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Breast MRI
The study on the application value of DKI in the classification of invasive breast carcinoma and its correlation with prognostic factors
CHENG Fang  SUO Shi-teng  KANG Ji-wen  HUA Xiao-lan  GENG Xiao-chuan  ZHANG Ke-bei  ZHANG Qing  HUA Jia 

DOI:10.12015/issn.1674-8034.2017.03.002.


[Abstract] Objective: To evaluate the application value of diffusion kurtosis imaging (DKI) based MD, MK parameters in grading invasive breast carcinoma and compare their diagnostic potential.Materials and Methods: Collecting 53 patients with invasive breast carcinoma diagnosed by pathological examination in this study. One male patient and 52 female patients were included. All patients underwent breast magnetic resonance imaging, including traditional magnetic resonance imaging and diffusion kurtosis imaging. ADC, MK and MD were calculated by using post-processing software, Matlab 2011b. Compared with histological grade, the classification of invasive breast carcinoma and its correlation with prognostic factors were statistically analyzed.Results: ADC, MD and MK values have no significant difference in well, moderately and poorly differentiated invasive breast carcinoma (P>0.05). Only ADC and MD were significantly different in ER expression (P<0.05). ADC, MD and MK values showed no significant difference in PR, HER-2 and Ki-67 expression (P>0.05, P=0.055 with MK in Ki-67).Conclusions: DKI has limited value in the evaluation on the classification of invasive breast carcinoma. However it provides useful information in the assessment of tumor proliferation activity.
[Keywords] Breast neoplasms;Magnetic resonance imaging;Pathology, clinical

CHENG Fang Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China

SUO Shi-teng Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China

KANG Ji-wen Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China

HUA Xiao-lan Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China

GENG Xiao-chuan Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China

ZHANG Ke-bei Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China

ZHANG Qing Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China

HUA Jia* Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China

*Correspondence to: Hua J, E-mail: Jill_huajia@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This study was funded by the Multidisciplinary Cross-program Development Fund Project No. YG2014ZD05
Received  2016-12-16
Accepted  2017-01-20
DOI: 10.12015/issn.1674-8034.2017.03.002
DOI:10.12015/issn.1674-8034.2017.03.002.

[1]
Hui ES, Cheung MM, Qi L, et al. Towards better MR characterization of neural tissues using directional diffusion kurtosis analysis. Neuroimage, 2008, 42(1): 122-134.
[2]
Goldhirsch A, Wood WC, Coates AS, et al. Strategies for subtypes: dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol, 2011, 22 (8): 1736-1747.
[3]
Cheang MC, Chia SK, Voduc D, et al. Ki-67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst, 2009, 101(10): 736-750.
[4]
Goldhirseh A, Winer EP, Coates AS, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy ofEarly Breast Cancer 2013. Ann Oncol, 2013, 24(9): 2206-2223.
[5]
Klauschen F, Wienert S, Schmitt WD, et al. Standardized Ki-67 diagnostics using automated scoring-clinical validation in the GeparTrio breast cancer study. Clin Cancer Res, 2015, 21 (16): 3651-3657.
[6]
Inoue K, Kozawa E, Mizukoshi W, et al. Usefulness of diffusion-weighted imaging of breast tumors: quantitative and visual assessment. Jpn J Radiol, 2011, 29(6): 429-436.
[7]
Rinaldi P, Giuliani M, Belli P, et al. DWI in breast MRI: role of ADC value to determine diagnosis between recurrent tumor and surgical scar in operated patients. Eur J Radiol, 2010, 75(2): e114-e123.
[8]
Panek R, Borri M, Orton M, et al. Evaluation of diffusion models in breast cancer. Med Physics, 2015, 42(8): 4833-4839.
[9]
Suo S, Lin N, Wang H, et al. Intravoxel incoherent motion diffusion-weighted MR imaging of breast cancer at 3.0 tesla: comparison of different curve-fitting methods. J Magn Reson Imaging, 2015, 42(2): 362-370.
[10]
Raab P, Hattingen E, Franz K, et al. Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology, 2010, 254(3): 876-881.
[11]
Wu EX, Cheng MM. MR diffusion Kurtosis imaging for neural tissue characterization. NMR Biomed, 2010, 23(7): 836-848.
[12]
Jensen JH, Helpern JA. Ramani A, et al. diffusion kurtosis imaing: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med, 2005, 53(6): 1432-1440.
[13]
Motoshima S, Irie H, Nakazono T, et al. Diffusion-weighted MR imaging in gynecologic cancers. J Gynecol Oncol, 2011, 22(4): 275-287.
[14]
Woodhams R, Kakita S, Hata H, et al. Diffusion-weighted imaging of mucinous carcinoma of the breast: evaluation of apparent diffusion coefficient and signal intensity in correlation with histologic findings. AJR Am J Roentgenol, 2009, 193(1): 260-266.
[15]
Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI.1996. J Magn Reson, 2011, 213(2): 560-570.
[16]
Rosenkrantz AB, Sigmund EE, Johnson G, et al. Prostate cancer: feasibility and preliminary experience of a diffusional kurtosis model for detection and assessment of aggressiveness of peripheral zone cancer. Radiology, 2012, 264(1): 126-135.
[17]
Nogueira L, Brandão S, Matos E, et al. Application of the diffusion kurtosis model for the study of breast lesions. Eur Radiol, 2014, 24(6): 1197-1203.
[18]
Wu D, Li G, Zhang J, et al. Characterization of breast tumors using diffusion kurtosis imaging (DKI). Plos One, 2014, 9(11): e113240.
[19]
Sun K, Chen X, Chai W, et al. Breast cancer: diffusion kurtosis MR imaging-diagnostic accuracy and correlation with clinical-pathologic factors. Radiology, 2015, 277(1): 46-55.
[20]
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(1): 175-182.
[21]
Jeh SK, Kim SH, Kim HS, 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(1): 102-109.
[22]
Choi SY, Chang YW, Park HJ, et al. Correlation of the apparent diffusion coefficiency values on diffusion-weighted imaging with prognostic factors for breast cancer. Br J Radiol, 2012, 85(1016): e474-e479.
[23]
Fuckar D, Dekanic A, Stifter S, et al. VEGF expression is associated with negative estrogen receptor status in patients with breast cancer. Int J Surg Pathol, 2006, 14(1): 49-55.
[24]
Kim SH, Cha ES, Kim HS, et al. Diffusion-weighted imaging of breast cancer: correlation of the apparent diffusion coefficient value with prognostic factors. J Magn Reson Imaging, 2009, 30(3): 615-620.
[25]
de Azambuja E, Cardoso F, de Castro G, et al. Ki-67 as prognostic marker in early breast cancer: a meta-analysis of published studieds involving 12, 155 patients. Br J Cancer, 2007, 96(10): 1504-1503.

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