Diagnostic role of Dynamic Contrast-Enhanced Magnetic Resonance Imaging in differentiating Breast Lesions.

Main Article Content

Hussein Abed Dakhil
Ahmed Mohamedbaqer Easa
Ammar Yaser Hussein
Raad Ajeel Bustan
Hayder Suhail Najm

Keywords

DCE, MRI, breast cancer, differentiation, benign and malignant.

Abstract

Objective: this study aimed to assess the Diagnostic role of dynamic contrast-enhanced Perfusion weighted image (DCE-PWI) in the differentiation of benign from malignant breast lesions.


Patients and methods: The study comprised 32 women who had mammography and/or breast ultrasonography findings that were clinically questionable. All patients were fasting during the MRI test to avoid nausea or vomiting from the contrast medium.


Result: in our, study we observed the form of the dynamic curve (time and signal intensity curve) type I (persistent curve) was noted in 12 lesions (37.5%): 10 lesions were benign and 2 lesions were malignant; while type II (plateau curve) was noted in 8 lesions (25%): 3 lesions were benign and 5 lesions were malignant, and type III (washout curve) noted in 12 lesions (37.5%): 1 lesion was benign and 11 lesions were malignant.


Conclusion: the dynamic contrast-enhanced (DCE) magnetic resonance imaging perfusion technique play important role in Differentiate between benign and malignant tumours in breast cancer.

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References

1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49.
2. Berg WA, Zhang Z, Lehrer D, Jong RA, Pisano ED, Barr RG, et al. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. JAMA - J Am Med Assoc. 2012;307(13):1394–404.
3. Akram M, Iqbal M, Daniyal M, Khan AU. Awareness and current knowledge of breast cancer. Biol Res. 2017;50(1):1–23.
4. Labrèche F, Goldberg MS, Hashim D, Weiderpass E. Breast cancer. Occup Cancers. 2020;417–38.
5. Peters N, IH BR, NP Z, WP M, KF M, Peeters P. Meta-Analysis of MR Imaging in the Diagnosis of Breast Lesions 1 Purpose : Methods : Results : Conclusion : Radiology. 2008;246(1):116–24.
6. Menezes GLG, Knuttel FM, Stehouwer BL, Pijnappel RM, Van Den Bosch MAAJ. Magnetic resonance imaging in breast cancer: A literature review and future perspectives. World J Clin Oncol. 2014;5(2):61–70.
7. Salem DS, Kamal RM, Mansour SM, Salah LA, Wessam R. Breast imaging in the young: The role of magnetic resonance imaging in breast cancer screening, diagnosis and follow-up. J Thorac Dis. 2013;5(SUPPL.1).
8. Lee JS, Lee HY, Sung NS, Cheon KW, Moon JI, Lee SE, et al. Accuracy of physical examination, ultrasonography, and magnetic resonance imaging in predicting response to neo-adjuvant chemotherapy for breast cancer. 2016;125(11):55–9.
9. Selvi Radhakrishna, S. Agarwal1, Purvish M. Parikh2, K. Kaur3, Shikha Panwar4, Shelly Sharma5, Ashish Dey6 KKS, Madhavi Chandra5 SS. Role of magnetic resonance imaging in breast cancer management. South Asian J cancer. 2018;7(2):171–4.
10. Morrow M. Magnetic resonance imaging in the preoperative evaluation of breast cancer: Primum non nocere. J Am Coll Surg. 2004;198(2):240–1.
11. Huang W, Fisher PR, Dulaimy K, Tudorica LA, O’Hea B, Button TM. Detection of breast malignancy: Diagnostic MR protocol for improved specificity. Radiology. 2004;232(2):585–91.
12. Warren RML, Pointon L, Thompson D, Hoff R, Gilbert FJ, Padhani A, et al. Reading protocol for dynamic contrast-enhanced MR images of the breast: Sensitivity and specificity analysis. Radiology. 2005;236(3):779–88.
13. Nunes LW, Schnall M, Siegelman ES, Langlotz CP, Gorel S, Sullivan D, et al. Diagnostic performance characteristics of architectural features revealed by high spatial-resolution MR imaging of the breast. Am J Roentgenol. 1997;169(2):409–15.
14. Kuhl CK, Mielcareck P, Klaschik S, Leutner C, Wardelmann E, Gieseke J, et al. Dynamic breast MR imaging: Are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology. 1999;211(1):101–10.
15. Kul S, Cansu A, Alhan E, Dinc H, Gunes G, Reis A. Contribution of diffusion-weighted imaging to dynamic contrast-enhanced MRI in the characterization of breast tumors. Am J Roentgenol. 2011;196(1):210–7.
16. Sohns C, Scherrer M, Staab W, Obenauer S. Value of the BI-RADS classification in MR-Mammography for diagnosis of benign and malignant breast tumors. Eur Radiol. 2011;21(12):2475–83.
17. Bi-rads ACR, Mri B. ACR Bi-Rads® Atlas — Breast MRI. Am Coll Radiol [Internet]. 2013;125–43. Available from: https://www.acr.org/-/media/ACR/Files/RADS/BI-RADS/MRI-Reporting.pdf
18. Tozaki M, Fukuda K. High-spatial-resolution MRI of non-masslike breast lesions: Interpretation model based on BI-RADS MRI descriptors. Am J Roentgenol. 2006;187(2):330–7.
19. Newell D, Nie K, Chen JH, Hsu CC, Yu HJ, Nalcioglu O, et al. Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: Differences in lesions presenting as mass and non-mass-like enhancement. Eur Radiol. 2010;20(4):771–81.
20. Macura KJ, Ouwerkerk R, Jacobs MA, Bluemke DA. Patterns of enhancement on breast MR images: Interpretation and imaging pitfalls. Radiographics. 2006;26(6):1719–34.
21. Kim YR, Kim HS, Kim HW. Are irregular hypoechoic breast masses on ultrasound always malignancies?: A pictorial essay. Korean J Radiol. 2015;16(6):1266–75.
22. Article O. The Role of Dynamic Contrast Enhanced Magnetic Resonance Imaging in Differentiation of Soft Tissue Masses. Eur J Gen Med. 2016;13(1):37–44.
23. Schnall MD, Rosten S, Englander S, Orel SG, Nunes LW. A combined architectural and kinetic interpretation model for breast MR images. Acad Radiol. 2001;8(7):591–7.
24. Li T, Yu T, Li L, Lu L, Zhuo Y, Lian J, et al. Use of Diffusion Kurtosis Imaging and Quantitative Dynamic Contrast-Enhanced MRI for the Differentiation of Breast Tumors. 2018;
25. Hetta W. Role of diffusion weighted images combined with breast MRI in improving the detection and differentiation of breast lesions. Egypt J Radiol Nucl Med [Internet]. 2015;46(1):259–70. Available from: http://dx.doi.org/10.1016/j.ejrnm.2014.10.009
26. Cuenod CA, Balvay D. Perfusion and vascular permeability : Basic concepts and measurement in DCE-CT and DCE-MRI. Diagn Interv Imaging [Internet]. 2013;94(12):1187–204. Available
27. Dmitry Olegovich Bokov, Abduladheem Turki Jalil, Forat H. Alsultany, Mustafa Z. Mahmoud, Wanich Suksatan, Supat Chupradit, Maytham T. Qasim & Parvaneh Delir Kheirollahi Nezhad. Ir-decorated gallium nitride nanotubes as a chemical sensor for recognition of mesalamine drug: a DFT study, Molecular Simulation, 2022. DOI: 10.1080/08927022.2021.2025234
28. Ansari, M.J., Jasim, S.A., Taban, T.Z. et al. Anticancer Drug-Loading Capacity of Green Synthesized Porous Magnetic Iron Nanocarrier and Cytotoxic Effects Against Human Cancer Cell Line. J Clust Sci (2022). https://doi.org/10.1007/s10876-022-02235-4
29. Huldani Huldani, Saade Abdalkareem Jasim, Dmitry Olegovich Bokov, Walid Kamal Abdelbasset, Mohammed Nader Shalaby, Lakshmi Thangavelu, Ria Margiana, Maytham T. Qasim. Application of extracellular vesicles derived from mesenchymal stem cells as potential therapeutic tools in autoimmune and rheumatic diseases, International Immunopharmacology,Volume 106, 2022, 108634, ISSN 1567-5769, https://doi.org/10.1016/j.intimp.2022.108634.