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Year : 2016  |  Volume : 12  |  Issue : 2  |  Page : 744-750

Apparent diffusion coefficient value of diffusion-weighted imaging for differential diagnosis of ductal carcinoma in situ and infiltrating ductal carcinoma

Department of Radiology, Taizhou Hospital of Zhejiang Province, Linhai, China

Date of Web Publication25-Jul-2016

Correspondence Address:
Jing-Li Pan
Department of Radiology, Taizhou Hospital of Zhejiang Province, Simen Street No. 150, Linhai - 317 000
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0973-1482.154093

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 > Abstract 

Purpose: The present meta-analysis investigated the clinical value of apparent diffusion coefficient (ADC) values in diffusion-weighted imaging (DWI) for differential diagnosis of ductal carcinoma in situ (DCIS) and infiltrating ductal carcinoma (IDC).
Materials and Methods: Electronic databases searches were employed to identify relevant scientific literature, and the search results were screened to selected high-quality studies for this meta-analysis. Methodological quality of the enrolled studies was evaluated by quality evaluation of diagnostic accuracy studies (QUADAS). Summary odds ratios (ORs) and its corresponding 95% confidence interval (95% CI) were calculated for DCIS versus IDC category of ADC value using Z test.
Results: Our meta-analysis contained a combined total of 1,097 subjects (928 patients with IDC and 169 patients with DCIS) from 9 relevant high-quality cohort studies. Pooled ORs demonstrated that ADC value in IDC patients was significantly lower than DCIS patients. Subgroup analysis stratified by ethnicity indicated a higher ADC value in DCIS patients compared to IDC, in Asian population, but not in Caucasians. Magnetic resonance imaging (MRI) machine type-stratified analysis revealed that the ADC value of DWI obtained from both non- General Electric Company (GE) 1.5T and GE 1.5T machines were highly reliable in the differential diagnosis of DCIS and IDC.
Conclusion: Our meta-analysis provides evidence that ADC values in DWI accurately conveys the differences in tumor architecture between IDC and DCIS, which has high clinical value in differentiatal diagnosis of IDC and DCIS. This may lead to improved BC prediction and treatment.

Keywords: Apparent diffusion coefficient, breast cancer, diffusion-weighted imaging, ductal carcinoma in situ, infiltrating ductal carcinoma, machine type, magnetic resonance imaging, meta-analysis

How to cite this article:
Ding JR, Wang DN, Pan JL. Apparent diffusion coefficient value of diffusion-weighted imaging for differential diagnosis of ductal carcinoma in situ and infiltrating ductal carcinoma. J Can Res Ther 2016;12:744-50

How to cite this URL:
Ding JR, Wang DN, Pan JL. Apparent diffusion coefficient value of diffusion-weighted imaging for differential diagnosis of ductal carcinoma in situ and infiltrating ductal carcinoma. J Can Res Ther [serial online] 2016 [cited 2020 Jul 5];12:744-50. Available from: http://www.cancerjournal.net/text.asp?2016/12/2/744/154093

 > Introduction Top

Breast cancer (BC) is the most frequently diagnosed malignancy affecting women worldwide.[1] BC is the main cause of cancer-related death among women aged under 50 years in the US.[2] An estimated 209,000 BC cases were diagnosed, and approximately 40,000 patients died from BC in US in 2010.[3] In addition, the survival rates and prognosis for BC patients varies greatly depending on the cancer type, tumor stage at diagnosis, and the choice of treatment.[4] The etiology of BC is diverse and includes family history, early menarche, and late menopause as significant risk factors of BC.[5],[6] BC is classified into ductal carcinoma in situ (DCIS) and infiltrating ductal carcinoma (IDC) based on specific markers.[7],[8],[9] DCIS accounts for 20% of BC and is non-invasive.[10],[11] On the other hand, approximately 80 cases of 100 will be diagnosed with IDC, which is the most common type of BC.[8] Early detection and treatment increases the survival rate, and therefore significant efforts are directed at improving early diagnostic accuracy.[12] As a diagnostic parameter that correlates with tumor characteristics, apparent diffusion coefficient in diffusion-weighted imaging (DWI) has significant advantages in measuring tumor parameters, and can be applied to BCs in the diagnosis of DCIS and IDC.[13],[14]

DWI is one of the significant advances of magnetic resonance imaging (MRI) technique and estimates micro-structural features of tissues based on water diffusion in biological tissues.[13] The principle of DWI for clinical applications is based on the fact that tissue structures are probed by the molecular diffusion, propelled by the available thermal energy, when water molecules are diffusing randomly to drive displacement, and this random molecular motion of water is beyond the resolution capabilities of other imaging tools.[15] Therefore, by virtue of thermal energy, DWI can uniquely investigate the random motion water molecules by the apparent diffusion coefficient (ADC) to give valuable morphological information, important in management of several human diseases, such as ischemic cerebral infarction and breast cancer.[16] ADC is a quantitative parameter of DWI, useful in comparing lesion diffusivity, with important implications in evaluating BC.[1] To be specific, by measuring ADC values, DWI can differentiate malignant breast lesions from benign breast lesions, which differ in blood flow, tissue cellularity, and membrane permeability.[17] ADC values for benign tumors are higher than ADC values in malignant breast cancers.[18] More importantly, as a non-invasive subtype of primary BC, DCIS diagnostic accuracy is also increased by ADC values, but overlap in the ranges of ADC values between malignant and benign lesions do exist, making the diagnosis difficult for a subset of patients.[19] In IDC, ADC values did not associate with maximum standardized uptake values (MSUmax), although MSUmax correlated with histologic grade, tumor size, and tumor cellularity. The ADC values still have a potential role in predicting the prognosis of IDC, such as monitoring therapy response and detecting lesion aggressiveness.[13] Several previous studies have demonstrated that ADC values improve the diagnostic accuracy of DCIS and IDC,[20],[21] while other studies contradict these findings.[14],[19] In order to address the clinical value in BC, we conducted the present meta-analysis to evaluate the potential of the ADC values for improving the diagnostic accuracy for DCIS and IDC.

 > Materials and Methods Top

Data sources and keywords

Studies published prior to May 1, 2014, which assessed the differences in ADC value between adult subjects with IDC or DCIS, were retrieved by search of computerized databases [PubMed, Embase, Cochrane Library, Web of Science, CISCOM, Google Scholar, CINAHL, China BioMedicine (CBM), and China National Knowledge Infrastructure (CNKI)], applying selected common keywords (“Diffusion Magnetic Resonance Imaging” 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 (“Diffusion Magnetic Resonance Imaging” 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”). The language of all enrolled studies had no restriction. The bibliographies of eligible studies were also further searched manually to retrieve other relevant studies. The first authors were asked for clarifications when the enrolled studies provided unclear information in original publications.

Selection criteria

Published studies fulfilled the following inclusion criteria to be selected for this meta-analysis: (1) Studies include patients with IDC or DCIS; (2) human cohort studies with diagnosis tests of IDC or DCIS by diffusion MRI; (3) provide serviceable data for b value and ADC value; (4) report the odd ratios (ORs) with 95% confidence intervals (CI) for ADC value; (5) sample size as well as ample information about ADC value, b value, and the 4-fold (2 × 2) tables should be supplied; (6) number of samples in studies must greater than 25; (7) relate to the exactitude of diffusion MRI within differential diagnosis between IDC and DCIS.

Data extraction

Two investigators collected information separately from all enrolled studies under the selection criteria to minimize the bias and enhance the credibility, and reach an agreement on all the items through discussion. The following relevant information were enrolled from all included studies: Surname of first author, source of publication, year of publication, study design, study type, age, ethnicity, sample size, country, MRI machine type, “gold standard,” diagnostic accuracy, contrast agent, b value with ADC value in patients with IDC or DCIS. All the authors approved the final data extracted from the enrolled studies.

Quality assessment

The quality of all recruited studies was appraised independently by two authors with quality assessment of studies of diagnostic accuracy studies (QUADAS).[22] The QUADAS criteria have 14 detailed items. These items were scored as “yes” (2), “no” (0), or “unclear” (1). QUADAS score ranged from 0 to 28, with a score of over 22, indicating high quality.

Statistical analysis

In order to compute the effect size of every study, ORs at 95% CI were utilized for DCIS versus IDC category of ADC value by employing Z test. Random-effect model was applied when significant heterogeneity among the studies was detected otherwise fixed-effects model was employed. Subgroup meta-analyses based on ethnicity and MRI machine type was performed to investigate potential effect modification. Heterogeneity was estimated by the Cochran's Q-statistic (P <</i> 0.05 were treated as statistically significant).[23]I2 test (0%, no heterogeneity; 100%, maximal heterogeneity) was also used to detect heterogeneity among studies.[24] The sensitivity analysis was applied to evaluate if deleting a single study one by one affected the final result of our meta-analysis. The funnel plot was drawn to assess publication bias. Egger's linear regression test was further assessed by the symmetry of the funnel plot.[25] All tests were two-sided and P value lower than 0.05 was deemed to be statistically significant. In order to guarantee credibility and accuracy, data input and analysis was conducted separately, by two investigators. STATA software, version 12.0 (Stata Corp, College Station, TX, USA) was used for statistical analysis.

 > Results Top

Included studies

Our current meta-analysis enrolled a total of 15 cohort studies on the correlation between diffusion MRI and diagnosis of IDC and DCIS published between 2006 and 2014.[14],[19],[20],[21],[26],[27],[28],[29],[30],[31],[32],[33],[34],[35],[36] Initially, a sum of 603 papers was retrieved from the database searches. Our meta-analysis excluded several studies for the following reason: 3 studies for duplicates, 124 for letters, reviews or meta-analyses, 139 for non-human studies, and 141 studies not related to research topics. The remaining 196 studies were reviewed and additional 179 studies were excluded in that they were not case-control or cohort study (n = 44), irrelevant to diffusion MRI (n = 62), or irrelevant to IDC or DCIS (n = 73), and 2 studies were abandoned for not supplying enough information. Finally, 15 studies were included in the final analysis. All quality scores of the included studies were higher than 21 (high quality). Demographic information on the subjects with IDC or DCIS, and other characteristics and methodological quality of the enrolled studies are shown in [Table 1]. Eleven studies were conducted in Asians and 4 in Caucasians, and included a total of 928 IDC patients and 169 DCIS patients. The studies were performed in Korea (n = 3), Japan (n = 3), China (n = 5), USA (n = 1), Italy (n = 2), and Belgium (n = 1). MRI machine type in our meta-analysis included GE 3.0T (n = 2), GE 1.5T (n = 9), Siemens 3.0T (n = 1), Siemens 1.5T (n = 2), Philips 1.5T (n = 1). The study selection process is presented in [Figure 1].
Table 1: Main characteristics and methodological quality of eligibly studies

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Figure 1: Flow chart shows study selection procedure. Fifteen case-control studies were included

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Diagnostic value of diffusion MRI in IDC and DCIS

As shown in [Figure 2], the main result of our meta-analysis revealed that ADC value in DCIS patients was higher than IDC patients (OR = 0.99, 95% CI: 0.61–1.37, P <</i> 0.001). Subgroup analysis based on ethnicity showed a higher ADC value in DCIS patients compared to IDC patients in Asians (OR = 1.08, 95% CI: 0.75–1.40, P< 0.001), but a similar relationship was not found in Caucasians (P > 0.05) [as shown in [Figure 3]]. Subgroup analysis on the basis of MRI machine type revealed that ADC value of DWI is a diagnostic indicator for DCIS and IDC using both non-GE 1.5T (OR = 1.81, 95% CI: 1.13–2.50, P < 0.001), and GE 1.5T (OR = 0.50, 95% CI: 0.22–0.79, P < 0.001), as shown in [Figure 3]. Subgroup analysis by field strength showed that ADC value in DCIS patients was significantly higher compared with IDC patients (SMD = 1.91, 95% CI = 0.68 ~ 3.14, P = 0.002) using 3.0T field strength. ADC value in DCIS patients was also significantly higher in DCIS patients compared to IDC patients (SMD = 0.82, 95% CI = 0.41 ~ 1.23, P < 0.001) at a field strength of 1.5 T. Further subgroup analysis on b value showed a higher ADC value in DCIS patients compared to IDC patients with b values of < 800s/mm2 (SMD = 1.14, 95% CI = 0.24 ~ 2.04, P = 0.013). ADC value in DCIS patients was also higher than IDC patients with b value ≥ 800 s/mm2, and the differences showed statistical significance (SMD = 0.93, 95% CI = 0.49 ~ 1.37, P < 0.001) [Figure 4].
Figure 2: Forest plot of the association between Diagnostic value of diffusion magnetic resonance imaging (MRI) in infiltrating ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS)

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Figure 3: Subgroup analyses by ethnicity (a) and magnetic resonance imaging (MRI) machine (b) type for the association between Diagnostic value of diffusion MRI in infiltrating ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS)

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Figure 4: Subgroup analyses by field strength (a) and b value (b) for the association between diagnostic value of diffusion magnetic resonance imaging (MRI) in infiltrating ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS)

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Sensitivity analysis and publication bias

Sensitivity analysis was employed to assess the stability of the current meta-analysis. Each included study in the present meta-analysis was assessed one by one to reflect whether pooled ORs are significant. The statistical significance was unchanged when any single study was deleted. Therefore, our meta-analysis result was stable [Figure 5]. The graphical funnel plots of all enrolled 15 studies are symmetry, and Egger's test further confirm no publication bias (P > 0.05) [Figure 6].
Figure 5: Sensitivity analysis for the associations between diagnostic value of diffusion magnetic resonance imaging (MRI) in infiltrating ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS)

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Figure 6: Funnel plot of the association between diagnostic value of diffusion magnetic resonance imaging (MRI) in infiltrating ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS)

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 > Discussion Top

To examine the diagnostic value of ADC values of DWI in breast cancer disease setting for differential diagnosis of IDC and DCIS, a systematic meta-analysis was performed. The main result of our analysis showed that the ADC value of DCIS patients was significantly higher than IDC patients, confirming a reliable diagnostic value of ADC in differentiating IDC and DCIS. DWI is a non-contrast MRI technique that conveys critical information related to cell density, organization of cells, membrane integrity, and tissue structure by measuring the diffusion of water molecules through tumor tissues. Lower rate of loss of signal represents low water diffusion and higher rate of loss of signal represents high water diffusion.[34],[36] ADC is the frequently used value for diffusibility of water and measures the attenuation of signal and water molecular motion, which is high in tissues without obstacles for water motion, and low in tissues with barriers to molecular motion of water.[30],[31] ADC is quantified by mean diffusivity measurements in three orthogonal directions, and is influenced by tissue cellularity, fluid viscosity, permeability of membrane, and blood flow.[29] ADC values are relevant to many disease settings. The ADC value was found significantly higher in the areas of hyper-intense bone marrow than the healthy group which is due to the increased water molecular movement because of the edema in the bone marrow.[37] The higher ADC values in primary central nervous system (CNS) lymphoma and glioblastoma was explained by focal necrosis and edema which reduce extracellular water hindrance, increasing the ADC value.[38] In liver fibrosis patients, the ADC value was lower than healthy subjects because of the restriction of water molecule diffusion due to proteoglycan, glycosaminoglycan, and collagen fiber accumulation.[39] Lower ADC value was found in malignant breast cancer compared to benign tissues, which is closely related to increased cell density in malignant tumors.[18] Additionally, the average ADC value in IDC patients was found to be significantly lower than DCIS patients probably because cancer cells of DCIS mainly spread in the duct, thus the cell density is lower than IDC, while the IDC is a densely packed tumor, lacking fibrous stroma.[14],[32] Thus, ADC value is a reliable tool to differentiate IDC from DCIS, because different histological grade, vascular invasion, and lymph node metastasis influence cell density and other tumor parameters, which ultimately is reflected in the ADC value. In agreement with our study conclusion, Park et al. also found the mean ADC values in IDC and DCIS were lower than normal tissue and the mean ADC value of DCIS was significantly higher than IDC.[33] Rubesova et al. also demonstrated the ADC value differentiated malignant and benign tissues with good sensitivity and specificity.[35]

In consideration of other factors which might influence the diagnostic value of ADC value in differentiating IDC and DCIS, a subgroup analysis was conducted based on ethnicity, MR machine type, field strength, and b value. Ethnicity-stratified analysis revealed that the diagnostic value of ADC was significant in Asians but not in Caucasians, which may be related to differences in environment and genetic background. The MR machine type did not affect the diagnostic value of ADC in differentiate IDC and DCIS. In summary, our results support the significant diagnostic value of ADC in differentiating IDC and DCIS. Subgroup analysis by field strength showed that, irrespective of the field strength of 3.0T or 1.5 T, ADC value was significantly higher in DCIS patients compared to IDC patients. Further subgroup analysis on b value revealed higher ADC value in DCIS patients compared to IDC patients with b values of <800 s/mm2 and ≥800 s/mm2, indicating that ADC value dependence on b value should be taken into consideration in interpreting these quantitative measurements.

Several limitations in our analysis exist. First, our study included relatively small number of articles and small number of patients. Second, the BC patient population involved a mixture of varying screening and staging process in differentiating IDC and DCIS. There were no unified standardized criteria in the screening process, and the prevalence of IDC and DCIS may be higher in population. Third, BCs have heterogeneous internal tumor architecture that might contribute to variation in ADC values. Finally, the acquisition parameters, b value and the ADC value were not optimized because of retrospective design, which may be a possibly source of bias.

Our study is the first example to investigate the clinical value of DWI in differentiating IDC and DCIS in BC patients. Our results suggested that ADC values of IDC was lower than DCIS, conveying important histopathologic information useful in differentiating IDC or DCIS patients. Further clinical studies will be required to directly test our conclusions in prospective studies and predicting prognosis in BC patients.

 > Acknowledgments Top

We would like to express our thankfulness to the reviewers for their helpful comments on this paper.

 > References Top

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. 1
Kazama T, Kuroki Y, Kikuchi M, Sato Y, Nagashima T, Miyazawa Y, et al. Diffusion-weighted MRI as an adjunct to mammography in women under 50 years of age: An initial study. J Magn Reson Imaging 2012;36:139-44.  Back to cited text no. 2
Oliver PG, LoBuglio AF, Zhou T, Forero A, Kim H, Zinn KR, et al. Effect of anti-DR5 and chemotherapy on basal-like breast cancer. Breast Cancer Res Treat 2012;133:417-26.  Back to cited text no. 3
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 1H MR spectroscopy. J Magn Reson Imaging 2015;41:169-74.  Back to cited text no. 4
Park SK, Kim Y, Kang D, Jung EJ, Yoo KY. Risk factors and control strategies for the rapidly rising rate of breast cancer in Korea. J Breast Cancer 2011;14:79-87.  Back to cited text no. 5
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. 6
Roberti MP, Arriaga JM, Bianchini M, Quinta HR, Bravo AI, Levy EM, et al. Protein expression changes during human triple negative breast cancer cell line progression to lymph node metastasis in a xenografted model in nude mice. Cancer Biol Ther 2012;13:1123-40.  Back to cited text no. 7
Hawthorn L, Luce J, Stein L, Rothschild J. Integration of transcript expression, copy number and LOH analysis of infiltrating ductal carcinoma of the breast. BMC Cancer 2010;10:460.  Back to cited text no. 8
Allred DC, Anderson SJ, Paik S, Wickerham DL, Nagtegaal ID, Swain SM, et al. Adjuvant tamoxifen reduces subsequent breast cancer in women with estrogen receptor-positive ductal carcinoma in situ: A study based on NSABP protocol B-24. J Clin Oncol 2012;30:1268-73.  Back to cited text no. 9
Holmes P, Lloyd J, Chervoneva I, Pequinot E, Cornfield DB, Schwartz GF, et al. Prognostic markers and long-term outcomes in ductal carcinoma in situ of the breast treated with excision alone. Cancer 2011;117:3650-7.  Back to cited text no. 10
Spanu A, Sanna D, Chessa F, Cottu P, Manca A, Madeddu G. Breast scintigraphy with breast-specific gamma-camera in the detection of ductal carcinoma in situ: A correlation with mammography and histologic subtype. J Nucl Med 2012;53:1528-33.  Back to cited text no. 11
Nogueira L, Brandao S, Matos E, Nunes RG, Ferreira HA, Loureiro J, et al. Diffusion-weighted breast imaging at 3 T: Preliminary experience. Clin Radiol 2014;69:378-84.  Back to cited text no. 12
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. 13
Cheng L, Bai Y, Zhang J, Liu M, Li X, Zhang A, et al. Optimization of apparent diffusion coefficient measured by diffusion-weighted MRI for diagnosis of breast lesions presenting as mass and non-mass-like enhancement. Tumour Biol 2013;34:1537-45.  Back to cited text no. 14
Li XR, Cheng LQ, Liu M, Zhang YJ, Wang JD, Zhang AL, et al. DW-MRI ADC values can predict treatment response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. Med Oncol 2012;29:425-31.  Back to cited text no. 15
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. 16
Ko ES, Han BK, Kim RB, Cho EY, Ahn S, Nam SJ, et al. Apparent diffusion coefficient in estrogen receptor-positive invasive ductal breast carcinoma: Correlations with tumor-stroma ratio. Radiology 2014;271:30-7.  Back to cited text no. 17
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 2015;41:175-82.  Back to cited text no. 18
Ochi M, Kuroiwa T, Sunami S, Murakami J, Miyahara S, Nagaie T, et al. Diffusion-weighted imaging (b value = 1500 s/mm (2)) is useful to decrease false-positive breast cancer cases due to fibrocystic changes. Breast Cancer 2013;20:137-44.  Back to cited text no. 19
Hirano M, Satake H, Ishigaki S, Ikeda M, Kawai H, Naganawa S. Diffusion-weighted imaging of breast masses: Comparison of diagnostic performance using various apparent diffusion coefficient parameters. AJR Am J Roentgenol 2012;198:717-22.  Back to cited text no. 20
Kang BJ, Lipson JA, Planey KR, Zackrisson S, Ikeda DM, Kao J, et al. Rim sign in breast lesions on diffusion-weighted magnetic resonance imaging: Diagnostic accuracy and clinical usefulness. J Magn Reson Imaging 2014.  Back to cited text no. 21
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. 22
Zintzaras E, Ioannidis JP. HEGESMA: Genome search meta-analysis and heterogeneity testing. Bioinformatics 2005;21:3672-3.  Back to cited text no. 23
Akhbardeh A, Jacobs MA. Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation. Med Phys 2012;39:2275-89.  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
Gu YJ, Feng XY, Xiao Q, Wu B, Peng WJ, Yang WT, et al. Evaluation of breast cancer extension with diffusion-weighted MR imaging. Chin J Radiol 2007;41:959-64.  Back to cited text no. 26
He YL, Ji EH, Ma DQ, Liang TY, Wu XH, He W. Value of apparent diffusion coefficient on the differential diagnosis of different pathological breast cancer. J Clin Radiol 2009;28:1082-5.  Back to cited text no. 27
Yu XR, Zhang WJ, Li W, Wang H, Yang JM, Tang W. Quantitative MR diffusion-weighted imaging in differentiating diagnosis of breast lesions. J Pract Radiol 2010;26:1333-6, 49.  Back to cited text no. 28
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. 29
Costantini M, Belli P, Rinaldi P, Bufi E, Giardina G, Franceschini G, et al. Diffusion-weighted imaging in breast cancer: Relationship between apparent diffusion coefficient and tumour aggressiveness. Clin Radiol 2010;65:1005-12.  Back to cited text no. 30
Long-Hua Q, Qin X, Wen-Tao Y, Feng T, Kun-Wei S, Bin W, et al. Diffusion-weighted MRI evaluation of breast cancer extension. Clin Imaging 2010;34:89-96.  Back to cited text no. 31
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. 32
Park MJ, Cha ES, Kang BJ, Ihn YK, Baik JH. The role of diffusion-weighted imaging and the apparent diffusion coefficient (ADC) values for breast tumors. Korean J Radiol 2007;8:390-6.  Back to cited text no. 33
Partridge SC, Demartini WB, Kurland BF, Eby PR, White SW, Lehman CD. Differential diagnosis of mammographically and clinically occult breast lesions on diffusion-weighted MRI. J Magn Reson Imaging 2010;31:562-70.  Back to cited text no. 34
Rubesova E, Grell AS, De Maertelaer V, Metens T, Chao SL, Lemort M. Quantitative diffusion imaging in breast cancer: A clinical prospective study. J Magn Reson Imaging 2006;24:319-24.  Back to cited text no. 35
Belli P, Costantini M, Bufi E, Magistrelli A, La Torre G, Bonomo L. Diffusion-weighted imaging in breast lesion evaluation. Radiol Med 2010;115:51-69.  Back to cited text no. 36
Gezmis E, Donmez FY, Agildere M. Diagnosis of early sacroiliitis in seronegative spondyloarthropathies by DWI and correlation of clinical and laboratory findings with ADC values. Eur J Radiol 2013;82:2316-21.  Back to cited text no. 37
White NS, McDonald CR, Farid N, Kuperman JM, Kesari S, Dale AM. Improved conspicuity and delineation of high-grade primary and metastatic brain tumors using “restriction spectrum imaging”: Quantitative comparison with high B-value DWI and ADC. AJNR Am J Neuroradiol 2013;34:958-64, S1.  Back to cited text no. 38
Cece H, Ercan A, Yildiz S, Karakas E, Karakas O, Boyaci FN, et al. The use of DWI to assess spleen and liver quantitative ADC changes in the detection of liver fibrosis stages in chronic viral hepatitis. Eur J Radiol 2013;82:e307-12.  Back to cited text no. 39


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

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