The Role of Multidirectional Diffusion Weighted Imaging in the diagnosis of breast carcinoma in Magnetic Resonance Imaging DW-MRI in breast carcinoma diagnosis

Leman Gunbey Karabekmez (1)
(1) Department of Radiology Ankara Yildirim Beyazit University, Ankara, Turkey, Turkey

Abstract

Background: Magnetic resonance imaging (MRI) is increasingly used in breast imaging. Diffusion imaging (DWI) is used in conjunction with contrast enhanced series. There will be a signal difference between the stationary and moving water molecules in DWI, due to the fact that all molecules will receive a first gradient pulse and then another pulse at the 180 degree-reverse direction of the first one. The stationary molecules will have zero signal after the two of the pulses and show restriction (low signal). Whereas, the moving molecule will not be at the same location and escape from the 180 degree pulse and have an energy in the end of the gradients. Multidirectional Diffusion-Weighted Imaging (MDDWI) gives information signifying water’s capacity to move freely in a direction according to its physiological and pathological boundaries, which is referred to as fractional anisotropy (FA). This study aimed to determine the usefulness of FA maps in in differentiating benign and malignant breast lesions.


Methods: The patients had breast MRI including MDDWI series and went through pathological evaluation (Seventy-nine patients with 86 lesions) were included in the study. The FA values were measured in addition to the conventional Diffusion-ADC values. Also, diffusion restriction and pathology results were noted. The lesion FA and ADC values, diffusion assessment, and pathology results were compared using the Student t-test.


Results: The patients were between 23 and 76 years and mean age for benign lesions was 43.9 whereas, it was 50.4 for the malignant lesions. Fourty-five patients had benign lesions and 41 had malignant. Mean ADC values were significant between benign and malignant lesions (correspondingly; 1256.5x103 mm²/s. and 978.7x103 mm²/s.) The FASD value of each lesion was found to be significant for malignant lesions (100x103), especially those with restricted diffusion. In addition, for lesions with restricted diffusion, the maximum FA (75x103) and mean FA(200x103 ) values were found to be significant for malignancy. A cut-off point of 500×10–3 of FA max was found to be a value that could be used to increase the specificity of suspicious lesions with restricted diffusion


Conclusion: Restricted diffusion is used as a supporting finding for biopsy indication. But due to its lower specificity DWI cannot be very helpful in the aspect of increasing specificity of conventional breast MRI. In the search of finding a tool to increase the specificity of breast MRI, FA values seem to have the potential in differentiating benign lesions.

Full text article

Generated from XML file

References

Lehotska V, Rauova K, Vanovcanova L. MR-mammography - impact on disease extent determination and surgical treatment of invasive ductal and lobular breast cancers. Neoplasma. 2015;62(2):269-77. doi: 10.4149/neo_2015_032.

Tagliafico A, Rescinito G, Monetti F, Villa A, Chiesa F, Fisci E, et al. Diffusion tensor magnetic resonance imaging of the normal breast: reproducibility of DTI-derived fractional anisotropy and apparent diffusion coefficient at 3.0 T. La Radiologia medica. 2012;117(6):992-1003. doi: 10.1007/s11547-012-0831-9.

Baltzer PA, Schafer A, Dietzel M, Grassel D, Gajda M, Camara O, et al. Diffusion tensor magnetic resonance imaging of the breast: a pilot study. European radiology. 2011;21(1):1-10. doi: 10.1007/s00330-010-1901-9.

Partridge SC, DeMartini WB, Kurland BF, Eby PR, White SW, Lehman CD. Quantitative diffusion-weighted imaging as an adjunct to conventional breast MRI for improved positive predictive value. AJR American journal of roentgenology. 2009;193(6):1716-22. doi: 10.2214/AJR.08.2139.

Koremezli Keskin N, Balci P, Basara Akin I, Yavuz Gurkan E, Sevinc AI, Durak MG, et al. Detection of the differences in the apparent diffusion coefficient values in different histopathological types of malignant breast lesions and comparison of cellular region/ stroma ratio and histopathological results. Turk J Med Sci. 2018;48(4):817-25. doi: 10.3906/sag-1801-89.

Teruel JR, Goa PE, Sjobakk TE, Ostlie A, Fjosne HE, Bathen TF. Diffusion weighted imaging for the differentiation of breast tumors: From apparent diffusion coefficient to high order diffusion tensor imaging. Journal of magnetic resonance imaging:JMRI. 2016;43(5):1111-21. doi: 10.1002/jmri.25067.

Tsougos I, Svolos P, Kousi E, Athanassiou E, Theodorou K, Arvanitis D, et al. The contribution of diffusion tensor imaging and magnetic resonance spectroscopy for the differentiation of breast lesions at 3T. Acta radiologica. 2014;55(1):14-23. doi: 10.1177/0284185113492152.

Naranjo ID, Reymbaut A, Brynolfsson P, Lo Gullo R, Bryskhe K, Topgaard D, et al. Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study. Cancers (Basel). 2021;13(7). doi: 10.3390/cancers13071606.

Jiang R, Ma Z, Dong H, Sun S, Zeng X, Li X. Diffusion tensor imaging of breast lesions: evaluation of apparent diffusion coefficient and fractional anisotropy and tissue cellularity. The British journal of radiology. 2016;89(1064):20160076. doi: 10.1259/bjr.20160076.

Marino MA, Helbich T, Baltzer P, Pinker-Domenig K. Multiparametric MRI of the breast: A review. Journal of magnetic resonance imaging : JMRI. 2018;47(2):301-15. doi: 10.1002/jmri.25790.

Topgaard D. Multidimensional diffusion MRI. Journal of magnetic resonance. 2017;275:98-113. doi: 10.1016/j.jmr.2016.12.007.

Baxter GC, Graves MJ, Gilbert FJ, Patterson AJ. A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization. Radiology. 2019;291(3):632-41. doi: 10.1148/radiol.2019182510.

Luo J, Hippe DS, Rahbar H, Parsian S, Rendi MH, Partridge SC. Diffusion tensor imaging for characterizing tumor microstructure and improving diagnostic performance on breast MRI: a prospective observational study. Breast Cancer Res. 2019;21(1):102. doi: 10.1186/s13058-019-1183-3.

Malayeri AA, El Khouli RH, Zaheer A, Jacobs MA, Corona-Villalobos CP, Kamel IR, et al. Principles and applications of diffusion-weighted imaging in cancer detection, staging, and treatment follow-up. Radiographics : a review publication of the Radiological Society of North America, Inc. 2011;31(6):1773-91. doi: 10.1148/rg.316115515.

Afzali M, Pieciak T, Newman S, Garyfallidis E, Ozarslan E, Cheng H, et al. The sensitivity of diffusion MRI to microstructural properties and experimental factors. J Neurosci Methods. 2021;347:108951. doi: 10.1016/j.jneumeth.2020.108951.

Hagmann P, Jonasson L, Maeder P, Thiran JP, Wedeen VJ, Meuli R. Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics : a review publication of the Radiological Society of North America, Inc. 2006;26 Suppl 1:S205-23. doi: 10.1148/rg.26si065510.

Furman-Haran E, Grobgeld D, Nissan N, Shapiro-Feinberg M, Degani H. Can diffusion tensor anisotropy indices assist in breast cancer detection? Journal of magnetic resonance imaging : JMRI. 2016;44(6):1624-32. doi: 10.1002/jmri.25292.

Onaygil C, Kaya H, Ugurlu MU, Aribal E. Diagnostic performance of diffusion tensor imaging parameters in breast cancer and correlation with the prognostic factors. Journal of magnetic resonance imaging : JMRI. 2017;45(3):660-72. doi: 10.1002/jmri.25481.

Charles-Edwards EM, deSouza NM. Diffusion-weighted magnetic resonance imaging and its application to cancer. Cancer Imaging. 2006;6:135-43. doi: 10.1102/1470-7330.2006.0021.

Koh DM, Collins DJ. Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR American journal of roentgenology. 2007;188(6):1622-35. doi: 10.2214/AJR.06.1403.

Arponen O, Sudah M, Masarwah A, Taina M, Rautiainen S, Kononen M, et al. 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. 2015;10(10):e0138702. doi: 10.1371/journal.pone.0138702.

Boyd NF, Lockwood GA, Byng JW, Tritchler DL, Yaffe MJ. Mammographic densities and breast cancer risk. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 1998;7(12):1133-44.

Eyal E, Shapiro-Feinberg M, Furman-Haran E, Grobgeld D, Golan T, Itzchak Y, et al. Parametric diffusion tensor imaging of the breast. Investigative radiology. 2012;47(5):284-91. doi: 10.1097/RLI.0b013e3182438e5d.

Hardie AD, Egbert RE, Rissing MS. Improved differentiation between hepatic hemangioma and metastases on diffusion-weighted MRI by measurement of standard deviation of apparent diffusion coefficient. Clinical imaging. 2015;39(4):654-8. doi: 10.1016/j.clinimag.2015.04.001.

Menezes GL, Knuttel FM, Stehouwer BL, Pijnappel RM, van den Bosch MA. Magnetic resonance imaging in breast cancer: A literature review and future perspectives. World journal of clinical oncology. 2014;5(2):61-70. doi: 10.5306/wjco.v5.i2.61.

Stavros T. Breast Ultrasound. Lippincott Williams&Wilkins; 2004.

Authors

Leman Gunbey Karabekmez
lgkarabekmez@gmail.com (Primary Contact)
1.
Gunbey Karabekmez L. The Role of Multidirectional Diffusion Weighted Imaging in the diagnosis of breast carcinoma in Magnetic Resonance Imaging: DW-MRI in breast carcinoma diagnosis. Arch Breast Cancer [Internet]. 2022 Aug. 24 [cited 2024 Jul. 27];9(4):488-96. Available from: https://archbreastcancer.com/index.php/abc/article/view/603

Article Details