Top

Published in:

Open Access 01-03-2020 | Breast MRI | Breast

Diffusion-weighted imaging of the breast—a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group

Authors: Pascal Baltzer, Ritse M. Mann, Mami Iima, Eric E. Sigmund, Paola Clauser, Fiona J. Gilbert, Laura Martincich, Savannah C. Partridge, Andrew Patterson, Katja Pinker, Fabienne Thibault, Julia Camps-Herrero, Denis Le Bihan, On behalf of the EUSOBI international Breast Diffusion-Weighted Imaging working group

Published in: European Radiology | Issue 3/2020

Abstract

The European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications.

Key Points

• The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use.

• Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters.

• Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed.

Available only for authorised users

Woodhams R, Matsunaga K, Iwabuchi K et al (2005) Diffusion-weighted imaging of malignant breast tumors: the usefulness of apparent diffusion coefficient (ADC) value and ADC map for the detection of malignant breast tumors and evaluation of cancer extension. J Comput Assist Tomogr 29:644–649CrossRef

Woodhams R, Matsunaga K, Kan S et al (2005) ADC mapping of benign and malignant breast tumors. Magn Reson Med Sci 4:35–42CrossRef

Rubesova E, Grell AS, De Maertelaer V, Metens T, Chao SL, Lemort M (2006) Quantitative diffusion imaging in breast cancer: a clinical prospective study. J Magn Reson Imaging 24:319–324. https://doi.org/10.1002/jmri.20643

Wenkel E, Geppert C, Schulz-Wendtland R et al (2007) Diffusion weighted imaging in breast MRI: comparison of two different pulse sequences. Acad Radiol 14:1077–1083. https://doi.org/10.1016/j.acra.2007.06.006 CrossRefPubMed

Baltzer PAT, Renz DM, Herrmann K-H et al (2009) Diffusion-weighted imaging (DWI) in MR mammography (MRM): clinical comparison of echo planar imaging (EPI) and half-Fourier single-shot turbo spin echo (HASTE) diffusion techniques. Eur Radiol 19:1612–1620. https://doi.org/10.1007/s00330-009-1326-5 CrossRefPubMed

Partridge SC, DeMartini WB, Kurland BF, Eby PR, White SW, Lehman CD (2009) Quantitative diffusion-weighted imaging as an adjunct to conventional breast MRI for improved positive predictive value. AJR Am J Roentgenol 193:1716–1722. https://doi.org/10.2214/AJR.08.2139

Pinker K, Bickel H, Helbich TH et al (2013) Combined contrast-enhanced magnetic resonance and diffusion-weighted imaging reading adapted to the “Breast Imaging Reporting and Data System” for multiparametric 3-T imaging of breast lesions. Eur Radiol 23:1791–1802. https://doi.org/10.1007/s00330-013-2771-8 CrossRefPubMed

Chen X, Li WL, Zhang YL, Wu Q, Guo YM, Bai ZL (2010) Meta-analysis of quantitative diffusion-weighted MR imaging in the differential diagnosis of breast lesions. BMC Cancer 10:693. https://doi.org/10.1186/1471-2407-10-693

Dorrius MD, Dijkstra H, Oudkerk M, Sijens PE (2014) Effect of b value and pre-admission of contrast on diagnostic accuracy of 1.5-T breast DWI: a systematic review and meta-analysis. Eur Radiol 24:2835–2847. https://doi.org/10.1007/s00330-014-3338-z CrossRefPubMed

10.

Shi R, Yao Q, Wu L, Xu J (2018) Breast lesions: diagnosis using diffusion weighted imaging at 1.5 T and 3.0 T—systematic review and meta-analysis. Clin Breast Cancer 18:e305–e320. https://doi.org/10.1016/j.clbc.2017.06.011 CrossRefPubMed

11.

Iima M, Kataoka M, Kanao S et al (2018) Intravoxel incoherent motion and quantitative non-Gaussian diffusion MR imaging: evaluation of the diagnostic and prognostic value of several markers of malignant and benign breast lesions. Radiology 287:432–441. https://doi.org/10.1148/radiol.2017162853 CrossRefPubMed

12.

Iima M, Yano K, Kataoka M et al (2015) Quantitative non-Gaussian diffusion and intravoxel incoherent motion magnetic resonance imaging: differentiation of malignant and benign breast lesions. Invest Radiol 50:205–211. https://doi.org/10.1097/RLI.0000000000000094 CrossRefPubMed

13.

Rahbar H, Zhang Z, Chenevert TL et al (2019) Utility of diffusion-weighted imaging to decrease unnecessary biopsies prompted by breast MRI: a trial of the ECOG-ACRIN cancer research group (A6702). Clin Cancer Res 25:1756–1765. https://doi.org/10.1158/1078-0432.CCR-18-2967 CrossRefPubMedPubMedCentral

14.

Partridge SC, Mullins CD, Kurland BF et al (2010) Apparent diffusion coefficient values for discriminating benign and malignant breast MRI lesions: effects of lesion type and size. AJR Am J Roentgenol 194:1664–1673. https://doi.org/10.2214/AJR.09.3534 CrossRefPubMed

15.

Rahbar H, Partridge SC, Eby PR et al (2011) Characterization of ductal carcinoma in situ on diffusion weighted breast MRI. Eur Radiol 21:2011–2019. https://doi.org/10.1007/s00330-011-2140-4 CrossRefPubMed

16.

Bickel H, Pinker-Domenig K, Bogner W et al (2015) Quantitative apparent diffusion coefficient as a noninvasive imaging biomarker for the differentiation of invasive breast cancer and ductal carcinoma in situ. Invest Radiol 50:95–100. https://doi.org/10.1097/RLI.0000000000000104 CrossRefPubMed

17.

Iima M, Le Bihan D, Okumura R et al (2011) Apparent diffusion coefficient as an MR imaging biomarker of low-risk ductal carcinoma in situ: a pilot study. Radiology 260:364–372. https://doi.org/10.1148/radiol.11101892 CrossRefPubMed

18.

Ding J-R, Wang D-N, Pan J-L (2016) Apparent diffusion coefficient value of diffusion-weighted imaging for differential diagnosis of ductal carcinoma in situ and infiltrating ductal carcinoma. J Cancer Res Ther 12:744–750. https://doi.org/10.4103/0973-1482.154093 CrossRefPubMed

19.

Pickles MD, Gibbs P, Lowry M, Turnbull LW (2006) Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer. Magn Reson Imaging 24:843–847. https://doi.org/10.1016/j.mri.2005.11.005 CrossRefPubMed

20.

Richard R, Thomassin I, Chapellier M et al (2013) Diffusion-weighted MRI in pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer. Eur Radiol 23:2420–2431. https://doi.org/10.1007/s00330-013-2850-x CrossRefPubMed

21.

Galbán CJ, Ma B, Malyarenko D et al (2015) Multi-site clinical evaluation of DW-MRI as a treatment response metric for breast cancer patients undergoing neoadjuvant chemotherapy. PLoS One 10:e0122151. https://doi.org/10.1371/journal.pone.0122151 CrossRefPubMedPubMedCentral

22.

Li X, Abramson RG, Arlinghaus LR et al (2015) Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer. Invest Radiol 50:195–204. https://doi.org/10.1097/RLI.0000000000000100 CrossRefPubMedPubMedCentral

23.

Leong KM, Lau P, Ramadan S (2015) Utilisation of MR spectroscopy and diffusion weighted imaging in predicting and monitoring of breast cancer response to chemotherapy. J Med Imaging Radiat Oncol 59:268–277. https://doi.org/10.1111/1754-9485.12310 CrossRefPubMed

24.

Partridge SC, Zhang Z, Newitt DC et al (2018) Diffusion-weighted MRI findings predict pathologic response in neoadjuvant treatment of breast cancer: the ACRIN 6698 multicenter trial. Radiology 289:618–627. https://doi.org/10.1148/radiol.2018180273 CrossRefPubMedPubMedCentral

25.

Newitt DC, Zhang Z, Gibbs JE et al (2019) Test-retest repeatability and reproducibility of ADC measures by breast DWI: results from the ACRIN 6698 trial. J Magn Reson Imaging 49:1617–1628. https://doi.org/10.1002/jmri.26539 CrossRefPubMed

26.

Le Bihan D (2013) Apparent diffusion coefficient and beyond: what diffusion MR imaging can tell us about tissue structure. Radiology 268:318–322. https://doi.org/10.1148/radiol.13130420 CrossRefPubMed

27.

D’Orsi CJ, Sickles EA, Mendelson EB, Morris EA (2013) ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. Reston, VA, American College of Radiology.

28.

Weinreb JC, Barentsz JO, Choyke PL et al (2016) PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. Eur Urol 69:16–40. https://doi.org/10.1016/j.eururo.2015.08.052 CrossRef

29.

Bickel H, Pinker K, Polanec S et al (2017) Diffusion-weighted imaging of breast lesions: Region-of-interest placement and different ADC parameters influence apparent diffusion coefficient values. Eur Radiol 27:1883–1892. https://doi.org/10.1007/s00330-016-4564-3 CrossRefPubMed

30.

Giannotti E, Waugh S, Priba L, Davisa Z, Crowe E, Vinnicombe S (2015) Assessment and quantification of sources of variability in breast apparent diffusion coefficient (ADC) measurements at diffusion weighted imaging. Eur J Radiol 84:1729–1736. https://doi.org/10.1016/j.ejrad.2015.05.032

31.

O’Flynn EAM, Morgan VA, Giles SL, deSouza NM (2012) Diffusion weighted imaging of the normal breast: reproducibility of apparent diffusion coefficient measurements and variation with menstrual cycle and menopausal status. Eur Radiol 22:1512–1518. https://doi.org/10.1007/s00330-012-2399-0 CrossRefPubMed

32.

Aliu SO, Jones EF, Azziz A et al (2014) Repeatability of quantitative MRI measurements in normal breast tissue. Transl Oncol 7:130–137CrossRef

33.

Spick C, Bickel H, Pinker K et al (2016) Diffusion-weighted MRI of breast lesions: a prospective clinical investigation of the quantitative imaging biomarker characteristics of reproducibility, repeatability, and diagnostic accuracy. NMR Biomed 29:1445–1453. https://doi.org/10.1002/nbm.3596 CrossRefPubMed

34.

Spick C, Pinker-Domenig K, Rudas M, Helbich TH, Baltzer PA (2014) MRI-only lesions: application of diffusion-weighted imaging obviates unnecessary MR-guided breast biopsies. Eur Radiol 24:1204–1210. https://doi.org/10.1007/s00330-014-3153-6

35.

Baltzer A, Dietzel M, Kaiser CG, Baltzer PA (2016) Combined reading of contrast enhanced and diffusion weighted magnetic resonance imaging by using a simple sum score. Eur Radiol 26:884–891. https://doi.org/10.1007/s00330-015-3886-x CrossRefPubMed

36.

Kessler LG, Barnhart HX, Buckler AJ et al (2015) The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat Methods Med Res 24:9–26. https://doi.org/10.1177/0962280214537333 CrossRefPubMed

37.

Winfield JM, Payne GS, Weller A, deSouza NM (2016) DCE-MRI, DW-MRI, and MRS in cancer: challenges and advantages of implementing qualitative and quantitative multi-parametric imaging in the clinic. Top Magn Reson Imaging 25:245–254. https://doi.org/10.1097/RMR.0000000000000103 CrossRefPubMedPubMedCentral

38.

Winfield JM, Tunariu N, Rata M et al (2017) Extracranial soft-tissue tumors: repeatability of apparent diffusion coefficient estimates from diffusion-weighted MR imaging. Radiology 284:88–99. https://doi.org/10.1148/radiol.2017161965 CrossRefPubMedPubMedCentral

39.

Barnes A, Alonzi R, Blackledge M et al (2018) UK quantitative WB-DWI technical workgroup: consensus meeting recommendations on optimisation, quality control, processing and analysis of quantitative whole-body diffusion-weighted imaging for cancer. Br J Radiol 91:20170577. https://doi.org/10.1259/bjr.20170577 CrossRefPubMed

40.

Zhang L, Tang M, Min Z, Lu J, Lei X, Zhang X (2016) Accuracy of combined dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted imaging for breast cancer detection: a meta-analysis. Acta Radiol 57:651–660. https://doi.org/10.1177/0284185115597265

41.

Mann RM, Kuhl CK, Kinkel K, Boetes C (2008) Breast MRI: guidelines from the European Society of Breast Imaging. Eur Radiol 18:1307–1318. https://doi.org/10.1007/s00330-008-0863-7 CrossRefPubMedPubMedCentral

42.

Sardanelli F, Boetes C, Borisch B et al (2010) Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group. Eur J Cancer 46:1296–1316. https://doi.org/10.1016/j.ejca.2010.02.015 CrossRefPubMed

43.

American College of Radiology (ACR) (2014) ACR practice parameter for the performance of contrast-enhanced magnetic resonance imaging (MRI) of the breast. Resolution 34. [Revised 2018]. Available at: https://www.acr.org/-/media/ACR/Files/Practice-Parameters/mr-contrastbreast.pdf. Accessed 25 Oct 2019

44.

ESUR guidelines on Contrast Media - ESUR 22ND European Symposium on Urogenital Radiology September 16-19 2015. http://www.esur-cm.org/index.php/en. Accessed 22 Sep 2018

45.

Benndorf M, Schelhorn J, Dietzel M, Kaiser WA, Baltzer PA (2012) Diffusion weighted imaging of liver lesions suspect for metastases: Apparent diffusion coefficient (ADC) values and lesion contrast are independent from Gd-EOB-DTPA administration. Eur J Radiol 81:e849–e853. https://doi.org/10.1016/j.ejrad.2012.03.027

46.

Newitt DC, Tan ET, Wilmes LJ et al (2015) Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the american college of radiology imaging network 6698 breast cancer trial. J Magn Reson Imaging 42:908–919. https://doi.org/10.1002/jmri.24883 CrossRefPubMedPubMedCentral

47.

Tan ET, Marinelli L, Slavens ZW, King KF, Hardy CJ (2013) Improved correction for gradient nonlinearity effects in diffusion-weighted imaging. J Magn Reson Imaging 38:448–453. https://doi.org/10.1002/jmri.23942

48.

Teruel JR, Fjøsne HE, Østlie A et al (2015) Inhomogeneous static magnetic field-induced distortion correction applied to diffusion weighted MRI of the breast at 3 T. Magn Reson Med 74:1138–1144. https://doi.org/10.1002/mrm.25489 CrossRefPubMed

49.

Arlinghaus LR, Welch EB, Chakravarthy AB et al (2011) Motion correction in diffusion-weighted MRI of the breast at 3 T. J Magn Reson Imaging 33:1063–1070. https://doi.org/10.1002/jmri.22562 CrossRefPubMedPubMedCentral

50.

Porter DA, Heidemann RM (2009) High resolution diffusion-weighted imaging using readout-segmented echo-planar imaging, parallel imaging and a two-dimensional navigator-based reacquisition. Magn Reson Med 62:468–475. https://doi.org/10.1002/mrm.22024 CrossRefPubMed

51.

Xing D, Papadakis NG, Huang CL, Lee VM, Carpenter TA, Hall LD (1997) Optimised diffusion-weighting for measurement of apparent diffusion coefficient (ADC) in human brain. Magn Reson Imaging 15:771–784

52.

Giannelli M, Toschi N (2016) On the use of trace-weighted images in body diffusional kurtosis imaging. Magn Reson Imaging 34:502–507. https://doi.org/10.1016/j.mri.2015.12.013 CrossRefPubMed

53.

Reeder SB, Wintersperger BJ, Dietrich O et al (2005) Practical approaches to the evaluation of signal-to-noise ratio performance with parallel imaging: application with cardiac imaging and a 32-channel cardiac coil. Magn Reson Med 54:748–754. https://doi.org/10.1002/mrm.20636 CrossRefPubMed

54.

Kellman P, McVeigh ER (2005) Image reconstruction in SNR units: a general method for SNR measurement. Magn Reson Med 54:1439–1447. https://doi.org/10.1002/mrm.20713 CrossRefPubMedPubMedCentral

55.

Malyarenko D, Galbán CJ, Londy FJ et al (2013) Multi-system repeatability and reproducibility of apparent diffusion coefficient measurement using an ice-water phantom. J Magn Reson Imaging 37:1238–1246. https://doi.org/10.1002/jmri.23825 CrossRefPubMed

56.

Belli G, Busoni S, Ciccarone A et al (2016) Quality assurance multicenter comparison of different MR scanners for quantitative diffusion-weighted imaging. J Magn Reson Imaging 43:213–219. https://doi.org/10.1002/jmri.24956 CrossRefPubMed

57.

Delakis I, Moore EM, Leach MO, De Wilde JP (2004) Developing a quality control protocol for diffusion imaging on a clinical MRI system. Phys Med Biol 49:1409–1422CrossRef

58.

Giannelli M, Sghedoni R, Iacconi C et al (2014) MR scanner systems should be adequately characterized in diffusion-MRI of the breast. PLoS One 9:e86280. https://doi.org/10.1371/journal.pone.0086280 CrossRefPubMedPubMedCentral

59.

Nogueira L, Brandão S, Nunes RG, Ferreira HA, Loureiro J, Ramos I (2015) Breast DWI at 3 T: influence of the fat-suppression technique on image quality and diagnostic performance. Clin Radiol 70:286–294. https://doi.org/10.1016/j.crad.2014.11.012

60.

Baron P, Dorrius MD, Kappert P, Oudkerk M, Sijens PE (2010) Diffusion-weighted imaging of normal fibroglandular breast tissue: influence of microperfusion and fat suppression technique on the apparent diffusion coefficient. NMR Biomed 23:399–405. https://doi.org/10.1002/nbm.1475

61.

Nogueira L, Brandão S, Matos E et al (2014) Diffusion-weighted breast imaging at 3 T: preliminary experience. Clin Radiol 69:378–384. https://doi.org/10.1016/j.crad.2013.11.005 CrossRefPubMed

62.

Iima M, Nobashi T, Imai H et al (2018) Effects of diffusion time on non-Gaussian diffusion and intravoxel incoherent motion (IVIM) MRI parameters in breast cancer and hepatocellular carcinoma xenograft models. Acta Radiol Open 7:2058460117751565. https://doi.org/10.1177/2058460117751565 CrossRefPubMedPubMedCentral

63.

Nogueira L, Brandão S, Matos E et al (2015) Region of interest demarcation for quantification of the apparent diffusion coefficient in breast lesions and its interobserver variability. Diagn Interv Radiol 21:123–127. https://doi.org/10.5152/dir.2014.14217 CrossRefPubMedPubMedCentral

64.

Arponent O, Sudah M, Masarwah A et al (2015) Diffusion-weighted imaging in 3.0 Tesla breast MRI: diagnostic performance and tumor characterization using small subregions vs. whole tumor regions of interest. PLoS One 10:e0138702. https://doi.org/10.1371/journal.pone.0138702 CrossRefPubMedPubMedCentral

65.

Baltzer PA, Dietzel M, Vag T et al (2009) Diffusion weighted imaging-useful in all kinds of lesions? A systematic review. Eur Radiol 19 (Suppl 4):S765–S974

66.

Hussein H, Chung C, Moshonov H, Miller N, Kulkarni SR, Scaranelo AM (2015) Evaluation of apparent diffusion coefficient to predict grade, microinvasion, and invasion in ductal carcinoma in situ of the breast. Acad Radiol 22:1483–1488. https://doi.org/10.1016/j.acra.2015.08.004

67.

Woodhams R, Kakita S, Hata H et al (2009) Diffusion-weighted imaging of mucinous carcinoma of the breast: evaluation of apparent diffusion coefficient and signal intensity in correlation with histologic findings. AJR Am J Roentgenol 193:260–266. https://doi.org/10.2214/AJR.08.1670 CrossRefPubMed

68.

Youk JH, Son EJ, Chung J, Kim JA, Kim EK (2012) Triple-negative invasive breast cancer on dynamic contrast-enhanced and diffusion-weighted MR imaging: comparison with other breast cancer subtypes. Eur Radiol 22:1724–1734. https://doi.org/10.1007/s00330-012-2425-2

69.

Keenan KE, Ainslie M, Barker AJ et al (2018) Quantitative magnetic resonance imaging phantoms: a review and the need for a system phantom. Magn Reson Med 79:48–61. https://doi.org/10.1002/mrm.26982 CrossRefPubMed

70.

Chenevert TL, Galbán CJ, Ivancevic MK et al (2011) Diffusion coefficient measurement using a temperature-controlled fluid for quality control in multicenter studies. J Magn Reson Imaging 34:983–987. https://doi.org/10.1002/jmri.22363 CrossRefPubMedPubMedCentral

71.

Newitt DC, Malyarenko D, Chenevert TL et al (2017) Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network. J Med Imaging (Bellingham) 5:011003. https://doi.org/10.1117/1.JMI.5.1.011003 CrossRef

72.

Keenan KE, Wilmes LJ, Aliu S et al (2016) Design of a breast phantom for quantitative MRI. J Magn Reson Imaging 44:610–619. https://doi.org/10.1002/jmri.25214 CrossRefPubMedPubMedCentral

73.

Nissan N, Furman-Haran E, Shapiro-Feinberg M, Grobgeld D, Degani H (2014) Diffusion-tensor MR imaging of the breast: hormonal regulation. Radiology 271:672–680. https://doi.org/10.1148/radiol.14132084

74.

Kim JY, Suh HB, Kang HJ et al (2016) Apparent diffusion coefficient of breast cancer and normal fibroglandular tissue in diffusion-weighted imaging: the effects of menstrual cycle and menopausal status. Breast Cancer Res Treat 157:31–40. https://doi.org/10.1007/s10549-016-3793-0 CrossRefPubMed

75.

Xie T, Zhao Q, Fu C et al (2019) Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging. Eur Radiol 29:2535–2544. https://doi.org/10.1007/s00330-018-5804-5 CrossRefPubMed

76.

Kim JY, Kim JJ, Lee JW et al (2019) Risk stratification of ductal carcinoma in situ using whole-lesion histogram analysis of the apparent diffusion coefficient. Eur Radiol 29:485–493. https://doi.org/10.1007/s00330-018-5666-x CrossRefPubMed

77.

Leithner D, Bernard-Davila B, Martinez DF et al (2019) Radiomic signatures derived from diffusion-weighted imaging for the assessment of breast cancer receptor status and molecular subtypes. Mol Imaging Biol. https://doi.org/10.1007/s11307-019-01383-w

78.

Partridge SC, Ziadloo A, Murthy R et al (2010) Diffusion tensor MRI: preliminary anisotropy measures and mapping of breast tumors. J Magn Reson Imaging 31:339–347. https://doi.org/10.1002/jmri.22045 CrossRefPubMed

79.

Baltzer PAT, Schäfer A, Dietzel M et al (2011) Diffusion tensor magnetic resonance imaging of the breast: a pilot study. Eur Radiol 21:1–10. https://doi.org/10.1007/s00330-010-1901-9 CrossRefPubMed

80.

Eyal E, Shapiro-Feinberg M, Furman-Haran E et al (2012) Parametric diffusion tensor imaging of the breast. Invest Radiol 47:284–291. https://doi.org/10.1097/RLI.0b013e3182438e5d CrossRefPubMed

81.

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:e801–e806. https://doi.org/10.1016/j.ejrad.2013.09.001 CrossRefPubMed

82.

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 45:660–672. https://doi.org/10.1002/jmri.25481 CrossRefPubMed

83.

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. https://doi.org/10.1002/jmri.25855

84.

Liu C, Liang C, Liu Z, Zhang S, Huang B (2013) Intravoxel incoherent motion (IVIM) in evaluation of breast lesions: comparison with conventional DWI. Eur J Radiol 82:e782–e789. https://doi.org/10.1016/j.ejrad.2013.08.006

85.

Bokacheva L, Kaplan JB, Giri DD et al (2014) Intravoxel incoherent motion diffusion-weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma. J Magn Reson Imaging 40:813–823. https://doi.org/10.1002/jmri.24462 CrossRefPubMed

86.

Lee YJ, Kim SH, Kang BJ et al (2017) Intravoxel incoherent motion (IVIM)-derived parameters in diffusion-weighted MRI: associations with prognostic factors in invasive ductal carcinoma. J Magn Reson Imaging 45:1394–1406. https://doi.org/10.1002/jmri.25514 CrossRefPubMed

87.

Cho GY, Moy L, Kim SG et al (2016) Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors. Eur Radiol 26:2547–2558. https://doi.org/10.1007/s00330-015-4087-3 CrossRefPubMed

88.

Cho GY, Moy L, Zhang JL et al (2015) Comparison of fitting methods and b-value sampling strategies for intravoxel incoherent motion in breast cancer. Magn Reson Med 74:1077–1085. https://doi.org/10.1002/mrm.25484 CrossRefPubMed

89.

Sigmund EE, Cho GY, Kim S et al (2011) Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer. Magn Reson Med 65:1437–1447. https://doi.org/10.1002/mrm.22740 CrossRefPubMedPubMedCentral

90.

Partridge SC, Nissan N, Rahbar H, Kitsch AE Sigmund EE (2017) Diffusion-weighted breast MRI: clinical applications and emerging techniques. J Magn Reson Imaging 45:337–355. https://doi.org/10.1002/jmri.25479

91.

Sun K, Chen X, Chai W et al (2015) Breast cancer: diffusion kurtosis MR imaging-diagnostic accuracy and correlation with clinical-pathologic factors. Radiology 277:46–55. https://doi.org/10.1148/radiol.15141625 CrossRefPubMed

92.

Suo S, Cheng F, Cao M et al (2017) Multiparametric diffusion-weighted imaging in breast lesions: association with pathologic diagnosis and prognostic factors. J Magn Reson Imaging 46:740–750. https://doi.org/10.1002/jmri.25612 CrossRefPubMed

93.

Wu D, Li G, Zhang J, Chang S, Hu J, Dai Y (2014) Characterization of breast tumors using diffusion kurtosis imaging (DKI). PLoS One 9:e113240. https://doi.org/10.1371/journal.pone.0113240

94.

Baxter GC, Graves MJ, Gilbert FJ, Patterson AJ (2019) A meta-analysis of the diagnostic performance of diffusion MRI for breast lesion characterization. Radiology 291:632–641. https://doi.org/10.1148/radiol.2019182510 CrossRefPubMed

Title: Diffusion-weighted imaging of the breast—a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group
Authors: Pascal Baltzer
Ritse M. Mann
Mami Iima
Eric E. Sigmund
Paola Clauser
Fiona J. Gilbert
Laura Martincich
Savannah C. Partridge
Andrew Patterson
Katja Pinker
Fabienne Thibault
Julia Camps-Herrero
Denis Le Bihan
On behalf of the EUSOBI international Breast Diffusion-Weighted Imaging working group
Publication date: 01-03-2020
Publisher: Springer Berlin Heidelberg
Keywords: Breast MRI
Biomarkers
Published in: European Radiology / Issue 3/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-019-06510-3

At a glance: The STEP trials

Springer Medicine

Diffusion-weighted imaging of the breast—a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 3/2020

Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models

Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer

Diagnostic performance of tomoelastography of the liver and spleen for staging hepatic fibrosis

New severity assessment in cystic fibrosis: signal intensity and lung volume compared to LCI and FEV1: preliminary results

Impact of the Kaiser score on clinical decision-making in BI-RADS 4 mammographic calcifications examined with breast MRI

High insertion of conjoint tendon is associated with inguinal-related groin pain: a MRI study