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ONCOIMAGING  
Year : 2020  |  Volume : 30  |  Issue : 1  |  Page : 13-19
Diffusion weighted imaging in breast cancer – Can it be a noninvasive predictor of nuclear grade?


Division of Breast Imaging, Department of Diagnostic and Interventional Radiology, Kovai Medical Center and Hospital, Coimbatore, Tamil Nadu, India

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Date of Submission05-Mar-2019
Date of Acceptance30-Oct-2019
Date of Web Publication30-Mar-2020
 

   Abstract 


Background: DWI and ADC values are noninvasive MRI techniques, which provide quantitative information about tumor heterogeneity. Aim: To determine the minimum and mean ADC values in breast carcinoma and to correlate ADC values with various prognostic factors. Settings and Design: Prospective observational study. Materials and Methods: Fifty-five patients with biopsy-proven breast carcinoma were included in this study. MRI with DWI was performed with Siemens 3T Skyra scanner. ADC values were measured by placing regions of interest (ROIs) within the targeted lesions on ADC maps manually. The histopathological and immunohistochemical analysis of surgical specimen was done to determine the prognostic factors. Statistical Analysis: Students T test and ANOVA were used to study the difference in ADC between two groups. Pearson correlation coefficient was used to quantify the correlation between ADC values and prognostic factors. Results: Lower grade (grade I) breast carcinoma had a significantly high ADC value as compared to higher grade carcinoma (grade II and III). For differentiating Grade I tumors from grade II and III, a minimum ADC cut-off value was 0.79 × 10-3 mm2/sec (83% sensitivity and 84% specificity) and a mean ADC cut-off value was 0.82 × 10-3 mm2/sec (83% sensitivity and 71% specificity) was derived. There was no significant correlation between ADC and other prognostic factors. Conclusion: ADC values can be used to differentiate lower grade breast carcinoma (grade I) from higher grades (grade II and III). Minimum ADC values are more accurate in predicting the grade of the breast tumor than mean ADC value.

Keywords: Breast cancer; diffusion; imaging; predictor; prognosis

How to cite this article:
Rupa R, Thushara R, Swathigha S, Athira R, Meena N, Cherian MP. Diffusion weighted imaging in breast cancer – Can it be a noninvasive predictor of nuclear grade?. Indian J Radiol Imaging 2020;30:13-9

How to cite this URL:
Rupa R, Thushara R, Swathigha S, Athira R, Meena N, Cherian MP. Diffusion weighted imaging in breast cancer – Can it be a noninvasive predictor of nuclear grade?. Indian J Radiol Imaging [serial online] 2020 [cited 2020 Sep 26];30:13-9. Available from: http://www.ijri.org/text.asp?2020/30/1/13/281582



   Introduction Top


Breast cancer is the commonest cancer among women in the world.[1] In India the incidence rate of breast cancer is less. However, the mortality rate is at par with western countries. Further Indian women tend to have tumors (use - tend to have) 10 years earlier than western population with occurrence of more aggressive tumors.[2],[3]

Purpose and rationale of the study

Breast cancer is a multispectrum disease.[4] The most important factors which determine its prognostication are histological grade (Elston-Ellis modification of Bloom-Richardson histologic grading system or Nottingham combined histologic grading system), histopathological type of cancer, Estrogen Receptor/Progesterone Receptor (ER/PR) status, Human Epidermal Growth Receptor (HER2) Neu status, lymph node status, and tumor size.[5],[6],[7] Prognosis also depends on other factors like lymphovascular and perineural invasion.[8],[9]

MRI acts as a complementary diagnostic tool to Mammogram and ultrasound in evaluation of breast cancer, especially in young women with dense breast.[2],[10],[11] The sensitivity and specificity of Dynamic Contrast Enhancement (DCE)-MRI in detecting breast cancer is approximately 85-99% and 72% respectively.[12],[13],[14] With the addition of Diffusion Weighted Imaging (DWI), the sensitivity increases to 85-100% and specificity to 37-88%[15],[16]

DWI assesses the physiological and functional environment of the lesion by studying the random motion of water molecules, tissue cellularity, fluid viscosity, membrane permeability, and blood flow which is measured quantitatively by Apparent Diffusion Coefficient (ADC) values.[17] Previous studies have shown lower apparent diffusion coefficient values for malignant breast lesions than for benign lesions and normal breast tissue.[16],[18],[19] In addition to differentiation of benign and malignant lesions, DWI is thought to have a wider role in assessing prognosis of breast carcinoma.[20] In the current study we have attempted to get cut-off values for minimum and mean ADC for breast cancer and correlated the ADC values with the histopathological prognostic factors, which helps to predict the tumor aggressiveness preoperatively and thus helps in treatment planning.


   Materials and Methods Top


This observational study was done to evaluate the role of ADC values in assessing the prognosis of breast carcinoma. This study has been approved (use- was or had been) by the institutional review board and written informed consent was obtained from all patients. Study population included women diagnosed to have breast carcinoma by histopathological study of the biopsy specimen and underwent breast MRI before any therapeutic interventions during a period of 2 years (from May 2015 to June 2017). Patients with motion artifacts on DWI, prior neoadjuvant chemotherapy, excision biopsy, those with in situ ductal cancer, and those with small invasive focus detected only in histopathology were excluded from the study. After exclusion, total of 55 patients with histopathologically proven breast carcinoma were included in the study (3 patients had bilateral malignancy).

Image analysis

MRI examinations were performed within 2 weeks before surgery. All patients were scanned in the prone position with Siemens (AG Healthcare, Erlanger, Germany) 3T Skyra scanner with a dedicated 4-channel phased array breast coil. Before administration of contrast media, axial bilateral fat-suppressed T2-weighted fast spin-echo and DWI series were acquired. DWI was performed using spin-echo single shot echo-planar imaging with the following parameters: TR/TE 6230/65; FOV 340 × 340 mm; matrix 164 × 274; thickness 5.0 mm; gap 0.1 mm. Spectral presaturation with inversion recovery (SPAIR) was used for fat suppression. Motion-probing gradients in three orthogonal orientations were applied with b values of 50 and 800 s/mm2. Isotropic diffusion-weighted images were reconstructed for each b value. For quantitative analysis of the data acquired from DWI, ADC maps were automatically created using software provided by the MRI system manufacturer using two b values (50 and 800 s/mm2).

Finally, dynamic axial bilateral breast images of fat-suppressed high-resolution T1-weighted 3D fast gradient-echo images (VIEWS) were sequentially acquired before and 60, 120, 180, 240 and 300 seconds after the administration of contrast medium. For the dynamic study, gadopentetate dimeglumine (Magnevist) was administered intravenously using a power injection at a dose of 0.1 mmol/kg of body weight at a flow rate of 2 mL/s, followed by flushing with 20 mL of saline.

ADC values of the lesions were measured by a senior radiologist with more than 10 years of experience in breast imaging by placing regions of interest (ROIs) of 5-10 mm2 within the target lesions on ADC maps manually. The regions with high T2 within the lesion such as cystic part, necrosis, hematoma or fat, and the visual artifacts of DWI are avoided. The minimum and mean ADC values are automatically calculated from the ROI drawn.(please mention minimum size of tumor required for the ROI and put the rest of the cases i.e., smaller cancers in exclusion criteria).

Histopathological analysis

All the 55 participants who had done breast MRI underwent surgery (either breast conservation or modified radical mastectomy). The histopathological and immunohistochemical analysis of the surgical specimen was done to determine the prognostic factors like histopathological type of tumor, grade of the tumor, ER, PR status, Her 2Neu status, lymphovascular, and perineural invasion. The tumor size and axillary lymphnode status were also assessed.

Statistical analysis

Statistical analysis was performed using SPSS (Statistical package for social science version 16.0 for windows). Students T test and Analysis of Variance (ANOVA) were used to study the difference in ADC between two groups. Pearson correlation coefficient was used to quantify the correlation between ADC values and prognostic factors. The efficiency of the ADC was evaluated using receiver operating characteristic (ROC) analysis. A P value <0.05 was taken to indicate statistical significance.


   Results Top


The age of the patients ranged from 35 to 85 years. 28 had lesion on the right side, 24 had lesion on the left side and 3 had bilateral invasive breast carcinoma [Figure 1]. The maximum size was 7.2 cm and minimum size was 0.7 cm. All the 58 index lesions showed enhancement at DCE-MRI, out of which 45 (77.6%) had type III enhancement kinetics and 13 (22.4%) had type II enhancement kinetics. All the index lesions showed restricted diffusion on DWI. Both minimum and mean ADC values were calculated for all lesions in the study.
Figure 1 (A-D): Axial section of DWI (A), ADC map (B), post-contrast (C) and post-contrast MIP (D) images showing bilateral invasive carcinoma (no special type). Minimum and mean ADC values of the lesion were 0.57 and 0.79 × 10-3 mm2/sec respectively

Click here to view


Out of all the total 58 breast lesions, 44 lesions were (75.9%) single, while 9 (15.5%) were multifocal carcinoma and 5 (8.6%) were multicentric multifocal breast carcinoma. Histological types included invasive ductal carcinoma no special type (53) invasive lobular carcinoma (1) mucinous carcinoma (2) papillary carcinoma (1) medullary carcinoma (1). The minimum and maximum size of the tumors in HPE were 0.5 cm and 7.5 cm respectively [Graph 1]. Majority of the patients (35 out of 57, about 60.3%) were included in T2 stage of TNM staging.



Tumors with lower grade showed significantly higher ADC value (P < 0.001) compared to tumors with higher grade [Table 1]. On post hoc analysis, there was significant difference in the ADC values (both minimum and mean ADC values) of tumors of grade I and II (P < 0.001) as well as grade I and III (P < 0.001) and insignificant difference between grade II and III (P value - 1.0). The ADC value was correlated with pathological grading of the tumor (r = -0.497, P < 0.001).
Table 1: Statistical analysis of ADC values with prognostic factors

Click here to view


Further evaluation of the relationship between grade and minimum and mean ADC values was carried out using ROC analysis. The area under curve (AUC) was more for minimum ADC (0.922) than that for mean ADC (0.817) [Figure 2]. In ROC curve analysis in which grade I was compared with grade II and III, the minimum ADC cut-off value was calculated as 0.79 × 10-3 mm2/sec with 83% sensitivity and 84% specificity. For mean ADC cutoff was calculated as 0.82 × 10-3 mm2/sec with a sensitivity of 83% and specificity of 71%.
Figure 2: ROC curve for ADC values for grade of carcinoma

Click here to view


There was no statistically significant difference in minimum or mean ADC values in patients with positive and negative ER, PR status, Her 2 neu status, lymph node metastasis, lymphovascular, and perineural invasion. On Pearson correlation there was no significant correlation between ADC values and ER PR status and HER2 Neu status.


   Discussion Top


Diffusion weighted imaging is a functional imaging technique that detects early changes at the molecular level, cellularity, and cell membrane permeability which correlate with tumor biology.[20] So there is a possibility that diffusion weighted imaging and ADC values would identify tumors with high malignant potential and aid in preoperative prognostication and treatment planning.

In this study, both minimum and mean ADC values were separately assessed for all lesions using a 3T MRI system. It can acquire high-resolution images while retaining high signal to noise ratio. Compared to 1.5T MRI, 3T MRI allows data acquisition with higher b values and high signal to noise ratio.[21],[22]

Tumor grade of breast cancer is the most important prognostic factor which predicts the invasive behavior of tumor and its long-term prognosis. In this study the grade I carcinoma was found to have significantly higher ADC values than grade II and grade III [Figure 3]. But there was no significant difference in ADC values between grade II and grade III [Figure 4] and [Figure 5]. The mucinous tumors had the highest ADC values [Figure 6] and the lowest ADC obtained in the study in a grade 3 tumor was 0.3 × 10-3 mm2/sec [Figure 7]. In the ROC analysis, minimum ADC was having an accuracy of 92.2% (AUC 0.922 graded as Excellent) and the mean ADC was having an accuracy of 81.7% (AUC 0.817 graded as Good). This suggests that minimum ADC is a more reliable measurement for predicting preoperatively, the grade of the tumor and can be used for future analysis. A cut-off value of 0.79 × 10-3 mm2/sec was calculated by ROC analysis for minimum ADC for differentiation of grade I tumors from grade II and III with a sensitivity of 83% and specificity of 84%. For mean ADC, cut off is calculated as 0.82 × 10-3 mm2/sec with a sensitivity of 83% and specificity of 71%. Though both the cut offs are closer to each other minimum ADC is found to be more specific in differentiating grade I from other grades of tumor.
Figure 3 (A-D): Axial section of DWI (A), ADC map (B), post-contrast (C) and post-contrast MIP (D) images of invasive carcinoma (no special type) of right breast. Histopathologically proven grade I carcinoma. Minimum and mean ADC values of the lesion were 0.8 and 0.85 × 10-3 mm2/sec respectively

Click here to view
Figure 4 (A-C): Axial section of DWi (A), ADC map (B) and post-contrast (C) images of invasive carcinoma (no special type) of left breast. Histopathologically proven grade II carcinoma. Minimum and mean ADC values of the lesion were 0.63 and 0.76 × 10-3 mm2/sec respectively

Click here to view
Figure 5 (A-C): Axial section of DWI (A), ADC map (B) and post-contrast (C) images of invasive carcinoma (no special type) of right breast. Histopathologically proven grade III carcinoma. Minimum and mean ADC values of the lesion were 0.69 and 0.75 × 10-3 mm2/sec respectively

Click here to view
Figure 6 (A-D): Axial sections of DWI (A), ADC map (B), post-contrast (C) and post-contrast MIP (D) images of mucinous carcinoma of right breast. Minimum and mean ADC values of the lesion were 1.2 and 1.3 × 10-3 mm2/sec respectively

Click here to view
Figure 7 (A and B): Axial section of DWI (A), ADC map (B) images of invasive carcinoma (no special type) of right breast. Histopathologicallyproven grade III carcinoma. Minimum and mean ADC values of the lesion were 0.3 and 0.45 × 10-3 mm2/sec respectively

Click here to view


In a study done by Kizildag yirgin et al. a cut off of 1.05 × 10-3 mm2/sec was proposed for differentiating grade I from higher grade tumors.[23] This value is higher as compared to the cut off derived in our study. A statistically significant inverse correlation between ADC values and tumor grading was seen in few other previous studies.[20],[24] However Park et al. and Martincich et al. observed no statistically significant difference between ADC values and grade of tumor.[25],[26] The difference in observations may be due to the heterogeneity of the lesion or due to observer variability or variability based on spatial focus of observation of slide for assessing grade.(this statement is vague and does not explain anything. Please explain properly and provide the references).

The Non-significant P values in axillary lymphnode metastasis in our study can be explained by the fact that these values also depend on the duration of the disease, size of index lesion and disease progression. A similar result was also observed by Choi et al.[27]

The immunohistochemical markers like ER and PR are intracellular steroid receptor proteins and are indicators of prognosis and guide to hormonal and endocrine therapy.[28] In this study there was no significant association between ER and PR with ADC values. This is consistent with various previous studies.[25],[29],[30],[31]

It is known that, HER2-positive cells have a higher cell proliferation rate, more invasion and metastasis. So ADC values of HER2-positive Intra Ductal Carcinoma (IDC) are assumed to be lower because of its increased cellularity. In our study, on the contrast, the mean ADC values of lesions with Her-2-Neu positive were more than that of lesions with Her2-Neu negative cases, but they were statistically not significant. This can be explained by the increased angiogenesis suppressing diffusion restriction.[26] This result is consistent with other studies done by Martincich et al. and Park et al.[25],[26]

Lymphovascular invasion indicates increased risk of axillary lymph node involvement and distant metastasis in node negative cancer.[8] this high; lighted statement is unclear. please explain properly with references). No significant relation was noted between the lymphovascular invasion and ADC values in our study which is similar to that reported by Guvenc et al.[22]

Perineural invasion is reported in conjunction with lymphovascular invasion confounding its significance as an independent prognostic factor.[9] In this study no statistically significant relation is noted between the ADC values and perineural invasion.

DWI takes short acquisition time and post-processing time and does not need administration of contrast and provides a real quantitative functional parameter for assessing tumor aggressiveness. ADC values provide a powerful noninvasive predictive tool for the prognostication of the breast cancer preoperatively and thus helps in treatment planning and patient follow-up.


   Conclusion Top


MRI studies using DWI can identify the biological heterogeneity of the tumor tissue and ADC values can vary significantly according to the biological features of the breast cancer. Lower grade (grade I) breast carcinoma has a significantly high ADC value as compared to higher grade carcinoma (grade II and III). According to this study, a minimum ADC of more than 0.79 × 10-3 mm2/sec and a mean ADC of more than 0.82 × 10-3 mm2/sec are good prognostic indicators of which minimum ADC has more specificity. Infiltrating ductal carcinomas have lower ADC values than other types of carcinoma reflecting its more aggressive nature. There was no significant correlation between ADC and other prognostic factors like ER PR status, Her 2 Neu status, axillary lymph node status, lymphovascular, and perineural invasion.

Limitations

  1. As the study was conducted in patients with invasive cancers with a variety of histologies, it could not rule out bias due to histological variability. Invasive ductal carcinoma was dominant in the sub-group distribution of tumors. The number of special types of breast carcinoma and lobular carcinoma included in the study was too low for definitive assessment of a statistically significant mean ADC values for each histological type
  2. Some of the tumors were small and ADCs of these tumors might be inaccurate due to partial volume effect
  3. Patients undergoing neoadjuvant chemotherapy were excluded from the study. By doing so all the patients with aggressive breast cancers were excluded from the study resulting in selection bias
  4. The ROI used for calculating the ADC values were small and need not represent the biological character of the whole tumor.


Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
Breast cancer statistics | World Cancer Research Fund International [Internet]. Available from: http://www.wcrf.org/int/cancer-facts-figures/data-specific-cancers/breast-cancer-statistics. [Last cited on 2017 May 10].   Back to cited text no. 1
    
2.
Gupta A, Shridhar K, Dhillon PK. A review of breast cancer awareness among women in India: Cancer literate or awareness deficit? Eur J Cancer 2015;51:2058-66.  Back to cited text no. 2
    
3.
Malvia S, Bagadi SA, Dubey US, Saxena S. Epidemiology of breast cancer in Indian women: Breast cancer epidemiology. Asia Pac J Clin Oncol [Internet]. 2017 Feb; Available from: http://doi.wiley.com/10.1111/ajco.12661. [Last cited on 2017 Jun 03].  Back to cited text no. 3
    
4.
Breast Cancer Facts & Figures | American Cancer Society [Internet]. Available from: https://www.cancer.org/research/cancer-facts-statistics/breast-cancer-facts-figures.html. [Last cited on 2017 May 09].  Back to cited text no. 4
    
5.
Wolff AC, Hammond MEH, Schwartz JN, Hagerty KL, Allred DC, Cote RJ, et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. J Clin Oncol 2006;25:118-45.  Back to cited text no. 5
    
6.
Wolff AC, Hammond MEH, Hicks DG, Dowsett M, McShane LM, Allison KH, et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. J Clin Oncol 2013;31:3997-4013.  Back to cited text no. 6
    
7.
Pleşan DM, Georgescu M, Georgescu CV, Patrana N, Nina T, Pleşan C. Immunohistochemical evaluation of hormone receptors with predictive value in mammary carcinomas. Rom J Morphol Embryol 2011;52:1331-6.  Back to cited text no. 7
    
8.
Woo CS, Silberman H, Nakamura SK, Ye W, Sposto R, Colburn W, et al. Lymph node status combined with lymphovascular invasion creates a more powerful tool for predicting outcome in patients with invasive breast cancer. Am J Surg 184:337-40.  Back to cited text no. 8
    
9.
Liebig C, Ayala G, Wilks JA, Berger DH, Albo D. Perineural invasion in cancer: A review of the literature. Cancer 2009;115:3379-91.  Back to cited text no. 9
    
10.
Bostan Bozkurt T, Koc G, Sezgin G, Altay C, Gelal MF, Oyar O. Value of apparent diffusion coefficient values in differentiating malignant and benign breast lesions. Balk Med J 2016;33:294-300.  Back to cited text no. 10
    
11.
Saslow D, Boetes C, Burke W, Harms S, Leach MO, Lehman CD, et al. American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin 2007;57:75-89.  Back to cited text no. 11
    
12.
Gupta VK, Liu W, Wang R, Ye Y, Jiang J. Differentiation between benign and malignant breast lesions using adc on diffusion-weighted imaging at 3.0 T. Open J Radiol 2016;06:1-9.  Back to cited text no. 12
    
13.
Kul S, Cansu A, Alhan E, Dinc H, Gunes G, Reis A. Contribution of diffusion-weighted imaging to dynamic contrast-enhanced MRI in the characterization of breast tumors. Am J Roentgenol 2011;196:210-7.  Back to cited text no. 13
    
14.
Menezes GL. Magnetic resonance imaging in breast cancer: A literature review and future perspectives. World J Clin Oncol 2014;5:61-70.  Back to cited text no. 14
    
15.
Bogner W, Gruber S, Pinker K, Grabner G, Stadlbauer A, Weber M, et al. Diffusion-weighted MR for differentiation of breast lesions at 3.0 T: How does selection of diffusion protocols affect diagnosis? Radiology 2009;253:341-51.  Back to cited text no. 15
    
16.
Min Q, Shao K, Zhai L, Liu W, Zhu C, Yuan L, et al. Differential diagnosis of benign and malignant breast masses using diffusion-weighted magnetic resonance imaging. World J Surg Oncol 2015;13:32.  Back to cited text no. 16
    
17.
Abdulghaffar W, Tag-Aldeen MM. Role of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) in differentiating between benign and malignant breast lesions. Egypt J Radiol Nucl Med 2013;44:945-51.  Back to cited text no. 17
    
18.
Partridge SC, Mullins CD, Kurland BF, Allain MD, DeMartini WB, Eby PR, et al. Apparent diffusion coefficient values for discriminating benign and malignant breast MRI lesions: Effects of lesion type and size. Am J Roentgenol 2010;194:1664-73.  Back to cited text no. 18
    
19.
Partridge SC, McDonald ES. Diffusion weighted magnetic resonance imaging of the breast. Magn Reson Imaging Clin N Am 2013;21:601-24.  Back to cited text no. 19
    
20.
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. 20
    
21.
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. Am J Roentgenol 2012;198:717-22.  Back to cited text no. 21
    
22.
Guvenc I, Akay S, Ince S, Yildiz R, Kilbas Z, Oysul FG, et al. Apparent diffusion coefficient value in invasive ductal carcinoma at 3.0 Tesla: Is it correlated with prognostic factors? Br J Radiol 2016;89:20150614.  Back to cited text no. 22
    
23.
Kizildag Yirgin I, Arslan G, Ozturk E, Yirgin H, Tasdemir N, Akdogan Gemici A, et al. Diffusion weighted mr imaging of breast and correlation of prognostic factors in breast cancer. Balk Med J 2016;33:301-7.  Back to cited text no. 23
    
24.
Razek AAKA, 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. 24
    
25.
Park EK, Cho KR, Seo BK, Woo OH, Cho SB, Bae JW. Additional Value of diffusion-weighted imaging to evaluate prognostic factors of breast cancer: Correlation with the apparent diffusion coefficient. Iran J Radiol [Internet]. 2016 Jan 16;13. Available from: http://www. Iranjradiol.com/?page=article and article_id=33133. [Last cited on 2017 Jun 13].  Back to cited text no. 25
    
26.
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. 26
    
27.
Choi SY, Chang Y-W, 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. 27
    
28.
Donegan WL. Tumor-related prognostic factors for breast cancer. CA Cancer J Clin 1997;47:28-51.  Back to cited text no. 28
    
29.
Available from: https://www.ncbi.nlm.nih.gov/books/NBK174467/pdf/Bookshelf_NBK174467.pdf. [Last cited on 2017 May 10].  Back to cited text no. 29
    
30.
Akin Y, Ugurlu MU, Kaya H, Aribal E. Diagnostic value of diffusion-weighted imaging and apparent diffusion coefficient values in the differentiation of breast lesions, histpathologic subgroups and correlation with prognostic factors using 3.0 Tesla MR. J Breast Health 2016;12:123-32.  Back to cited text no. 30
    
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. 31
    

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Correspondence Address:
Dr. Mathew P Cherian
Department of Radiology Services, Kovai Medical Center and Hospital, 99, Avinashi Road, Coimbatore - 641 014, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijri.IJRI_97_19

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