Browse > Article
http://dx.doi.org/10.7314/APJCP.2014.15.19.8271

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)
Publication Information
Asian Pacific Journal of Cancer Prevention / v.15, no.19, 2014 , pp. 8271-8277 More about this Journal
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
Diffusion weighted MRI; apparent diffusion coefficient; breast cancer; meta-analysis;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 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.   DOI
2 Basser PJ and Jones DK (2002). Diffusion-tensor MRI: theory, experimental design and data analysis-a technical review. NMR Biomed, 15, 456-67.   DOI   ScienceOn
3 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.
4 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.   DOI
5 Coughlin SS and Ekwueme DU (2009). Breast cancer as a global health concern. Cancer Epidemiol, 33, 315-8.   DOI   ScienceOn
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.   DOI
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.   DOI
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.   DOI
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.   DOI   ScienceOn
10 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.   DOI   ScienceOn
11 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.   DOI
12 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.   DOI
13 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.   DOI
14 Jackson D, White IR, Riley RD (2012). Quantifying the impact of between-study heterogeneity in multivariate meta-analyses. Stat Med, 31, 3805-20.   DOI   ScienceOn
15 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.   DOI
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.   DOI   ScienceOn
17 Le Bihan D, Mangin JF, Poupon C, et al (2001). Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging, 13, 534-46.   DOI   ScienceOn
18 Lehman CD (2012). Diffusion weighted imaging (DWI) of the breast: ready for clinical practice? Eur J Radiol, 81, 80-1.   DOI
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.   DOI
20 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.   DOI
21 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.   DOI
22 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.
23 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.   DOI
24 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.   DOI
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.   DOI   ScienceOn
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.   DOI
27 Pediconi F, Napoli A, Di Mare L, et al (2012). MRgFUS: from diagnosis to therapy. Eur J Radiol, 81, 118-20.   DOI
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.   DOI   ScienceOn
29 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.   DOI
30 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.   DOI
31 Siegel R, Naishadham D and Jemal A (2013). Cancer statistics, 2013. CA Cancer J Clin, 63, 11-30.   DOI   ScienceOn
32 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.   DOI
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.   DOI   ScienceOn
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.   DOI   ScienceOn
35 Woodhams R, Ramadan S, Stanwell P, et al (2011). Diffusionweighted imaging of the breast: principles and clinical applications. Radiographics, 31, 1059-84.   DOI   ScienceOn
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.   DOI
37 Zintzaras E and Ioannidis JP (2005). HEGESMA: genome search meta-analysis and heterogeneity testing. Bioinformatics, 21, 3672-3.   DOI   ScienceOn