European Radiology

, Volume 30, Issue 2, pp 767–777 | Cite as

Breast MRI during lactation: effects on tumor conspicuity using dynamic contrast-enhanced (DCE) in comparison with diffusion tensor imaging (DTI) parametric maps

  • Noam NissanEmail author
  • Tanir Allweis
  • Tehillah Menes
  • Asia Brodsky
  • Shani Paluch-Shimon
  • Ilana Haas
  • Orit Golan
  • Yaheli Miller
  • Hani Barlev
  • Einat Carmon
  • Malka Brodsky
  • Debbie Anaby
  • Philip Lawson
  • Osnat Halshtok-Neiman
  • Anat Shalmon
  • Michael Gotlieb
  • Renata Faermann
  • Eli Konen
  • Miri Sklair-Levy



To investigate the effect of lactation on breast cancer conspicuity on dynamic contrast-enhanced (DCE) MRI in comparison with diffusion tensor imaging (DTI) parametric maps.

Materials and methods

Eleven lactating patients with 16 biopsy-confirmed pregnancy-associated breast cancer (PABC) lesions were prospectively evaluated by DCE and DTI on a 1.5-T MRI for pre-treatment evaluation. Additionally, DCE datasets of 16 non-lactating age-matched breast cancer patients were retrospectively reviewed, as control. Contrast-to-noise ratio (CNR) comprising two regions of interests of the normal parenchyma was used to assess the differences in the tumor conspicuity on DCE subtraction images between lactating and non-lactating patients, as well as in comparison against DTI parametric maps of λ1, λ2, λ3, mean diffusivity (MD), fractional anisotropy (FA), and maximal anisotropy index, λ1–λ3.


CNR values of breast cancer on DCE MRI among lactating patients were reduced by 62% and 58% (p < 0.001) in comparison with those in non-lactating patients, when taking into account the normal contralateral parenchyma and an area of marked background parenchymal enhancement (BPE), respectively. Among the lactating patients, DTI parameters of λ1, λ2, λ3, MD, and λ1–λ3 were significantly decreased, and FA was significantly increased in PABC, relative to the normal lactating parenchyma ROIs. When compared against DCE in the lactating cohort, the CNR on λ1, λ2, λ3, and MD was significantly superior, providing up to 138% more tumor conspicuity, on average.


Breast cancer conspicuity on DCE MRI is markedly reduced during lactation owing to the marked BPE. However, the additional application of DTI can improve the visualization and quantitative characterization of PABC, therefore possibly suggesting an additive value in the diagnostic workup of PABC.

Key Points

• Breast cancer conspicuity on DCE MRI has decreased by approximately 60% among lactating patients compared with non-lactating controls.

• DTI-derived diffusion coefficients and the anisotropy indices of PABC lesions were significantly different than those of the normal lactating fibroglandular tissue.

• Among lactating patients, breast cancer conspicuity on DTI-derived parametric maps provided up to 138% increase in contrast-to-noise ratio compared with DCE imaging.


Breast Lactating Breastfeeding Diffusion Magnetic resonance imaging 



Apparent diffusion coefficient


Background parenchymal enhancement


Contrast-to-noise ratio


Dynamic contrast-enhanced


Ductal carcinoma in situ


Diffusion tensor imaging


Fractional anisotropy


Field of view


Invasive ductal carcinoma


Invasive lobular carcinoma


Mean diffusivity


Pregnancy-associated breast cancer


Region of interest


Echo time


Repetition time



NN thanks Prof. Hadassa Degani from the Weizmann Institute of Science for long hours of stimulating discussions, as well as for the permission to use the proprietary DTI software.

Funding information

This study has received funding from The Israel Cancer Association and the Sheba Medical Center and Weizmann Institute of Science Research collaboration biomedical research grant.

Compliance with ethical standards


The scientific guarantor of this publication is Dr. Noam Nissan.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.


• prospective

• case-control study/experimental

• performed at one institution, but patients were recruited in several centers

Supplementary material

330_2019_6435_MOESM1_ESM.docx (2.3 mb)
Supplementary Figure 1 (DOCX 2378 kb)


  1. 1.
    McManaman JL, Neville MC (2003) Mammary physiology and milk secretion. Adv Drug Deliv Rev 55:629–641PubMedCrossRefGoogle Scholar
  2. 2.
    Vashi R, Hooley R, Butler R, Geisel J, Philpotts L (2013) Breast imaging of the pregnant and lactating patient: physiologic changes and common benign entities. AJR Am J Roentgenol 200:329–336PubMedCrossRefGoogle Scholar
  3. 3.
    Andersson TM, Johansson AL, Hsieh CC, Cnattingius S, Lambe M (2009) Increasing incidence of pregnancy-associated breast cancer in Sweden. Obstet Gynecol 114(3):568–572PubMedCrossRefGoogle Scholar
  4. 4.
    Langer A, Mohallem M, Stevens D, Rouzier R, Lerebours F, Chérel P (2014) A single-institution study of 117 pregnancy-associated breast cancers (pabc): presentation, imaging, clinicopathological data and outcome. Diagn Interv Imaging 95(4):435–441PubMedCrossRefGoogle Scholar
  5. 5.
    Vashi R, Hooley R, Butler R, Geisel J, Philpotts L (2013) Breast imaging of the pregnant and lactating patient: imaging modalities and pregnancy-associated breast cancer. AJR Am J Roentgenol 200(2):321–328PubMedCrossRefGoogle Scholar
  6. 6.
    Sardanelli F, Boetes C, Borisch B et al (2010) Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group. Eur J Cancer 46(8):1296–1316PubMedCrossRefGoogle Scholar
  7. 7.
    Oh SW, Lim HS, Moon SM et al (2017) MR imaging characteristics of breast cancer diagnosed during lactation. Br J Radiol 90(1078)PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    Myers KS, Green LA, Lebron L, Morris EA (2017) Imaging appearance and clinical impact of preoperative breast MRI in pregnancy-associated breast cancer. AJR Am J Roentgenol 209(3):W177–W183PubMedCrossRefGoogle Scholar
  9. 9.
    Taron J, Fleischer S, Preibsch H, Nikolaou K, Gruber I, Bahrs S (2018) Background parenchymal enhancement in pregnancy-associated breast cancer: a hindrance to diagnosis? Eur Radiol 29(3):1187–1193PubMedCrossRefGoogle Scholar
  10. 10.
    Espinosa LA, Daniel BL, Vidarsson L, Zakhour M, Ikeda DM, Herfkens RJ (2005) The lactating breast: contrast-enhanced MR imaging of normal tissue and cancer. Radiology. 237(2):429–436PubMedCrossRefGoogle Scholar
  11. 11.
    diFlorio-Alexander RM, Slanetz PJ, Moy L et al (2018) ACR appropriateness criteria® breast imaging of pregnant and lactating women. J Am Coll Radiol 15(11S):S263–S275PubMedCrossRefGoogle Scholar
  12. 12.
    Carmichael H, Matsen C, Freer P et al (2017) Breast cancer screening of pregnant and breastfeeding women with BRCA mutations. Breast Cancer Res Treat 162(2):225–230PubMedCrossRefGoogle Scholar
  13. 13.
    Partridge SC, Nissan N, Rahbar H, Kitsch AE, Sigmund EE (2016) Diffusion-weighted breast MRI: clinical applications and emerging techniques. J Magn Reson Imaging 14:1–19Google Scholar
  14. 14.
    Partridge SC, Murthy RS, Ziadloo A, White SW, Allison KH, Lehman CD (2010) Diffusion tensor magnetic resonance imaging of the normal breast. Magn Reson Imaging 28(3):320–328PubMedCrossRefGoogle Scholar
  15. 15.
    Eyal E, Shapiro-Feinberg M, Furman-Haran E et al (2012) Parametric diffusion tensor imaging of the breast. Invest Radiol 47(5):284–291PubMedCrossRefGoogle Scholar
  16. 16.
    Baltzer PA, Schäfer A, Dietzel M et al (2011) Diffusion tensor magnetic resonance imaging of the breast: a pilot study. Eur Radiol 21(1):1–10PubMedCrossRefGoogle Scholar
  17. 17.
    Partridge SC, Ziadloo A, Murthy R et al (2010) Diffusion tensor MRI: preliminary anisotropy measures and mapping of breast tumors. J Magn Reson Imaging 31(2):339–347PubMedCrossRefGoogle Scholar
  18. 18.
    Le Bihan D, Mangin JF, Poupon C et al (2001) Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 13(4):534–546PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Plaza MJ, Morris EA, Thakur SB (2016) Diffusion tensor imaging in the normal breast: influences of fibroglandular tissue composition and background parenchymal enhancement. Clin Imaging 40(3):506–511PubMedCrossRefGoogle Scholar
  20. 20.
    Wiederer J, Pazahr S, Leo C, Nanz D, Boss A (2013) Quantitative breast MRI: 2D histogram analysis of diffusion tensor parameters in normal tissue. MAGMA 27(2):185–193CrossRefGoogle Scholar
  21. 21.
    Teruel JR, Cho GY, Moccaldi Rt M et al (2017) Stimulated echo diffusion tensor imaging (STEAM-DTI) with varying diffusion times as a probe of breast tissue. J Magn Reson Imaging 45(1):84–93PubMedCrossRefGoogle Scholar
  22. 22.
    Nissan N, Furman-Haran E, Shapiro-Feinberg M, Grobgeld D, Degani H (2014) Diffusion-tensor MR imaging of the breast: hormonal regulation. Radiology. 271(3):672–680PubMedCrossRefGoogle Scholar
  23. 23.
    Nissan N, Furman-Haran E, Shapiro-Feinberg M, Grobgeld D, Degani H (2017) Monitoring in-vivo the mammary gland microstructure during morphogenesis from lactation to post-weaning using diffusion tensor MRI. J Mammary Gland Biol Neoplasia 22(3):193–202PubMedCrossRefGoogle Scholar
  24. 24.
    Cakir O, Arslan A, Inan N et al (2013) Comparison of the diagnostic performances of diffusion parameters in diffusion weighted imaging and diffusion tensor imaging of breast lesions. Eur J Radiol 82(12):e801–e806PubMedCrossRefGoogle Scholar
  25. 25.
    Teruel JR, Goa PE, Sjøbakk TE, Østlie A, Fjøsne HE, Bathen TF (2016) Diffusion weighted imaging for the differentiation of breast tumors: from apparent diffusion coefficient to high order diffusion tensor imaging. J Magn Reson Imaging 43(5):1111–1121PubMedCrossRefGoogle Scholar
  26. 26.
    Onaygil C, Kaya H, Ugurlu MU, Aribal E (2017) Diagnostic performance of diffusion tensor imaging parameters in breast cancer and correlation with the prognostic factors. J Magn Reson Imaging 45(3):660–672PubMedCrossRefGoogle Scholar
  27. 27.
    Kosmin M, Makris A, Joshi PV, Ah-See ML, Woolf D, Padhani AR (2017) The addition of whole-body magnetic resonance imaging to body computerised tomography alters treatment decisions in patients with metastatic breast cancer. Eur J Cancer 77:109–116PubMedCrossRefGoogle Scholar
  28. 28.
    Furman-Haran E, Grobgeld D, Nissan N, Shapiro-Feinberg M, Degani H (2016) Can diffusion tensor anisotropy indices assist in breast cancer detection? J Magn Reson Imaging 44(6):1624–1632PubMedCrossRefGoogle Scholar
  29. 29.
    Tsougos I, Bakosis M, Tsivaka D et al (2019) Diagnostic performance of quantitative diffusion tensor imaging for the differentiation of breast lesions at 3 T MRI. Clin Imaging 53:25–31PubMedCrossRefGoogle Scholar
  30. 30.
    Wilmes LJ, Li W, Shin HJ et al (2016) Diffusion tensor imaging for assessment of response to neoadjuvant chemotherapy in patients with breast cancer. Tomography. 2(4):438–447PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Furman-Haran E, Nissan N, Ricart-Selma V, Martinez-Rubio C, Degani H, Camps-Herrero J (2017) Quantitative evaluation of breast cancer response to neoadjuvant chemotherapy by diffusion tensor imaging: initial results. J Magn Reson Imaging 47(4):1080–1090PubMedCrossRefGoogle Scholar
  32. 32.
    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(2):341–351PubMedCrossRefGoogle Scholar
  33. 33.
    Partridge SC, Singer L, Sun R et al (2011) Diffusion-weighted MRI: influence of intravoxel fat signal and breast density on breast tumor conspicuity and apparent diffusion coefficient measurements. Magn Reson Imaging 29(9):1215–1221PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Djonov V, Andres AC, Ziemiecki A (2001) Vascular remodelling during the normal and malignant life cycle of the mammary gland. Microsc Res Tech 52(2):182–189PubMedCrossRefGoogle Scholar
  35. 35.
    Sah RG, Agarwal K, Sharma U, Parshad R, Seenu V, Jagannathan NR (2015) Characterization of malignant breast tissue of breast cancer patients and the normal breast tissue of healthy lactating women volunteers using diffusion MRI and in vivo 1H MR spectroscopy. J Magn Reson Imaging 41(1):169–174 36PubMedCrossRefGoogle Scholar
  36. 36.
    Iima M, Kataoka M, Sakaguchi R et al (2018) Intravoxel incoherent motion (IVIM) and non-Gaussian diffusion MRI of the lactating breast. Eur J Radiol Open 5:24–30PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Nissan N, Furman-Haran E, Allweis T et al (2018) Noncontrast breast MRI during pregnancy using diffusion tensor imaging: a feasibility study. J Magn Reson Imaging 49(2):508–517PubMedCrossRefGoogle Scholar
  38. 38.
    Nissan N, Furman-Haran E, Feinberg-Shapiro M et al (2014) Tracking the mammary architectural features and detecting breast cancer with magnetic resonance diffusion tensor imaging. J Vis Exp (94):1–18Google Scholar
  39. 39.
    Furman-Haran E, Eyal E, Shapiro-Feinberg M et al (2012) Advantages and drawbacks of breast DTI. Eur J Radiol 81:S45–S47PubMedCrossRefGoogle Scholar
  40. 40.
    Shapiro-Feinberg M, Weisenberg N, Zehavi T et al (2012) Clinical results of DTI. Eur J Radiol 81(1):S151–152. PubMedCrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Noam Nissan
    • 1
    • 2
    Email author
  • Tanir Allweis
    • 3
  • Tehillah Menes
    • 4
  • Asia Brodsky
    • 5
  • Shani Paluch-Shimon
    • 6
  • Ilana Haas
    • 7
  • Orit Golan
    • 8
  • Yaheli Miller
    • 3
  • Hani Barlev
    • 9
  • Einat Carmon
    • 10
  • Malka Brodsky
    • 1
  • Debbie Anaby
    • 1
  • Philip Lawson
    • 1
  • Osnat Halshtok-Neiman
    • 1
  • Anat Shalmon
    • 1
  • Michael Gotlieb
    • 1
  • Renata Faermann
    • 1
  • Eli Konen
    • 1
  • Miri Sklair-Levy
    • 1
  1. 1.Department of RadiologySheba Medical CenterTel HashomerIsrael
  2. 2.Sackler School of MedicineTel Aviv UniversityTel AvivIsrael
  3. 3.Department of General SurgeryKaplan Medical CenterRehovotIsrael
  4. 4.Department of General SurgeryTel Aviv Souraski Medical CenterTel AvivIsrael
  5. 5.Department of General SurgeryBnai Zion Medical CenterHaifaIsrael
  6. 6.Department of OncologyShaare Zedek Medical CenterJerusalemIsrael
  7. 7.Department of General SurgeryMeir Medical CenterSabaIsrael
  8. 8.Department of RadiologyTel Aviv Souraski Medical CenterTel AvivIsrael
  9. 9.Department of General SurgeryLaniado Medical CenterNetanyaIsrael
  10. 10.Department of General SurgeryHadassah Medical CenterJerusalemIsrael

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