Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 

 Table of Contents  
Year : 2016  |  Volume : 12  |  Issue : 1  |  Page : 36-42

Apparent diffusion coefficient value measurements with diffusion magnetic resonance imaging correlated with the expression levels of estrogen and progesterone receptor in breast cancer: A meta-analysis

The Second Laboratory of Cancer Research Institute, The First Hospital of Medical University, Shenyang, People’s Republic of China

Date of Web Publication13-Apr-2016

Correspondence Address:
Ping Ma
The Second Laboratory of Cancer Research Institute, The First Hospital of China Medical University, Nanjing North Street, Heping District, No. 155, Shenyang 110001
People’s Republic of China
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0973-1482.150418

Rights and Permissions
 > Abstract 

Aim: Apparent diffusion coefficient (ADC) value measurement of nodes in diffusion-weighted images (DWI) has been widely used for diagnosis and differential diagnosis of human tumors. We evaluated whether the ADC provided by DWI varies according to the state of estrogen receptors (ER) and progesterone receptors (PR) in breast cancer (BC).
Materials and Methods: English and Chinese electronic databases were searched for potentially relevant studies followed by a comprehensive literature search. Summary standardized mean difference (SMD) with 95% confidence interval (CI) was used for ER and PR category of ADC value with the use of the Z test.
Results: Final analysis from 6 eligible cohort studies between 2010 and 2012 were selected for inclusion. Overall estimates revealed that the mean ADC of ER-positive or PR-positive cancer was significantly lower than that of ER-negative or PR-negative breast cancer (ER- VS. ER+: SMD = 0.64, 95% CI: 0.02–1.27, P = 0.042; PR- VS. PR+: SMD = 0.45, 95% CI: 0.04–0.86, P = 0.032). Country-stratified analysis indicated the mean ADC of ER-positive cancer was significantly lower in China (SMD = 3.92, 95% CI: 2.22–5.62, P < 0.001). Further subgroup analysis by MRI machine type implied that ADC values may be a potential diagnostic indicator for PR levels in breast cancer only when using Non-Phillips 1.5T scanner (SMD = 0.88, 95% CI: 0.64–1.12, P < 0.001).
Conclusion: ADC values vary significantly according to the ER and PR levels in BC patients, indicating that cancer heterogeneity affects imaging parameters. DWI could therefore be critical for clinical applications of ADC values.

Keywords: Apparent diffusion coefficient, breast cancer, diffusion weighted MRI, estrogen receptor, meta-analysis, progesterone receptor

How to cite this article:
Meng L, Ma P. Apparent diffusion coefficient value measurements with diffusion magnetic resonance imaging correlated with the expression levels of estrogen and progesterone receptor in breast cancer: A meta-analysis. J Can Res Ther 2016;12:36-42

How to cite this URL:
Meng L, Ma P. Apparent diffusion coefficient value measurements with diffusion magnetic resonance imaging correlated with the expression levels of estrogen and progesterone receptor in breast cancer: A meta-analysis. J Can Res Ther [serial online] 2016 [cited 2021 Jan 20];12:36-42. Available from: https://www.cancerjournal.net/text.asp?2016/12/1/36/150418

 > Introduction Top

Breast cancer (BC) remains the second leading cause of cancer-related death and makes up the largest number of new cancer cases in women worldwide.[1] The chances of suffering from BC for women are high, approximately 12.5%. the rate of BC incidence in Malaysia may be 1 in 20, making it the most prevalent cancer type in the country.[2],[3] The diagnosis rate of BC is more serious in developed countries than in developing countries, and the onset rate of BC in the US may account for 30% of all other cancer cases among women.[4] An estimated 70% of patients with BC in India are diagnosed at advanced stages due to axillary lymph metastasis. This has a great impact on the postoperative prognosis and survival rate which depends on cancer size, stage, type and stage of detection.[5] Another big issue for BC patients is that various complications, especially lymphedema, disturb them for months or years after radiotherapy for the treatment of BC.[6] It is well established that the risk factors for BC include age, having given birth or not, early menarche, tobacco smoking and alcohol consumption.[7] In addition, a large amounts of studies have investigated the possibility that viral infections may result in the development of BC, such as Epstein-Barr virus, mouse mammary tumor virus, human papillomavirus and human cytomegalovirus.[8] Recently, multi observational studies have illustrated that the level of hormone receptor (HR) expression, positive or negative, especially estrogen receptor (ER) and progesterone receptor (PR), is associated with the stage and subtype of BC and prognosis.[9],[10] Diffusion-weighted imaging (DWI), a breakthrough magnetic resonance imaging system characterized by noninvasiveness and accuracy, is currently used to detect the random motion of water protons due to thermal power, known as Brown motion.[11] Because it uses the diffusion property of water molecules among tissues, DWI calculates a quantitative parameter, known as the apparent diffusion coefficient (ADC) value, which determines cellularity.[12] Moreover, having the ability to explore the mobility of water within cells and offer morphological images, DWI may clearly reflect data on cell organization, cell density and microstructure, like cancer cells management, based on ADC values.[13] Calculating ADC values of malignant tumors with precancerous lesions by DWI shows low values due to the reduction of molecular diffusion space in water. This suggests that DWI can be utilized to differentiate between malignant and benign tumors and to evaluate cancer's response to therapy.[14] It is well established that ADC is highly sensitive to subtle changes of tumor cellularity. Thus, DWI is applied to all kinds of cancers, such as brain, liver, kidney, cervix and prostate.[15] In addition, multiple studies have displayed that DWI can be regarded as the imaging standard for evaluating patients diagnosed with breast cancer.[16],[17] It has been observed that ADC values measured by DWI may be correlated with breast cancer tumor grade and size. Lower ADC values represent higher grade, larger size and the invasive metastasis of axillary lymph nodes.[18] Furthermore, increasing evidence from a large number of studies has proven that ER is expressed negatively in high-grade breast cancers, and ADC values are relatively lower.[11] Accordingly, more and more studies are showing the possibility that ADC values may be negative correlated with ER and PR expression.[19],[20] However, several reports presented inconsistent conclusions.[14],[21] Due to these controversial results, we wanted to perform an updated meta-analysis based on previous studies to explore the relationship of ADC values derived from DWI and the levels of ER and PR.

 > Materials and Methods Top

Data sources and keywords

The PubMed, Embase, Web of Science, Cochrane Library, CISCOM, CINAHL, Google Scholar, China BioMedicine (CBM) and China National Knowledge Infrastructure (CNKI) searches were conducted, and terms searched for in the titles and abstracts of the articles were: (”Diffusion Magnetic Resonance Imaging”) and (”breast neoplasms”) combined with the selected common keywords (or “Diffusion MRI” or “Diffusion Weighted MRI” or “DWI” or “diffusion-weighted magnetic resonance imaging” or “MRI-DWI” or “diffusion-weighted imaging” or “diffusion-weighted-MRI”) and (”breast cancer” or “breast tumor” or “breast carcinoma” or “mammary cancer” or “mammary carcinoma” or “mammary neoplasms” or “mastocarcinoma” or “mastocarcinoma”). We limited our search to publications in English and Chinese, and data published no earlier than June 30, 2009. Further scanning of the bibliographies of relevant articles was carried out manually to search for other relevant studies. If the enrolled papers were found to supply unclear data in their original publications, the first authors would be contacted and asked for clarifications.

Selection criteria

Inclusion criteria were as follows: (1) Papers had to include patients with BC and pathology had to be varied within the dataset; (2) papers had to be human-associated clinical cohort studies or diagnostic tests focusing on the diagnosis of ER and PR alternation in BC patients by diffusion MRI; (3) papers had to provide available data for histopathologic analysis whether performed at surgery or biopsy; (4) included BC patients had to be confirmed by histological examination, and follow-up by ultrasound, mammography, or MRI in accordance with standard operating procedure for these tests [22]; (5) papers had to supply the sample number and sufficient information about b_value, ADC value and the four-fold (2 × 2) tables; (6) papers had to have a sample size greater than 35. When the same data or subsets of data were found in more than one paper, the studies that were the most recently published or the most detailed were selected. At the same time, if a study presented results of various MRI series, like the contrast-enhanced imaging, and DWI in combination and the DWI results could not be assessed individually, it would also be excluded.

Data extraction

Two investigators selected eligible studies, extracted relevant data from the retrieved papers independently, examined results from each article cooperatively and arrived at a consensus on all the items through discussion and reexamination. A third reviewer would be consulted to resolve any disagreements between the two investigators. The majority opinion was chosen for subsequent analysis. The following relevant data was extracted from eligible studies: Surname of first author, year of publication, source of publication, study type, study design, sample size, age, ethnicity and country of origin, type of MRI machine, “gold standard”, contrast agent, diagnostic accuracy, b_value and ADC value in patients with IDC or DCIS. All authors approved the final decision on what studies to enroll.

Quality assessment

The quality of included studies was assessed independently by two authors based on the quality assessment of studies of diagnostic accuracy studies (QUADAS), a quality assessment tool specifically established for systemic analysis of diagnostic accuracy.[23] The QUADAS criteria included 14 assessment items. Each of these items was scored as “yes” (2), “no” (0), or “unclear” (1). QUADAS score ranged from 0 to 28; a score ≥22 indicates good quality.

Statistical analysis

The summary standardized mean difference (SMD) with 95% confidence interval (CI) was used for the ER and PR categories of ADC values using the Z test. The Chi-square value test, Q statistic and inconsistency index (I-squared, I2) was applied to estimate heterogeneity of individual studies and between-study heterogeneity producing apooled estimate.[24],[25] A random-effect model was applied when heterogeneity existed among studies, while fixed-effects model was applied when there was no statistical heterogeneity. The subgroup meta-analyses were also conducted by ethnicity and MRI machine type to explore potential effect modification. A one-way sensitivity analysis was conducted, specifically, when suspicious studies were excluded the pooled estimates were reappraised, and the reappraised results were compared with the original results to judge whether or not one or more studies would affect the overall results. In this way, we were able to analyze the stability and reliability of our meta-analysis A funnel plot was constructed to evaluate any publication bias which might affect the validity of the estimates. The symmetry of the funnel plot was evaluated by Egger's linear regression test.[25] All tests were two-sided and a P < 0.05 was regarded as statistically significant. STATA software, version 12.0 (Stata Corp, College Station, TX, USA) was used for statistical analysis.

 > Results Top

Included studies

Our present meta-analysis included a total of 6 cohort studies that provided information on the correlation between diffusion MRI and diagnosis of ER and PR changes in BC between 2010 and 2012.[11],[14],[19],[20],[21],[26] Demographic information on adult subjects with ER or PR and other characteristics, along with the methodological quality of the extracted studies involved in this analysis are presented in [Table 1]. Five studies were conducted in populations of Asian descent and one in a population of Caucasian descent, including 767 subjects total. The countries where the studies were performed were Korea (N = 3), Japan (N = 1), China (N = 1), and Italy (N = 1). MRI machine brand included in this meta-analysis are GE 1.5T (N = 1), Siemens 3.0T (N = 1), Siemens 1.5T (N = 1), Philips 1.5T (N = 3).A flow chart showing the study selection process is displayed in [Figure 1]. Initially, a total of 603 papers were selected from the 9 databases through screening the title and key words. After excluding duplicates (N = 2), letters, reviews or meta-analyses (N = 123), non-human studies (N = 137), and studies not related to research topics (N = 141), the remaining studies (N = 199) were reviewed. Subsequently, an additional 190 studies were excluded because they were not case-control or cohort studies (N = 52), not relevant to diffusionMRI (N = 67), or not relevant to IDC or DCIS (N = 71). After the remaining 9 studies were further reviewed, 6 papers were enrolled for analysis. During the final selection process, the major reason for abandoning a paper was that it did not supply enough information (N = 2). All quality scores of the included studies were higher than 21 (high quality). From 2001 to 2014, the number of articles selected from the searched electronic databases is shown in [Figure 2].
Table 1: Effect of different hospital grade and departments on misdiagnosis

Click here to view
Figure 1: Flow chart showing study selection procedure. Six cohort studies were included in this meta-analysis

Click here to view
Figure 2: The distribution of the amount of topic-related literature in electronic databases over the last decade

Click here to view

Diagnostic value of diffusion MRI in IDC and DCIS

As shown in [Figure 3], the result of our meta-analysis was that the mean ADC of ER-positive or PR-positive cancer was significantly lower than that of ER-negative or PR -negative BC (ER- VS. ER+: SMD = 0.64, 95% CI: 0.02-1.27, P = 0.042; PR- VS. PR+: SMD = 0.45, 95% CI: 0.04-0.86, P = 0.032). Subgroup analysis based on country suggested that the mean ADC of ER-positive cancer was significantly lower among Chinese subjects (SMD = 3.92, 95% CI: 2.22-5.62, P < 0.001), but the same was not detected in Italian or Korean study participants (all P > 0.05 as shown in [Figure 4]). There appeared to be no significant difference among those countries included in the experiments (Korea and Japan) with regards to the PR-positive or PR-negative factor (all P > 0.05). Further subgroup analysis based on MRI machine brand implied that ADC values from diffusion MRI may only be a potential diagnostic indicator for PR level in BC when using the brands other than Phillips 1.5 T (SMD = 0.88, 95% CI: 0.64-1.12, P < 0.001).
Figure 3: Forest plots on the relationship between ADC values and expression levels of estrogen receptors and progesterone receptors in breast cancer patients

Click here to view
Figure 4: Subgroup analyses by MRI machine type and country on the relationship between ADC values and expression levels of estrogen receptors and progesterone receptors in breast cancer patients

Click here to view

Sensitivity analysis and publication bias

Sensitivity analysis results of this meta-analysis indicated that the overall statistical significance does not change when any single study is omitted. Therefore, the current meta-analysis data is relatively stable and credible [Figure 5]. The graphical funnel plots of ER- VS. ER + appear to be un-symmetrical. Egger's test showed a possibility for the existence of publication bias (t = 6.67, P = 0.022). In the PR- VS. PR+ comparison as well, there appeared to be a potential for publication bias since the graphical funnel plots were significantly symmetrical (t = -1.35, P = 0.309) [Figure 6].
Figure 5: Sensitivity analysis of the summary odds ratio coefficients for the relationship between ADC values and expression levels of estrogen receptors and progesterone receptors in breast cancer patients

Click here to view
Figure 6: Funnel plot of publication biases on the relationship between ADC values and expression levels of estrogen receptors and progesterone receptors in breast cancer patients

Click here to view

 > Discussion Top

In order to study the relationship of ADC values in the DWI detection of BC patients and the expression of ER and PR, a systematic meta-analysis was undertaken. The main result shows a connection between lower ADC values and the positive ER and PR expression. As a non-invasive MR imaging technique, DWI could show the micro-structural characteristics of tissue on the basis of the water molecular diffusion around the cells.[16] DWI has been applied for differentiation between benign tumors and malignant tumors and for identifying histopathological features.[11],[14] The ADC values derived from DWI could reflect the protons random thermal motion and provide quantitative measurements of water diffusivity, especially in the differentiation of malignant tumors and benign tumors because malignant tumors have lower ADC values due to their high cellularity.[13] Thus, the ADC value of DWI could be a convenient tool in BC screening without the need for contrast medium.[27] ER, with ER-α and ER-β isoforms, could play an important part in the development of normal breast tissues and the progression of BC since ER-α expression is necessary for the development of normal breast tissues. Moreover, a dramatic increase of ER-α has been observed in malignant lesions.[28] PR could be activated by progesterone to regulate the proliferation of BC.[29] As predictive markers for BC, the positive status of ER and PR indicate high sensitivity to hormone therapy.[30],[31] The relationship between lower ADC values in DWI detection performed on BC patients and the positive status of ER and PR has been confirmed by many studies.[32],[33] The possible mechanism might be that the ER-positive group could block the pathway of blood vessels and reduce blood perfusion, resulting in decreased ADC values.[14] Furthermore, ER-positive tumors often have a high cellularity which could restrict the diffusion of water molecules and result in lower ADC values.[34] From our analysis, we could conclude that lower ADC values have a strong relationship with the positive expression of ER and PR, which has an important role in the proliferation and metastasis of BC. This is because ER and PR positive BC often has a reduced perfusion and a high cellularity, resulting in the restriction of water molecule motion and decreased ADC value. In agreement with our conclusion, Choi and his colleagues found that ER and PR positive expression and increased levels of Ki-67 have a strong relationship with the lower ADC values found in BC patients.[20]

A subgroup analysis was conducted to evaluate the influence of country and MR machine type on the relationship between lower ADC values and positive expression of ER and PR. From the country-stratified analysis, we found that ER expression was not affected by ADC values in Korea and China, but not in Italy, however, ADC values were affected by PR both in Korea and Japan, all of which could be explained by the genetic variants and environment differences in different countries. As for the MR machine type subgroup analysis, a correlation between ADC values and ER expression was not found when Philips 1.5 T was used, but was found when the other brands were used. The reverse was found in the correlation between ADC values and PR expression. One possible explanation could be a deviation of different brand of detection methods. In summary, the relationship between low ADC values and the positive expression of ER and PR have been confirmed in our analysis. This is consistent with some previous studies that have shown evidence for the potential of ADC in the prediction of the expression of ER and PR and the diagnosis and treatment of BC patients.

Although our meta-analysis was a practical way to generate a more powerful estimate of effect-size with less random error than individual studies, it did have some limitations. First, our dataset was small, and the included number of BC tissues with positive ER and PR was too small relative to the number of negative ER and PR tissues. Because we only examined such a small number of ER-positive and PR-positive invasive BC patients, our results lack high statistical power, and our results cannot be generalized to all types of BC. Second, the internal architecture of breast tumors were heterogeneous, therefore the precise areas where the ADC was measured might be significantly different from one patient to the next. Finally, usual reliable statistical packages (STATA) are only able to calculate unweighted kappa coefficients for multiple raters, and they are inappropriate for ordinal scales because they treat all discrepancies equally.

Regardless, on the whole, our meta-analysis has verified the connection between low ADC values and the positive expression of ER and PR. Consequently, detectability of signal intensity by DWI combined with threshold ADC values may contribute to the prediction of the expression of ER and PR and the diagnosis and treatment of BC. Further studies with larger sample sizes are warranted for further evaluations of ADC values as a diagnostic factor for BC patients. The information and understanding of tumor biological heterogeneity such studies would bring about may influence imaging parameters and be critical for the clinical applications of ADC values.

 > Acknowledgments Top

We would like to acknowledge the reviewers for their helpful comments on this paper.

 > References Top

Baba S, Isoda T, Maruoka Y, Kitamura Y, Sasaki M, Yoshida T, et al. Diagnostic and prognostic value of pretreatment SUV in 18F-FDG/PET in breast cancer: Comparison with apparent diffusion coefficient from diffusion-weighted MR imaging. J Nucl Med 2014;55:736-42.  Back to cited text no. 1
Guinan EM, Hussey J, McGarrigle SA, Healy LA, O'Sullivan JN, Bennett K, et al. A prospective investigation of predictive and modifiable risk factors for breast cancer in unaffected BRCA1 and BRCA2 gene carriers. BMC Cancer 2013;13:138.  Back to cited text no. 2
Tan SL, Rahmat K, Rozalli FI, Mohd-Shah MN, Aziz YF, Yip CH, et al. Differentiation between benign and malignant breast lesions using quantitative diffusion-weighted sequence on 3 T MRI. Clin Radiol 2014;69:63-71.  Back to cited text no. 3
Luo N, Su D, Jin G, Liu L, Zhu X, Xie D, et al. Apparent diffusion coefficient ratio between axillary lymph node with primary tumor to detect nodal metastasis in breast cancer patients. J Magn Reson Imaging 2013;38:824-8.  Back to cited text no. 4
Sah RG, Agarwal K, Sharma U, Parshad R, Seenu V, Jagannathan NR. Characterization of malignant breast tissue of breast cancer patients and the normal breast tissue of healthy lactating women volunteers using diffusion MRI and in vivo H MR spectroscopy. J Magn Reson Imaging 2013;22.  Back to cited text no. 5
Dominick SA, Madlensky L, Natarajan L, Pierce JP. Risk factors associated with breast cancer-related lymphedema in the WHEL Study. J Cancer Surviv 2013;7:115-23.  Back to cited text no. 6
Bjerkaas E, Parajuli R, Weiderpass E, Engeland A, Maskarinec G, Selmer R, et al. Smoking duration before first childbirth: An emerging risk factor for breast cancer? Results from 302,865 Norwegian women. Cancer Causes Control 2013;24:1347-56.  Back to cited text no. 7
Taher C, de Boniface J, Mohammad AA, Religa P, Hartman J, Yaiw KC, et al. High prevalence of human cytomegalovirus proteins and nucleic acids in primary breast cancer and metastatic sentinel lymph nodes. PLoS One 2013;8:e56795.  Back to cited text no. 8
Cabezon T, Gromova I, Gromov P, Serizawa R, Timmermans Wielenga V, Kroman N, et al. Proteomic profiling of triple-negative breast carcinomas in combination with a three-tier orthogonal technology approach identifies Mage-A4 as potential therapeutic target in estrogen receptor negative breast cancer. Mol Cell Proteomics 2013;12:381-94.  Back to cited text no. 9
Upstill-Goddard R, Eccles D, Ennis S, Rafiq S, Tapper W, Fliege J, et al. Support Vector Machine classifier for estrogen receptor positive and negative early-onset breast cancer PloS one 2013;8:e68606.  Back to cited text no. 10
Martincich L, Deantoni V, Bertotto I, Redana S, Kubatzki F, Sarotto I, et al. Correlations between diffusion-weighted imaging and breast cancer biomarkers. Eur Radiol 2012;22:1519-28.  Back to cited text no. 11
Tamura T, Usui S, Murakami S, Arihiro K, Fujimoto T, Yamada T, et al. Comparisons of multi b-value DWI signal analysis with pathological specimen of breast cancer. Magn Reson Med 2012;68:890-7.  Back to cited text no. 12
Park SH, Moon WK, Cho N, Chang JM, Im SA, Park IA, et al. Comparison of diffusion-weighted MR imaging and FDG PET/CT to predict pathological complete response to neoadjuvant chemotherapy in patients with breast cancer. Eur Radiol 2012;22:18-25.  Back to cited text no. 13
Choi BB, Kim SH, Kang BJ, Lee JH, Song BJ, Jeong SH, et al. 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 2012;10:126.  Back to cited text no. 14
Sigmund EE, Cho GY, Kim S, Finn M, Moccaldi M, Jensen JH, et al. Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer. Magn Reson Med 2011;65:1437-47.  Back to cited text no. 15
Junping W, Tongguo S, Yunting Z, Chunshui Y, Renju B. Discrimination of axillary metastatic from nonmetastatic lymph nodes with PROPELLER diffusion-weighted MR imaging in a metastatic breast cancer model and its correlation with cellularity. J Magn Reson Imaging 2012;36:624-31.  Back to cited text no. 16
Nakai G, Matsuki M, Harada T, Tanigawa N, Yamada T, Barentsz J, et al. Evaluation of axillary lymph nodes by diffusion-weighted MRI using ultrasmall superparamagnetic iron oxide in patients with breast cancer: Initial clinical experience. J Magn Reson Imaging 2011;34:557-62.  Back to cited text no. 17
Razek AA, Gaballa G, Denewer A, Nada N. Invasive ductal carcinoma: Correlation of apparent diffusion coefficient value with pathological prognostic factors. NMR Biomed 2010;23:619-23.  Back to cited text no. 18
Wang KX, Xing W, Yu SN, Qiu JG, Chen J, Yang HX. [Diffusion weighted imaging in differential diagnosis of breast lesions]. Chin Comput Med Imaging 2011;17:323-6.  Back to cited text no. 19
Choi SY, Chang YW, Park HJ, Kim HJ, Hong SS, Seo DY. Correlation of the apparent diffusion coefficiency values on diffusion-weighted imaging with prognostic factors for breast cancer. Br J Radiol 2012;85:e474-9.  Back to cited text no. 20
Jeh SK, Kim SH, Kim HS, Kang BJ, Jeong SH, Yim HW, et al. Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma. J Magn Reson Imaging 2011;33:102-9.  Back to cited text no. 21
Chen X, Li WL, Zhang YL, Wu Q, Guo YM, Bai ZL. Meta-analysis of quantitative diffusion-weighted MR imaging in the differential diagnosis of breast lesions. BMC Cancer 2010;10:693.  Back to cited text no. 22
Whiting PF, Weswood ME, Rutjes AW, Reitsma JB, Bossuyt PN, Kleijnen J. Evaluation of QUADAS, a tool for the quality assessment of diagnostic accuracy studies. BMC Med Res Methodol 2006;6:9.  Back to cited text no. 23
Jackson D, White IR, Riley RD. Quantifying the impact of between-study heterogeneity in multivariate meta-analyses. Stat Med 2012;31:3805-20.  Back to cited text no. 24
Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L. Comparison of two methods to detect publication bias in meta-analysis. JAMA 2006;295:676-80.  Back to cited text no. 25
Nakajo M, Kajiya Y, Kaneko T, Kaneko Y, Takasaki T, Tani A, et al. FDG PET/CT and diffusion-weighted imaging for breast cancer: Prognostic value of maximum standardized uptake values and apparent diffusion coefficient values of the primary lesion. Eur J Nucl Med Mol Imaging 2010;37:2011-20.  Back to cited text no. 26
Yabuuchi H, Matsuo Y, Sunami S, Kamitani T, Kawanami S, Setoguchi T, et al. Detection of non-palpable breast cancer in asymptomatic women by using unenhanced diffusion-weighted and T2-weighted MR imaging: Comparison with mammography and dynamic contrast-enhanced MR imaging. Eur Radiol 2011;21:11-7.  Back to cited text no. 27
Thakkar JP, Mehta DG. A review of an unfavorable subset of breast cancer: Estrogen receptor positive progesterone receptor negative. Oncologist 2011;16:276-85.  Back to cited text no. 28
Allred DC. Issues and updates: Evaluating estrogen receptor-alpha, progesterone receptor, and HER2 in breast cancer. Mod Pathol 2010;23:S52-9.  Back to cited text no. 29
Zhou XL, Fan W, Yang G, Yu MX. The clinical significance of PR, ER, NF- kappa B, and TNF- alpha in breast cancer. Dis Markers 2014;2014:494581.  Back to cited text no. 30
Shapochka DO, Zaletok SP, Gnidyuk MI. Relationship between NF-kappaB, ER, PR, Her2/neu, Ki67, p53 expression in human breast cancer. Exp Oncol 2012;34:358-63.  Back to cited text no. 31
Kim SH, Cha ES, Kim HS, Kang BJ, Choi JJ, Jung JH, et al. Diffusion-weighted imaging of breast cancer: Correlation of the apparent diffusion coefficient value with prognostic factors. J Magn Reson Imaging 2009;30:615-20.  Back to cited text no. 32
Kamitani T, Matsuo Y, Yabuuchi H, Fujita N, Nagao M, Jinnouchi M, et al. Correlations between apparent diffusion coefficient values and prognostic factors of breast cancer. Magn Reson Med Sci 2013;12:193-9.  Back to cited text no. 33
Park SH, Choi HY, Hahn SY. Correlations between apparent diffusion coefficient values of invasive ductal carcinoma and pathologic factors on diffusion-weighted MRI at 3.0 Tesla. J Magn Reson Imaging 2013.  Back to cited text no. 34


  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]

  [Table 1]


Similar in PUBMED
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

  >Abstract>Introduction>Materials and Me...>Results>Discussion>Acknowledgments>Article Figures>Article Tables
  In this article

 Article Access Statistics
    PDF Downloaded201    
    Comments [Add]    

Recommend this journal