A meta-analysis study to comparing MR spectroscopy and Intravoxel Incoherent motion (DWI) in the differentiation of benign and malignant breast lesions
Main Article Content
Keywords
Diffusion-weighted imaging, Intravoxel incoherent motion (IVIM), magnetic resonance spectroscopy , Classification tumors of the breast
Abstract
Background: Advanced diffusion models are none-contrast MR imaging techniques for the assessment of breast lesions. The value of accuracy in detecting BC in the diagnosis of breast cancer has not been systematically evaluated. The study aimed, using meta-analysis approach, to examine the diagnostic ability of MRI spectroscopy and intravoxel incoherent motion (DWI) used to assess the differentiation between benign and malignant breast lesions.
Materials and Methods: The study searched PubMed, MEDLINE, Cochrane, Embase, Scopus, Google Scholar, was used to find recent original studies that assessed the use of Non-Gaussian DWI model (Intravoxel Incoherent Motion; perfusion fraction ‘f’ ; real diffusivity ‘D’ and pseudo-diffusivity ‘D*’) and spectroscopy (1H) for the detection of breast cancer. The standardized mean difference (SMD) , pooling the sensitivity, specificity, and area under the curve were used to organize and summarize the studies. The QUADAS-2 and QUIPS programs were used to assess the quality of the included studies.
Results: fifty studies were included, with IVIM and spectroscopy being the most investigated . The presence of significant heterogeneity (P˂0.05) was observed for all parameters. The results showed high differentiation ability found between malignant and benign cancer, where, malignant cancer has significantly lower D values (SMD=-1.54; P<0.0001) than benign cancer . While D*, f, and 1H values were higher than benign caner (SMD=0.08, 0.71, and 1.42; P=0.0001). The best diagnostic performance was shown for D (sensitivity=86%; specificity= 0.86; AUC≤0.92) and for 1H, f, and D* in breast tumors’ differential diagnosis (sensitivity=0.83, 0.78, 0.69; specificity= 0.76, 0.69, 0.63; AUC 0.73), respectively.
Conclusion: Our findings showed that their parameters play a potential role in differentiating breast tumours. Superior diagnostic accuracy, alongside, the high sensitivity and specificity for diffusion-weighted advanced imaging means that these approaches can be used as a suitable method in differentiating breast tumors.
References
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3. Boisserie-Lacroix M, Hurtevent-Labrot G, Ferron S, Lippa N, Bonnefoi H, Mac Grogan G. Correlation between imaging and molecular classification of breast cancers. Diagnostic and interventional imaging. 2013;94(11):1069-80.
4. Morris EA. Diagnostic breast MR imaging: current status and future directions. Radiologic Clinics of North America. 2007;45(5):863-80.
5. Plana MN, Carreira C, Muriel A, Chiva M, Abraira V, Emparanza JI, et al. Magnetic resonance imaging in the preoperative assessment of patients with primary breast cancer: systematic review of diagnostic accuracy and meta-analysis. European radiology. 2012;22(1):26-38.
6. Arian A, Dinas K, Pratilas GC, Alipour S. The breast imaging-reporting and data system (BI-RADS) Made Easy. Iranian Journal of Radiology. 2022;19(1).
7. Arian A, Delazar S, Aghasi M, Jahanbin B, Ahmadinejad N. Does MRI have added value in ultrasound-detected BIRADS-3 breast masses in candidates for assisted reproductive therapy? European Journal of Radiology Open. 2023;10:100474.
8. Brandão AC, Lehman CD, Partridge SC. Breast magnetic resonance imaging: diffusion-weighted imaging. Magnetic Resonance Imaging Clinics. 2013;21(2):321-36.
9. Arian A, Ahmadi E, Gity M, Setayeshpour B, Delazar S. Diagnostic value of T2 and diffusion-weighted imaging (DWI) in local staging of endometrial cancer. Journal of Medical Imaging and Radiation Sciences. 2023.
10. Basser PJ. Inferring microstructural features and the physiological state of tissues from diffusion‐weighted images. NMR in Biomedicine. 1995;8(7):333-44.
11. Padhani AR, Liu G, Mu-Koh D, Chenevert TL, Thoeny HC, Takahara T, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009;11(2):102-25.
12. Woodhams R, Ramadan S, Stanwell P, Sakamoto S, Hata H, Ozaki M, et al. Diffusion-weighted imaging of the breast: principles and clinical applications. Radiographics. 2011;31(4):1059-84.
13. 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. 2011;31(6):1773-91.
14. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology. 1986;161(2):401-7.
15. Koh D-M, Collins DJ, Orton MR. Intravoxel incoherent motion in body diffusion-weighted MRI: reality and challenges. American Journal of Roentgenology. 2011;196(6):1351-61.
16. Le Bihan D, Breton E, Lallemand D, Aubin M, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988;168(2):497-505.
17. Suo S, Lin N, Wang H, Zhang L, Wang R, Zhang S, et al. Intravoxel incoherent motion diffusion‐weighted MR imaging of breast cancer at 3.0 tesla: comparison of different curve‐fitting methods. Journal of Magnetic Resonance Imaging. 2015;42(2):362-70.
18. Kim Y, Ko K, Kim D, Min C, Kim SG, Joo J, et al. Intravoxel incoherent motion diffusion-weighted MR imaging of breast cancer: association with histopathological features and subtypes. The British journal of radiology. 2016;89(1063):20160140.
19. Sigmund E, Cho G, Kim S, Finn M, Moccaldi M, Jensen J, et al. Intravoxel incoherent motionimaging of tumor microenvironment in locally advanced breast cancer. Magnetic resonance in medicine. 2011;65(5):1437-47.
20. Sardanelli F, Fausto A, Di Leo G, De Nijs R, Vorbuchner M, Podo F. In vivo proton MR spectroscopy of the breast using the total choline peak integral as a marker of malignancy. American journal of roentgenology. 2009;192(6):1608-17.
21. Tozaki M, Fukuma E. 1H MR spectroscopy and diffusion-weighted imaging of the breast: are they useful tools for characterizing breast lesions before biopsy? American Journal of Roentgenology. 2009;193(3):840-9.
22. Sharma U, Jagannathan NR. In vivo MR spectroscopy for breast cancer diagnosis. BJR| Open. 2019;1(1):20180040.
23. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of internal medicine. 2011;155(8):529-36.
24. Hayden JA, van der Windt DA, Cartwright JL, Côté P, Bombardier C. Assessing bias in studies of prognostic factors. Annals of internal medicine. 2013;158(4):280-6.
25. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. Bmj. 1997;315(7109):629-34.
26. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Bmj. 2003;327(7414):557-60.
27. He M, Ruan H, Ma M, Zhang Z. Application of diffusion weighted imaging techniques for differentiating benign and malignant breast lesions. Frontiers in Oncology. 2021;11:694634.
28. Meng N, Wang XJ, Sun J, Huang L, Wang Z, Wang KY, et al. Comparative Study of Amide Proton Transfer‐Weighted Imaging and Intravoxel Incoherent Motion Imaging in Breast Cancer Diagnosis and Evaluation. Journal of Magnetic Resonance Imaging. 2020;52(4):1175-86.
29. Chan S-W, Chang Y-C, Huang P-W, Ouyang Y-C, Chang Y-T, Chang R-F, et al. Breast tumor detection and classification using intravoxel incoherent motion hyperspectral imaging techniques. BioMed research international. 2019;2019.
30. Song SE, Cho KR, Seo BK, Woo OH, Park KH, Son YH, et al. Intravoxel incoherent motion diffusion‐weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer‐aided diagnosis. Journal of Magnetic Resonance Imaging. 2019;49(1):118-30.
31. Zhao M, Fu K, Zhang L, Guo W, Wu Q, Bai X, et al. Intravoxel incoherent motion magnetic resonance imaging for breast cancer: a comparison with benign lesions and evaluation
of heterogeneity in different tumor regions with prognostic factors and molecular classification. Oncology Letters. 2018;16(4):5100-12.
32. Mao X, Zou X, Yu N, Jiang X, Du J. Quantitative evaluation of intravoxel incoherent motion diffusion-weighted imaging (IVIM) for differential diagnosis and grading prediction of benign and malignant breast lesions. Medicine. 2018;97(26).
33. Jiang L, Lu X, Hua B, Gao J, Zheng D, Zhou Y. Intravoxel incoherent motion diffusion-weighted imaging versus dynamic contrast-enhanced magnetic resonance imaging: comparison of the diagnostic performance of perfusion-related parameters in breast. Journal of Computer Assisted Tomography. 2018;42(1):6-11.
34. Iima M, Kataoka M, Kanao S, Onishi N, Kawai M, Ohashi A, et al. 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. 2018;287(2):432-41.
35. Suo S, Cheng F, Cao M, Kang J, Wang M, Hua J, et al. Multiparametric diffusion‐weighted imaging in breast lesions: Association with pathologic diagnosis and prognostic factors. Journal of Magnetic Resonance Imaging. 2017;46(3):740-50.
36. Lin N, Chen J, Hua J, Zhao J, Zhao J, Lu J. Intravoxel incoherent motion MR imaging in breast cancer: quantitative analysis for characterizing lesions. Int J Clin Exp Med. 2017;10(1):1705-14.
37. Kawashima H, Miyati T, Ohno N, Ohno M, Inokuchi M, Ikeda H, et al. Differentiation between luminal-A and luminal-B breast cancer using intravoxel incoherent motion and dynamic contrast-enhanced magnetic resonance imaging. Academic Radiology. 2017;24(12):1575-81.
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Jun 4, 2023
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Mohanad Ahmed Sahib, Arian Arvin, Raad Ajeel Bustan, & Nasrin Ahmadinejad. (2023). A meta-analysis study to comparing MR spectroscopy and Intravoxel Incoherent motion (DWI) in the differentiation of benign and malignant breast lesions. Journal of Population Therapeutics and Clinical Pharmacology, 30(13), 390–402. https://doi.org/10.47750/jptcp.2023.30.13.040
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