Reproducible automated breast density measure with no ionizing radiation using fat-water decomposition MRI

Quantitative measurement of mammographic density in breast-tissue explants using portable NMR: Precision and accuracy

PURPOSE

Single-sided portable NMR (pNMR) has previously been demonstrated to be suitable for quantification of mammographic density (MD) in excised breast tissue samples. Here we investigate the precision and accuracy of pNMR measurements of MD ex vivo as compared with the gold standards.

METHODS

Forty-five breast-tissue explants from 9 prophylactic mastectomy patients were measured. The relative tissue water content was taken as the MD-equivalent quantity. In each sample, the water content was measured using some combination of three pNMR techniques (apparent T2, diffusion, and T1 measurements) and two gold-standard techniques (computed microtomography [μCT] and hematoxylin and eosin [H&E] histology). Pairwise correlation plots and Bland-Altman analysis were used to quantify the degree of agreement between pNMR techniques and the gold standards.

RESULTS

Relative water content measured from both apparent T2 relaxation spectra, and diffusion decays exhibited strong correlation with the H&E and μCT results. Bland-Altman analysis yielded average bias values of -0.4, -2.6, 2.6, and 2.8 water percentage points (pp) and 95% confidence intervals of 13.1, 7.5, 11.2, and 11.8 pp for the H&E - T2, μCT - T2, H&E - diffusion, and μCT - diffusion comparison pairs, respectively. T1-based measurements were found to be less reliable, with the Bland-Altman confidence intervals of 27.7 and 33.0 pp when compared with H&E and μCT, respectively.

CONCLUSION

Apparent T2-based and diffusion-based pNMR measurements enable quantification of MD in breast-tissue explants with the precision of approximately 10 pp and accuracy of approximately 3 pp or better, making pNMR a promising measurement modality for radiation-free quantification of MD.

MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction

BACKGROUND

The volume of the implant is the most critical element of breast reconstruction, so it is necessary to accurately assess the preoperative volume of the healthy and affected breasts and select the appropriate implant for placement. Accurate and automated methods for quantitative assessment of breast volume can optimize breast reconstruction surgery and assist physicians in clinical decision making. The aim of this study was to develop an artificial intelligence model for automated segmentation of the breast and measurement of volume.

MATERIAL AND METHODS

A total of 249 subjects undergoing breast reconstruction surgery were enrolled in this study. Subjects underwent preoperative breast MRI, and the breast region manually outlined by the imaging physician served as the gold standard for volume measurement by the automated segmentation model. In this study, we developed three automated algorithms for automatic segmentation of breast regions, including a simple alignment model, an alignment dynamic encoding model, and a deep learning model. The volumetric agreement between the three automated segmentation algorithms and the breast regions manually segmented by imaging physicians was evaluated by calculating the mean square error (MSE) and intragroup correlation coefficient (ICC), and the reproducibility of the automated segmentation of the breast regions was assessed by the test-retest step.

RESULTS

The three breast automated segmentation models developed in this study (simple registration model, dynamic programming model, and deep learning model) showed strong ICC with manual segmentation of the breast region, with MSEs of 1.124, 0.693, and 0.781, and ICCs of 0.975 (95% CI, 0.869-0.991), 0.986 (95% CI, 0.967-0.996), and 0.983 (95% CI, 0.961-0.992), respectively. Regarding the test-retest results of breast volume, the dynamic programming model performed the best with an MSE of 0.370 and an ICC of 0.993 (95% CI, 0.982-0.997), followed by the deep learning algorithm with an MSE of 0.741 and an ICC of 0.983 (95% CI, 0.956-0.993), and the simple registration algorithm with an MSE of 0.763 and an ICC of 0.982 (95% CI, 0.949-0.993). The reproducibility of the breast region segmented by the three automated algorithms was higher than that of manual segmentation by different radiologists.

CONCLUSION

The three automated breast segmentation algorithms developed in this study generate accurate and reliable breast regions, enable highly reproducible breast region segmentation and automated volume measurements, and provide a valuable tool for surgical selection of appropriate prostheses.

NO LEVEL ASSIGNED

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Assessing breast density using the chemical-shift encoding-based proton density fat fraction in 3-T MRI

Objectives

There is a clinical need for a non-ionizing, quantitative assessment of breast density, as one of the strongest independent risk factors for breast cancer. This study aims to establish proton density fat fraction (PDFF) as a quantitative biomarker for fat tissue concentration in breast MRI and correlate mean breast PDFF to mammography.

Methods

In this retrospective study, 193 women were routinely subjected to 3-T MRI using a six-echo chemical shift encoding-based water-fat sequence. Water-fat separation was based on a signal model accounting for a single T2* decay and a pre-calibrated 7-peak fat spectrum resulting in volumetric fat-only, water-only images, PDFF- and T2*-values. After semi-automated breast segmentation, PDFF and T2* values were determined for the entire breast and fibroglandular tissue. The mammographic and MRI-based breast density was classified by visual estimation using the American College of Radiology Breast Imaging Reporting and Data System categories (ACR A-D).

Results

The PDFF negatively correlated with mammographic and MRI breast density measurements (Spearman rho: −0.74, p < .001) and revealed a significant distinction between all four ACR categories. Mean T2* of the fibroglandular tissue correlated with increasing ACR categories (Spearman rho: 0.34, p < .001). The PDFF of the fibroglandular tissue showed a correlation with age (Pearson rho: 0.56, p = .03).

Conclusion

The proposed breast PDFF as an automated tissue fat concentration measurement is comparable with mammographic breast density estimations. Therefore, it is a promising approach to an accurate, user-independent, and non-ionizing breast density assessment that could be easily incorporated into clinical routine breast MRI exams.

Key Points

• The proposed PDFF strongly negatively correlates with visually determined mammographic and MRI-based breast density estimations and therefore allows for an accurate, non-ionizing, and user-independent breast density measurement.

• In combination with T2*, the PDFF can be used to track structural alterations in the composition of breast tissue for an individualized risk assessment for breast cancer.

Two fully automated data-driven 3D whole-breast segmentation strategies in MRI for MR-based breast density using image registration and U-Net with a focus on reproducibility

Presence of higher breast density (BD) and persistence over time are risk factors for breast cancer. A quantitatively accurate and highly reproducible BD measure that relies on precise and reproducible whole-breast segmentation is desirable. In this study, we aimed to develop a highly reproducible and accurate whole-breast segmentation algorithm for the generation of reproducible BD measures. Three datasets of volunteers from two clinical trials were included. Breast MR images were acquired on 3 T Siemens Biograph mMR, Prisma, and Skyra using 3D Cartesian six-echo GRE sequences with a fat-water separation technique. Two whole-breast segmentation strategies, utilizing image registration and 3D U-Net, were developed. Manual segmentation was performed. A task-based analysis was performed: a previously developed MR-based BD measure, MagDensity, was calculated and assessed using automated and manual segmentation. The mean squared error (MSE) and intraclass correlation coefficient (ICC) between MagDensity were evaluated using the manual segmentation as a reference. The test-retest reproducibility of MagDensity derived from different breast segmentation methods was assessed using the difference between the test and retest measures (Δ2-1), MSE, and ICC. The results showed that MagDensity derived by the registration and deep learning segmentation methods exhibited high concordance with manual segmentation, with ICCs of 0.986 (95%CI: 0.974-0.993) and 0.983 (95%CI: 0.961-0.992), respectively. For test-retest analysis, MagDensity derived using the registration algorithm achieved the smallest MSE of 0.370 and highest ICC of 0.993 (95%CI: 0.982-0.997) when compared to other segmentation methods. In conclusion, the proposed registration and deep learning whole-breast segmentation methods are accurate and reliable for estimating BD. Both methods outperformed a previously developed algorithm and manual segmentation in the test-retest assessment, with the registration exhibiting superior performance for highly reproducible BD measurements.

Preoperative prediction of lymph node metastasis using deep learning-based features

Lymph node involvement increases the risk of breast cancer recurrence. An accurate non-invasive assessment of nodal involvement is valuable in cancer staging, surgical risk, and cost savings. Radiomics has been proposed to pre-operatively predict sentinel lymph node (SLN) status; however, radiomic models are known to be sensitive to acquisition parameters. The purpose of this study was to develop a prediction model for preoperative prediction of SLN metastasis using deep learning-based (DLB) features and compare its predictive performance to state-of-the-art radiomics. Specifically, this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar resolution. Dynamic contrast-enhancement images from 198 patients (67 positive SLNs) were used in this study. Of these subjects, 163 had an in-plane resolution of 0.7 × 0.7 mm², which were randomly divided into a training set (approximately 67%) and a validation set (approximately 33%). The remaining 35 subjects with a different in-plane resolution (0.78 × 0.78 mm²) were treated as independent testing set for generalizability. Two methods were employed: (1) conventional radiomics (CR), and (2) DLB features which replaced hand-curated features with pre-trained VGG-16 features. The threshold determined using the training set was applied to the independent validation and testing dataset. Same feature reduction, feature selection, model creation procedures were used for both approaches. In the validation set (same resolution as training), the DLB model outperformed the CR model (accuracy 83% vs 80%). Furthermore, in the independent testing set of the dissimilar resolution, the DLB model performed markedly better than the CR model (accuracy 77% vs 71%). The predictive performance of the DLB model outperformed the CR model for this task. More interestingly, these improvements were seen particularly in the independent testing set of dissimilar resolution. This could indicate that DLB features can ultimately result in a more generalizable model.

Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence

BACKGROUND

Although lumbar bone marrow fat fraction (BMFF) has been demonstrated to be predictive of osteoporosis, its utility is limited by the requirement of manual segmentation. Additionally, quantitative features beyond simple BMFF average remain to be explored. In this study, we developed a fully automated radiomic pipeline using deep learning-based segmentation to detect osteoporosis and abnormal bone density (ABD) using a <20 s modified Dixon (mDixon) sequence.

METHODS

In total, 222 subjects underwent quantitative computed tomography (QCT) and lower back magnetic resonance imaging (MRI). Bone mineral density (BMD) were extracted from L1-L3 using QCT as the reference standard; 206 subjects (48.8±14.9 years old, 140 females) were included in the final analysis, and were divided temporally into the training/validation set (142/64 subjects). A deep-learning network was developed to perform automated segmentation. Radiomic models were built using the same training set to predict ABD and osteoporosis using the mDixon maps. The performance was evaluated using the temporal validation set comprised of 64 subjects, along with the automated segmentation. Additional 25 subjects (56.1±8.8 years, 14 females) from another site and a different scanner vendor was included as independent validation to evaluate the performance of the pipeline.

RESULTS

The automated segmentation achieved an outstanding mean dice coefficient of 0.912±0.062 compared to manual in the temporal validation. Task-based evaluation was performed in the temporal validation set, for predicting ABD and osteoporosis, the area under the curve, sensitivity, specificity, and accuracy were 0.925/0.899, 0.923/0.667, 0.789/0.873, 0.844/0.844, respectively. These values were comparable to that of manual segmentation. External validation (cross-vendor) was also performed; the area under the curve, sensitivity, specificity, and accuracy were 0.688/0.913, 0.786/0.857, 0.545/0.944, 0.680/0.920 for ABD and osteoporosis prediction, respectively.

CONCLUSIONS

Our work is the first attempt using radiomics to predict osteoporosis with BMFF map, and the deep-learning based segmentation will further facilitate the clinical utility of the pipeline as a screening tool for early detection of ABD.

Proton density water fraction as a reproducible MR-based measurement of breast density

PURPOSE

To introduce proton density water fraction (PDWF) as a confounder-corrected (CC) MR-based biomarker of mammographic breast density, a known risk factor for breast cancer.

METHODS

Chemical shift encoded (CSE) MR images were acquired using a low flip angle to provide proton density contrast from multiple echo times. Fat and water images, corrected for known biases, were produced by a six-echo CC CSE-MRI algorithm. Fibroglandular tissue (FGT) volume was calculated from whole-breast segmented PDWF maps at 1.5T and 3T. The method was evaluated in (1) a physical fat-water phantom and (2) normal volunteers. Results from two- and three-echo CSE-MRI methods were included for comparison.

RESULTS

Six-echo CC-CSE-MRI produced unbiased estimates of the total water volume in the phantom (mean bias 3.3%) and was reproducible across protocol changes (repeatability coefficient [RC] = 14.8 cm³ and 13.97 cm³ at 1.5T and 3.0T, respectively) and field strengths (RC = 51.7 cm³ ) in volunteers, while the two- and three-echo CSE-MRI approaches produced biased results in phantoms (mean bias 30.7% and 10.4%) that was less reproducible across field strengths in volunteers (RC = 82.3 cm³ and 126.3 cm³ ). Significant differences in measured FGT volume were found between the six-echo CC-CSE-MRI and the two- and three-echo CSE-MRI approaches (p = 0.002 and p = 0.001, respectively).

CONCLUSION

The use of six-echo CC-CSE-MRI to create unbiased PDWF maps that reproducibly quantify FGT in the breast is demonstrated. Further studies are needed to correlate this quantitative MR biomarker for breast density with mammography and overall risk for breast cancer.

A randomized controlled trial of metformin in women with components of metabolic syndrome: intervention feasibility and effects on adiposity and breast density

PURPOSE

Obesity is a known risk factor for post-menopausal breast cancer and may increase risk for triple negative breast cancer in premenopausal women. Intervention strategies are clearly needed to reduce obesity-associated breast cancer risk.

METHODS

We conducted a Phase II double-blind, randomized, placebo-controlled trial of metformin in overweight/obese premenopausal women with components of metabolic syndrome to assess the potential of metformin for primary breast cancer prevention. Eligible participants were randomized to receive metformin (850 mg BID, n = 76) or placebo (n = 75) for 12 months. Outcomes included breast density, assessed by fat/water MRI with change in percent breast density as the primary endpoint, anthropometric measures, and intervention feasibility.

RESULTS

Seventy-six percent in the metformin arm and 83% in the placebo arm (p = 0.182) completed the 12-month intervention. Adherence to study agent was high with more than 80% of participants taking ≥ 80% assigned pills. The most common adverse events reported in the metformin arm were gastrointestinal in nature and subsided over time. Compared to placebo, metformin intervention led to a significant reduction in waist circumference (p < 0.001) and waist-to-hip ratio (p = 0.019). Compared to placebo, metformin did not change percent breast density and dense breast volume but led to a numerical but not significant decrease in non-dense breast volume (p = 0.070).

CONCLUSION

We conclude that metformin intervention resulted in favorable changes in anthropometric measures of adiposity and a borderline decrease in non-dense breast volume in women with metabolic dysregulation. More research is needed to understand the impact of metformin on breast cancer risk reduction.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02028221. Registered January 7, 2014, https://clinicaltrials.gov/ct2/show/NCT02028221.

Sulindac, a Nonselective NSAID, Reduces Breast Density in Postmenopausal Women with Breast Cancer Treated with Aromatase Inhibitors

PURPOSE

To evaluate the effect of sulindac, a nonselective anti-inflammatory drug (NSAID), for activity to reduce breast density (BD), a risk factor for breast cancer.

EXPERIMENTAL DESIGN

An open-label phase II study was conducted to test the effect of 12 months' daily sulindac at 150 mg twice daily on change in percent BD in postmenopausal hormone receptor-positive breast cancer patients on aromatase inhibitor (AI) therapy. Change in percent BD in the contralateral, unaffected breast was measured by noncontrast magnetic resonance imaging (MRI) and reported as change in MRI percent BD (MRPD). A nonrandomized patient population on AI therapy (observation group) with comparable baseline BD was also followed for 12 months. Changes in tissue collagen after 6 months of sulindac treatment were explored using second-harmonic generated microscopy in a subset of women in the sulindac group who agreed to repeat breast biopsy.

RESULTS

In 43 women who completed 1 year of sulindac (86% of those accrued), relative MRPD significantly decreased by 9.8% [95% confidence interval (CI), -14.6 to -4.7] at 12 months, an absolute decrease of -1.4% (95% CI, -2.5 to -0.3). A significant decrease in mean breast tissue collagen fiber straightness (P = 0.032), an investigational biomarker of tissue inflammation, was also observed. MRPD (relative or absolute) did not change in the AI-only observation group (N = 40).

CONCLUSIONS

This is the first study to indicate that the NSAID sulindac may reduce BD. Additional studies are needed to verify these findings and determine if prostaglandin E₂ inhibition by NSAIDs is important for BD or collagen modulation.

Volumetric breast density estimation on MRI using explainable deep learning regression

To purpose of this paper was to assess the feasibility of volumetric breast density estimations on MRI without segmentations accompanied with an explainability step. A total of 615 patients with breast cancer were included for volumetric breast density estimation. A 3-dimensional regression convolutional neural network (CNN) was used to estimate the volumetric breast density. Patients were split in training (N = 400), validation (N = 50), and hold-out test set (N = 165). Hyperparameters were optimized using Neural Network Intelligence and augmentations consisted of translations and rotations. The estimated densities were evaluated to the ground truth using Spearman’s correlation and Bland–Altman plots. The output of the CNN was visually analyzed using SHapley Additive exPlanations (SHAP). Spearman’s correlation between estimated and ground truth density was ρ = 0.81 (N = 165, P < 0.001) in the hold-out test set. The estimated density had a median bias of 0.70% (95% limits of agreement = − 6.8% to 5.0%) to the ground truth. SHAP showed that in correct density estimations, the algorithm based its decision on fibroglandular and fatty tissue. In incorrect estimations, other structures such as the pectoral muscle or the heart were included. To conclude, it is feasible to automatically estimate volumetric breast density on MRI without segmentations, and to provide accompanying explanations.

Whole Breast Sound Speed Measurement from US Tomography Correlates Strongly with Volumetric Breast Density from Mammography

Objective

To assess the feasibility of using tissue sound speed as a quantitative marker of breast density.

Methods

This study was carried out under an Institutional Review Board-approved protocol (written consent required). Imaging data were selected retrospectively based on the availability of US tomography (UST) exams, screening mammograms with volumetric breast density data, patient age of 18 to 80 years, and weight less than 300 lbs. Sound speed images from the UST exams were used to measure the volume of dense tissue, the volume averaged sound speed (VASS), and the percent of high sound speed tissue (PHSST). The mammographic breast density and volume of dense tissue were estimated with three-dimensional (3D) software. Differences in volumes were assessed with paired t-tests. Spearman correlation coefficients were calculated to determine the strength of the correlations between the mammographic and UST assessments of breast density.

Results

A total of 100 UST and 3D mammographic data sets met the selection criteria. The resulting measurements showed that UST measured a more than 2-fold larger volume of dense tissue compared to mammography. The differences were statistically significant (P < 0.001). A strong correlation of r_S = 0.85 (95% CI: 0.79-0.90) between 3D mammographic breast density (BD) and the VASS was noted. This correlation is significantly stronger than those reported in previous two-dimensional studies (r_S = 0.85 vs r_S = 0.71). A similar correlation was found for PHSST and mammographic BD with r_S = 0.86 (95% CI: 0.80-0.90).

Conclusion

The strong correlations between UST parameters and 3D mammographic BD suggest that breast sound speed should be further studied as a potential new marker for inclusion in clinical risk models.

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.

Fatty infiltration of paraspinal muscles is associated with bone mineral density of the lumbar spine

A total of 88 subjects were enrolled to investigate the relationship between paraspinal muscle fatty infiltration and lumbar bone mineral density (BMD) using chemical shift encoding-based water-fat MRI and quantitative computed tomography (QCT), respectively. A moderate inverse correlation between paraspinal muscle proton density fat fraction and lumbar QCT-BMD was found with age, sex, and BMI controlled.

PURPOSE

To investigate the relationship between paraspinal muscle fatty infiltration and lumbar bone mineral density (BMD).

METHODS

A total of 88 subjects were enrolled in this study (52 females, 36 males; age, 46.6 ± 14.2 years old; BMI, 23.2 ± 3.49 kg/m²). Proton density fat fractions (PDFF) of paraspinal muscles (erector spinae, multifidus, and psoas) were measured at L2/3, L3/4, and L4/5 levels using chemical shift encoding-based water-fat MRI. Quantitative computed tomography (QCT) was used to assess BMD of L1, L2, and L3. The differences in paraspinal muscle PDFF among subjects with normal bone density, osteopenia, and osteoporosis were tested using one-way ANOVA. The relationship between paraspinal muscle PDFF and QCT-BMD was analyzed using linear regression with age, sex, and BMI variables.

RESULTS

PDFF of the erector spinae, multifidus, and psoas of subjects with normal bone density were all significantly less than those with osteopenia and those with osteoporosis (all p < 0.001). There was an inverse correlation between paraspinal muscle PDFF and BMD after controlling for age, sex, and BMI (standardized beta coefficient, - 0.21~- 0.29; all p < 0.05).

CONCLUSIONS

Paraspinal muscle fatty infiltration increased while lumbar BMD decreased after adjusting for age, sex, and BMI. Paraspinal muscles and vertebrae are interacting tissues. Paraspinal muscle fatty infiltration may be a marker of low lumbar BMD. Chemical shift imaging is an efficient and fast quantitative method and can be easily added to the clinical protocol to measure paraspinal muscle PDFF when the patient underwent the routine lumbar MRI with low-back pain.

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.

Background parenchymal enhancement on breast MRI: A comprehensive review

The degree of normal fibroglandular tissue that enhances on breast MRI, known as background parenchymal enhancement (BPE), was initially described as an incidental finding that could affect interpretation performance. While BPE is now established to be a physiologic phenomenon that is affected by both endogenous and exogenous hormone levels, evidence supporting the notion that BPE frequently masks breast cancers is limited. However, compelling data have emerged to suggest BPE is an independent marker of breast cancer risk and breast cancer treatment outcomes. Specifically, multiple studies have shown that elevated BPE levels, measured qualitatively or quantitatively, are associated with a greater risk of developing breast cancer. Evidence also suggests that BPE could be a predictor of neoadjuvant breast cancer treatment response and overall breast cancer treatment outcomes. These discoveries come at a time when breast cancer screening and treatment have moved toward an increased emphasis on targeted and individualized approaches, of which the identification of imaging features that can predict cancer diagnosis and treatment response is an increasingly recognized component. Historically, researchers have primarily studied quantitative tumor imaging features in pursuit of clinically useful biomarkers. However, the need to segment less well-defined areas of normal tissue for quantitative BPE measurements presents its own unique challenges. Furthermore, there is no consensus on the optimal timing on dynamic contrast-enhanced MRI for BPE quantitation. This article comprehensively reviews BPE with a particular focus on its potential to increase precision approaches to breast cancer risk assessment, diagnosis, and treatment. It also describes areas of needed future research, such as the applicability of BPE to women at average risk, the biological underpinnings of BPE, and the standardization of BPE characterization. Level of Evidence: 3 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2020;51:43-61.

Comparison of Lipid and Water Contents by Time-domain Diffuse Optical Spectroscopy and Dual-energy Computed Tomography in Breast Cancer Patients

We previously compared time-domain diffuse optical spectroscopy (TD-DOS) with magnetic resonance imaging (MRI) using various water/lipid phantoms. However, it is difficult to conduct similar comparisons in the breast, because of measurement differences due to modality-dependent differences in posture. Dual-energy computed tomography (DECT) examination is performed in the same supine position as a TD-DOS measurement. Therefore, we first verified the accuracy of the measured fat fraction of fibroglandular tissue in the normal breast on DECT by comparing it with MRI in breast cancer patients (n = 28). Then, we compared lipid and water signals obtained in TD-DOS and DECT from normal and tumor-tissue regions (n = 16). The TD-DOS breast measurements were carried out using reflectance geometry with a source–detector separation of 3 cm. A semicircular region of interest (ROI), with a transverse diameter of 3 cm and a depth of 2 cm that included the breast surface, was set on the DECT image. Although the measurement area differed between the modalities, the correlation coefficients of lipid and water signals between TD-DOS and DECT were rs = 0.58 (p < 0.01) and rs = 0.90 (p < 0.01), respectively. These results indicate that TD-DOS captures the characteristics of the lipid and water contents of the breast.

Quantitative assessment of breast density using transmission ultrasound tomography

Purpose

Breast density is important in the evaluation of breast cancer risk. At present, breast density is evaluated using two‐dimensional projections from mammography with or without tomosynthesis using either (a) subjective assessment or (b) a computer‐aided approach. The purpose of this work is twofold: (a) to establish an algorithm for quantitative assessment of breast density using quantitative three‐dimensional transmission ultrasound imaging; and (b) to determine how these quantitative assessments compare with both subjective and objective mammographic assessments of breast density.

Methods

We described and verified a threshold‐based segmentation algorithm to give a quantitative breast density (QBD) on ultrasound tomography images of phantoms of known geometric forms. We also used the algorithm and transmission ultrasound tomography to quantitatively determine breast density by separating fibroglandular tissue from fat and skin, based on imaged, quantitative tissue characteristics, and compared the quantitative tomography segmentation results with subjective and objective mammographic assessments.

Results

Quantitative breast density (QBD) measured in phantoms demonstrates high quantitative accuracy with respect to geometric volumes with average difference of less than 0.1% of the total phantom volumes. There is a strong correlation between QBD and both subjective mammographic assessments of Breast Imaging ‐ Reporting and Data System (BI‐RADS) breast composition categories and Volpara density scores — the Spearman correlation coefficients for the two comparisons were calculated to be 0.90 (95% CI: 0.71–0.96) and 0.96 (95% CI: 0.92–0.98), respectively.

Conclusions

The calculation of breast density using ultrasound tomography and the described segmentation algorithm is quantitatively accurate in phantoms and highly correlated with both subjective and Food and Drug Administration (FDA)‐cleared objective assessments of breast density.