Share：
Share this content in WeChat
X
[Chinese][PDF]103456
• Experience Exchanges •
Study on the correlation between MRI diffusion-weighted imaging apparent diffusion coefficient and Ki-67 index of triple negative and non-triple negative breast cancer
QIAN Jifang  ZHANG Rong  ZHAO Li  LI Yunzhi  XU Shengfang  YANG Aiping 

Cite this article as: Qian JF, ZHANG R, ZHAO L, et al. Study on the correlation between MRI diffusion-weighted imaging apparent diffusion coefficient and Ki-67 index of triple negative and non-triple negative breast cancer[J]. Chin J Magn Reson Imaging, 2021, 12(5): 69-72. DOI:10.12015/issn.1674-8034.2021.05.015.


[Abstract] Objective To explore the the utility of the minimum apparent diffusion coeffcient (ADC), average ADC, maximum ADC, and ADC difference value and to find optimum ADC parameters for differentiation between triple negative and non-triple negative breast cancer in diffusion-weighted imaging (DWI),and further investigated the relationship between ADC values and the Ki-67 proliferation index. Materials andMethods One hundred and forty-eight patients, who underwent breast MRI and collectively had 148 pathologically proven invasive carcinomas, were retrospectively enrolled. The apparent diffusion coefficient (ADC) value of the breast cancer lesions with each molecular subtype were identified and assessed jointly. all patients were female, aged 25—80 (51.4±10.5) years, 28 case TNBC and 120 case nTNBC patients. Receiver operating characteristic curves were drawn to evaluate the differentiating accuracy of ADC values. The Ki-67 proliferation index of the solid tumor components was also measured to explore its relationship with ADC values.Results Statistical analysis of ADC values showed that ADCmean [(0.769±0.117) vs. (0.897±0.088)×10-3 mm2/s] and ADCmin [(0.633±0.091) vs. (0.712±0.121)×10-3 mm2/s] were significantly lower in TNBC than nTNBC (all P＜0.05). The ADCDR of TNBC was significantly higher than that of nTNBC [(0.692±0.082) vs. (0.468±0.133)×10-3 mm2/s], P＜0.05). ADCmax value was not statistically significant (P＞0.05) in the two groups. ADCDR best distinguished the two groups, with an area under the curve value of 0.925. Using 0.635×10-3 mm2/s as the optimal threshold, the sensitivity, specificity of the two groups were 78.6%, 93.3%, respectively. ADCmean (r=-0.321) and ADCmin (r=-0.316) showed significant negative correlations with the Ki-67 proliferation index, ADCmax (r=0.249) and ADCmin (r=0.447) showed significant positive correlations with the Ki-67 proliferation index (all P＜0.01).Conclusions Quantitative analysis of ADC value can identify TNBC and nTNBC. ADCDR value may be the best single parameter for DWI to identify the two groups, and there is a certain correlation between ADC value and Ki-67 proliferation index.
[Keywords] triple negative breast cancer；magnetic resonance imaging；apparent diffusion coefficient；Ki-67；correlation

QIAN Jifang1   ZHANG Rong2*   ZHAO Li1   LI Yunzhi1   XU Shengfang1   YANG Aiping1  

1 Medical Imaging Center, Gansu Provincial Maternity and Child-care Hospital, Lanzhou 730050, China

2 The Second Clinical Medical College of Lanzhou University, Lanzhou 730030, China

Zhang R, E-mail: zhangronglzu@163.com

Conflicts of interest   None.

Received  2020-12-21
Accepted  2021-03-25
DOI: 10.12015/issn.1674-8034.2021.05.015
Cite this article as: Qian JF, ZHANG R, ZHAO L, et al. Study on the correlation between MRI diffusion-weighted imaging apparent diffusion coefficient and Ki-67 index of triple negative and non-triple negative breast cancer[J]. Chin J Magn Reson Imaging, 2021, 12(5): 69-72. DOI:10.12015/issn.1674-8034.2021.05.015.

1
Wang N, Liu S, Yang L, et al. Interpretation on the report of global cancer statistics 2018[J]. J Multidisciplinary Cancer Management, 2019, 5(1): 87-97.
2
Lyons TG. Targeted therapies for triple-negative breast cancer[J]. Curr Treat Options Oncol, 2019, 20(11): 82. DOI: 10.1007/s11864-019-0682-x.
3
Kim JY, Kim JJ, Hwangbo L, et al. Diffusion-weighted imaging of invasive breast cancer: relationship to distant metastasis-free survival[J]. Radiology, 2019, 291(2): 300-307. DOI: 10.1148/radiol.2019181706.
4
Kumar P, Aggarwal R. An overview of triple-negative breast cancer[J]. Arch Gynecol Obstet, 2016, 293(2): 247-269. DOI: 10.1007/s00404-015-3859-y.
5
Park GE, Kim SH, Kim EJ, et al. Histogram analysis of volume-based apparent diffusion coefficient in breast cancer[J]. Acta Radiol, 2017, 58(11): 1294-1302. DOI: 10.1177/0284185117694507.
6
Xianwang L, Lei H, Hong L, et al. Apparent diffusion coefficient to evaluate adult intracranial ependymomas: relationship to Ki-67 proliferation index. J Neuroimaging, 2020. [ DOI: ] DOI: 10.1111/jon.12789.
7
María del RTS, Teresa SM, Petit A, et al. Digital quantification of KI-67 in breast cancer[J]. Virchows Archiv, 2019, 474(2): 169-176. DOI: 10.1007/s00428-018-2481-3.
8
Alexey S, Paola C, Yun-Woo C, et al. Can diffusion-weighted imaging predict tumor grade and expression of Ki-67 in breast cancer? A multicenter analysis[J]. Breast Cancer Res, 2018, 20(1): 58-62. DOI: 10.1186/s13058-018-0991-1.
9
Wang XZ, Zuo WS, Liu Q, et al. International Expert consensus gathering at St Gallen Breast Cancer Conference 2013[J]. Chin J Cancer Preven Treatment, 2013, 20(23): 1859-1864.
10
Savas P, Salgado R, Denkert C, et al. Clinical relevance of host immunity in breast cancer: from TILs to the clinic[J]. Nat Rev Clin Oncol, 2016, 13(4): 228-241. DOI: 10.1038/nrclinonc.2015.215.
11
Bianchini G, Balko JM, Mayer IA, et al. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease[J]. Nature Reviews Clin Oncol, 2016, 13(11): 674-690. DOI: 10.1038/nrclinonc.2016.66.
12
O'Conor CJ, Chen T, González I, et al. Cancer stem cells in triple-negative breast cancer: a potential target and prognostic marker[J]. Biomark Med, 2018, 12(7): 813-820. DOI: 10.2217/bmm-2017-0398.
13
Onishi N, Kanao S, Kataoka M, et al. Apparent diffusion coefficient as a potential surrogate marker for Ki-67 index in mucinous breast carcinoma[J]. Magn Reson Imaging, 2015, 41(3): 610-615. DOI: 10.1002/jmri.24615.
14
Razek AA, Gaballa G, Denewer A, et al. Invasive ductal carcinoma: correlation of apparent diffusion coefficient value with pathological prognostic factors[J]. NMR Biomed, 2010, 23(6): 619-623. DOI: 10.1002/nbm.1503.
15
Hirano M, Satake H, Ishigaki S, et al. Diffusion-weighted imaging of breast masses: comparison of diagnostic performance using various apparent diffusion coefficient parameters[J]. AJR Am J Roentgenol, 2012, 198(3): 717-722. DOI: 10.2214/AJR.11.7093.
16
Mori N, Ota H, Mugikura S, et al. Detection of invasive components in cases of breast ductal carcinoma in situ on biopsy by using apparent diffusion coefficient MR parameters[J]. Eur Radiol, 2013, 23(10): 2705-2712. DOI: 10.1007/s00330-013-2902-2.
17
Hu J, Wang DB, Cai WM, et al. MR imaging characteristics of triple-negativebreastcancer[J]. Radiol Pract, 2010, 25(9): 1011-10157.
18
Kato F, Kudo K, Yamashita H, et al. Differences in morphological features and minimum apparent diffusion coefficient values among breast cancer subtypes using 3-tesla MRI[J]. Eur J Radiol, 2016, 85(1): 96-102. DOI: 10.1016/j.ejrad.2015.10.018.
19
Kato F, Kudo K, Yamashita H, et al. Predicting metastasis in clinically negative axillary lymph nodes with minimum apparent diffusion coefficient value in luminal A-like breast cancer[J]. Breast Cancer, 2019, 26(5): 628-636. DOI: 10.1007/s12282-019-00969-0.
20
Sharma U, Sah RG, Agarwal K, et al. Potential of diffusion-weighted imaging in the characterization of malignant, benign, and healthy breast tissues and molecular subtypes of breast cancer[J]. Front Oncol, 2016, 6: 126. DOI: 10.3389/fonc.2016.00126.
21
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[J]. Br J Radiol, 2012, 85(1016): e474-e479. DOI: 10.1259/bjr/79381464.
22
Boroughs LK, DeBerardinis RJ. Metabolic pathways promoting cancer cell survival and growth[J]. Nat Cell Biol, 2015, 17(4): 351-359. DOI: 10.1038/ncb3124.
23
Wu P, Cui L, Guo HB, et al. Differential diagnosis of breast ductal carcinoma in situ and microinvasive breast carcinoma by minimal apparent diffusivity, diffusion coefficient difference and enhanced magnetic resonance imaging[J]. J Pract Med, 2019, 41(6): 737-745.
24
Wang W, Wu J, Zhang P, et al. Prognostic and predictive value of Ki-67 in triple-negative breast cancer[J]. Oncotarget, 2016, 7(21): 31079-31087. DOI: 10.18632/oncotarget.9075.
25
Martincich L, Deantoni V, Bertotto I, et al. Correlations between diffusion-weighted imaging and breast cancer biomarkers[J]. Eur Radiol, 2012, 22(7): 1519-1528. DOI: 10.1007/s00330-012-2403-8.
26
Rahbar H, Partridge SC, Eby PR, et al. Characterization of ductal carcinoma in situ on diffusion weighted breast MRI[J]. Eur Radiol, 2011, 21(9): 2011-2019. DOI: 10.1007/s00330-011-2140-4.

PREV Value of MRI and cerebrospinal fluid examination in evaluating the curative effect of anti-tuberculosis treatment on patients with intracranial tuberculosis
NEXT Assessment of angiogenesis and differentiation grade of extrahepatic cholangiocarcinoma by diffusion weighted imaging
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn