1650–1659.
73. Garteiser P, Doblas S, Daire JL, et al. MR elastography of liver
tumours: value of viscoelastic properties for tumour characterization.
Eur Radiol 2012;22:2169–2177.
74. Sinkus R, Tanter M, Xydeas T, Catheline S, Bercoff J, Fink M. Viscoelastic shear properties of in vivo breast lesions measured by MR elastography. Magn Reson Imaging 2005;23:159–165.
75. Shi Y, Gao F, Li Y, et al. Differentiation of benign and malignant solid
pancreatic masses using magnetic resonance elastography with spinecho echo planar imaging and three-dimensional inversion reconstruction: a prospective study. Eur Radiol 2017 [Epub ahead of print].
76. Prezzi D, Neji R, Kelly-Morland C, et al. Characterization of small renal
tumors with MR elastography. Invest Radiol (in press).
77. Li SP, Taylor NJ, Makris A, et al. Primary human breast adenocarcinoma: imaging and histologic correlates of intrinsic susceptibilityweighted MR imaging before and during chemotherapy. Radiology
2010;257:643–652.
78. Remmele S, Sprinkart AM, Muller A, et al. Dynamic and simultaneous
MR measurement of R1 and R2* changes during respiratory challenges for the assessment of blood and tissue oxygenation. Magn
Reson Med 2013;70:136–146.
79. Liu W, Turkbey B, Senegas J, et al. Accelerated T2 mapping for characterization of prostate cancer. Magn Reson Med 2011;65:1400–1406.
25
Journal of Magnetic Resonance Imaging
80. Gibbs P, Liney GP, Pickles MD, Zelhof B, Rodrigues G, Turnbull LW.
Correlation of ADC and T2 measurements with cell density in prostate
cancer at 3.0 Tesla. Invest Radiol 2009;44:572–576.
81. Dregely I, Margolis DAJ, Sung K, et al. Rapid quantitative T2 mapping
of the prostate using three-dimensional dual echo steady state MRI at
3T. Magn Reson Med 2016;76:1720–1729
82. Sabouri S, Chang SD, Savdie R, et al. Luminal water imaging: a new
MR imaging T2 mapping technique for prostate cancer diagnosis.
Radiology 2017;284:161687.
83. Evanochoko WT, Ng TC, Glickson JD. Application of in vivo NMR
spectroscopy to cancer. Magn Reson Med 1984;1:508–534.
84. Bruhn H, Frahm J, Gyngell ML, et al. Noninvasive differentiation of
tumors with use of localized H-1MR spectroscopy in vivo: initial experiments in patients with cerebral tumors. Radiology 1989;172:541–548.
85. Kurhanewicz J, Vigneron DB, Hricak H, Narayan P, Carroll P, Nelson
SJ. Three-dimensional H-1 MR spectroscopic imaging of the in situ
human prostate with high (0.24-0.7-cm3) spatial resolution. Radiology
1996;198:795–805.
86. Heerschap A, Jager GJ, van der Graaf M, et al. In vivo proton MR
spectroscopy reveals altered metabolite content in malignant prostate
tissue. Anticancer Res 1997;17:1455–1460.
87. Kurhanewicz J, Vigneron DB, Nelson SJ, et al. Citrate as an in vivo
marker to discriminate prostate cancer from benign prostatic hyperplasia and normal prostate peripheral zone: detection via localized
proton spectroscopy. Urology 1995;45:459–466
5FU in colorectal metastases predict clinical response? Cancer Chemother Pharmacol 2011;68:29–36.
98. Gallagher FA, Kettunen MI, Day SE, et al. Magnetic resonance imaging of pH in vivo using hyperpolarized 13C-labelled bicarbonate.
Nature 2008;453:940–943.
99. Day SE, Kettunen MI, Gallagher FA, et al. Detecting tumor response
to treatment using hyperpolarized 13C magnetic resonance imaging
and spectroscopy. Nat Med 2007;13:1382–1387.
100. Ding Y, Mason RP, McColl RW, et al. Simultaneous measurement of
tissue oxygen level-dependent (TOLD) and blood oxygenation leveldependent (BOLD) effects in abdominal tissue oxygenation level studies. J Magn Reson Imaging 2013;38:1230–1236
101. Skorpil M, Brynolfsson P, Engstr€
om M. Motion corrected DWI with
integrated T2-mapping for simultaneous estimation of ADC, T2relaxation and perfusion in prostate cancer. Magn Reson Imaging
2017;39:162–167
102. Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature 2013;495:187–192.
103. Chen Y, Jiang Y, Pahwa S, et al. MR fingerprinting for rapid quantitative abdominal imaging. Radiology 2016;279:278–286
104. Yu AC, Badve C, Ponsky LE, et al. Development of a combined MR
fingerprinting and diffusion examination for prostate cancer. Radiology 2017;283:729–738
105. Badve C, Yu A, Dastmalchian S, et al. MR fingerprinting of adult brain
tumors: Initial experience. Am J Neuroradiol 2017;38:492–499
88. Scheidler J, Hricak H, Vigneron DB, et al. Prostate cancer: localization
with three-dimensional proton MR spectroscopic imaging—clinicopathologic study. Radiology 1999;213:473–480
106. Keenan KE, Ainslie M, Barker AJ, et al. Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom. Magn Reson Med 2017;61:48–61.
89. Goffeney N, Bulte JW, Duyn J, Bryant LH Jr, van Zijl PC. Sensitive
NMR detection of cationic-polymer-based gene delivery systems using
saturation transfer via proton exchange. J Am Chem Soc 2001;123:
8628–8629.
107. Davnall F, Yip CS, Ljungqvist G, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging
2012;3:573–589.
90. Zhou J, Payen JF, Wilson DA, Traystman RJ, van Zijl PC. Using the
amide proton signals of intracellular proteins and peptides to detect
pH effects in MRI. Nat Med 2003;9:1085–1090.
91. Weinmann HJ, Brasch RC, Press WR, Wesbey GE. Characteristics of
gadolinium-DTPA complex: a potential NMR contrast agent. AJR Am
J Roentgenol 1984;142:619–624
92. Qiao R, Yang C, Gao M. Superparamagnetic iron oxide nanoparticles:
from preparations to in vivo MRI applications. J Mater Chem 2009;19:
6274.
93. Li L, Jiang W, Luo K, et al. Superparamagnetic iron oxide nanoparticles as MRI contrast agents for non-invasive stem cell labeling and
tracking. Theranostics 2013;3:595–615.
94. Korchinski DJ, Taha M, Yang R, Nathoo N, Dunn JF. iron oxide as an
MRI contrast agent for cell tracking. Magn Reson Insights 2015;8:15–29.
95. Mason RP, RodbumrungW, Antich PP. Hexafluorobenzene: a sensitive
19F NMR indicator of tumor oxygenation. NMR Biomed 1996;9:125–
134.
96. Diepart C, Magat J, Jordan BF, Gallez B. In vivo mapping of tumor
oxygen consumption using 19F MRI relaxometry. NMR Biomed 2011;
24:458–463.
97. McIntyre DJ, Howe FA, Ladroue C, Lofts F, Stubbs M, Griffiths JR. Can
localised (19)F magnetic resonance spectroscopy pharmacokinetics of
26
108. Woodard GA, Ray KM, Joe BN, Price ER. Qualitative radiogenomics:
association between oncotype DX test recurrence score and BI-RADS
mammographic and breast MR imaging features. Radiology 2018;286:
60–70.
109. Grimm LJ. Breast MRI radiogenomics: Current status and research
implications. J Magn Reson Imaging 2016;43:1269–1278.
110. Mehta S, Hughes NP, Li S, et al. Radiogenomics monitoring in breast
cancer identifies metabolism and immune checkpoints as early actionable mechanisms of resistance to anti-angiogenic treatment. EBioMedicine 2016;10:109–116.
111. Shinagare AB, Vikram R, Jaffe C, et al. Radiogenomics of clear cell
renal cell carcinoma: preliminary findings of The Cancer Genome
Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group.
Abdom Imaging 2015;40:1684–1692.
112. Kickingereder P, Bonekamp D, Nowosielski M, et al. Radiogenomics
of glioblastoma: machine learning-based classification of molecular
characteristics by using multiparametric and multiregional MR imaging
features. Radiology 2016;281:907–918.
113. Hu LS, Ning S, Eschbacher JM, et al. Radiogenomics to characterize
regional genetic heterogeneity in glioblastoma. Neuro Oncol 2017;19:
128–137.
114. Gourtsoyianni S, Doumou G, Prezzi D, et al. Primary rectal cancer:
repeatability of global and local-regional MR imaging texture features.
Radiology 2017:161375.
Volume 48, No. 1

Purchase answer to see full
attachment




Why Choose Us

  • 100% non-plagiarized Papers
  • 24/7 /365 Service Available
  • Affordable Prices
  • Any Paper, Urgency, and Subject
  • Will complete your papers in 6 hours
  • On-time Delivery
  • Money-back and Privacy guarantees
  • Unlimited Amendments upon request
  • Satisfaction guarantee

How it Works

  • Click on the “Place Order” tab at the top menu or “Order Now” icon at the bottom and a new page will appear with an order form to be filled.
  • Fill in your paper’s requirements in the "PAPER DETAILS" section.
  • Fill in your paper’s academic level, deadline, and the required number of pages from the drop-down menus.
  • Click “CREATE ACCOUNT & SIGN IN” to enter your registration details and get an account with us for record-keeping and then, click on “PROCEED TO CHECKOUT” at the bottom of the page.
  • From there, the payment sections will show, follow the guided payment process and your order will be available for our writing team to work on it.