Diffusion-weighted
MRI (DW-MRI) exploits the incoherent motion of water molecules within tissues
to generate contrast. Many solid tumours exhibit restricted diffusion of water
molecules compared to many normal tissues, leading to bright signal on
diffusion-weighted images and low values of Apparent Diffusion Coefficient
(ADC). The strength of the diffusion-weighting is determined by the magnitudes
and timings of the diffusion-weighting gradients and is commonly described by a
summary parameter known as the b-value. Estimates of ADC may be derived from
fitting a mono-exponential function to the signal measured at two or more
b-values. Restricted diffusion within tumours has been shown to be related to increased
cellularity and reduction of extra-cellular space. Increase in necrosis / cell
death after treatment increases ADC which has been shown to be predictive of
response to chemotherapy in various tumour sites1, 2, 3.
DW-MRI can be
carried out on most modern MR scanners and does not require administration of
exogenous contrast agents. Echo-planar imaging (EPI) is usually employed in
order to reduce sensitivity to motion. EPI is, however, sensitive to field
inhomogeneities and chemical shift artefacts and optimisation of sequence
parameters is required to obtain good quality images. Good B0
homogeneity, minimal eddy current effects, high SNR and good fat suppression
are required. ADC maps are provided by the manufacturer’s software. Many
studies also use in-house software for definition of Regions of Interest (ROIs)
and calculation of ADCs.
Repeatability
studies have reported within-patient coefficients of variation (wCV) between
4 % and 15 %3, 4. Multi-centre studies of healthy
volunteers have shown significant differences in ADC estimates between scanners
from different manufacturers in abdominal organs5 and in grey matter
and white matter6.
ADC
has been shown to be negatively correlated with histological measures of cell density
in glioma7 and in colorectal liver metastases8. Negative
correlation has also been shown between ADC and proportion of collagenous
fibres in pancreatic cancer9. A study of 32 patients with locally-advanced
gastro-oesophageal cancers showed correlation of the change in ADC estimates
after neaoadjuvant treatment and the tumour regression grade determined from
histology10. Although an increase in ADC estimates between
pre-treatment and post-treatment measurements has been shown to be predictive
of response to treatment in many cases, some studies have not demonstrated
correlation between change in ADC and response3.
The use of
diffusion-weighted MRI as a biomarker has revolutionized oncological diagnosis.
As it can be used both qualitatively by viewing the high b-value images and ADC
maps as well as quantitatively to generate mean or median tumour ADCs, it has
been exploited as a diagnostic tool, and as a prognostic /predictive biomarker
as well as for longitudinally monitoring treatment response. In several tumour
types e.g liver11, 12, lung13, kidney14,
breast15, prostate16 and cervix17 it is used
as a means to differentiate tumour from non-tumour tissue. However, its
quantitative potential has been exploited to predict the aggressiveness of
disease in prostate cancer18 and histological grade in renal cancer19
and cervix cancer20. It has also quantified for predicting
therapeutic response in cervix cancer21, colorectal liver metastases22
and ovarian cancer4 as well as for predicting local recurrence in
rectal cancer23, endometrial cancer24 and biochemical
recurrence in prostate cancer25. More recently, whole body
diffusion-weighted MRI has become possible through advancements in hardware (rf
coil technology) as well as software for integrating image stacks into a visual
representation of the whole body in a 3-D multiplanar re-format. This type of
image has been used for metastases screening, particularly in cases where bone
lesions may be the only site of disease and bone scintigraphy is negative e.g.
multiple myeloma26. In this whole-body mode it has the advantage of
being able to generate a total tumour burden for skeletal metastases and follow
their response to treatment, which has hitherto not been possible27.
A major
limitation in the validation and qualification of ADC as a biomarker in
oncology is the lack of standardization for data acquisition and analysis.
There are currently no standard sequences which can be implemented on all
platforms which limits the extent to which data acquisition can be standardised
in multi-centre projects. Technical limitations, for example non-uniformity in
ADC estimates, may introduce errors in ADC estimates particularly when
employing large fields-of-view. Software and methods for analysis are also not
standardized, leading variation in definition of ROIs and calculation of ADCs.
The
physiological basis of the diffusion-weighted signal is not fully understood
and validation, for example by correlation with histopathology, remains an area
of current and future work. The temporal evolution of ADC in response to
treatment may be influenced by factors such as cell swelling, cell shrinkage,
necrosis, fat infiltration and fibrosis. Moreover, attempts to use
pre-treatment ADC estimates as a predictive biomarker have yielded mixed
results with some studies showing correlation between pre-treatment ADC and
response to treatment while many other studies showed no correlation3.
A limitation of many studies is that numbers of patients are small and
meta-analyses are impeded by differences between imaging protocols, patient
populations and treatment regimens.
References
1. Sinkus R, Van Beers BE, Vilgrain V, deSouza N,
Waterton JC. Apparent diffusion coefficient from
magnetic resonance imaging as a biomarker in oncology drug development.
Eur J Cancer. 2012;48:425-31.
2. Patterson DM, Padhani AR, Collins DJ. Technology insight: water diffusion MRI--a potential new biomarker of
response to cancer therapy. Nat Clin
Pract Oncol. 2008;5: 220-33.
3. Heijmen L, Verstappen MC, Ter Voert EE, Punt
CJ, Oyen WJ, de Geus-Oei LF, Hermans JJ, Heerschap A, van Laarhoven HW. Tumour response prediction by diffusion-weighted MR
imaging: ready for clinical use? Crit Rev
Oncol Hematol. 2012; 83: 194-207.
4. Kyriazi S1, Collins DJ, Messiou C, Pennert K, Davidson RL, Giles SL, Kaye SB, deSouza NM.
Metastatic ovarian and primary peritoneal cancer:
assessing chemotherapy response with diffusion-weighted MR imaging--value of
histogram analysis of apparent diffusion coefficients. Radiology. 2011; 261:182-192
5. Donati OF, Chong D, Nanz D, Boss A, Froehlich
JM, Andres E, Seifert B, Thoeny HC. Diffusion-weighted MR imaging of upper
abdominal organs: field strength and intervendor variability of apparent diffusion coefficients.
Radiology. 2014; 270: 454-463.
6. Sasaki M, Yamada K, Watanabe Y, Matsui M, Ida
M, Fujiwara S, Shibata E; Acute Stroke Imaging Standardization Group-Japan
(ASIST-Japan) Investigators. Variability in absolute apparent diffusion coefficient
values across different platforms may be substantial: a multivendor,
multi-institutional comparison study. Radiology. 2008; 249: 624-30.
7. Sugahara T1, Korogi Y, Kochi M, Ikushima I, Shigematu Y, Hirai T, Okuda T, Liang L, Ge Y, Komohara Y, Ushio Y, Takahashi M. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging. 1999;
9: 53-60.
8. Heijmen L, Ter Voert EE, Nagtegaal ID, Span P,
Bussink J, Punt CJ, de Wilt JH, Sweep FC, Heerschap A, van Laarhoven HW. Diffusion-weighted MR imaging in liver
metastases of colorectal cancer: reproducibility and biological validation.
Eur Radiol. 2013; 23: 748-756.
9. Muraoka N, Uematsu H, Kimura H, Imamura Y,
Fujiwara Y, Murakami M, Yamaguchi A, Itoh H. Apparent diffusion coefficient in pancreatic cancer:
characterization and histopathological correlations. J
Magn Reson Imaging. 2008; 27: 1302-1308.
10. De Cobelli F1, Giganti F, Orsenigo E, Cellina M, Esposito A, Agostini G, Albarello L, Mazza E, Ambrosi A, Socci C, Staudacher C, Del Maschio A. Apparent diffusion coefficient modifications in assessing gastro-oesophageal cancer response
to neoadjuvant treatment: comparison with tumour regression grade at histology.
Eur Radiol. 2013; 23: 2165-2174.
11. Haradome H, Grazioli L, Morone M,
Gambarini S, Kwee TC, Takahara T, Colagrande S. T2-weighted and diffusion-weighted MRI for discriminating benign from
malignant focal liver lesions: diagnostic abilities of single versus combined
interpretations. J
Magn Reson Imaging. 2012; 35:1388-1396.
12. Cieszanowski A, Anysz-Grodzicka A,
Szeszkowski W, Kaczynski B, Maj E, Gornicka B, Grodzicki M, Grudzinski IP,
Stadnik A, Krawczyk M, Rowinski O. Characterization of focal liver lesions using quantitative
techniques: comparison of apparent diffusion coefficient values and T2 relaxation
times. Eur Radiol.
2012; 22: 2514-2524.
13. Wu LM, Xu JR, Hua J, Gu HY, Chen J,
Haacke EM, Hu J. Can diffusion-weighted imaging be used as a reliable sequence in the
detection of malignant pulmonary nodules and masses? Magn Reson Imaging. 2013; 31: 235-46.
14. Razek AA,
Farouk A, Mousa A, Nabil N. Role of diffusion-weighted magnetic resonance imaging in
characterization of renal tumors. J
Comput Assist Tomogr. 2011; 35: 332-336.
15. Ei Khouli RH, Jacobs MA, Mezban SD,
Huang P, Kamel IR, Macura KJ, Bluemke DA. Diffusion-weighted imaging improves the diagnostic accuracy of
conventional 3.0-T breast MR imaging. Radiology. 2010; 256:64-73.
16. Mazaheri Y, Shukla-Dave A, Hricak
H, Fine SW, Zhang J, Inurrigarro G, Moskowitz CS, Ishill NM, Reuter VE, Touijer
K, Zakian KL, Koutcher JA. Prostate cancer: identification with combined diffusion-weighted MR imagingand 3D 1H MR spectroscopic imaging--correlation with pathologic findings. Radiology.
2008; 246: 480-488.
17. Charles-Edwards EM, Messiou C,
Morgan VA, De Silva SS, McWhinney NA, Katesmark M, Attygalle AD, DeSouza NM. Diffusion-weighted imaging in cervical
cancer with an endovaginal technique: potential value for improving tumor detection in
stage Ia and Ib1 disease. Radiology.
2008; 249: 541-550.
18. deSouza NM, Riches SF, Vanas NJ, Morgan VA,
Ashley SA, Fisher C, Payne GS, Parker C. Diffusion-weighted magnetic resonance imaging: a potential non-invasive marker of tumour aggressiveness
in localized prostate cancer. Clin Radiol.
2008; 63: 774-782.
19. Goyal A,
Sharma R, Bhalla AS, Gamanagatti S, Seth A, Iyer VK, Das P. Diffusion-weighted MRI in renal cell carcinoma: a surrogate marker for predicting
nuclear grade and histological subtype. Acta
Radiol. 2012; 53: 349-358.
20. Payne GS,
Schmidt M, Morgan VA, Giles S, Bridges J, Ind T, deSouza NM. Evaluation of magnetic resonance diffusion and spectroscopy measurements as
predictive biomarkers in stage 1 cervical cancer.Gynecol Oncol.
2010; 116: 246-252.
21. Zhang Y, Chen JY, Xie CM, Mo YX,
Liu XW, Liu Y, Wu PH. Diffusion-weighted magnetic resonance imaging for
prediction of response of advanced cervical cancer to chemoradiation.
J Comput Assist Tomogr. 2011; 35: 102-107.
22. Wybranski C, Zeile M, Löwenthal D,
Fischbach F, Pech M, Röhl FW, Gademann G, Ricke J, Dudeck O. Value of diffusion weighted MR imaging as an early surrogate parameter for
evaluation of tumor response to high-dose-rate
brachytherapy of colorectal liver metastases. Radiat Oncol.
2011; 6:43.
23. Elmi A, Hedgire SS, Covarrubias D,
Abtahi SM, Hahn PF, Harisinghani M. Apparent diffusion coefficient
as a non-invasive predictor of treatment response and recurrence in locally
advanced rectal cancer. Clin
Radiol. 2013; 68: e524-531.
24. Nakamura K, Imafuku N, Nishida T,
Niwa I, Joja I, Hongo A, Kodama J, Hiramatsu Y. Measurement of the minimum apparent diffusion coefficient
(ADCmin) of the primary tumor and CA125 are predictive of
disease recurrence for patients with endometrial cancer. Gynecol Oncol.
2012; 124: 335-339.
25. Park SY, Kim CK, Park BK, Lee HM,
Lee KS. Prediction of biochemical recurrence following radical
prostatectomy in men with prostate cancer by diffusion-weighted magnetic resonance imaging: initial results. Eur Radiol. 2011; 21: 1111-1118.
26. Giles SL, Messiou C, Collins DJ,
Morgan VA, Simpkin CJ, West S, Davies FE, Morgan GJ, deSouza NM. Whole-Body Diffusion-weighted MR Imaging for Assessment
of Treatment Response in Myeloma. Radiology.
2014; 271: 785-794.
27. Blackledge MD, Collins DJ, Tunariu
N, Orton MR, Padhani AR,
Leach MO,
Koh DM. Assessment of treatment response by total tumor volume and
global apparentdiffusion coefficient using diffusion-weighted MRI in
patients with metastatic bone disease: a feasibility study. PLoS One.
2014; 9: e91779.