Association of Breast Cancer Risk, Density, and Stiffness: Global Tissue Stiffness on Breast MR Elastography (MRE)

Association of Breast Cancer Risk, Density, and Stiffness: Global Tissue Stiffness on Breast MR Elastography (MRE) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of Breast Cancer Risk, Density, and Stiffness: Global Tissue Stiffness on Breast MR Elastography (MRE) Bhavika Patel, Kay Pepin, Kathy Brandt, Gina Mazza, Barbara Pockaj, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-611943/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Purpose: Quantify in vivo biomechanical tissue properties in various breast densities and in normal risk and high risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association between breast biomechanical properties and cancer risk. Methods: Patients with normal risk or high risk of breast cancer underent 3.0 T breast MR imaging and elastography. Breast parenchymal enhancement (BPE), density (from most recent mammogram), stiffness, elasticity, and viscosity were recorded. Within each breast density group (non-dense versus dense), stiffness, elasticity, and viscosity were compared across risk groups (normal versus high). A multivariable logistic regression model was used to evaluate whether the MRE parameters (separately for stiffness, elasticity, and viscosity) predicted risk status after controlling for clinical factors. Results: 50 normal risk and 86 high risk patients were included. Risk groups were similar on age, density, and menopausal status. Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high risk patients ( N = 55) compared to normal risk patients ( N = 34; all p < 0.001). Stiffness remained a significant predictor of risk status (OR=4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, BPE, age, and menopausal status. Similar results were seen for elasticity and viscosity. Conclusion: A structurally-based, quantitative biomarker of tissue stiffness obtained from MRE is associated with differences in breast cancer risk in dense breasts. Tissue stiffness could provide a novel prognostic marker to help identify high risk women with dense breasts who would benefit from increased surveillance and/or risk reduction measures. Cancer Biology breast cancer risk dense breasts breast tissue stiffness breast MR imaging breast elastography breast stroma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Fifty percent of screening-eligible women have high mammographic breast density (MBD). MBD is a strong risk factor for breast cancer, with a 4 to 6 fold increased risk in women with dense tissue compared to those with minimal dense tissue 1 . As such, increased areas of breast density may be more susceptible to the initation and promotion of breast cancers. However, breast cancer risk is not uniformly elevated across women with dense breasts 2 . Because half of all women have dense breat tissue, there is significant interest in developing imaging biomarkers to improve personalized risk stratification and identify which women with dense breasts are, in fact, at elevated risk for breast cancer. Doing so will enable appropriate imaging surveillance, reduce unnecessary additional screening, and enable effective risk-reducing strategies for women with dense breasts. Studies have shown that the variation in the risk associated with breast density may be related to differences in the breast microenvironment at a microscopic level 3–6 . More recently, studies using quantitative proteomics, collagen analyses, and mechanical measurements have demonstrated that mammographichally-dense tissues have increased collagen density, breast stroma stiffness, and higher epithelial cell density 7–9 . These mechanical properties of tissue play a major role in the development and progression of disease states and have been studied in multiple parts of the body 10–12 . Preliminary studies, performed in vitro , in animal models, or in small numbers of patients (livers and other organs), show this hypothesis to be true and also valid in relation to breast cancer development. An in vivo study in mice 13 demonstrated a causal link between increased stroma collagen deposition and enhanced tumorigenesis, local invasion, and metastases. These interactions may in turn affect oncogenesis and contribute to increased cancer risk in dense breasted women 4,14,15 . With the advent of MR elastography, quantitative biomechanical properties, such as tissue stiffness, elasticity and viscosity, have become viable imaging biomarkers to better understand the tissue microenvironment. MRE is a promising, noninvasive imaging technology capable of quantifying tissue mechanical properties 16–19 in vivo in patients. It assesses the viscoelastic shear properties of lesions through direct MR visualization of acoustic waves and demonstrates decreased elasticity in malignant tumors. As such, MRE allows an objective, quantitative analysis of the viscoelastic properties of normal and diseased breast tissues, providing greater insight into the study of and treatment of breast cancer. We hypothesize that the distinctive biomechanical properties of microenvironments can be assessed by MRE, and may account for the varying risk patterns for breast cancer in patients with similar densities. In this study, we will quantify in vivo biomechanical tissue properties, such as breast stiffness, elasticity and viscosity, in various breast densities in both normal and high risk women using Magnetic Resonance Imaging (MRI)/MRE. We will also examine the association of biomechanical properties of the breast with cancer risk. Materials And Methods Patients In this institutional review board–approved prospective single-institution study, we recruited two groups of women with different breast cancer risk to undergo a 3.0 T dynamic contrast enhanced MRI/MRE of the breast. Normal risk women were defined as having no personal history of breast cancer, no prior high risk breast biopsies, no self-reported significant family history of breast cancer, and a negative mammogram within 12 months (Table 1 ). Normal risk patients were at least 35 years old, not pregnant, not breastfeeding, had no imaging abnormality on mammography or sonography (Breast Imaging Reporting and Data System [BI-RADS] 1 or 2), and had no contraindications to MRI or contrast agents. High-risk breast cancer patients were recruited from patients who received standard of care breast MR for routine evaluation (patients with prior breast cancer history or those who were deemed at lifetime risk of 20% or greater based on Tryir Czuick scores 20–23 ). All prospectively collected patients provided informed written consent. High-risk patients were at least 26 years of age, not pregnant, not breastfeeding, had no imaging abnormality on mammography or sonography (Breast Imaging Reporting and Data System [BI-RADS] 1 or 2), and had no contraindications to MRI or contrast agents. Exclusion criteria were incomplete examinations and technical failures. Imaging All patients received combined 3-T multiparametric contrast-enhanced breast MRI and breast elastography. Breast MRE Magnetic resonance elastography (MRE) is a safe and novel non-invasive imaging technique to quantitatively assess the mechanical properties of soft tissues using MR imaging. The key component of elasticity imaging is to characterize the tissue response of the stress. In general, MRE imaging consists of the following steps: 1) Deliver shear waves in the tissues to be imaged; 2) Measure tissue displacement; 3) Generate quantitative stiffness maps (elastograms). Our breast MRE exam is naturally integrated into the established breast CEMRI / MRI clinical protocol, avoiding the need to switch RF coils or reposition patients. Our current breast MRE protocol is previously published 24 and the breast MRE sequence is added at the conclusion of the clinical portion of the exam. The breast MRE protocol is performed using a gradient echo phase contrast sequence 25 . A pillow-like passive driver is positioned between the breasts under the sternum while the patient lies prone in a standard breast coil 24 . Low frequency vibrations of 40 Hz are transmitted to the passive driver from an active driver (Resoundant, Inc., Rochester, MN) located outside the scan room during the entire duration of image acquisition (5–7 minutes). Breast MRE data is obtained from 40 axial slices, centered at the breast along the superior-inferior direction. A modified 3D-GREMRE pulse sequence is used with the following major parameters [ 9 ]: vibration frequency = 40 Hz; FOVx/y/z = 38.4/38.4/12 cm; flip angle = 8°; TR = 22.2–22.9 ms, TE = 15.9–18.1 ms (fat/water in-phase); axial bilateral imaging plane; matrix = 96 × 96 × 30; 8 motion cycles per 9 TRs (3 phase offsets); motion sensitivity (MENC) = 12.3 micron/radian; motion encoding directions = x/y/z; scan time = 5’25’’ − 5’42” (free breathing). 3D wave images are processed with 3D multiple-model direct inversions without directional filtering, generating 3D elastograms of bilateral breasts. Breast MRE measurements Regions of interest are then drawn over the central slice (at the level of the nipple) and custom, in-house software is used to obtain MRE stiffness values. Quantitative breast stiffness and viscosity measurements were obtained as described previously 24 by drawing whole breast region of interest (ROI) around the breast tissue (at the center slice). This was drawn on the magnitude images which demonstrate anatomy the best.. The wave images were analyzed to assess the adequacy of wave penetration through breast tissue. The ROI was then copied and translated onto the stiffness and viscosity maps to obtain the global breast MRE measurement. Multiparametric breast MRI A dynamic contrast enhanced image set was also acquired with the first series being an unenhanced fat-saturated gradient-recalled echo T1-weighted sequence (VIBRANT) followed by three dynamic contrast-enhanced fat-saturated T1-weighted gradient-recalled echo series (VIBRANT) performed after IV administration of Gadobutrol (Gadavist, Bayer) at 30 sec, 3 min and 6 min with the use of a T1-weight-based dosing protocol. The dynamic contrast images were acquired in the sagittal orientation (TR/TE = 5.1/2.4 ms; matrix = 256 x 256). Automatic post-processing included the generation of subtraction images between pre and post contrast images produced after each phase. Late gadolinium fat-suppressed T1- weighted fast-spoiled-gradient-echo (FSPGR) sequences were also acquired for both right and left side separately in the axial orientation (TR/TE = 115/3.15 ms; matrix = 256 x192, NEX = 2). T2 weighted sagittal and T1 weighted FSE AX images were obtained pre-contrast. Statistical Analysis In both the normal risk and high risk groups, the following breast imaging features were recorded: breast parenchymal enhancement (BPE); breast density (from most recent MG); tissue stiffness, elasticity, and viscosity. Patient demographic and disease characteristics were summarized for the full sample and by risk status; comparisons were conducted across risk groups (normal versus high) using two-sample t -tests (for continuous variables) or Fisher’s exact test (for categorical variables). Breast stiffness, elasticity, and viscosity were averaged across the left and right breasts. If breast stiffness, elasticity, or viscosity was missing for one breast, then the value observed for the contralateral breast was used. For the full sample and within each breast density group (non-dense versus dense), two-sample t -tests allowing for unequal variances were conducted to compare breast stiffness, elasticity, and viscosity across risk groups (normal versus high). Separately for breast stiffness, elasticity, and viscosity, a multivariable logistic regression model was used to evaluate whether the MRE parameter predicted risk status after controlling for clinical factors. A nominal significance level of α = 0.05 was used for all analyses. Results Patients From February 2017 to November 2019, there were 57 normal risk and 86 high risk patients recruited to the study. Of the 57 low-average risk patients, 50 who met the inclusion criteria were included in the normal risk portion of the study (mean age = 55.6 years, range = 39.0–74.0; Table 1 ); 2 were excluded for incomplete examinations and 5 were excluded for technical failures. Eighty-six patients who fulfilled the inclusion criteria were included in the high risk portion of the study (mean age = 53.6 years, range = 26.0–76.0; Table 1 ). As shown in Table 1 , normal risk and high risk women did not significantly differ in their mean age (mean age = 55.6 vs. 53.6 years, p = 0.27), density (68.0% vs. 64.0% dense breasts, p = 0.71), or menopausal status (66.0% vs. 69.8% were post-menopausal, p = 0.70). Table 1 Patient Demographic Characteristics by Risk Status Characteristic Normal Risk ( N = 50) High Risk ( N = 86) Total ( N = 136) p Value 1 Age in Years (Mean, SD ) 55.6 (10.2) 53.6 (11.3) 54.3 (10.9) 0.27 Breast Density (#, % Dense) 34 (68.0%) 55 (64.0%) 89 (65.4%) 0.71 Prior Breast Cancer (#, % Yes) 0 (0.0%) 28 (32.6%) 28 (20.6%) — Biopsy-Proven High Risk Lesion (#, % Yes) 0 (0.0%) 12 (14.0%) 12 (8.8%) — Biopsy or Surgery in Either Breast (#, % Yes) 7 (14.0%) 50 (58.1%) 57 (41.9%) 0.999 1st Degree Relative with Breast Cancer (#, % Yes) 0 (0.0%) 64 (74.4%) 64 (47.1%) — 1 For age in years, the p -value was based on a two-sample t -test allowing for unequal variances. For all other patient demographic and disease characteristics, the p -value was based on Fisher’s exact test. Em dashes indicate that the p -value was not calculated because high risk status was defined by prior breast cancer, biopsy-proven high risk lesion, and family history of breast cancer. Breast MRI BPE measurements Breast background parenchymal enhancement was routinely assessed on a BIRADS scale 26 (minimal, mild, moderate, marked) by standard of care within the imaging reports by one of five breast fellowship trained radiologists. Background parenchymal enhancement was stratified into 2 cohorts on MRI (mininal or mild versus moderate or marked) and did not significantly differ between the normal risk and high risk cohorts, p = 0.56. Breast MRE measurements Quantitative breast stiffness and viscosity measurements were significantly higher in the high-risk dense breast cohort compared to that of the normal risk dense breast cohort (Figs. 1, 2). The means were not significantly different between those with dense versus non-dense breasts in the normal risk patient cohort ( p = 0.14 for breast stiffness, p = 0.78 for breast viscosity; Table 2 ). Similarly, the means were not significantly different between those with dense versus non-dense breasts in the high risk patient cohort ( p = 0.09 for breast stiffness, p = 0.13 for breast viscosity; Table 2 , Fig. 3 ). In those women who had a prior breast surgery or biopsy, mean breast stiffness did not significantly differ between the surgerized or biopsied breast versus contralateral breast (Table 3 , Figs. 4 and 5 ). Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high risk patients ( N = 55) compared to normal risk patients ( N = 34; all p < 0.001). In the multivariable logistic regression model, breast stiffness remained a significant predictor of risk status (OR = 4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, MRI BPE, age, and menopausal status. Similar results were seen for breast elasticity (OR = 4.88, 95% CI [2.08, 11.43]) and viscosity (OR = 11.49, 95% CI [1.15, 114.89]). Table 2 Magnetic Resonance Elastography (MRE) Parameters and Magnetic Resonance Imaging (MRI) Background Parenchymal Enhancement (BPE) by Risk Status and Breast Density Characteristic Normal Risk High Risk Non-Dense ( N = 16) Dense ( N = 34) p Value 1 Non-Dense ( N = 31) Dense ( N = 55) p Value 1 Breast Stiffness 0.14 0.09 Mean ( SD ) 1.07 (0.18) 1.16 (0.21) 1.69 (1.19) 2.14 (1.12) Median (Range) 1.05 (0.65–1.44) 1.15 (0.76–1.67) 1.09 (0.54–4.36) 1.37 (0.92–4.09) Breast Elasticity 0.01 0.06 Mean ( SD ) 0.96 (0.15) 1.10 (0.20) 1.57 (1.08) 2.03 (1.08) Median (Range) 0.96 (0.61–1.21) 1.10 (0.74–1.58) 1.03 (0.52–3.85) 1.32 (0.74–3.99) Breast Viscosity 0.78 0.13 Mean ( SD ) 0.23 (0.09) 0.23 (0.07) 0.26 (0.27) 0.35 (0.24) Median (Range) 0.23 (0.08–0.39) 0.23 (0.11–0.44) 0.20 (0.0–1.39) 0.27 (-0.05–1.05) BPE (#, % Minimal or Mild) 14 (87.5%) 20 (58.8%) 0.06 28 (90.3%) 35 (63.6%) 0.01 Breast stiffness, elasticity, and viscosity were averaged across the left and right breasts. 1 For breast stiffness, elasticity, and viscosity, the p -value was based on a two-sample t -test allowing for unequal variances. For BPE, the p -value was based on Fisher’s exact test. Table 3 Breast Stiffness in Patients with 1 Surgerized Breast and 1 Unaffected Breast Patient Group N Breast Stiffness Mean ( SD ) p Value 1 Unaffected Breast Surgerized Breast Normal Risk 7 1.07 (0.12) 1.07 (0.15) — High Risk 33 2.33 (1.23) 2.22 (1.17) — Total 40 2.11 (1.21) 2.02 (1.15) 0.14 1 The p -value was based on a paired t -test comparing breast stiffness in the unaffected versus surgerized breast. The sample was limited to patients who underwent biopsy or surgery on one breast but not the other. Discussion The most striking results in our prospective study to understand global breast stiffness in normal and high risk patients, is the significantly higher stiffness of breast tissue in the high-risk (prior breast cancer history or high risk based on lifetime risk of greater than 20%) dense breasted patients compared to the normal risk dense breasted patients. Patients that had prior unilateral post-operative and/or post-treatment related radtaion change did not show a significant difference in MRE parameters between the treated and untreated breast. Our study suggests that the structurally-based (tissue stiffness) risk information obtained from MRE could be used to identify the subset of women with dense breasts most likely to benefit from increased surveillance and serve as a prognostic biomarker in screening breast cancer patients. These differences in stiffness were also seen between high risk and normal risk patients with non-dense breasts, although is less clinically significant as the performance of screening mammography is better in non-dense breasts. Through translational studies 27,28 , one biologic explanation for the increased risk of breast cancer in dense breasted patients suggests that increased collagen deposition, tissue tensile strength and collagen structural differences may contribute to increased mammographic density which then is associated with breast cancer initiation 4,14,15,29−36 . In fact, increasing evidence indicates that the tumor microenvironment plays a critical role in regulating the biologic behavior of, and predisposition to breast cancer 37,38 . Recent studies have shown that organization and stiffness of the collagen matrix are important in mediating tumor growth and invasion 3,4,28,39−41 . Abnormal changes in the amount and organization of extracellular matrix lead to altered biochemical, physical, and biomechanical properties of tumor-associated ECM that contribute to tumor progression 42,43 . In a similar light, a 2002 study investigated the feasibility of MRE to distinguish malignant and benign properties of breast tissue. Analysis of MR elastograms using a 65Hz frequency on 20 female patients with malignant and benign breast lesions and 15 healthy volunteers as the control demonstrated that cancerous growths consistently demonstrated higher elasticity values (greater stiffness) in contrast to surrounding tissue. Malignant breast tumors measured a median stiffness of 15.9 kPa while adjacent breast parenchyma and fatty tissue measured a median of only 2.5 kPa and 2.0 kPa, respectively. Furthermore, the difference in stiffness between malignant and benign tumors were also evident through MRE where benign breast lesions were determined to be at a median value of 7.0 kPa. In fact, quantitatively, the stiffness value of breast carcinomas was, on average, 418% higher than the value of the adjacent tissues, ( p < 0.05) 44 . The differing values of malignant tumors, benign tumors, and surrounding breast tissue illustrates that MRE as an imaging technique is able to accurately differentiate these tissues, and therefore of clinical importance 44–46 . Clinically, we know that over 40% of our breast cancer screening patients have dense breasts 47 . As a result of relatively new dense breasts notification laws, these patients are now being told that they have dense breast tissue (which could result in decreased sensitivity of mammography screening and increase their risk of breast cancer). Providers are encouraged to discuss these details with patients and where appropriate, offer various forms of supplemental dense breast screening 48–50 . These supplemental screening examinations can find additional cancers, however, also result in increased false positive results 23,51,52 . Since the risks and benefits of screening test are functions of each patient’s density features and risk factors, personal screening policies tailored to individuals are necessary and the new paradigm of breast cancer screening. Personalized breast cancer screening algorithms will result in cost efficiencies 53–58 and reduced false positive rates. As such, our data is promising whereby imaging biomarker of global breast tissue stiffness could provide an additional component in a clinical decision making tool to guide providers of screening and risk-reduction measures based on patient values and preferences. In our study we observed a nonsignificant increase in stiffness in both the dense and non-dense breasts. These observed differences with increasing stiffness with density are consistent with other smaller studies 59–61 , however the lack of a statistical significance is unique in our study. It should be noted that our study had few cases included along the the extreme spectrum of breast densities, fatty (n = 1) and extremely dense (n = 3) in our normal risk cohort, which may be affecting our results of statistical significance. Also it should be noted in our study, we chose to use the central slice as a first step to assess global 3D MRE values and used a 40Hz frequency on a 3T scanner. The region of interest included the central breast tissues, including fatty tissue and breast parenchyma on the slice, sparing the edges. This technique is similar to the annotations drawn and lessons learned on clinical diagnostic liver MRE. Contrast this technique to that of a 2017 study that demonstrated that dense breasts had significantly higher stiffness measurements compared with non-dense breasts (p < 0.05 61 ) the authors used a 60Hz frequency on the 3T scanner which requires less power for penetration. We speculate that the stiffness is highly dependent on the measurement method and the tissues that are covered. Limitations within our study highlight the need for further inquiry to fill the gaps of current research before implementation in clinical practice. Our study is one of the largest to date, other published breast MRE studies have been performed using a small sample cohort of approximately 20 to 50 patients, limiting the representation of breast tissue density. In addition, the high risk volunteers used for testing the efficacy of MRE had a included those with a personal history breast cancer, making it difficult to extrapolate the results to the general screening population. Thus, future research should aim to encompass a larger set of patients to account for individual differences in breast tissue and a variety of lesion characteristics. Given the current study is based on self- reported family history, we will also plan to calculate lifetime and 5 year risks for patients based on acccepted clinical breast cancer risk models 62–64 . Doing so, will allow us to have more accurate correlations to lifetime risk rather than the current stratification provided here. Another limitation is the fairly homogenous demographics of the patient population in this study. Therefore adding demographic variation to the population will be of utmost interest given the variations seen in breast density based on racial differences 65–67 . Also, future studies should include computer-generated volumetric breast density from MG to classify pateints as dense or non dense, given the considerable inter- and intra-reader variability within the mammographic density reporting system 68,69 . As more personalized approach within the breast cancer screening regimen develops, further risk stratification using quantitative imaging biomarker of global breast stiffness could inform more effective personalized screening regimens.We anticipate the results of this study and ours will give light to more investigations on how tissue stiffness correlates with breast cancer risk and prognosis. These prior studies and our results provide support for the further study of MRE given its ability to assess the viscoelastic properties of breast tumors and the surrounding tissue 70–72 . While the studies on MRE’s application to in vivo breast tissue are limited, this trial substantiates the capabilities of MRE and its potential for a risk stratification tool in women with dense breasts. Conclusions In conclusion, our work above suggests structurally-based, quantitative biomarker of tissue stiffness obtained from global 3D breast MRE could provide a novel prognostic marker used to identify the subset of high-risk women with dense breasts. As such, a stiffness values could help stratify dense breasted pateints into those who are at elevated risk and would benefit from increased surveillance with supplemental imaging techniques and/or risk reduction measures. Declarations Acknowledgments: We would like to think the Mayo Clinic Cancer Center for providing grant funding for this study. We would also like to acknowledge the Women's Health staff who have been instrumental with recruitment for this study and the breast imaging staff who helped with imaging patients using this new technology. Special thanks to Diana Almader-Smith who assisted with formatting the references for this manuscript. Funding: This study was funded by the Mayo Clinic Cancer Center MEGA grant. Conflict of Interest: BKP has received unrelated grant funding from GRAIL Inc. and Hologic Inc. with monies directed to Mayo Clinic. Dr. Kay Pepin is an employee at Resoundant Inc. Dr. Jun Chen is an employee, holds patents and intellectual property at Resoundant Inc. Dr. Yuxiang Zhou holds intellectual property in interventional MRI. Dr. Northfelt has research funding with Genentech/Roche (Inst); GlaxoSmithKline (Inst); Incyte (Inst); Merck (Inst); Novartis (Inst); Pfizer (Inst), Dr. Anderson has Stock and Other Ownership Interests - FlexBioTech; SafeGen Therapeutics, Consulting or Advisory Role and research funding from Merck, Dr. Vachon I don't have insurance owns stock inexact sciences, receives research funding from grail, has intellectual property with breast density software algorithms and has received travel expenses from GRAIL Inc. Dr Ehman owns stock, has research funding, and discloses uncompensated relationships with Resoundant. 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N Engl J Med 342(8):564–571 Brentnall AR, Cohn WF, Knaus WA, Yaffe MJ, Cuzick J, Harvey JA (2019) A Case-Control Study to Add Volumetric or Clinical Mammographic Density into the Tyrer-Cuzick Breast Cancer Risk Model. J Breast Imaging 1(2):99–106 Tice JA, Cummings SR, Ziv E, Kerlikowske K (2005) Mammographic breast density and the Gail model for breast cancer risk prediction in a screening population. Breast Cancer Res Treat 94(2):115–122 del Carmen MG, Halpern EF, Kopans DB et al (2007) Mammographic breast density and race. AJR Am J Roentgenol 188(4):1147–1150 McCarthy AM, Keller BM, Pantalone LM et al. Racial Differences in Quantitative Measures of Area and Volumetric Breast Density. J Natl Cancer Inst. 2016;108(10) Razzaghi H, Troester MA, Gierach GL, Olshan AF, Yankaskas BC, Millikan RC (2012) Mammographic density and breast cancer risk in White and African American Women. Breast Cancer Res Treat 135(2):571–580 Gard CC, Aiello Bowles EJ, Miglioretti DL, Taplin SH, Rutter CM (2015) Misclassification of Breast Imaging Reporting and Data System (BI-RADS) Mammographic Density and Implications for Breast Density Reporting Legislation. Breast J 21(5):481–489 Redondo A, Comas M, Macià F et al (2012) Inter- and intraradiologist variability in the BI-RADS assessment and breast density categories for screening mammograms. Br J Radiol 85(1019):1465–1470 Balleyguier C, Lakhdar AB, Dunant A, Mathieu MC, Delaloge S, Sinkus R. Value of whole breast magnetic resonance elastography added to MRI for lesion characterization. NMR in biomedicine. 2018;31(1) McKnight AL, Kugel JL, Rossman PJ, Manduca A, Hartmann LC, Ehman RL (2002) MR elastography of breast cancer: preliminary results. AJR American journal of roentgenology 178(6):1411–1417 Sinkus R, Lorenzen J, Schrader D, Lorenzen M, Dargatz M, Holz D (2000) High-resolution tensor MR elastography for breast tumour detection. Physics in medicine biology 45(6):1649–1664 Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Jun, 2021 Reviewers invited by journal 11 Jun, 2021 Editor assigned by journal 10 Jun, 2021 First submitted to journal 10 Jun, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-611943","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":32886328,"identity":"9426fab8-8404-4d43-810f-46a4878a5685","order_by":0,"name":"Bhavika Patel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYLCCBwwHEhgYGJsZGCqgIjx41TMzMCTAtZwhTQuQxdhGhBZz9vMHHyQw3Mnjn5HcbPBz3jZ5g+MNjA/etuHWYtmTzGyQwPCsWOJGYnNi77bbhhvOHGA2nItHi8GBZDaJBIbDiQ1nDjYf4N12m3HDjQQ2aV58Ws4/Zv8B0jIfqOXg3zm37YFa2H/j1XIjmY0BpGXD8cbmZN6G24kgW5jxabGc8dhYIsHgWeJGoBZjmWO3k2cCrZOccw63FnP+xIcfPlTcSZx3mP2x5Jua27Z9x5sPfnhThsdhSCQEKBxgbMCtHk0xBMjj1zAKRsEoGAUjEAAA/KVgavnei/sAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8486-955X","institution":"Mayo Clinic Arizona","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Bhavika","middleName":"","lastName":"Patel","suffix":""},{"id":32886329,"identity":"8bf4d19f-8dc9-475c-b7e1-c818620ae2bd","order_by":1,"name":"Kay Pepin","email":"","orcid":"","institution":"Mayo Clinic Rochester: Mayo Clinic Minnesota","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kay","middleName":"","lastName":"Pepin","suffix":""},{"id":32886330,"identity":"e679f369-743f-47f5-b2ff-174f8229fe3d","order_by":2,"name":"Kathy Brandt","email":"","orcid":"","institution":"Mayo Clinic Rochester: Mayo Clinic Minnesota","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kathy","middleName":"","lastName":"Brandt","suffix":""},{"id":32886331,"identity":"e7e1544c-3e6c-4bbe-b5e8-d69c2ee60853","order_by":3,"name":"Gina Mazza","email":"","orcid":"","institution":"Mayo Clinic Arizona","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gina","middleName":"","lastName":"Mazza","suffix":""},{"id":32886332,"identity":"330a89fc-7691-4a2c-baa8-41d2e2444e56","order_by":4,"name":"Barbara Pockaj","email":"","orcid":"","institution":"Mayo Clinic Arizona","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Barbara","middleName":"","lastName":"Pockaj","suffix":""},{"id":32886333,"identity":"7dc6fe2e-62e3-4714-b3a1-2b52224238f4","order_by":5,"name":"Jun Chen","email":"","orcid":"","institution":"Mayo Clinic Rochester","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Chen","suffix":""},{"id":32886334,"identity":"b3f4eeaf-f0b7-4b83-a0d9-30293ef69c27","order_by":6,"name":"Yuxiang Zhou","email":"","orcid":"","institution":"Mayo Clinic Arizona","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuxiang","middleName":"","lastName":"Zhou","suffix":""},{"id":32886335,"identity":"e170742b-28d3-4dc0-81e9-614c39f7b688","order_by":7,"name":"Donald Northfelt","email":"","orcid":"","institution":"Mayo Clinic Arizona","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Donald","middleName":"","lastName":"Northfelt","suffix":""},{"id":32886336,"identity":"fa0b7f62-eab6-4bd5-9eac-c0703c5515c6","order_by":8,"name":"Karen Anderson","email":"","orcid":"","institution":"Mayo Clinic Arizona","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Karen","middleName":"","lastName":"Anderson","suffix":""},{"id":32886337,"identity":"16a599e2-cf55-4bc9-9481-a769c8d556c7","order_by":9,"name":"Juliana Kling","email":"","orcid":"","institution":"Mayo Clinic Arizona","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Juliana","middleName":"","lastName":"Kling","suffix":""},{"id":32886338,"identity":"488ebe1f-21d9-4843-950c-e4917affc4b1","order_by":10,"name":"Kristin Swanson","email":"","orcid":"","institution":"Mayo Clinic Arizona","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kristin","middleName":"","lastName":"Swanson","suffix":""},{"id":32886339,"identity":"9b6efabc-3c84-44f4-aba4-8b7be5681927","order_by":11,"name":"Mehdi Nikkhah","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Nikkhah","suffix":""},{"id":32886340,"identity":"59cab03b-275b-4876-bee7-d6d27bd3d009","order_by":12,"name":"Richard Ehman","email":"","orcid":"","institution":"Mayo Clinic: Mayo Clinic Minnesota","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Ehman","suffix":""}],"badges":[],"createdAt":"2021-06-11 04:31:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-611943/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-611943/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":10395962,"identity":"165d8d1b-cab0-4780-afb5-d4f4fe68754c","added_by":"auto","created_at":"2021-06-15 15:45:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165101,"visible":true,"origin":"","legend":"50 year old average risk female with a) dense breasts on negative screening mammography, b) Post-contrast breast MRI was negative for imaging abnormalities, mild bilateral benign background parenchymal enhancement, c) MR magnitude map demonstrates anatomical anatomy to delineate central slice d) corresponding elastogram map shows mild symmetric bilateral stiffness ranging within 0.5-1.0 kPa.\n\n","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-611943/v1/995058bab8670fa7a1f8f8d1.jpg"},{"id":10395855,"identity":"1dd62faa-efc5-42cc-99ae-9f11c07b2301","added_by":"auto","created_at":"2021-06-15 15:42:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151334,"visible":true,"origin":"","legend":"41 year old female with significant history of maternal aunt with BRCA 2 mutation carrier, maternal history of breast cancer, IBIS model 29% a) dense breasts on negative screening mammography, b) post-contrast breast MRI was negative for imaging abnormalities, mild bilateral benign background parenchymal enhancement, c) MR magnitude map demonstrates anatomical anatomy to delineate central slice d) corresponding elastogram map shows mild symmetric bilateral stiffness ranging within 2.0-4.0 kPa.","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-611943/v1/08c9b229b5642e7aa455bcd8.jpg"},{"id":10395857,"identity":"bebe887b-fa5a-4fac-bc4d-af90770f085f","added_by":"auto","created_at":"2021-06-15 15:42:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33697,"visible":true,"origin":"","legend":"Box and whisker plot of breast stiffness (averaged across the left and right breasts) by breast density and risk status. High risk patients with dense breasts had greater breast stiffness compared to normal risk patients with dense breasts. The box represents the interquartile range, the horizontal line within each box represents the median, and the diamond within each box represents the mean.","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-611943/v1/a362ffeab2d6ceeaad68a36a.jpg"},{"id":10395508,"identity":"c98e1a93-84de-4075-9637-d347b57b1b11","added_by":"auto","created_at":"2021-06-15 15:39:04","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58636,"visible":true,"origin":"","legend":"Box and whisker plot of breast stiffness (averaged across the left and right breasts) by biopsy/surgery in high risk patients. 24 high risk women with dense breasts and no biopsy/surgery and 30 high risk women with dense breasts and biopsy/surgery in either or both breasts. The horizontal line within each box represents the median, and the diamond within each box represents the mean. The breast stiffness median = 1.34 versus 2.56 in the no biopsy/surgery versus biopsy surgery group. The breast stiffness mean = 1.92 versus 2.35 in the no biopsy/surgery versus biopsy surgery group. These means were not significantly different. ","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-611943/v1/a2ebe039654d480ddcaca257.jpg"},{"id":10395854,"identity":"f343265e-0422-49a4-9799-8384daa6bb63","added_by":"auto","created_at":"2021-06-15 15:42:04","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":59116,"visible":true,"origin":"","legend":"Stiffness of surgerized/unaffected breast scatterplot. The blue markers represent normal risk women, and the red markers represent high risk women. The diagonal line represents perfect agreement for stiffness between a woman’s biopsied/surgerized breast and unaffected breast. ","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-611943/v1/20cede3bc95249fc6478195f.jpg"},{"id":13698742,"identity":"8cd51be3-3309-4efe-9d13-ea0cce16b1b9","added_by":"auto","created_at":"2021-09-17 13:16:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":606248,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-611943/v1/99733746-767b-4a87-bf09-8eb912f1932d.pdf"}],"financialInterests":"","formattedTitle":"Association of Breast Cancer Risk, Density, and Stiffness: Global Tissue Stiffness on Breast MR Elastography (MRE)","fulltext":[{"header":"Background","content":" \u003cp\u003eFifty percent of screening-eligible women have high mammographic breast density (MBD). MBD is a strong risk factor for breast cancer, with a 4 to 6 fold increased risk in women with dense tissue compared to those with minimal dense tissue\u003csup\u003e1\u003c/sup\u003e. As such, increased areas of breast density may be more susceptible to the initation and promotion of breast cancers. However, breast cancer risk is not uniformly elevated across women with dense breasts\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBecause half of all women have dense breat tissue, there is significant interest in developing imaging biomarkers to improve personalized risk stratification and identify which women with dense breasts are, in fact, at elevated risk for breast cancer. Doing so will enable appropriate imaging surveillance, reduce unnecessary additional screening, and enable effective risk-reducing strategies for women with dense breasts.\u003c/p\u003e \u003cp\u003eStudies have shown that the variation in the risk associated with breast density may be related to differences in the breast microenvironment at a microscopic level\u003csup\u003e3\u0026ndash;6\u003c/sup\u003e. More recently, studies using quantitative proteomics, collagen analyses, and mechanical measurements have demonstrated that mammographichally-dense tissues have increased collagen density, breast stroma stiffness, and higher epithelial cell density\u003csup\u003e7\u0026ndash;9\u003c/sup\u003e. These mechanical properties of tissue play a major role in the development and progression of disease states and have been studied in multiple parts of the body\u003csup\u003e10\u0026ndash;12\u003c/sup\u003e. Preliminary studies, performed \u003cem\u003ein vitro\u003c/em\u003e, in animal models, or in small numbers of patients (livers and other organs), show this hypothesis to be true and also valid in relation to breast cancer development. An \u003cem\u003ein vivo\u003c/em\u003e study in mice\u003csup\u003e13\u003c/sup\u003e demonstrated a causal link between increased stroma collagen deposition and enhanced tumorigenesis, local invasion, and metastases. These interactions may in turn affect oncogenesis and contribute to increased cancer risk in dense breasted women\u003csup\u003e4,14,15\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith the advent of MR elastography, quantitative biomechanical properties, such as tissue stiffness, elasticity and viscosity, have become viable imaging biomarkers to better understand the tissue microenvironment. MRE is a promising, noninvasive imaging technology capable of quantifying tissue mechanical properties\u003csup\u003e16\u0026ndash;19\u003c/sup\u003e \u003cem\u003ein vivo\u003c/em\u003e in patients. It assesses the viscoelastic shear properties of lesions through direct MR visualization of acoustic waves and demonstrates decreased elasticity in malignant tumors. As such, MRE allows an objective, quantitative analysis of the viscoelastic properties of normal and diseased breast tissues, providing greater insight into the study of and treatment of breast cancer. We hypothesize that the distinctive biomechanical properties of microenvironments can be assessed by MRE, and may account for the varying risk patterns for breast cancer in patients with similar densities. In this study, we will quantify \u003cem\u003ein vivo\u003c/em\u003e biomechanical tissue properties, such as breast stiffness, elasticity and viscosity, in various breast densities in both normal and high risk women using Magnetic Resonance Imaging (MRI)/MRE. We will also examine the association of biomechanical properties of the breast with cancer risk.\u003c/p\u003e "},{"header":"Materials And Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eIn this institutional review board\u0026ndash;approved prospective single-institution study, we recruited two groups of women with different breast cancer risk to undergo a 3.0 T dynamic contrast enhanced MRI/MRE of the breast. Normal risk women were defined as having no personal history of breast cancer, no prior high risk breast biopsies, no self-reported significant family history of breast cancer, and a negative mammogram within 12 months (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Normal risk patients were at least 35 years old, not pregnant, not breastfeeding, had no imaging abnormality on mammography or sonography (Breast Imaging Reporting and Data System [BI-RADS] 1 or 2), and had no contraindications to MRI or contrast agents.\u003c/p\u003e \u003cp\u003eHigh-risk breast cancer patients were recruited from patients who received standard of care breast MR for routine evaluation (patients with prior breast cancer history or those who were deemed at lifetime risk of 20% or greater based on Tryir Czuick scores\u003csup\u003e20\u0026ndash;23\u003c/sup\u003e). All prospectively collected patients provided informed written consent. High-risk patients were at least 26 years of age, not pregnant, not breastfeeding, had no imaging abnormality on mammography or sonography (Breast Imaging Reporting and Data System [BI-RADS] 1 or 2), and had no contraindications to MRI or contrast agents.\u003c/p\u003e \u003cp\u003eExclusion criteria were incomplete examinations and technical failures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eImaging\u003c/h2\u003e \u003cp\u003eAll patients received combined 3-T multiparametric contrast-enhanced breast MRI and breast elastography.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBreast MRE\u003c/h2\u003e \u003cp\u003eMagnetic resonance elastography (MRE) is a safe and novel non-invasive imaging technique to quantitatively assess the mechanical properties of soft tissues using MR imaging. The key component of elasticity imaging is to characterize the tissue response of the stress. In general, MRE imaging consists of the following steps: 1) Deliver shear waves in the tissues to be imaged; 2) Measure tissue displacement; 3) Generate quantitative stiffness maps (elastograms).\u003c/p\u003e \u003cp\u003eOur breast MRE exam is naturally integrated into the established breast CEMRI / MRI clinical protocol, avoiding the need to switch RF coils or reposition patients. Our current breast MRE protocol is previously published\u003csup\u003e24\u003c/sup\u003e and the breast MRE sequence is added at the conclusion of the clinical portion of the exam.\u003c/p\u003e \u003cp\u003eThe breast MRE protocol is performed using a gradient echo phase contrast sequence\u003csup\u003e25\u003c/sup\u003e. A pillow-like passive driver is positioned between the breasts under the sternum while the patient lies prone in a standard breast coil\u003csup\u003e24\u003c/sup\u003e. Low frequency vibrations of 40 Hz are transmitted to the passive driver from an active driver (Resoundant, Inc., Rochester, MN) located outside the scan room during the entire duration of image acquisition (5\u0026ndash;7 minutes). Breast MRE data is obtained from 40 axial slices, centered at the breast along the superior-inferior direction. A modified 3D-GREMRE pulse sequence is used with the following major parameters [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]: vibration frequency\u0026thinsp;=\u0026thinsp;40 Hz; FOVx/y/z\u0026thinsp;=\u0026thinsp;38.4/38.4/12 cm; flip angle\u0026thinsp;=\u0026thinsp;8\u0026deg;; TR\u0026thinsp;=\u0026thinsp;22.2\u0026ndash;22.9 ms, TE\u0026thinsp;=\u0026thinsp;15.9\u0026ndash;18.1 ms (fat/water in-phase); axial bilateral imaging plane; matrix\u0026thinsp;=\u0026thinsp;96 \u0026times; 96 \u0026times; 30; 8 motion cycles per 9 TRs (3 phase offsets); motion sensitivity (MENC)\u0026thinsp;=\u0026thinsp;12.3 micron/radian; motion encoding directions\u0026thinsp;=\u0026thinsp;x/y/z; scan time\u0026thinsp;=\u0026thinsp;5\u0026rsquo;25\u0026rsquo;\u0026rsquo; \u0026minus;\u0026thinsp;5\u0026rsquo;42\u0026rdquo; (free breathing). 3D wave images are processed with 3D multiple-model direct inversions without directional filtering, generating 3D elastograms of bilateral breasts.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eBreast MRE measurements\u003c/h2\u003e \u003cp\u003eRegions of interest are then drawn over the central slice (at the level of the nipple) and custom, in-house software is used to obtain MRE stiffness values. Quantitative breast stiffness and viscosity measurements were obtained as described previously\u003csup\u003e24\u003c/sup\u003e by drawing whole breast region of interest (ROI) around the breast tissue (at the center slice). This was drawn on the magnitude images which demonstrate anatomy the best.. The wave images were analyzed to assess the adequacy of wave penetration through breast tissue. The ROI was then copied and translated onto the stiffness and viscosity maps to obtain the global breast MRE measurement.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMultiparametric breast MRI\u003c/h2\u003e \u003cp\u003eA dynamic contrast enhanced image set was also acquired with the first series being an unenhanced fat-saturated gradient-recalled echo T1-weighted sequence (VIBRANT) followed by three dynamic contrast-enhanced fat-saturated T1-weighted gradient-recalled echo series (VIBRANT) performed after IV administration of Gadobutrol (Gadavist, Bayer) at 30 sec, 3 min and 6 min with the use of a T1-weight-based dosing protocol. The dynamic contrast images were acquired in the sagittal orientation (TR/TE\u0026thinsp;=\u0026thinsp;5.1/2.4 ms; matrix\u0026thinsp;=\u0026thinsp;256 x 256). Automatic post-processing included the generation of subtraction images between pre and post contrast images produced after each phase. Late gadolinium fat-suppressed T1- weighted fast-spoiled-gradient-echo (FSPGR) sequences were also acquired for both right and left side separately in the axial orientation (TR/TE\u0026thinsp;=\u0026thinsp;115/3.15 ms; matrix\u0026thinsp;=\u0026thinsp;256 x192, NEX\u0026thinsp;=\u0026thinsp;2). T2 weighted sagittal and T1 weighted FSE AX images were obtained pre-contrast.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIn both the normal risk and high risk groups, the following breast imaging features were recorded: breast parenchymal enhancement (BPE); breast density (from most recent MG); tissue stiffness, elasticity, and viscosity. Patient demographic and disease characteristics were summarized for the full sample and by risk status; comparisons were conducted across risk groups (normal versus high) using two-sample \u003cem\u003et\u003c/em\u003e-tests (for continuous variables) or Fisher\u0026rsquo;s exact test (for categorical variables). Breast stiffness, elasticity, and viscosity were averaged across the left and right breasts. If breast stiffness, elasticity, or viscosity was missing for one breast, then the value observed for the contralateral breast was used. For the full sample and within each breast density group (non-dense versus dense), two-sample \u003cem\u003et\u003c/em\u003e-tests allowing for unequal variances were conducted to compare breast stiffness, elasticity, and viscosity across risk groups (normal versus high). Separately for breast stiffness, elasticity, and viscosity, a multivariable logistic regression model was used to evaluate whether the MRE parameter predicted risk status after controlling for clinical factors. A nominal significance level of \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05 was used for all analyses.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003ePatients\u003c/h2\u003e\n\u003cp\u003eFrom February 2017 to November 2019, there were 57 normal risk and 86 high risk patients recruited to the study. Of the 57 low-average risk patients, 50 who met the inclusion criteria were included in the normal risk portion of the study (mean age\u0026thinsp;=\u0026thinsp;55.6 years, range\u0026thinsp;=\u0026thinsp;39.0\u0026ndash;74.0; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e); 2 were excluded for incomplete examinations and 5 were excluded for technical failures. Eighty-six patients who fulfilled the inclusion criteria were included in the high risk portion of the study (mean age\u0026thinsp;=\u0026thinsp;53.6 years, range\u0026thinsp;=\u0026thinsp;26.0\u0026ndash;76.0; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, normal risk and high risk women did not significantly differ in their mean age (mean age\u0026thinsp;=\u0026thinsp;55.6 vs. 53.6 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27), density (68.0% vs. 64.0% dense breasts, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.71), or menopausal status (66.0% vs. 69.8% were post-menopausal, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.70).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePatient Demographic Characteristics by Risk Status\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNormal\u0026nbsp;Risk\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026nbsp;=\u0026nbsp;50)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHigh\u0026nbsp;Risk\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026nbsp;=\u0026nbsp;86)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026nbsp;=\u0026nbsp;136)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003eValue\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge in Years (Mean, \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.6\u0026nbsp;(10.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.6\u0026nbsp;(11.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.3\u0026nbsp;(10.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreast Density (#, % Dense)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (68.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55 (64.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89 (65.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrior Breast Cancer (#, % Yes)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026nbsp;(0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (32.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (20.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBiopsy-Proven High Risk Lesion\u0026nbsp;(#,\u0026nbsp;%\u0026nbsp;Yes)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026nbsp;(0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (14.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (8.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBiopsy or Surgery in Either Breast (#, % Yes)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (14.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (58.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57 (41.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMenopausal Status (#, % Post-Menopausal)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (66.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 (69.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (68.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOn\u0026nbsp;Hormone\u0026nbsp;Replacement\u0026nbsp;Therapy\u0026nbsp;(#,\u0026nbsp;%\u0026nbsp;Yes)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (10.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (9.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (9.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1st Degree Relative with Breast Cancer (#, % Yes)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026nbsp;(0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64 (74.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64 (47.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eFor age in years, the \u003cem\u003ep\u003c/em\u003e-value was based on a two-sample \u003cem\u003et\u003c/em\u003e-test allowing for unequal variances. For all other patient demographic and disease characteristics, the \u003cem\u003ep\u003c/em\u003e-value was based on Fisher\u0026rsquo;s exact test. Em dashes indicate that the \u003cem\u003ep\u003c/em\u003e-value was not calculated because high risk status was defined by prior breast cancer, biopsy-proven high risk lesion, and family history of breast cancer.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n\u003ch2\u003eBreast MRI BPE measurements\u003c/h2\u003e\n\u003cp\u003eBreast background parenchymal enhancement was routinely assessed on a BIRADS scale\u003csup\u003e26\u003c/sup\u003e (minimal, mild, moderate, marked) by standard of care within the imaging reports by one of five breast fellowship trained radiologists. Background parenchymal enhancement was stratified into 2 cohorts on MRI (mininal or mild versus moderate or marked) and did not significantly differ between the normal risk and high risk cohorts, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n\u003ch2\u003eBreast MRE measurements\u003c/h2\u003e\n\u003cp\u003eQuantitative breast stiffness and viscosity measurements were significantly higher in the high-risk dense breast cohort compared to that of the normal risk dense breast cohort \u003cstrong\u003e(Figs.\u0026nbsp;1, 2).\u003c/strong\u003e The means were not significantly different between those with dense versus non-dense breasts in the normal risk patient cohort (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14 for breast stiffness, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.78 for breast viscosity; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, the means were not significantly different between those with dense versus non-dense breasts in the high risk patient cohort (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09 for breast stiffness, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.13 for breast viscosity; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cstrong\u003eFig.\u0026nbsp;3\u003c/strong\u003e). In those women who had a prior breast surgery or biopsy, mean breast stiffness did not significantly differ between the surgerized or biopsied breast versus contralateral breast (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cstrong\u003eFigs.\u0026nbsp;4 and 5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAmong patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high risk patients (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;55) compared to normal risk patients (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34; all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eIn the multivariable logistic regression model, breast stiffness remained a significant predictor of risk status (OR\u0026thinsp;=\u0026thinsp;4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, MRI BPE, age, and menopausal status. Similar results were seen for breast elasticity (OR\u0026thinsp;=\u0026thinsp;4.88, 95% CI [2.08, 11.43]) and viscosity (OR\u0026thinsp;=\u0026thinsp;11.49, 95% CI [1.15, 114.89]).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMagnetic Resonance Elastography (MRE) Parameters and Magnetic Resonance Imaging\u0026nbsp;(MRI) Background Parenchymal Enhancement (BPE) by Risk Status and Breast Density\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eNormal Risk\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eHigh Risk\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNon-Dense\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026nbsp;=\u0026nbsp;16)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDense\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026nbsp;=\u0026nbsp;34)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003eValue\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNon-Dense\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026nbsp;=\u0026nbsp;31)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDense\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026nbsp;=\u0026nbsp;55)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003eValue\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreast\u0026nbsp;Stiffness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07 (0.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.16 (0.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.69 (1.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.14 (1.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian (Range)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05\u0026nbsp;(0.65\u0026ndash;1.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.15 (0.76\u0026ndash;1.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.09 (0.54\u0026ndash;4.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.37 (0.92\u0026ndash;4.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreast\u0026nbsp;Elasticity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96 (0.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.10 (0.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.57 (1.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.03 (1.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian (Range)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96 (0.61\u0026ndash;1.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.10 (0.74\u0026ndash;1.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03 (0.52\u0026ndash;3.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.32 (0.74\u0026ndash;3.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreast Viscosity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.23 (0.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.23 (0.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.26 (0.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35 (0.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian (Range)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.23 (0.08\u0026ndash;0.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.23 (0.11\u0026ndash;0.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.20 (0.0\u0026ndash;1.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.27 (-0.05\u0026ndash;1.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBPE\u0026nbsp;(#,\u0026nbsp;%\u0026nbsp;Minimal\u0026nbsp;or\u0026nbsp;Mild)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u0026nbsp;(87.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u0026nbsp;(58.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (90.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (63.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eBreast stiffness, elasticity, and viscosity were averaged across the left and right breasts.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eFor breast stiffness, elasticity, and viscosity, the \u003cem\u003ep\u003c/em\u003e-value was based on a two-sample \u003cem\u003et\u003c/em\u003e-test allowing for unequal variances. For BPE, the \u003cem\u003ep\u003c/em\u003e-value was based on Fisher\u0026rsquo;s exact test.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBreast Stiffness in Patients with 1 Surgerized Breast and 1 Unaffected Breast\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePatient Group\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBreast Stiffness Mean (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003eValue\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUnaffected Breast\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSurgerized Breast\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal\u0026nbsp;Risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07 (0.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07 (0.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh Risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.33 (1.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.22 (1.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.11\u0026nbsp;(1.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.02\u0026nbsp;(1.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eThe \u003cem\u003ep\u003c/em\u003e-value was based on a paired \u003cem\u003et\u003c/em\u003e-test comparing breast stiffness in the unaffected versus surgerized breast. The sample was limited to patients who underwent biopsy or surgery on one breast but not the other.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":" \u003cp\u003eThe most striking results in our prospective study to understand global breast stiffness in normal and high risk patients, is the significantly higher stiffness of breast tissue in the high-risk (prior breast cancer history or high risk based on lifetime risk of greater than 20%) dense breasted patients compared to the normal risk dense breasted patients. Patients that had prior unilateral post-operative and/or post-treatment related radtaion change did not show a significant difference in MRE parameters between the treated and untreated breast. Our study suggests that the structurally-based (tissue stiffness) risk information obtained from MRE could be used to identify the subset of women with dense breasts most likely to benefit from increased surveillance and serve as a prognostic biomarker in screening breast cancer patients. These differences in stiffness were also seen between high risk and normal risk patients with non-dense breasts, although is less clinically significant as the performance of screening mammography is better in non-dense breasts.\u003c/p\u003e \u003cp\u003eThrough translational studies\u003csup\u003e27,28\u003c/sup\u003e, one biologic explanation for the increased risk of breast cancer in dense breasted patients suggests that increased collagen deposition, tissue tensile strength and collagen structural differences may contribute to increased mammographic density which then is associated with breast cancer initiation\u003csup\u003e4,14,15,29\u0026minus;36\u003c/sup\u003e. In fact, increasing evidence indicates that the tumor microenvironment plays a critical role in regulating the biologic behavior of, and predisposition to breast cancer\u003csup\u003e37,38\u003c/sup\u003e. Recent studies have shown that organization and stiffness of the collagen matrix are important in mediating tumor growth and invasion\u003csup\u003e3,4,28,39\u0026minus;41\u003c/sup\u003e. Abnormal changes in the amount and organization of extracellular matrix lead to altered biochemical, physical, and biomechanical properties of tumor-associated ECM that contribute to tumor progression\u003csup\u003e42,43\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn a similar light, a 2002 study investigated the feasibility of MRE to distinguish malignant and benign properties of breast tissue. Analysis of MR elastograms using a 65Hz frequency on 20 female patients with malignant and benign breast lesions and 15 healthy volunteers as the control demonstrated that cancerous growths consistently demonstrated higher elasticity values (greater stiffness) in contrast to surrounding tissue. Malignant breast tumors measured a median stiffness of 15.9 kPa while adjacent breast parenchyma and fatty tissue measured a median of only 2.5 kPa and 2.0 kPa, respectively. Furthermore, the difference in stiffness between malignant and benign tumors were also evident through MRE where benign breast lesions were determined to be at a median value of 7.0 kPa. In fact, quantitatively, the stiffness value of breast carcinomas was, on average, 418% higher than the value of the adjacent tissues, (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003csup\u003e44\u003c/sup\u003e. The differing values of malignant tumors, benign tumors, and surrounding breast tissue illustrates that MRE as an imaging technique is able to accurately differentiate these tissues, and therefore of clinical importance \u003csup\u003e44\u0026ndash;46\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eClinically, we know that over 40% of our breast cancer screening patients have dense breasts\u003csup\u003e47\u003c/sup\u003e. As a result of relatively new dense breasts notification laws, these patients are now being told that they have dense breast tissue (which could result in decreased sensitivity of mammography screening and increase their risk of breast cancer). Providers are encouraged to discuss these details with patients and where appropriate, offer various forms of supplemental dense breast screening\u003csup\u003e48\u0026ndash;50\u003c/sup\u003e. These supplemental screening examinations can find additional cancers, however, also result in increased false positive results\u003csup\u003e23,51,52\u003c/sup\u003e. Since the risks and benefits of screening test are functions of each patient\u0026rsquo;s density features and risk factors, personal screening policies tailored to individuals are necessary and the new paradigm of breast cancer screening. Personalized breast cancer screening algorithms will result in cost efficiencies\u003csup\u003e53\u0026ndash;58\u003c/sup\u003e and reduced false positive rates. As such, our data is promising whereby imaging biomarker of global breast tissue stiffness could provide an additional component in a clinical decision making tool to guide providers of screening and risk-reduction measures based on patient values and preferences.\u003c/p\u003e \u003cp\u003eIn our study we observed a nonsignificant increase in stiffness in both the dense and non-dense breasts. These observed differences with increasing stiffness with density are consistent with other smaller studies\u003csup\u003e59\u0026ndash;61\u003c/sup\u003e, however the lack of a statistical significance is unique in our study. It should be noted that our study had few cases included along the the extreme spectrum of breast densities, fatty (n\u0026thinsp;=\u0026thinsp;1) and extremely dense (n\u0026thinsp;=\u0026thinsp;3) in our normal risk cohort, which may be affecting our results of statistical significance. Also it should be noted in our study, we chose to use the central slice as a first step to assess global 3D MRE values and used a 40Hz frequency on a 3T scanner. The region of interest included the central breast tissues, including fatty tissue and breast parenchyma on the slice, sparing the edges. This technique is similar to the annotations drawn and lessons learned on clinical diagnostic liver MRE. Contrast this technique to that of a 2017 study that demonstrated that dense breasts had significantly higher stiffness measurements compared with non-dense breasts (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003csup\u003e61\u003c/sup\u003e) the authors used a 60Hz frequency on the 3T scanner which requires less power for penetration. We speculate that the stiffness is highly dependent on the measurement method and the tissues that are covered.\u003c/p\u003e \u003cp\u003eLimitations within our study highlight the need for further inquiry to fill the gaps of current research before implementation in clinical practice. Our study is one of the largest to date, other published breast MRE studies have been performed using a small sample cohort of approximately 20 to 50 patients, limiting the representation of breast tissue density. In addition, the high risk volunteers used for testing the efficacy of MRE had a included those with a personal history breast cancer, making it difficult to extrapolate the results to the general screening population. Thus, future research should aim to encompass a larger set of patients to account for individual differences in breast tissue and a variety of lesion characteristics. Given the current study is based on self- reported family history, we will also plan to calculate lifetime and 5 year risks for patients based on acccepted clinical breast cancer risk models\u003csup\u003e62\u0026ndash;64\u003c/sup\u003e. Doing so, will allow us to have more accurate correlations to lifetime risk rather than the current stratification provided here. Another limitation is the fairly homogenous demographics of the patient population in this study. Therefore adding demographic variation to the population will be of utmost interest given the variations seen in breast density based on racial differences\u003csup\u003e65\u0026ndash;67\u003c/sup\u003e. Also, future studies should include computer-generated volumetric breast density from MG to classify pateints as dense or non dense, given the considerable inter- and intra-reader variability within the mammographic density reporting system\u003csup\u003e68,69\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs more personalized approach within the breast cancer screening regimen develops, further risk stratification using quantitative imaging biomarker of global breast stiffness could inform more effective personalized screening regimens.We anticipate the results of this study and ours will give light to more investigations on how tissue stiffness correlates with breast cancer risk and prognosis. These prior studies and our results provide support for the further study of MRE given its ability to assess the viscoelastic properties of breast tumors and the surrounding tissue \u003csup\u003e70\u0026ndash;72\u003c/sup\u003e. While the studies on MRE\u0026rsquo;s application to \u003cem\u003ein vivo\u003c/em\u003e breast tissue are limited, this trial substantiates the capabilities of MRE and its potential for a risk stratification tool in women with dense breasts.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eIn conclusion, our work above suggests structurally-based, quantitative biomarker of tissue stiffness obtained from global 3D breast MRE could provide a novel prognostic marker used to identify the subset of high-risk women with dense breasts. As such, a stiffness values could help stratify dense breasted pateints into those who are at elevated risk and would benefit from increased surveillance with supplemental imaging techniques and/or risk reduction measures.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments: We would like to think the Mayo Clinic Cancer Center for providing grant funding for this study. We would also like to acknowledge the Women\u0026apos;s Health staff who have been instrumental with recruitment for this study and the breast imaging staff who helped with imaging patients using this new technology. Special thanks to Diana Almader-Smith who assisted with formatting the references for this manuscript.\u003c/p\u003e\n\u003cp\u003eFunding: This study was funded by the Mayo Clinic Cancer Center MEGA grant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflict of Interest: \u0026nbsp;BKP has received unrelated grant funding from GRAIL Inc. and Hologic Inc. with monies directed to Mayo Clinic. Dr. Kay Pepin is an employee at Resoundant Inc. Dr. Jun Chen is an employee, holds patents and intellectual property at Resoundant Inc. Dr. Yuxiang Zhou holds intellectual property in interventional MRI. Dr. Northfelt has research funding with Genentech/Roche (Inst); GlaxoSmithKline (Inst); Incyte (Inst); Merck (Inst); Novartis (Inst); Pfizer (Inst), Dr. Anderson has Stock and Other Ownership Interests - FlexBioTech; SafeGen Therapeutics, Consulting or Advisory Role and research funding from Merck, Dr. Vachon I don\u0026apos;t have insurance owns stock inexact sciences, receives research funding from grail, has intellectual property with breast density software algorithms and has received travel expenses from GRAIL Inc. Dr Ehman owns stock, has research funding, and discloses uncompensated relationships with Resoundant. Dr. Swanson discloses Precision Oncology Insights Inc. Drs. Ehman and Mayo Clinic have financial interest and intellectual properties related to MR Elastography.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBoyd NF, Lockwood GA, Byng JW, Tritchler DL, Yaffe MJ (1998) Mammographic densities and breast cancer risk. Cancer Epidemiology Prevention Biomarkers 7(12):1133\u0026ndash;1144\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVachon CM, Brandt KR, Ghosh K et al (2007) Mammographic breast density as a general marker of breast cancer risk. Cancer Epidemiology Prevention Biomarkers 16(1):43\u0026ndash;49\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcerbi I, Cassereau L, Dean I et al (2015) Human breast cancer invasion and aggression correlates with ECM stiffening and immune cell infiltration. 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Br J Radiol 85(1019):1465\u0026ndash;1470\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalleyguier C, Lakhdar AB, Dunant A, Mathieu MC, Delaloge S, Sinkus R. Value of whole breast magnetic resonance elastography added to MRI for lesion characterization. \u003cem\u003eNMR in biomedicine.\u003c/em\u003e 2018;31(1)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKnight AL, Kugel JL, Rossman PJ, Manduca A, Hartmann LC, Ehman RL (2002) MR elastography of breast cancer: preliminary results. AJR American journal of roentgenology 178(6):1411\u0026ndash;1417\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinkus R, Lorenzen J, Schrader D, Lorenzen M, Dargatz M, Holz D (2000) High-resolution tensor MR elastography for breast tumour detection. Physics in medicine biology 45(6):1649\u0026ndash;1664\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"breast cancer risk, dense breasts, breast tissue stiffness, breast MR imaging, breast elastography, breast stroma","lastPublishedDoi":"10.21203/rs.3.rs-611943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-611943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePurpose:\u003c/p\u003e\u003cp\u003eQuantify \u003cem\u003ein vivo \u003c/em\u003ebiomechanical tissue properties in various breast densities and in normal risk and high risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association between breast biomechanical properties and cancer risk.\u003c/p\u003e\u003cp\u003eMethods: Patients with normal risk or high risk of breast cancer underent 3.0 T breast MR imaging and elastography. Breast parenchymal enhancement (BPE), density (from most recent mammogram), stiffness, elasticity, and viscosity were recorded. Within each breast density group (non-dense versus dense), stiffness, elasticity, and viscosity were compared across risk groups (normal versus high).\u0026nbsp;A multivariable logistic regression model was used to evaluate whether the MRE parameters (separately for stiffness, elasticity, and viscosity) predicted risk status after controlling for clinical factors.\u003c/p\u003e\u003cp\u003eResults: 50 normal risk and 86 high risk patients were included.\u0026nbsp;Risk groups were similar on age, density, and menopausal status. Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high risk patients (\u003cem\u003eN\u003c/em\u003e\u0026nbsp;=\u0026nbsp;55) compared to normal risk patients (\u003cem\u003eN\u003c/em\u003e\u0026nbsp;=\u0026nbsp;34; all \u003cem\u003ep\u003c/em\u003e\u0026nbsp;\u0026lt;\u0026nbsp;0.001). Stiffness remained a significant predictor of risk status (OR=4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, BPE, age, and menopausal status. Similar results were seen for elasticity and viscosity.\u003c/p\u003e\u003cp\u003eConclusion: A structurally-based, quantitative biomarker of tissue stiffness obtained from MRE is associated with differences in breast cancer risk in dense breasts. Tissue stiffness could provide a novel prognostic marker to help identify high risk women with dense breasts who would benefit from increased surveillance and/or risk reduction measures.\u003c/p\u003e","manuscriptTitle":"Association of Breast Cancer Risk, Density, and Stiffness: Global Tissue Stiffness on Breast MR Elastography (MRE)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-06-15 15:39:02","doi":"10.21203/rs.3.rs-611943/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-06-16T00:00:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-06-12T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-06-11T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research and Treatment","date":"2021-06-10T18:52:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7f1fa1b0-a9ba-4bc8-99e2-2b6ae60f42b0","owner":[],"postedDate":"June 15th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":5035999,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2022-04-12T05:04:44+00:00","versionOfRecord":[],"versionCreatedAt":"2021-06-15 15:39:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-611943","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-611943","identity":"rs-611943","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}