[Home ] [Archive]    
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
IJRR Information::
For Authors::
For Reviewers::
News & Events::
Web Mail::
Search in website

Advanced Search
Receive site information
Enter your Email in the following box to receive the site news and information.
Hard Copy 2322-3243
Online 2345-4229
Online Submission
Now you can send your articles to IJRR office using the article submission system.



:: Volume 21, Issue 4 (10-2023) ::
Int J Radiat Res 2023, 21(4): 827-832 Back to browse issues page
Application of intravoxel incoherent motion diffusion-weighted magnetic resonance imaging on the diagnosis of breast cancer
S. Chen , D. Wang , P. Kong , H. Gu , Z. Yu
Department of breast surgery, The Second Hospital of Shandong University, Jinan, Shandong, 250033, China , yuzhigang@sdu.edu.cn
Abstract:   (459 Views)
Background: To explore the application of intravoxel incoherent motion (IVIM) parameters based on mono- and bi-exponential models for diagnosing breast cancer (BC). Materials and Methods: 43 patients underwent breast magnetic resonance (MR) scanned before operation and the images were transferred to AW Volume Share 5 post-processing workstation. The mono-exponential and bi-exponential model was used to measure the slow diffusion coefficient (DC) (D), fast DC (D*) and fraction of perfusion (f). And the association between different parameter values and BC grading was analyzed via Spearman’s rank correlation coefficient. Results: The D mono value of 13 patients with benign breast diseases was higher than that of 30 patients with BC (4.04 ± 0.23 vs 2.59 ± 0.64p<0.05) while there was no statistical significance on the value of D* mono, f mono, D bi, D* bi and f bi. Additionally, the parameter differences of D mono, D* mono, D bi and D* bi had statistical significance between the different BC grades. And the grades of BC had a negative correlation with D mono and D bi while were positively related to D* mono and D* bi. It was most closely related to D* bi than D bi. D mono value in ER-positive were higher than that in ER-negative group (3.09±0.37 vs 1.03±0.09, p=.0095). Conclusion: IVIM could be used to diagnose BC, predict histological grading and ER expression and provide valuable imaging for the clinical treatment and prognosis of BC patients.
Keywords: Breast cancer, magnetic resonance imaging, intravoxel incoherent motion, histological grading, diffusion-weighted imaging.
Full-Text [PDF 664 kb]   (274 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
1. 1. Trapani D, Ginsburg O, Fadelu T, et al. (2022) Global challenges and policy solutions in breast cancer control. Cancer Treat Rev, 104:102339. [DOI:10.1016/j.ctrv.2022.102339] [PMID]
2. Fan L, Strasser-Weippl K, Li JJ, et al. (2014) Breast cancer in China. Lancet Oncol, 15(7):e279-89. [DOI:10.1016/S1470-2045(13)70567-9]
3. Eshaghi M (2020) The effect of pain management on pain reduction in women with breast cancer. SRPH Journal of Medical Sciences, 2(2) :1-5. [DOI:10.29252/sjmshm.2.2.1]
4. Zhang M, Peng P, Gu K, et al. (2018) Time-varying effects of prognostic factors associated with long-term survival in breast cancer. Endocr Relat Cancer, 25(5): 509-21. [DOI:10.1530/ERC-17-0502] [PMID] []
5. Cang B, zhou W, Cai L (2011) Analysis of survival related factors in 174 breast cancer. Practical Oncology Journal, 25(4): 3.
6. Tagliafico AS, Piana M, Schenone D, et al. (2020) Overview of radiomics in breast cancer diagnosis and prognostication. Breast, 49: 74-80. [DOI:10.1016/j.breast.2019.10.018] [PMID] []
7. Fowler AM and Strigel RM (2022) Clinical advances in PET-MRI for breast cancer. Lancet Oncol, 23(1): e32-e43. [DOI:10.1016/S1470-2045(21)00577-5]
8. Liang J, Zeng S, Li Z, et al. (2020) Intravoxel incoherent motion diffusion-weighted imaging for quantitative differentiation of breast tumors: A meta-analysis. Front Oncol, 10: 585486. [DOI:10.3389/fonc.2020.585486] [PMID] []
9. Uslu H, Onal T, Tosun M, et al. (2021) Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with molecular subtypes and histological grades. Magn Reson Imaging, 78: 35-41. [DOI:10.1016/j.mri.2021.02.005] [PMID]
10. Guo Y, Cai YQ, Cai ZL, et al. (2002) Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging, 16(2): 172-8. [DOI:10.1002/jmri.10140] [PMID]
11. Sinha S, Lucas-Quesada FA, Sinha U, et al. (2002) In-vivo diffusion-weighted MRI of the breast: potential for lesion characterization. J Magn Reson Imaging, 15(6): 693-704. [DOI:10.1002/jmri.10116] [PMID]
12. Le Bihan D, Breton E, Lallemand D, et al. (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology, 168(2): 497-505. [DOI:10.1148/radiology.168.2.3393671] [PMID]
13. Le Bihan D and Turner R. (1992) The capillary network: a link between IVIM and classical perfusion. Magn Reson Med, 27(1): 171-8. [DOI:10.1002/mrm.1910270116] [PMID]
14. De Robertis R, Tinazzi Martini P, Demozzi E, et al. (2015) Diffusion-weighted imaging of pancreatic cancer. World J Radiol, 7(10): 319-28. [DOI:10.4329/wjr.v7.i10.319] [PMID] []
15. Fang S, Yang Y, Chen B, et al. (2022) DWI and IVIM Imaging in a Murine Model of Rhabdomyosarcoma: Correlations with Quantitative Histopathologic Features. J Magn Reson Imaging, 55(1): 225-33. [DOI:10.1002/jmri.27828] [PMID]
16. Zhang Y, Luo D, Guo W, et al. (2023) Utility of mono-exponential, bi-exponential, and stretched exponential signal models of intravoxel incoherent motion (IVIM) to predict prognosis and survival risk in laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) patients after chemoradiotherapy. Jpn J Radiol, 41: 712-722. [DOI:10.1007/s11604-023-01399-x] [PMID] []
17. Chen W and Zheng R (2015) Incidence, mortality and survival analysis of breast cancer in China. Chinese Journal of Clinical Oncology, 24: 668-674.
18. Bloom HJ and Richardson WW (1957) Histological grading and prognosis in breast cancer; a study of 1409 cases of which 359 have been followed for 15 years. Br J Cancer, 11(3): 359-77. [DOI:10.1038/bjc.1957.43] [PMID] []
19. Parham DM (1995) Mitotic activity and histological grading of breast cancer. Pathol Annu, 30( Pt 1):189-207.
20. Carter D. Interpretation of Breast Biopsies. 4th Edition, 2002, Lippincott Williams & Wilkins, USA.
21. Le Doussal V, Tubiana-Hulin M, Friedman S, et al. (1989) Prognostic value of histologic grade nuclear components of Scarff-Bloom-Richardson (SBR). An improved score modification based on a multivariate analysis of 1262 invasive ductal breast carcinomas. Cancer, 64(9): 1914-21. https://doi.org/10.1002/1097-0142(19891101)64:9<1914::AID-CNCR2820640926>3.0.CO;2-G [DOI:10.1002/1097-0142(19891101)64:93.0.CO;2-G] [PMID]
22. Elston CW (1995) Pathology of the breast. The Breast, 4(1): 74-5. [DOI:10.1016/0960-9776(95)90059-4]
23. Aberle DR, Chiles C, Gatsonis C, et al. (2005) Imaging and cancer: research strategy of the American College of Radiology Imaging Network. Radiology, 235(3): 741-51. [DOI:10.1148/radiol.2353041760] [PMID]
24. Leung JW (2005) Screening mammography reduces morbidity of breast cancer treatment. AJR Am J Roentgenol, 184(5): 1508-9. [DOI:10.2214/ajr.184.5.01841508] [PMID]
25. Hopton DS, Thorogood J, Clayden AD, MacKinnon D (1989) Histological grading of breast cancer; significance of grade on recurrence and mortality. Eur J Surg Oncol, 15(1): 25-31.
26. Freedman LS, Edwards DN, McConnell EM, Downham DY (1979) Histological grade and other prognostic factors in relation to survival of patients with breast cancer. Br J Cancer, 40(1): 44-55. [DOI:10.1038/bjc.1979.139] [PMID] []
27. Peters NH, Borel Rinkes IH, Zuithoff NP, et al. (2008) Meta-analysis of MR imaging in the diagnosis of breast lesions. Radiology, 246(1): 116-24. [DOI:10.1148/radiol.2461061298] [PMID]
28. Jones EF, Sinha SP, Newitt DC, et al. (2013) MRI enhancement in stromal tissue surrounding breast tumors: association with recurrence free survival following neoadjuvant chemotherapy. PLoS One, 8(5): e61969. [DOI:10.1371/journal.pone.0061969] [PMID] []
29. Costantini M, Belli P, Rinaldi P, et al. (2010) Diffusion-weighted imaging in breast cancer: relationship between apparent diffusion coefficient and tumour aggressiveness. Clin Radiol, 65(12): 1005-12. [DOI:10.1016/j.crad.2010.07.008] [PMID]
30. Qin Y, Wu F, Hu Q, et al. (2023) Histogram analysis of multi-model high-resolution diffusion-weighted MRI in breast cancer: correlations with molecular prognostic factors and subtypes. Front Oncol, 13: 1139189. [DOI:10.3389/fonc.2023.1139189] [PMID] []
Send email to the article author

Add your comments about this article
Your username or Email:


XML     Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Chen S, Wang D, Kong P, Gu H, Yu Z. Application of intravoxel incoherent motion diffusion-weighted magnetic resonance imaging on the diagnosis of breast cancer. Int J Radiat Res 2023; 21 (4) :827-832
URL: http://ijrr.com/article-1-5091-en.html

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 21, Issue 4 (10-2023) Back to browse issues page
International Journal of Radiation Research
Persian site map - English site map - Created in 0.05 seconds with 50 queries by YEKTAWEB 4642