Literature DB >> 29713771

Diffusion tensor magnetic resonance imaging of breast cancer: associations between diffusion metrics and histological prognostic factors.

Jin You Kim1,2, Jin Joo Kim3, Suk Kim3, Ki Seok Choo4, Ahrong Kim5, Taewoo Kang6, Heesung Park6.   

Abstract

OBJECTIVES: To investigate whether quantitative diffusion metrics derived from diffusion tensor imaging (DTI) are associated with histological prognostic factors in breast cancer patients.
METHODS: This retrospective study was approved by the institutional review board, and informed consent was waived. Between 2016 and 2017, 251 consecutive women (mean age, 53.8 years) with breast cancer (230 invasive, 21 in situ) who underwent preoperative magnetic resonance (MR) imaging with DTI were identified. Diffusion gradients were applied in 20 directions (b values, 0 and 1,000 s/mm2). DTI metrics - mean diffusivity (MD) and fractional anisotropy (FA) - were measured for breast lesions and contralateral normal breast by two radiologists and were correlated with histological findings using the Mann-Whitney U-test and linear regression analysis.
RESULTS: MD and FA were significantly lower for breast cancers than for normal fibroglandular tissues (1.03 ± 0.25×10-3 mm2/s vs. 1.60 ± 0.19×10-3 mm2/s, p < 0.001 and 0.29 ± 0.09 vs. 0.33 ± 0.06, p < 0.001, respectively). Significant differences were observed in MD between invasive cancer and ductal carcinoma in situ lesions (p < 0.001). Multivariate linear analysis showed that larger size (>2 cm) (p = 0.007), high histological grade (grade 3) (p = 0.045) and axillary node metastasis (p = 0.009) were significantly associated with lower MD in invasive breast cancer patients. Larger size (p < 0.001) and high histological grade (p = 0.025) were significantly associated with lower FA.
CONCLUSIONS: DTI-derived diffusion metrics, such as MD and FA, are associated with histological prognostic factors in breast cancer patients. KEY POINTS: • MD was significantly lower for breast cancers than for normal breast tissues. • FA was significantly lower for breast cancers than for normal breast tissues. • Reduced DTI metrics were associated with poor prognostic factors of breast cancer. • DTI may provide valuable information concerning biological aggressiveness in breast cancer.

Entities:  

Keywords:  Anisotropy; Breast neoplasms; Diffusion tensor imaging; Magnetic resonance imaging; Prognosis

Mesh:

Year:  2018        PMID: 29713771     DOI: 10.1007/s00330-018-5429-8

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  32 in total

1.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI.

Authors:  P J Basser; C Pierpaoli
Journal:  J Magn Reson B       Date:  1996-06

2.  Histogram analysis of apparent diffusion coefficient at 3.0t: Correlation with prognostic factors and subtypes of invasive ductal carcinoma.

Authors:  Eun Jeong Kim; Sung Hun Kim; Ga Eun Park; Bong Joo Kang; Byung Joo Song; Yun Ju Kim; Dongeon Lee; Hyunsoo Ahn; Inah Kim; Yo Han Son; Robert Grimm
Journal:  J Magn Reson Imaging       Date:  2015-04-27       Impact factor: 4.813

3.  Parametric diffusion tensor imaging of the breast.

Authors:  Erez Eyal; Myra Shapiro-Feinberg; Edna Furman-Haran; Dov Grobgeld; Talia Golan; Yacov Itzchak; Raphael Catane; Moshe Papa; Hadassa Degani
Journal:  Invest Radiol       Date:  2012-05       Impact factor: 6.016

4.  Comparison of the diagnostic performances of diffusion parameters in diffusion weighted imaging and diffusion tensor imaging of breast lesions.

Authors:  Ozgur Cakir; Arzu Arslan; Nagihan Inan; Yonca Anık; Tahsin Sarısoy; Sevtap Gumustas; Gur Akansel
Journal:  Eur J Radiol       Date:  2013-09-13       Impact factor: 3.528

5.  Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging.

Authors:  Yong Guo; You-Quan Cai; Zu-Long Cai; Yuan-Gui Gao; Ning-Yu An; Lin Ma; Srikanth Mahankali; Jia-Hong Gao
Journal:  J Magn Reson Imaging       Date:  2002-08       Impact factor: 4.813

6.  Apparent diffusion coefficient of breast cancer and normal fibroglandular tissue in diffusion-weighted imaging: the effects of menstrual cycle and menopausal status.

Authors:  Jin You Kim; Hie Bum Suh; Hyun Jung Kang; Jong Ki Shin; Ki Seok Choo; Kyung Jin Nam; Seok Won Lee; Young Lae Jung; Young Tae Bae
Journal:  Breast Cancer Res Treat       Date:  2016-04-18       Impact factor: 4.872

7.  Can diffusion tensor anisotropy indices assist in breast cancer detection?

Authors:  Edna Furman-Haran; Dov Grobgeld; Noam Nissan; Myra Shapiro-Feinberg; Hadassa Degani
Journal:  J Magn Reson Imaging       Date:  2016-04-19       Impact factor: 4.813

8.  Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient.

Authors:  Shiteng Suo; Kebei Zhang; Mengqiu Cao; Xinjun Suo; Jia Hua; Xiaochuan Geng; Jie Chen; Zhiguo Zhuang; Xiang Ji; Qing Lu; He Wang; Jianrong Xu
Journal:  J Magn Reson Imaging       Date:  2015-09-07       Impact factor: 4.813

9.  Quantitative diffusion-weighted MR imaging in the differential diagnosis of breast lesion.

Authors:  C Marini; C Iacconi; M Giannelli; A Cilotti; M Moretti; C Bartolozzi
Journal:  Eur Radiol       Date:  2007-03-14       Impact factor: 7.034

10.  p53 protein accumulation predicts resistance to endocrine therapy and decreased post-relapse survival in metastatic breast cancer.

Authors:  Hiroko Yamashita; Tatsuya Toyama; Mariko Nishio; Yoshiaki Ando; Maho Hamaguchi; Zhenhuan Zhang; Shunzo Kobayashi; Yoshitaka Fujii; Hirotaka Iwase
Journal:  Breast Cancer Res       Date:  2006       Impact factor: 6.466

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  3 in total

1.  Diagnostic Performance of Diffusion Tensor Imaging for Characterizing Breast Tumors: A Comprehensive Meta-Analysis.

Authors:  Kai Wang; Zhipeng Li; Zhifeng Wu; Yucong Zheng; Sihui Zeng; Linning E; Jianye Liang
Journal:  Front Oncol       Date:  2019-11-18       Impact factor: 6.244

Review 2.  Diffusion-Weighted Magnetic Resonance Imaging of the Breast: Standardization of Image Acquisition and Interpretation.

Authors:  Su Hyun Lee; Hee Jung Shin; Woo Kyung Moon
Journal:  Korean J Radiol       Date:  2020-08-28       Impact factor: 3.500

Review 3.  Diffusion Breast MRI: Current Standard and Emerging Techniques.

Authors:  Ashley M Mendez; Lauren K Fang; Claire H Meriwether; Summer J Batasin; Stéphane Loubrie; Ana E Rodríguez-Soto; Rebecca A Rakow-Penner
Journal:  Front Oncol       Date:  2022-07-08       Impact factor: 5.738

  3 in total

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