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Diagnostic Significance of Apparent Diffusion Coefficient Values with Diffusion Weighted MRI in Breast Cancer: a Meta-Analysis

  • Sun, Jiang-Hong (Department of Radiology, Harbin Medical University Cancer Hospital) ;
  • Jiang, Li (Department of Tumor, Harbin Medical University Cancer Hospital) ;
  • Guo, Fei (Department of Radiology, Harbin Medical University Cancer Hospital) ;
  • Zhang, Xiu-Shi (Department of Radiology, Harbin Medical University Cancer Hospital)
  • Published : 2014.10.23

Abstract

Aims: Apparent diffusion coefficient (ADC) values of nodes in diffusion-weighted imaging (DWI) are widely used in differentiating metastatic from non-metastatic lymph nodes. The purpose of this meta-analysis was to demonstrate whether DWI could contribute to the precise diagnosis of breast cancer (BC) with and without lymph node metastasis (LNM). Materials and Methods: English and Chinese electronic databases were searched for relevant studies followed by a comprehensive literature search. Two reviewers independently assessed the methodological quality of the included trials based on the quality assessment of diagnostic accuracy studies (QUADAS). Summary odds ratios (ORs) and corresponding 95% confidence intervals (95% CIs) were calculated. Results: Final analysis of 624 BC subjects (patients with LNM = 254, patients without LNM = 370) were incorporated into the current meta-analysis from 9 eligible cohort studies. Combined ORs of ADCs suggested that ADC values in BC patients without LNM were higher than in patients with LNM (OR=0.56, 95%CI: 0.11-1.01, p=0.015). Subgroup analysis stratified by country indicated a low ADC value in BC patients with LNM rather than those without LNM among Chinese (OR=1.27, 95%CI: 0.89-1.66, p<0.001), Italians (OR=0.75, 95%CI: 0.13-1.38, p=0.018), and Egyptians (OR=1.27, 95%CI: 0.71-1.84, p<0.001). The findings of subgroup analysis by MRI machine type revealed that ADC values from diffusion MRI may be potential diagnostic indicators for BC using Non-Philips 1.5T (OR=1.10, 95%CI: 0.84-1.36, p<0.001). Conclusions: The main findings of our meta-analysis demonstrated that increased signal intensity on DWI and decreased signals on ADC are helpful in diagnosis of BC patients with or without LNM. DWI could therefore be an important imaging investigation in patients suspected of BC.

Keywords

References

  1. Basser PJ and Jones DK (2002). Diffusion-tensor MRI: theory, experimental design and data analysis-a technical review. NMR Biomed, 15, 456-67. https://doi.org/10.1002/nbm.783
  2. Bokacheva L, Kaplan JB, Giri DD, et al (2013). 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, 813-23.
  3. Choi BB, Kim SH, Kang BJ, et al (2012). Diffusion-weighted imaging and FDG PET/CT: predicting the prognoses with apparent diffusion coefficient values and maximum standardized uptake values in patients with invasive ductal carcinoma. World J Surg Oncol, 10, 126. https://doi.org/10.1186/1477-7819-10-126
  4. Cosottini M, Giannelli M, Siciliano G, et al (2005). Diffusiontensor MR imaging of corticospinal tract in amyotrophic lateral sclerosis and progressive muscular atrophy. Radiology, 237, 258-64. https://doi.org/10.1148/radiol.2371041506
  5. Coughlin SS and Ekwueme DU (2009). Breast cancer as a global health concern. Cancer Epidemiol, 33, 315-8. https://doi.org/10.1016/j.canep.2009.10.003
  6. Downey K, Riches SF, Morgan VA, et al (2013). Relationship between imaging biomarkers of stage I cervical cancer and poor-prognosis histologic features: quantitative histogram analysis of diffusion-weighted MR images. Am J Roentgenol, 200, 314-20. https://doi.org/10.2214/AJR.12.9545
  7. Fornasa F, Nesoti MV, Bovo C and Bonavina MG (2012). Diffusion-weighted magnetic resonance imaging in the characterization of axillary lymph nodes in patients with BC. J Magn Reson Imaging, 36, 858-64. https://doi.org/10.1002/jmri.23706
  8. Gibson LJ, Hery C, Mitton N, et al (2010). Risk factors for BC among Filipino women in Manila. Int J Cancer, 126, 515-21. https://doi.org/10.1002/ijc.24769
  9. Hamstra DA, Rehemtulla A and Ross BD (2007). Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology. J Clin Oncol, 25, 4104-9. https://doi.org/10.1200/JCO.2007.11.9610
  10. Han X, Y. Dong, J. J. Xiu, et al (2014) Diffusion-weighted imaging for the left hepatic lobe has higher diagnostic accuracy for malignant focal liver lesions. Asian Pac J Cancer Prev, 15, 6155-60. https://doi.org/10.7314/APJCP.2014.15.15.6155
  11. Inoue K, Kozawa E, Mizukoshi W, et al (2011). Usefulness of diffusion-weighted imaging of breast tumors: quantitative and visual assessment. Jpn J Radiol, 29, 429-36. https://doi.org/10.1007/s11604-011-0575-9
  12. Jackson D, White IR, Riley RD (2012). Quantifying the impact of between-study heterogeneity in multivariate meta-analyses. Stat Med, 31, 3805-20. https://doi.org/10.1002/sim.5453
  13. Jeh SK, Kim SH, Kim HS, et al (2011). Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma. J Magn Reson Imaging, 33, 102-9. https://doi.org/10.1002/jmri.22400
  14. Kamitani T, Matsuo Y, Yabuuchi H, et al (2013). Correlations between apparent diffusion coefficient values and prognostic factors of BC. Magn Reson Med Sci, 12, 193-9. https://doi.org/10.2463/mrms.2012-0095
  15. Kim SH, Cha ES, Kim HS, et al (2009). Diffusion-weighted imaging of BC: correlation of the apparent diffusion coefficient value with prognostic factors. J Magn Reson Imaging, 30, 615-20. https://doi.org/10.1002/jmri.21884
  16. Koh DM, Takahara T, Imai Y, Collins DJ (2007). Practical aspects of assessing tumors using clinical diffusion-weighted imaging in the body. Magn Reson Med Sci, 6, 211-24. https://doi.org/10.2463/mrms.6.211
  17. Le Bihan D, Mangin JF, Poupon C, et al (2001). Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging, 13, 534-46. https://doi.org/10.1002/jmri.1076
  18. Lehman CD (2012). Diffusion weighted imaging (DWI) of the breast: ready for clinical practice? Eur J Radiol, 81, 80-1. https://doi.org/10.1016/S0720-048X(12)70032-3
  19. Luo N, Su D, Jin G, et al (2013). Apparent diffusion coefficient ratio between axillary lymph node with primary tumor to detect nodal metastasis in BC patients. J Magn Reson Imaging, 38, 824-8. https://doi.org/10.1002/jmri.24031
  20. Luo YB, Su DK, Liu LD, et al (2012). Evaluation of axillary lymph node metastases using diffusion-weighted imaging. Chin J Oncol Prev Treatment, 4, 194-6.
  21. McCullough ML, Stevens VL, Patel R, et al (2009). Serum 25-hydroxyvitamin D concentrations and postmenopausal BC risk: a nested case control study in the Cancer Prevention Study-II Nutrition Cohort. Breast Cancer Res, 11, 64. https://doi.org/10.1186/bcr2356
  22. Nakai G, Matsuki M, Harada T, et al (2011). Evaluation of axillary lymph nodes by diffusion-weighted MRI using ultrasmall superparamagnetic iron oxide in patients with BC: initial clinical experience. J Magn Reson Imaging, 34, 557-62. https://doi.org/10.1002/jmri.22651
  23. Nakajo M, Kajiya Y, Kaneko T, et al (2010). FDG PET/CT and diffusion-weighted imaging for BC: prognostic value of maximum standardized uptake values and apparent diffusion coefficient values of the primary lesion. Eur J Nucl Med Mol Imaging, 37, 2011-20. https://doi.org/10.1007/s00259-010-1529-7
  24. Padhani AR, Liu G, Koh DM, et al (2009). Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia, 11, 102-25. https://doi.org/10.1593/neo.81328
  25. Park SH, Moon WK, Cho N, et al (2010). Diffusion-weighted MR imaging: pretreatment prediction of response to neoadjuvant chemotherapy in patients with BC. Radiology, 257, 56-63. https://doi.org/10.1148/radiol.10092021
  26. Parsian S, Rahbar H, Allison KH, et al (2012). Nonmalignant breast lesions: ADCs of benign and high-risk subtypes assessed as false-positive at dynamic enhanced MR imaging. Radiology, 265, 696-706. https://doi.org/10.1148/radiol.12112672
  27. Pediconi F, Napoli A, Di Mare L, et al (2012). MRgFUS: from diagnosis to therapy. Eur J Radiol, 81, 118-20. https://doi.org/10.1016/j.ejrad.2011.05.003
  28. Peters JL, Sutton AJ, Jones DR, et al (2006). Comparison of two methods to detect publication bias in meta-analysis. JAMA, 295, 676-80. https://doi.org/10.1001/jama.295.6.676
  29. Razek AA, Gaballa G, Denewer A, Nada N (2010). Invasive ductal carcinoma: correlation of apparent diffusion coefficient value with pathological prognostic factors. NMR Biomed, 23, 619-23. https://doi.org/10.1002/nbm.1503
  30. Siegel R, Naishadham D and Jemal A (2013). Cancer statistics, 2013. CA Cancer J Clin, 63, 11-30. https://doi.org/10.3322/caac.21166
  31. Usuda K, M. Sagawa, N. Motomo, et al (2014a) Recurrence and metastasis of lung cancer demonstrate decreased diffusion on diffusion-weighted magnetic resonance imaging. Asian Pac J Cancer Prev, 15, 6843-8. https://doi.org/10.7314/APJCP.2014.15.16.6843
  32. Usuda K, M. Sagawa, N. Motono, et al (2014b) Diagnostic performance of diffusion weighted imaging of malignant and benign pulmonary nodules and masses: comparison with positron emission tomography. Asian Pac J Cancer Prev, 15, 4629-35. https://doi.org/10.7314/APJCP.2014.15.11.4629
  33. Whiting PF, Weswood ME, Rutjes AW, et al (2006). Evaluation of QUADAS, a tool for the quality assessment of diagnostic accuracy studies. BMC Med Res Methodol, 6, 9. https://doi.org/10.1186/1471-2288-6-9
  34. Woodhams R, Matsunaga K, Iwabuchi K, et al (2005). Diffusionweighted 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, 644-9. https://doi.org/10.1097/01.rct.0000171913.74086.1b
  35. Woodhams R, Ramadan S, Stanwell P, et al (2011). Diffusionweighted imaging of the breast: principles and clinical applications. Radiographics, 31, 1059-84. https://doi.org/10.1148/rg.314105160
  36. Wu SG, He ZY, Li Q, et al (2013). Prognostic value of metastatic axillary lymph node ratio for Chinese BC patients. PLoS One, 8, 61410. https://doi.org/10.1371/journal.pone.0061410
  37. Zintzaras E and Ioannidis JP (2005). HEGESMA: genome search meta-analysis and heterogeneity testing. Bioinformatics, 21, 3672-3. https://doi.org/10.1093/bioinformatics/bti536

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