Skip to main content
Top
Published in: European Radiology 6/2014

01-06-2014 | Breast

Application of the diffusion kurtosis model for the study of breast lesions

Authors: Luísa Nogueira, Sofia Brandão, Eduarda Matos, Rita Gouveia Nunes, Joana Loureiro, Isabel Ramos, Hugo Alexandre Ferreira

Published in: European Radiology | Issue 6/2014

Login to get access

Abstract

Objectives

To evaluate diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in the differentiation and characterisation of breast lesions.

Methods

Thirty-six women underwent breast magnetic resonance imaging (MRI) including a DWI sequence with multiple b-values (50–3,000 s/mm2). Mean values for apparent diffusion coefficient (ADC), mean diffusivity (MD) and mean kurtosis (MK) were calculated by lesion type and histological subtype. Differences and correlation between parameters were determined.

Results

Forty-four lesions were found. There were significant differences between benign and malignant lesions for all parameters (ADC, p = 0.017; MD, p = 0.028; MK, p = 0.017). ADC and MD were higher for benign (1.96 ± 0.41 × 10−3 and 2.17 ± 0.42 × 10−3 mm2/s, respectively) than for malignant lesions (1.33 ± 0.18 × 10−3 and 1.52 ± 0.50 × 10−3 mm2/s). MK was higher for malignant (0.61 ± 0.27) than benign lesions (0.37 ± 0.18). We found differences between invasive ductal carcinoma (IDC) and fibroadenoma (FA) for all parameters (ADC, MD and MK): p = 0.016, 0.022 and 0.016, respectively. FA and fibrocystic change (FC) showed differences only in MK (p = 0.016).

Conclusions

Diffusion in breast lesions follows a non-Gaussian distribution. MK enables differentiation and characterisation of breast lesions, providing new insights into microstructural complexity. To confirm these results, further investigation in a broader sample should be performed.

Key Points

• The diffusion kurtosis model provides new information regarding breast lesions
• MD and MK are valid parameters to characterise tissue microstructure
• MK enables improved lesion differentiation
• MK is able to differentiate lesions that display similar ADC values
Literature
1.
go back to reference Guo Y, Cai YQ, Cai ZL, Gao YG, An NY, Ma L (2002) Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging 16:172–178PubMedCrossRef Guo Y, Cai YQ, Cai ZL, Gao YG, An NY, Ma L (2002) Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging 16:172–178PubMedCrossRef
2.
go back to reference Bogner W, Gruber S, Pinker K et al (2009) Diffusion-weighted MR for differentiation of breast lesions at 3.0 T: how does selection of diffusion protocols affect diagnosis? Radiology 253:341–351PubMedCrossRef Bogner W, Gruber S, Pinker K et al (2009) Diffusion-weighted MR for differentiation of breast lesions at 3.0 T: how does selection of diffusion protocols affect diagnosis? Radiology 253:341–351PubMedCrossRef
3.
go back to reference Peters N, Vincken K, Van den Bosch M, Luijten P, Mali W, Bartels L (2010) Quantitative diffusion weighted imaging for differentiation of benign and malignant breast lesions: the influence of the choice of b-values. J Magn Reson Imaging 31:1100–1105PubMedCrossRef Peters N, Vincken K, Van den Bosch M, Luijten P, Mali W, Bartels L (2010) Quantitative diffusion weighted imaging for differentiation of benign and malignant breast lesions: the influence of the choice of b-values. J Magn Reson Imaging 31:1100–1105PubMedCrossRef
4.
go back to reference El Khouli R, Jacobs AM, Mezban DS, Huang P, Kamel JK, Bluemke AD (2010) Diffusion-weighted imaging improves the diagnostic accuracy of conventional 3.0-T breast MR imaging. Radiology 256:64–73CrossRef El Khouli R, Jacobs AM, Mezban DS, Huang P, Kamel JK, Bluemke AD (2010) Diffusion-weighted imaging improves the diagnostic accuracy of conventional 3.0-T breast MR imaging. Radiology 256:64–73CrossRef
5.
go back to reference Lo G, Ai V, Chan J et al (2009) Diffusion-weighted magnetic resonance imaging of breast lesions: first experiences at 3 T. J Comput Assist Tomogr 33:63–69PubMedCrossRef Lo G, Ai V, Chan J et al (2009) Diffusion-weighted magnetic resonance imaging of breast lesions: first experiences at 3 T. J Comput Assist Tomogr 33:63–69PubMedCrossRef
6.
go back to reference Pereira F, Martins G, Oliveira R (2011) Diffusion magnetic resonance imaging of the breast. Magn Reson Imaging Clin N Am 19:95–110PubMedCrossRef Pereira F, Martins G, Oliveira R (2011) Diffusion magnetic resonance imaging of the breast. Magn Reson Imaging Clin N Am 19:95–110PubMedCrossRef
7.
go back to reference Basser PJ, Jones DK (2002) Diffusion-tensor MRI: theory, experimental design and data analysis—a technical review. NMR Biomed 15:456–67PubMedCrossRef Basser PJ, Jones DK (2002) Diffusion-tensor MRI: theory, experimental design and data analysis—a technical review. NMR Biomed 15:456–67PubMedCrossRef
8.
go back to reference Gillies R, Raghunand N, Karczmar G, Bhujwalla Z (2002) MRI of the tumor microenvironment. J Magn Reson Imaging 16:430–450PubMedCrossRef Gillies R, Raghunand N, Karczmar G, Bhujwalla Z (2002) MRI of the tumor microenvironment. J Magn Reson Imaging 16:430–450PubMedCrossRef
9.
go back to reference Jensen J, Helpern J, Ramani A, Lu H, Kaczynski K (2005) Diffusion kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 53:1432–1440PubMedCrossRef Jensen J, Helpern J, Ramani A, Lu H, Kaczynski K (2005) Diffusion kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 53:1432–1440PubMedCrossRef
10.
go back to reference Tamura T, Usui S, Murakami S et al (2012) Comparisons of multi b-value DWI signal analysis with pathological specimen of breast cancer. Magn Reson Med 68:890–897PubMedCrossRef Tamura T, Usui S, Murakami S et al (2012) Comparisons of multi b-value DWI signal analysis with pathological specimen of breast cancer. Magn Reson Med 68:890–897PubMedCrossRef
11.
go back to reference Poot DH, Den Dekker AJ, Achten E, Verhoye M, Sijbers J (2010) Optimal experimental design for diffusion kurtosis imaging. IEEE Trans Med Imaging 29:3CrossRef Poot DH, Den Dekker AJ, Achten E, Verhoye M, Sijbers J (2010) Optimal experimental design for diffusion kurtosis imaging. IEEE Trans Med Imaging 29:3CrossRef
12.
go back to reference Jansen J, Stambuk H, Koutcher J, Shukla-Dave A (2010) Non-Gaussian analysis of diffusion-weighted MR imaging in head and neck squamous cell carcinoma: a feasibility study. AJNR Am J Neuroradiol 31:741–748PubMedCentralPubMedCrossRef Jansen J, Stambuk H, Koutcher J, Shukla-Dave A (2010) Non-Gaussian analysis of diffusion-weighted MR imaging in head and neck squamous cell carcinoma: a feasibility study. AJNR Am J Neuroradiol 31:741–748PubMedCentralPubMedCrossRef
13.
go back to reference Quentin M, Blondin D, Klasen J et al (2012) Comparison of different mathematical models of diffusion-weighted prostate MR imaging. Magn Reson Imaging 30:1468–1474PubMedCrossRef Quentin M, Blondin D, Klasen J et al (2012) Comparison of different mathematical models of diffusion-weighted prostate MR imaging. Magn Reson Imaging 30:1468–1474PubMedCrossRef
14.
go back to reference Cheung M, Hui E, Chan K, Helpen J, Qi L, Wu E (2009) Does diffusion kurtosis imaging lead to better neural tissue characterization? A rodent brain maturation study. Neuroimage 45:386–392PubMedCrossRef Cheung M, Hui E, Chan K, Helpen J, Qi L, Wu E (2009) Does diffusion kurtosis imaging lead to better neural tissue characterization? A rodent brain maturation study. Neuroimage 45:386–392PubMedCrossRef
15.
16.
go back to reference Chen S, Pickard JD, Harris NG (2003) Time course of cellular pathology after controlled cortical impact injury. Exp Neurol 1:87–102CrossRef Chen S, Pickard JD, Harris NG (2003) Time course of cellular pathology after controlled cortical impact injury. Exp Neurol 1:87–102CrossRef
17.
go back to reference Raab P, Hattingen E, Franz K, Zanella FE, Lanferman H (2010) Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology 254:876–81PubMedCrossRef Raab P, Hattingen E, Franz K, Zanella FE, Lanferman H (2010) Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology 254:876–81PubMedCrossRef
18.
go back to reference Trampel R, Jensen JH, Lee RF, Kamenetskiy I, McGuinness G, Johnson G (2006) Diffusional kurtosis imaging in the lung using hyperpolarized 3He. Magn Reson Med 56:733–737PubMedCrossRef Trampel R, Jensen JH, Lee RF, Kamenetskiy I, McGuinness G, Johnson G (2006) Diffusional kurtosis imaging in the lung using hyperpolarized 3He. Magn Reson Med 56:733–737PubMedCrossRef
19.
go back to reference Borlinhas F, Lacerda L, Andrade A, Ferreira HA (2012) Diffusional kurtosis as a biomarker of breast tumors (E-poster presentation). European Congress of Radiology 2012, 1–5 March 2012, Vienna, Austria. doi:10.1594/erc2012/C-1369 Borlinhas F, Lacerda L, Andrade A, Ferreira HA (2012) Diffusional kurtosis as a biomarker of breast tumors (E-poster presentation). European Congress of Radiology 2012, 1–5 March 2012, Vienna, Austria. doi:10.​1594/​erc2012/​C-1369
20.
go back to reference Ikeda DM, Hylton NM, Kuhl CK et al (2003) BI-RADS: magnetic resonance imaging, 1st edn. In: D’Orsi CJ, Mendelson EB, Ikeda DM et al (eds) Breast imaging reporting and data system: ACR BI-RADS—breast imaging atlas. American College of Radiology, Reston Ikeda DM, Hylton NM, Kuhl CK et al (2003) BI-RADS: magnetic resonance imaging, 1st edn. In: D’Orsi CJ, Mendelson EB, Ikeda DM et al (eds) Breast imaging reporting and data system: ACR BI-RADS—breast imaging atlas. American College of Radiology, Reston
21.
go back to reference Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11:431–441CrossRef Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11:431–441CrossRef
22.
go back to reference Costantini M, Belli P, Rinaldi P (2010) Diffusion-weighted imaging in breast cancer: relationship between apparent diffusion coefficient and tumour aggressiveness. Clin Radiol 65:1008–1012CrossRef Costantini M, Belli P, Rinaldi P (2010) Diffusion-weighted imaging in breast cancer: relationship between apparent diffusion coefficient and tumour aggressiveness. Clin Radiol 65:1008–1012CrossRef
23.
go back to reference Paran Y, Bendel P, Margalit R, Degani H (2004) Water diffusion in the different microenvironments of breast cancer. NMR Biomed 17:170–180PubMedCrossRef Paran Y, Bendel P, Margalit R, Degani H (2004) Water diffusion in the different microenvironments of breast cancer. NMR Biomed 17:170–180PubMedCrossRef
24.
go back to reference Lyng H, Haraldseth O, Rofstad EK (2000) Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med 43:828–836PubMedCrossRef Lyng H, Haraldseth O, Rofstad EK (2000) Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med 43:828–836PubMedCrossRef
25.
go back to reference Sukstanskii AL, Yablonskiy DA (2002) Effects of restricted diffusion on MR signal formation. J Magn Reson 157:92–105PubMedCrossRef Sukstanskii AL, Yablonskiy DA (2002) Effects of restricted diffusion on MR signal formation. J Magn Reson 157:92–105PubMedCrossRef
26.
go back to reference Kiselev VG, Il’yasov KA (2007) Is the “biexponential diffusion” biexponential? Magn Reson Med 57:464–469PubMedCrossRef Kiselev VG, Il’yasov KA (2007) Is the “biexponential diffusion” biexponential? Magn Reson Med 57:464–469PubMedCrossRef
27.
go back to reference Pereira F, Martins G, Oliveira R (2011) Diffusion magnetic resonance imaging of the breast. Magn Reson Imaging Clin N Am 19:95–110PubMedCrossRef Pereira F, Martins G, Oliveira R (2011) Diffusion magnetic resonance imaging of the breast. Magn Reson Imaging Clin N Am 19:95–110PubMedCrossRef
28.
go back to reference De Santis S, Gabrielli A, Palombo M, Maraviglia B, Capuani S (2011) Non-Gaussian diffusion imaging: a brief practical review. Magn Reson Imaging 29:1410–1416PubMedCrossRef De Santis S, Gabrielli A, Palombo M, Maraviglia B, Capuani S (2011) Non-Gaussian diffusion imaging: a brief practical review. Magn Reson Imaging 29:1410–1416PubMedCrossRef
29.
go back to reference Fornasa F (2011) Diffusion-weighted magnetic resonance imaging: what makes water run fast or slow? J Clin Imaging Sci 1:1–7CrossRef Fornasa F (2011) Diffusion-weighted magnetic resonance imaging: what makes water run fast or slow? J Clin Imaging Sci 1:1–7CrossRef
30.
go back to reference Roth Y, Ocherashvilli A, Daniels D et al (2008) Quantification of water compartmentation in cell suspensions by diffusion-weighted and T2-weighted MRI. Magn Reson Imaging 26:88–102PubMedCrossRef Roth Y, Ocherashvilli A, Daniels D et al (2008) Quantification of water compartmentation in cell suspensions by diffusion-weighted and T2-weighted MRI. Magn Reson Imaging 26:88–102PubMedCrossRef
31.
go back to reference Koh D, Collins D (2007) Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol 188:1622–1635PubMedCrossRef Koh D, Collins D (2007) Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol 188:1622–1635PubMedCrossRef
Metadata
Title
Application of the diffusion kurtosis model for the study of breast lesions
Authors
Luísa Nogueira
Sofia Brandão
Eduarda Matos
Rita Gouveia Nunes
Joana Loureiro
Isabel Ramos
Hugo Alexandre Ferreira
Publication date
01-06-2014
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 6/2014
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-014-3146-5

Other articles of this Issue 6/2014

European Radiology 6/2014 Go to the issue