Similarity of fibroglandular breast tissue content measured from magnetic resonance and mammographic images and by a mathematical algorithm

Risk Analysis of Breast Cancer by Using Bilateral Mammographic Density Differences: A Case-Control Study

The identification of risk factors helps radiologists assess the risk of breast cancer. Quantitative factors such as age and mammographic density are established risk factors for breast cancer. Asymmetric breast findings are frequently encountered during diagnostic mammography. The asymmetric area may indicate a developing mass in the early stage, causing a difference in mammographic density between the left and right sides. Therefore, this paper aims to propose a quantitative parameter named bilateral mammographic density difference (BMDD) for the quantification of breast asymmetry and to verify BMDD as a risk factor for breast cancer. To quantitatively evaluate breast asymmetry, we developed a semi-automatic method to estimate mammographic densities and calculate BMDD as the absolute difference between the left and right mammographic densities. And then, a retrospective case-control study, covering the period from July 2006 to October 2014, was conducted to analyse breast cancer risk in association with BMDD. The study included 364 women diagnosed with breast cancer and 364 matched control patients. As a result, a significant difference in BMDD was found between cases and controls (P < 0.001) and the case-control study demonstrated that women with BMDD > 10% had a 2.4-fold higher risk of breast cancer (odds ratio, 2.4; 95% confidence interval, 1.3-4.5) than women with BMDD ≤ 10%. In addition, we also demonstrated the positive association between BMDD and breast cancer risk among the subgroups with different ages and the Breast Imaging Reporting and Data System (BI-RADS) mammographic density categories. This study demonstrated that BMDD could be a potential risk factor for breast cancer.

Development and Validation of a Risk Model for Breast Cancer-Related Lymphedema

IMPORTANCE

Approximately 1 in 5 patients with breast cancer who undergo axillary lymph node dissection will develop lymphedema. To appropriately triage and monitor these patients for timely diagnosis and treatment, robust risk models are required.

OBJECTIVE

To evaluate the prognostic value of mammographic breast density in estimating lymphedema severity.

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study collected data from July 16, 2018, to March 3, 2020, from the electronic health records of patients of the Cancer Rehabilitation and Survivorship Program at the Princess Margaret Cancer Centre in Toronto, Ontario, Canada. Participants included women who had completed curative treatment for a first diagnosis of breast cancer and who were referred to the program. Also included were a sample of patients in the general breast oncology population who were receiving follow-up care at the center during the same period but who were not referred to the program. All patients attended follow-up appointments at the Princess Margaret Cancer Centre from January 1, 2016, to May 1, 2018. The cohort was randomly split 2:1 to group patients into a training cohort and a validation cohort.

EXPOSURES

Participant demographic and clinical characteristics included age, sex, body mass index (BMI), medical history, cancer characteristics, and cancer treatment.

MAIN OUTCOMES AND MEASURES

Spearman correlation coefficient between measured and predicted volume of lymphedema was calculated. Area under the curve (AUC) values were generated for predicting the occurrence of at least mild lymphedema (volume, >200 mL) and severe lymphedema (volume, >500 mL) at the time of initial lymphedema diagnosis.

RESULTS

A total of 373 female patients (median [interquartile range] age, 52.3 [45.9-60.1] years) were eligible for this analysis. Multivariate linear regression identified 3 patient factors (age, BMI, and mammographic breast density), 1 cancer factor (number of pathological lymph nodes), and 1 treatment factor (axillary lymph node dissection) as independent prognostic variables. In validation testing, Spearman correlation revealed a statistically significant moderate correlation (coefficient, 0.42; 95% CI, 0.26-0.56; P < .001) between measured volume and predicted volume of lymphedema. The AUC values were 0.72 (95% CI, 0.60-0.83) for predicting the occurrence of mild lymphedema and 0.83 (95% CI, 0.74-0.93) for severe lymphedema.

CONCLUSIONS AND RELEVANCE

This prognostic study found that patients with low breast density appeared to be at a higher risk of developing severe lymphedema. The finding suggests that by combining breast density with established risk factors a multivariate linear regression model could be used to predict the development of lymphedema and provide volumetric estimates of lymphedema severity in patients with breast cancer.

Quantitative Measurement of Breast Density Using Personalized 3D-Printed Breast Model for Magnetic Resonance Imaging

Despite the development and implementation of several MRI techniques for breast density assessments, there is no consensus on the optimal protocol in this regard. This study aimed to determine the most appropriate MRI protocols for the quantitative assessment of breast density using a personalized 3D-printed breast model. The breast model was developed using silicone and peanut oils to simulate the MRI related-characteristics of fibroglandular and adipose breast tissues, and then scanned on a 3T MRI system using non-fat-suppressed and fat-suppressed sequences. Breast volume, fibroglandular tissue volume, and percentage of breast density from these imaging sequences were objectively assessed using Analyze 14.0 software. Finally, the repeated-measures analysis of variance (ANOVA) was performed to examine the differences between the quantitative measurements of breast volume, fibroglandular tissue volume, and percentage of breast density with respect to the corresponding sequences. The volume of fibroglandular tissue and the percentage of breast density were significantly higher in the fat-suppressed sequences than in the non-fat-suppressed sequences (p < 0.05); however, the difference in breast volume was not statistically significant (p = 0.529). Further, a fat-suppressed T2-weighted with turbo inversion recovery magnitude (TIRM) imaging sequence was superior to the non-fat- and fat-suppressed T1- and T2-weighted sequences for the quantitative measurement of breast density due to its ability to represent the exact breast tissue compositions. This study shows that the fat-suppressed sequences tended to be more useful than the non-fat-suppressed sequences for the quantitative measurements of the volume of fibroglandular tissue and the percentage of breast density.

Development of patient-specific 3D-printed breast phantom using silicone and peanut oils for magnetic resonance imaging

Background

Despite increasing reports of 3D printing in medical applications, the use of 3D printing in breast imaging is limited, thus, personalized 3D-printed breast model could be a novel approach to overcome current limitations in utilizing breast magnetic resonance imaging (MRI) for quantitative assessment of breast density. The aim of this study is to develop a patient-specific 3D-printed breast phantom and to identify the most appropriate materials for simulating the MR imaging characteristics of fibroglandular and adipose tissues.

Methods

A patient-specific 3D-printed breast model was generated using 3D-printing techniques for the construction of the hollow skin and fibroglandular region shells. Then, the T1 relaxation times of the five selected materials (agarose gel, silicone rubber with/without fish oil, silicone oil, and peanut oil) were measured on a 3T MRI system to determine the appropriate ones to represent the MR imaging characteristics of fibroglandular and adipose tissues. Results were then compared to the reference values of T1 relaxation times of the corresponding tissues: 1,324.42±167.63 and 449.27±26.09 ms, respectively. Finally, the materials that matched the T1 relaxation times of the respective tissues were used to fill the 3D-printed hollow breast shells.

Results

The silicone and peanut oils were found to closely resemble the T1 relaxation times and imaging characteristics of these two tissues, which are 1,515.8±105.5 and 405.4±15.1 ms, respectively. The agarose gel with different concentrations, ranging from 0.5 to 2.5 wt%, was found to have the longest T1 relaxation times.

Conclusions

A patient-specific 3D-printed breast phantom was successfully designed and constructed using silicone and peanut oils to simulate the MR imaging characteristics of fibroglandular and adipose tissues. The phantom can be used to investigate different MR breast imaging protocols for the quantitative assessment of breast density.

Using Whole Breast Ultrasound Tomography to Improve Breast Cancer Risk Assessment: A Novel Risk Factor Based on the Quantitative Tissue Property of Sound Speed

Mammographic percent density (MPD) is an independent risk factor for developing breast cancer, but its inclusion in clinical risk models provides only modest improvements in individualized risk prediction, and MPD is not typically assessed in younger women because of ionizing radiation concerns. Previous studies have shown that tissue sound speed, derived from whole breast ultrasound tomography (UST), a non-ionizing modality, is a potential surrogate marker of breast density, but prior to this study, sound speed has not been directly linked to breast cancer risk. To that end, we explored the relation of sound speed and MPD with breast cancer risk in a case-control study, including 61 cases with recent breast cancer diagnoses and a comparison group of 165 women, frequency matched to cases on age, race, and menopausal status, and with a recent negative mammogram and no personal history of breast cancer. Multivariable odds ratios (ORs) and 95% confidence intervals (CIs) were estimated for the relation of quartiles of MPD and sound speed with breast cancer risk adjusted for matching factors. Elevated MPD was associated with increased breast cancer risk, although the trend did not reach statistical significance (OR per quartile = 1.27, 95% CI: 0.95, 1.70; p_trend = 0.10). In contrast, elevated sound speed was significantly associated with breast cancer risk in a dose–response fashion (OR per quartile = 1.83, 95% CI: 1.32, 2.54; p_trend = 0.0003). The OR trend for sound speed was statistically significantly different from that observed for MPD (p = 0.005). These findings suggest that whole breast sound speed may be more strongly associated with breast cancer risk than MPD and offer future opportunities for refining the magnitude and precision of risk associations in larger, population-based studies, including women younger than usual screening ages.

Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRI

Mammographic breast density (MBD) is the most commonly used method to assess the volume of fibroglandular tissue (FGT). However, MRI could provide a clinically feasible and more accurate alternative. There were three aims in this study: (1) to evaluate a clinically feasible method to quantify FGT with MRI, (2) to assess the inter-rater agreement of MRI-based volumetric measurements and (3) to compare them to measurements acquired using digital mammography and 3D tomosynthesis. This retrospective study examined 72 women (mean age 52.4 ± 12.3 years) with 105 disease-free breasts undergoing diagnostic 3.0-T breast MRI and either digital mammography or tomosynthesis. Two observers analyzed MRI images for breast and FGT volumes and FGT-% from T1-weighted images (0.7-, 2.0-, and 4.0-mm-thick slices) using K-means clustering, data from histogram, and active contour algorithms. Reference values were obtained with Quantra software. Inter-rater agreement for MRI measurements made with 2-mm-thick slices was excellent: for FGT-%, r = 0.994 (95% CI 0.990-0.997); for breast volume, r = 0.985 (95% CI 0.934-0.994); and for FGT volume, r = 0.979 (95% CI 0.958-0.989). MRI-based FGT-% correlated strongly with MBD in mammography (r = 0.819-0.904, P < 0.001) and moderately to high with MBD in tomosynthesis (r = 0.630-0.738, P < 0.001). K-means clustering-based assessments of the proportion of the fibroglandular tissue in the breast at MRI are highly reproducible. In the future, quantitative assessment of FGT-% to complement visual estimation of FGT should be performed on a more regular basis as it provides a component which can be incorporated into the individual's breast cancer risk stratification.

Qualitative and quantitative differences in estrogen biotransformation in human breast glandular and adipose tissues: implications for studies using mammary biospecimens

Because of its assumed role in breast cancer etiology, estrogen biotransformation (and interaction of compounds therewith) has been investigated in human biospecimens for decades. However, little attention has been paid to the well-known fact that large inter-individual variations exist in the proportion of breast glandular (GLT) and adipose (ADT) tissues and less to adequate tissue characterization. To assess the relevance of this, the present study compares estrogen biotransformation in GLT and ADT. GLT and ADT were isolated from 47 reduction mammoplasty specimens derived from women without breast cancer and were characterized histologically and by their percentages of oil. Levels of 12 unconjugated and five conjugated estrogens were analyzed by GC- and UHPLC-MS/MS, respectively, and levels of 27 transcripts encoding proteins involved in estrogen biotransformation by Taqman^® probe-based PCR. Unexpectedly, one-third of specimens provided neat GLT only after cryosection. Whereas 17β-estradiol, estrone, and estrone-3-sulfate were detected in both tissues, estrone-3-glucuronide and 2-methoxy-estrone were detected predominately in GLT and ADT, respectively. Estrogen levels as well as ratios 17β-estradiol/estrone and estrone-3-sulfate/estrone differed significantly between GLT and ADT, yet less than between individuals. Furthermore, estrogen levels in GLT and ADT correlated significantly with each other. In contrast, levels of most transcripts encoding enzymes involved in biotransformation differed more than between individuals and did not correlate between ADT and GLT. Thus, mixed breast tissues (and plasma) will not provide meaningful information on local estrogen biotransformation (and interaction of compounds therewith) whereas relative changes in 17β-estradiol levels may be investigated in the more abundant ADT.

Quantitative Measurements of Breast Density Using Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis

Breast density, a measure of dense fibroglandular tissue relative to non-dense fatty tissue, is confirmed as an independent risk factor of breast cancer. Although there has been an increasing interest in the quantitative assessment of breast density, no research has investigated the optimal technical approach of breast MRI in this aspect. Therefore, we performed a systematic review and meta-analysis to analyze the current studies on quantitative assessment of breast density using MRI and to determine the most appropriate technical/operational protocol. Databases (PubMed, EMBASE, ScienceDirect, and Web of Science) were searched systematically for eligible studies. Single arm meta-analysis was conducted to determine quantitative values of MRI in breast density assessments. Combined means with their 95% confidence interval (CI) were calculated using a fixed-effect model. In addition, subgroup meta-analyses were performed with stratification by breast density segmentation/measurement method. Furthermore, alternative groupings based on statistical similarities were identified via a cluster analysis employing study means and standard deviations in a Nearest Neighbor/Single Linkage. A total of 38 studies matched the inclusion criteria for this systematic review. Twenty-one of these studies were judged to be eligible for meta-analysis. The results indicated, generally, high levels of heterogeneity between study means within groups and high levels of heterogeneity between study variances within groups. The studies in two main clusters identified by the cluster analysis were also subjected to meta-analyses. The review confirmed high levels of heterogeneity within the breast density studies, considered to be due mainly to the applications of MR breast-imaging protocols and the use of breast density segmentation/measurement methods. Further research should be performed to determine the most appropriate protocol and method for quantifying breast density using MRI.

Comparison of subjective and fully automated methods for measuring mammographic density

Background Breast radiologists of the Norwegian Breast Cancer Screening Program subjectively classified mammographic density using a three-point scale between 1996 and 2012 and changed into the fourth edition of the BI-RADS classification since 2013. In 2015, an automated volumetric breast density assessment software was installed at two screening units. Purpose To compare volumetric breast density measurements from the automated method with two subjective methods: the three-point scale and the BI-RADS density classification. Material and Methods Information on subjective and automated density assessment was obtained from screening examinations of 3635 women recalled for further assessment due to positive screening mammography between 2007 and 2015. The score of the three-point scale (I = fatty; II = medium dense; III = dense) was available for 2310 women. The BI-RADS density score was provided for 1325 women. Mean volumetric breast density was estimated for each category of the subjective classifications. The automated software assigned volumetric breast density to four categories. The agreement between BI-RADS and volumetric breast density categories was assessed using weighted kappa (k_w). Results Mean volumetric breast density was 4.5%, 7.5%, and 13.4% for categories I, II, and III of the three-point scale, respectively, and 4.4%, 7.5%, 9.9%, and 13.9% for the BI-RADS density categories, respectively ( P for trend < 0.001 for both subjective classifications). The agreement between BI-RADS and volumetric breast density categories was k_w = 0.5 (95% CI = 0.47-0.53; P < 0.001). Conclusion Mean values of volumetric breast density increased with increasing density category of the subjective classifications. The agreement between BI-RADS and volumetric breast density categories was moderate.

Adiposity, breast density, and breast cancer risk: epidemiological and biological considerations

Excess total body fat and abdominal adipose tissue are recognized risk factors for metabolic diseases but also for some types of cancers, including breast cancer. Several biological mechanisms in connection with local and systemic effects of adiposity are believed to be implicated in breast cancer development, and may involve breast fat. Breast adipose tissue can be studied through mammography by looking at breast density features such as the nondense area mainly composed of fat, or the percent breast density, which is the proportion of fibroglandular tissue in relation to fat. The relation between adiposity, breast density features, and breast cancer is complex. Studies suggest a paradoxical association as adiposity and absolute nondense area correlate positively with each other, but in contrast to adiposity, absolute nondense area seems to be associated negatively with breast cancer risk. As breast density is one of the strongest risk factors for breast cancer, it is therefore critical to understand how these factors interrelate. In this review, we discuss these relations by first presenting how adiposity measurements and breast density features are linked to breast cancer risk. Then, we used a systematic approach to capture the literature to review the relation between adiposity and breast density features. Finally, the role of adipose tissue in carcinogenesis is discussed briefly from a biological perspective.