Literature DB >> 24533873

Diffusion-tensor MR imaging of the breast: hormonal regulation.

Noam Nissan1, Edna Furman-Haran, Myra Shapiro-Feinberg, Dov Grobgeld, Hadassa Degani.   

Abstract

PURPOSE: To investigate the parameters obtained with magnetic resonance (MR) diffusion-tensor imaging (DTI) of the breast throughout the menstrual cycle phases, during lactation, and after menopause, with and without hormone replacement therapy (HRT).
MATERIALS AND METHODS: All protocols were approved by the internal review board, and signed informed consent was obtained from all participants. Forty-five healthy volunteers underwent imaging by using T2-weighted and DTI MR sequences at 3 T. Premenopausal volunteers (n = 16) underwent imaging weekly, four times during one menstrual cycle. Postmenopausal volunteers (n = 19) and lactating volunteers (n = 10) underwent imaging once. The principal diffusion coefficients (λ1, λ2, and λ3), apparent diffusion coefficient (ADC), fractional anisotropy (FA), and maximal anisotropy (λ1-λ3) were calculated pixel by pixel for the fibroglandular tissue in the entire breast.
RESULTS: In all premenopausal volunteers, the DTI parameters exhibited high repeatability, remaining almost equal along the menstrual cycle, with a low mean within-subject coefficient of variance of λ1, λ2, λ3, and ADC (1%-2% for all) and FA (5%), as well as a high intraclass correlation of 0.92-0.98. The diffusion coefficients were significantly lower (a) in the group without HRT use as compared with the group with HRT use (P < .01) and premenopausal volunteers (P < .01) and (b) in the lactating volunteers as compared with the premenopausal volunteers (P < .005). No significant differences in DTI parameters were found between premenopausal volunteers free of oral contraceptives and those who used oral contraceptives (P = .28-0.82) and between premenopausal volunteers and postmenopausal volunteers who used HRT (P = .31-0.93).
CONCLUSION: DTI parameters are not sensitive to menstrual cycle changes, while menopause, long-term HRT, and presence of milk in lactating women affected the DTI parameters. Therefore, the timing for performing breast DTI is not restricted throughout the menstrual cycle, whereas the modulations in diffusion parameters due to HRT and lactation should be taken into account in DTI evaluation.

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Year:  2014        PMID: 24533873     DOI: 10.1148/radiol.14132084

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  17 in total

Review 1.  Various diffusion magnetic resonance imaging techniques for pancreatic cancer.

Authors:  Meng-Yue Tang; Xiao-Ming Zhang; Tian-Wu Chen; Xiao-Hua Huang
Journal:  World J Radiol       Date:  2015-12-28

2.  Monitoring In-Vivo the Mammary Gland Microstructure during Morphogenesis from Lactation to Post-Weaning Using Diffusion Tensor MRI.

Authors:  Noam Nissan; Edna Furman-Haran; Myra Shapiro-Feinberg; Dov Grobgeld; Hadassa Degani
Journal:  J Mammary Gland Biol Neoplasia       Date:  2017-07-13       Impact factor: 2.673

Review 3.  Diffusion-weighted breast MRI: Clinical applications and emerging techniques.

Authors:  Savannah C Partridge; Noam Nissan; Habib Rahbar; Averi E Kitsch; Eric E Sigmund
Journal:  J Magn Reson Imaging       Date:  2016-09-30       Impact factor: 4.813

4.  Predicting liver metastasis of gastrointestinal tract cancer by diffusion-weighted imaging of apparent diffusion coefficient values.

Authors:  De-Xian Zheng; Shu-Chun Meng; Qing-Jun Liu; Chuan-Ting Li; Xi-Dan Shang; Yu-Seng Zhu; Tian-Jun Bai; Shi-Ming Xu
Journal:  World J Gastroenterol       Date:  2016-03-14       Impact factor: 5.742

5.  Diffusion tensor imaging in the normal breast: influences of fibroglandular tissue composition and background parenchymal enhancement.

Authors:  Michael Jonathan Plaza; Elizabeth A Morris; Sunitha B Thakur
Journal:  Clin Imaging       Date:  2015-12-08       Impact factor: 1.605

6.  Breast MRI during lactation: effects on tumor conspicuity using dynamic contrast-enhanced (DCE) in comparison with diffusion tensor imaging (DTI) parametric maps.

Authors:  Noam Nissan; Tanir Allweis; Tehillah Menes; Asia Brodsky; Shani Paluch-Shimon; Ilana Haas; Orit Golan; Yaheli Miller; Hani Barlev; Einat Carmon; Malka Brodsky; Debbie Anaby; Philip Lawson; Osnat Halshtok-Neiman; Anat Shalmon; Michael Gotlieb; Renata Faermann; Eli Konen; Miri Sklair-Levy
Journal:  Eur Radiol       Date:  2019-09-16       Impact factor: 5.315

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

Authors:  Jin You Kim; Jin Joo Kim; Suk Kim; Ki Seok Choo; Ahrong Kim; Taewoo Kang; Heesung Park
Journal:  Eur Radiol       Date:  2018-04-30       Impact factor: 5.315

8.  Tracking the mammary architectural features and detecting breast cancer with magnetic resonance diffusion tensor imaging.

Authors:  Noam Nissan; Edna Furman-Haran; Myra Feinberg-Shapiro; Dov Grobgeld; Erez Eyal; Tania Zehavi; Hadassa Degani
Journal:  J Vis Exp       Date:  2014-12-15       Impact factor: 1.355

9.  Diffusion tensor magnetic resonance imaging of the pancreas.

Authors:  Noam Nissan; Talia Golan; Edna Furman-Haran; Sara Apter; Yael Inbar; Arie Ariche; Barak Bar-Zakay; Yuri Goldes; Michael Schvimer; Dov Grobgeld; Hadassa Degani
Journal:  PLoS One       Date:  2014-12-30       Impact factor: 3.240

10.  Assessing Detection, Discrimination, and Risk of Breast Cancer According to Anisotropy Parameters of Diffusion Tensor Imaging.

Authors:  Ruisheng Jiang; Xiangmin Zeng; Shihang Sun; Zhijun Ma; Ximing Wang
Journal:  Med Sci Monit       Date:  2016-04-20
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