Background parenchymal uptake during molecular breast imaging and associated clinical factors

Correlation between breast cancer and background parenchymal uptake on 18F-fluorodeoxyglucose positron emission tomography

PURPOSE

This study aimed to investigate differences in background parenchymal uptake (BPU) between patients with and without breast cancer using 18F-fluorodeoxyglucose positron emission tomography.

METHODS

Female patients (n = 130, 62.9 ± 12.7 years) with newly diagnosed breast cancer and 50 healthy participants (59.6 ± 13.3 years) without breast cancer were retrospectively included. BPU was evaluated using the maximum standardized uptake value. Data on participant age, body mass index, blood glucose level, and menopausal status were collected from medical records. Breast density was evaluated using mammography. Logistic regression analysis and receiver operating characteristic curves were used to examine the correlation between breast cancer and various characteristic factors, including BPU.

RESULTS

The BPU of patients with breast cancer was significantly higher than that of controls (P < 0.001). The results of logistic regression analysis regarding the presence of breast cancer demonstrated that BPU and menopausal status showed higher odds ratios of 13.6 and 4.25, respectively. The area under the receiver operating characteristic curve for BPU was 0.751.

CONCLUSIONS

Patients with breast cancer showed higher 18F-fluorodeoxyglucose-BPU. Glucose metabolism of mammary glands may correlate with the development of breast cancer.

Obesity and metabolic dysfunction correlate with background parenchymal enhancement in premenopausal women

OBJECTIVE

This study tested the hypothesis that obesity and metabolic abnormalities correlate with background parenchymal enhancement (BPE), the volume and intensity of enhancing fibroglandular breast tissue on dynamic contrast-enhanced magnetic resonance imaging.

METHODS

Participants included 59 premenopausal women at high risk of breast cancer. Obesity was defined as BMI ≥ 30 kg/m2 . Metabolic parameters included dual-energy x-ray absorptiometry-quantified body composition, plasma biomarkers of insulin resistance, adipokines, inflammation, lipids, and urinary sex hormones. BPE was assessed using computerized algorithms on dynamic contrast-enhanced magnetic resonance imaging.

RESULTS

BMI was positively correlated with BPE (r = 0.69; p < 0.001); participants with obesity had higher BPE than those without obesity (404.9 ± 189.6 vs. 261.8 ± 143.8 cm2 ; Δ: 143.1 cm2 [95% CI: 49.5-236.7]; p = 0.003). Total body fat mass (r = 0.68; p < 0.001), body fat percentage (r = 0.64; p < 0.001), visceral adipose tissue area (r = 0.65; p < 0.001), subcutaneous adipose tissue area (r = 0.60; p < 0.001), insulin (r = 0.59; p < 0.001), glucose (r = 0.35; p = 0.011), homeostatic model of insulin resistance (r = 0.62; p < 0.001), and leptin (r = 0.60; p < 0.001) were positively correlated with BPE. Adiponectin (r = -0.44; p < 0.001) was negatively correlated with BPE. Plasma biomarkers of inflammation and lipids and urinary sex hormones were not correlated with BPE.

CONCLUSIONS

In premenopausal women at high risk of breast cancer, increased BPE is associated with obesity, insulin resistance, leptin, and adiponectin.

Background parenchymal enhancement and uptake as breast cancer imaging biomarkers: A state-of-the-art review

Within the past decade, background parenchymal enhancement (BPE) and background parenchymal uptake (BPU) have emerged as novel imaging-derived biomarkers in the diagnosis and treatment monitoring of breast cancer. Growing evidence supports the role of breast parenchyma vascularity and metabolic activity as probable risk factors for breast cancer development. Furthermore, in the presence of a newly-diagnosed breast cancer, added clinically-relevant data was surprisingly found in the respective imaging properties of the non-affected contralateral breast. Evaluation of the contralateral BPE and BPU have been found to be especially instrumental in predicting the prognosis of a patient with breast cancer and even anticipating their response to neoadjuvant chemotherapy. Simultaneously, further research has found a link between these two biomarkers, even though they represent different physical properties. The aim of this review is to provide an up to date summary of the current clinical applications of BPE and BPU as breast cancer imaging biomarkers with the hope that it propels their further usage in clinical practice.

Background Parenchymal Uptake on Molecular Breast Imaging and Breast Cancer Risk: A Cohort Study

BACKGROUND. Background parenchymal uptake (BPU) on molecular breast imaging (MBI) was identified in a case-control study as a breast cancer risk factor beyond mammographic density. To our knowledge, this finding has not yet been confirmed in a cohort study. OBJECTIVE. The objectives of this study were to examine the association of BPU with breast cancer and to estimate the absolute risk and discriminatory accuracy of BPU in a cohort study. METHODS. A retrospective cohort was established that included women without a history of breast cancer who underwent MBI from 2004 to 2015. Radiologists who were blinded to future breast cancer diagnoses assessed BPU on baseline MBI examinations as low (photopenic or minimal) or elevated (mild, moderate, or marked). Associations of BPU with breast cancer were estimated using multivariable Cox proportional hazards models of the time to diagnosis. The 5-year absolute risk was calculated for study subgroups. The discriminatory accuracy of BPU was also assessed. RESULTS. Among 2992 women (mean age, 56.3 years; SD, 10.6 years) who underwent MBI, breast cancer events occurred in 144 women (median follow-up, 7.3 years). Median time to diagnosis after MBI was 4.2 years (range, 0.5-11.6 years). Elevated BPU was associated with a greater breast cancer risk (hazard ratio [HR], 2.39; 95% CI, 1.68-3.41; p ≤ .001). This association remained in postmenopausal women (HR, 3.50; 95% CI, 2.31-5.31; p < .001) but was not significant in premenopausal women (HR, 1.29; 95% CI, 0.72-2.32; p = .39). The 5-year absolute risk of breast cancer was 4.3% (95% CI, 2.9-5.7%) for women with elevated BPU versus 2.5% (95% CI, 1.8-3.1%) for those with low BPU. Postmenopausal women with dense breasts and elevated BPU had a 5-year absolute risk of 8.1% (95% CI, 4.3-11.8%) versus 2.8% (1.8-3.8%) for those with low BPU. Among postmenopausal women, discriminatory accuracy for invasive cancer was improved with the addition of BPU versus use of the Gail risk score alone (C statistic, 65.1 vs 59.1; p = .04) or use of the Breast Cancer Surveillance Consortium risk score alone (C statistic, 66.4 vs 60.4; p = .04). CONCLUSION. BPU on MBI is an independent risk factor for breast cancer, with the strongest association observed among postmenopausal women with dense breasts. In postmenopausal women, BPU provides incremental discrimination in predicting breast cancer when combined with either the Gail model or the Breast Cancer Surveillance Consortium model. CLINICAL IMPACT. Observation of elevated BPU on MBI may identify a subset of women with dense breasts who would benefit most from supplemental screening or preventive options.

Classification of Background Parenchymal Uptake on Molecular Breast Imaging Using a Convolutional Neural Network

PURPOSE

Background parenchymal uptake (BPU), which describes the level of radiotracer uptake in normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor. Our objective was to develop and validate a deep learning model using image convolution to automatically categorize BPU on MBI.

METHODS

MBI examinations obtained for clinical and research purposes from 2004 to 2015 were reviewed to classify the BPU pattern using a standardized five-category scale. Two expert radiologists provided interpretations that were used as the reference standard for modeling. The modeling consisted of training and validating a convolutional neural network to predict BPU. Model performance was summarized in data reserved to test the performance of the algorithm at the per-image and per-breast levels.

RESULTS

Training was performed on 24,639 images from 3,133 unique patients. The model performance on the withheld testing data (6,172 images; 786 patients) was evaluated. Using direct matching on the predicted classification resulted in an accuracy of 69.4% (95% CI, 67.4% to 71.3%), and if prediction within one category was considered, accuracy increased to 96.0% (95% CI, 95.2% to 96.7%). When considering the breast-level prediction of BPU, the accuracy remained strong, with 70.3% (95% CI, 68.0% to 72.6%) and 96.2% (95% CI, 95.3% to 97.2%) for the direct match and allowance for one category, respectively.

CONCLUSION

BPU provided a robust target for training a convolutional neural network. A validated computer algorithm will allow for objective, reproducible encoding of BPU to foster its integration into risk-stratification algorithms.

99mTC-sestamibi breast imaging: Current status, new ideas and future perspectives

Here we proposed the most recent innovations in the use of Breast Specific Gamma Imaging with ⁹⁹mTc-sestamibi for the management of breast cancer patients. To this end, we reported the recent discoveries concerning: a) the implementation of both instrumental devices and software, b) the biological mechanisms involved in the ⁹⁹mTc-sestamibi uptake in breast cancer cells, c) the evaluation of Breast Specific Gamma Imaging with ⁹⁹mTc-sestamibi as predictive markers of metastatic diseases. In this last case, we also reported preliminary data about the capability of Breast Specific Gamma Imaging with ⁹⁹mTc-sestamibi to identify breast cancer lesions with high propensity to form bone metastatic lesions due to the presence of Breast Osteoblast-Like Cells.

Impact of short-term low-dose tamoxifen on molecular breast imaging background parenchymal uptake: a pilot study

BACKGROUND

High background parenchymal uptake (BPU) on molecular breast imaging (MBI) has been identified as a breast cancer risk factor. We explored the feasibility of offering a short-term intervention of low-dose oral tamoxifen to women with high BPU and examined whether this intervention would reduce BPU.

METHODS

Women with a history of high BPU and no breast cancer history were invited to the study. Participants had an MBI exam, followed by 30 days of low-dose oral tamoxifen at either 5 mg or 10 mg/day, and a post-tamoxifen MBI exam. BPU on pre- and post-tamoxifen MBI exams was quantitatively assessed as the ratio of average counts in breast fibroglandular tissue vs. average counts in subcutaneous fat. Pre-tamoxifen and post-tamoxifen BPU were compared with paired t tests.

RESULTS

Of 47 women invited, 22 enrolled and 21 completed the study (10 taking 5 mg tamoxifen, 11 taking 10 mg tamoxifen). Mean age was 47.7 years (range 41-56 years). After 30 days low-dose tamoxifen, 8 of 21 women (38%) showed a decline in BPU, defined as a decrease from the pre-tamoxifen MBI of at least 15%; 11 of 21 (52%) had no change in BPU (within ± 15%); 2 of 21 (10%) had an increase in BPU of greater than 15%. Overall, the average post-tamoxifen BPU was not significantly different from pre-tamoxifen BPU (1.34 post vs. 1.43 pre, p = 0.11). However, among women taking 10 mg tamoxifen, 5 of 11 (45%) showed a decline in BPU; average BPU was 1.19 post-tamoxifen vs. 1.34 pre-tamoxifen (p = 0.005). In women taking 5 mg tamoxifen, 2 of 10 (20%) showed a decline in BPU; average BPU was 1.51 post-tamoxifen vs.1.53 pre-tamoxifen (p = 0.99).

CONCLUSIONS

Short-term intervention with low-dose tamoxifen may reduce high BPU on MBI for some patients. Our preliminary findings suggest that 10 mg tamoxifen per day may be more effective than 5 mg for inducing declines in BPU within 30 days. Given the variability in BPU response to tamoxifen observed among study participants, future study is warranted to determine if BPU response could predict the effectiveness of tamoxifen for breast cancer risk reduction within an individual.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02979301 . Registered 01 December 2016.

Dedicated Breast Gamma Camera Imaging and Breast PET: Current Status and Future Directions

Recent advances in nuclear medicine instrumentation have led to the emergence of improved molecular imaging techniques to image breast cancer: dedicated gamma cameras using γ-emitting ^99mTc-sestamibi and breast-specific PET cameras using ¹⁸F-fluorodeoxyglucose. This article focuses on the current role of such approaches in the clinical setting including diagnosis, assessing local extent of disease, monitoring response to therapy, and, for gamma camera imaging, possible supplemental screening in women with dense breasts. Barriers to clinical adoption and technologies and radiotracers under development are also discussed.

Breast Lesions Detected via Molecular Breast Imaging: Physiological Parameters Affecting Interpretation

RATIONALE AND OBJECTIVES

To evaluate correlations between molecular breast imaging (MBI) descriptor characteristics and positive predictive value (PPV) in detecting breast cancer.

MATERIALS AND METHODS

A retrospective review was performed on 193 suspicious findings from 153 women (31-81 years) with positive MBI examinations. We assessed associations between (i) lesion pattern (mass vs. nonmass) and PPV; (ii) lesion pattern and suspected likelihood of cancer (low vs. moderate vs. high); (iii) background parenchymal uptake (BPU) (homogeneous vs. heterogeneous) and PPV; (iv) breast density (dense vs. non-dense) and PPV; and (v) BPU and density.

RESULTS

One hundred ten of 153 patients were diagnosed with malignancy or high-risk pathology (PPV1 = 71.9%), and 130/193 biopsies resulted in malignant or high-risk lesions (PPV3 = 67.4%). Biopsies of mass vs. nonmass findings had comparable PPV3 (71.7% vs. 61.3%; P = .0717). Mass findings were correlated with higher suspicion for cancer than nonmass findings (P < .001). There was no significant difference in PPV3 when comparing biopsies from homogeneous vs. heterogeneous BPU (72.5% vs. 60.7%; P = .103). No association was found between patients' BPU and diagnosed cancer or high-risk lesions (P = .513). Biopsies from nondense breasts demonstrated higher PPV3 than biopsies from dense breasts (85.4% vs. 60.6%; P = .0025); patients with nondense breasts were more likely to be diagnosed with cancer or high-risk pathology (PPV1 = 87.8% vs. 66.0%; P = .00844). Dense breasts had a greater association with heterogeneous BPU (P = .0844).

CONCLUSION

Neither variability in mass or nonmass positive MBI findings, nor variability in BPU on MBI were significant determinants for the probability of malignancy. Dense breasts were associated with lower predictability and heterogeneous BPU on MBI.

Quantitative background parenchymal uptake on molecular breast imaging and breast cancer risk: a case-control study

BACKGROUND

Background parenchymal uptake (BPU), which refers to the level of Tc-99m sestamibi uptake within normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor, independent of mammographic density. Prior analyses have used subjective categories to describe BPU. We evaluate a new quantitative method for assessing BPU by testing its reproducibility, comparing quantitative results with previously established subjective BPU categories, and determining the association of quantitative BPU with breast cancer risk.

METHODS

Two nonradiologist operators independently performed region-of-interest analysis on MBI images viewed in conjunction with corresponding digital mammograms. Quantitative BPU was defined as a unitless ratio of the average pixel intensity (counts/pixel) within the fibroglandular tissue versus the average pixel intensity in fat. Operator agreement and the correlation of quantitative BPU measures with subjective BPU categories assessed by expert radiologists were determined. Percent density on mammograms was estimated using Cumulus. The association of quantitative BPU with breast cancer (per one unit BPU) was examined within an established case-control study of 62 incident breast cancer cases and 177 matched controls.

RESULTS

Quantitative BPU ranged from 0.4 to 3.2 across all subjects and was on average higher in cases compared to controls (1.4 versus 1.2, p < 0.007 for both operators). Quantitative BPU was strongly correlated with subjective BPU categories (Spearman's r = 0.59 to 0.69, p < 0.0001, for each paired combination of two operators and two radiologists). Interoperator and intraoperator agreement in the quantitative BPU measure, assessed by intraclass correlation, was 0.92 and 0.98, respectively. Quantitative BPU measures showed either no correlation or weak negative correlation with mammographic percent density. In a model adjusted for body mass index and percent density, higher quantitative BPU was associated with increased risk of breast cancer for both operators (OR = 4.0, 95% confidence interval (CI) 1.6-10.1, and 2.4, 95% CI 1.2-4.7).

CONCLUSION

Quantitative measurement of BPU, defined as the ratio of average counts in fibroglandular tissue relative to that in fat, can be reliably performed by nonradiologist operators with a simple region-of-interest analysis tool. Similar to results obtained with subjective BPU categories, quantitative BPU is a functional imaging biomarker of breast cancer risk, independent of mammographic density and hormonal factors.

Breast-specific gamma imaging of invasive breast cancer: Clinicopathologic factors affecting detectability and correlation with mammographic findings

PURPOSE

To investigate the factors affecting detectability of invasive breast cancers on BSGI.

MATERIAL AND METHODS

We evaluated BSGI, mammography and pathologic reports of 89 patients with invasive breast cancers.

RESULTS

87.6% were visible on BSGI. Cancer in old or postmenopausal women were more visible on BSGI (p = 0.003, 0.046). Cancers ≥ 1.0 cm in size were significantly more visible on BSGI than those <1 cm in size (p = 0.002). Cancers in fatty breasts were more visible than those in dense breasts (p = 0.042).

CONCLUSION

Invasive cancers in older, postmenopausal patients, cancers with size ≥1.0 cm, and those with fatty breast are better visualized by BSGI, than those in younger, premenopausal patients, with size <1.0 cm and dense breast.

BEST PRACTICES IN MOLECULAR BREAST IMAGING: A GUIDE FOR TECHNOLOGISTS

Molecular breast imaging (MBI) technologists are required to possess a combination of nuclear medicine skills and mammographic positioning techniques. Currently, no formal programs offer this type of hybrid technologist training. The purpose of this perspective is to provide a best practices guide for technologists performing MBI. Familiarity with best practices may aid in obtaining high-quality MBI examinations by decreasing the likelihood of image artifacts, positioning problems and other factors that contribute to false negative or false positive findings.

Molecular Breast Imaging in Breast Cancer Screening and Problem Solving

In the United States, legislative actions in over 28 states require radiologists to notify women who undergo breast screening mammography of their breast density. This has led to increased public interest in supplemental screening, but radiologists have not come to a consensus on a supplemental screening modality. In choosing between the most common options, whole-breast ultrasonography (US) and magnetic resonance (MR) imaging, one must weigh the benefits and drawbacks of each modality, as increased cancer detection may be accompanied by increased examination costs and biopsy rates. There has been recent interest in molecular breast imaging (MBI) for supplemental screening because of its high sensitivity, as well as its high specificity. This article describes how MBI fits into clinical practice alongside digital breast tomosynthesis (DBT), targeted US, and MR imaging. The authors describe their approach to breast cancer screening, which uses DBT as the primary imaging modality. DBT is complemented by automated density calculations and supplemented with functional imaging techniques, including MR imaging or MBI, for women with dense breasts. An algorithm based on the patient's breast cancer risk is used to determine if either MR imaging or MBI for supplemental screening is appropriate. MBI is also used as a problem-solving tool for the evaluation of clinical indications following complex mammography or US, or for unexplained physical findings. This article describes aspects related to implementing MBI in clinical practice, including the clinical workflow, patient management, radioactive tracer administration, and procedure reimbursement. ^© RSNA, 2017.

Adjunctive Breast-Specific Gamma Imaging for Detecting Cancer in Women with Calcifications at Mammography

BACKGROUND

Mammography detects calcium deposits sensitively, but the specificity for differentiating malignancy from benign calcifications is low. Thus, we investigated whether adjunctive breast-specific gamma imaging (BSGI) has incremental value for detecting cancer in women with suspicious calcifications detected by mammography, and compared BSGI with adjunctive ultrasonography (US).

METHODS

The medical records of women without a personal history of breast cancer who underwent mammography for breast evaluation from 2009 to 2014 were reviewed retrospectively. Patients who had calcifications detected by mammography, with a result of Breast Imaging Reporting and Data System (BI-RADS) categories 3-5, underwent adjunctive US and BSGI and were included in this study. A total of 302 breast lesions in 266 women (mean age ± standard deviation 49 ± 9 years) were selected for this study.

RESULTS

For detecting breast cancer using mammography plus BSGI, the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating curve with 95% confidence intervals were 94% (91-96), 90% (86-93), 91% (87-94), 94% (90-96), and 0.92 (0.89-0.95), respectively. For mammography plus US, the respective values were 97% (94-98), 51% (46-57), 68% (63-73), 94% (90-96), and 0.74 (0.70-0.78).

CONCLUSIONS

Adjunctive BSGI had higher specificity than adjunctive US without loss of sensitivity. This finding suggests that adjunctive BSGI may be a useful complementary initial imaging method to improve the detection of breast cancer in women who have calcifications with suspicious morphology at mammography.

Breast Imaging Utilizing Dedicated Gamma Camera and (99m)Tc-MIBI: Experience at the Tel Aviv Medical Center and Review of the Literature Breast Imaging

The scope of the current article is the clinical role of gamma cameras dedicated for breast imaging and (99m)Tc-MIBI tumor-seeking tracer, as both a screening modality among a healthy population and as a diagnostic modality in patients with breast cancer. Such cameras are now commercially available. The technology utilizing a camera composed of a NaI (Tl) detector is termed breast-specific gamma imaging. The technology of dual-headed camera composed of semiconductor cadmium zinc telluride detectors that directly converts gamma-ray energy into electronic signals is termed molecular breast imaging. Molecular breast imaging system has been installed at the Department of Nuclear medicine at the Tel Aviv Sourasky Medical Center, Tel Aviv in 2009. The article reviews the literature well as our own experience.

Background parenchymal uptake on molecular breast imaging as a breast cancer risk factor: a case-control study

BACKGROUND

Molecular breast imaging (MBI) is a functional test used for supplemental screening of women with mammographically dense breasts. Additionally, MBI depicts variable levels of background parenchymal uptake (BPU) within nonmalignant, dense fibroglandular tissue. We investigated whether BPU is a risk factor for breast cancer.

METHODS

We conducted a retrospective case-control study of 3027 eligible women who had undergone MBI between February 2004 and February 2014. Sixty-two incident breast cancer cases were identified. A total of 179 controls were matched on age, menopausal status, and MBI year. Two radiologists blinded to case status independently assessed BPU as one of four categories: photopenic, minimal to mild, moderate, or marked. Conditional logistic regression analysis was performed to estimate the associations (OR) of BPU categories (moderate or marked vs. minimal to mild or photopenic) and breast cancer risk, adjusted for other risk factors.

RESULTS

The median age was 60.2 years (range 38-86 years) for cases vs. 60.2 years (range 38-88 years) for controls (p = 0.88). Women with moderate or marked BPU had a 3.4-fold (95 % CI 1.6-7.3) and 4.8-fold (95 % CI 2.1-10.8) increased risk of breast cancer, respectively, compared with women with photopenic or minimal to mild BPU, for two radiologists. The results were similar after adjustment for BI-RADS density (OR 3.3 [95 % CI 1.6-7.2] and OR 4.6 [95 % CI 2.1-10.5]) or postmenopausal hormone use (OR 3.6 [95 % CI 1.7-7.7] and OR 5.0 [95 % CI 2.2-11.4]). The association of BPU with breast cancer remained in analyses limited to postmenopausal women only (OR 3.8 [95 % CI 1.5-9.3] and OR 4.1 [95 % CI 1.6-10.2]) and invasive breast cancer cases only (OR 3.6 [95 % CI 1.5-8.8] and OR 4.4 [95 % CI 1.7-11.1]). Variable BPU was observed among women with similar mammographic density; the distribution of BPU categories differed across density categories (p < 0.0001).

CONCLUSIONS

This study provides the first evidence for BPU as a risk factor for breast cancer. Among women with dense breasts, who comprise >40 % of the screening population, BPU may serve as a functional imaging biomarker to identify the subset at greatest risk.

The Dose-Response Effects of Aerobic Exercise on Body Composition and Breast Tissue among Women at High Risk for Breast Cancer: A Randomized Trial

Observational data indicate that behaviors that shift energetic homeostasis, such as exercise, may decrease the risk of developing breast cancer by reducing the amount of energy-dense, metabolically active adipose tissue. Between December 2008 and April 2013, we conducted a single-blind, 5-month, clinical trial that randomized premenopausal women at high risk of developing breast cancer to one of three groups: 150 min/wk of aerobic exercise (low dose), 300 min/wk of aerobic exercise (high dose), or control. Body composition was assessed using dual-energy x-ray absorptiometry. Background parenchymal enhancement (BPE) was quantified using computerized algorithms on breast dynamic contrast-enhanced MRI. Over 5 months, compared with the control group: the low-dose and high-dose groups lost -1.5 ± 0.5 and -1.3 ± 0.5 kg of body mass (linear Ptrend = 0.032); -1.5 ± 0.4 and -1.4 ± 0.3 kg of fat mass (linear Ptrend = 0.003); -1.3 ± 0.3 and -1.4 ± 0.3% of body fat (linear Ptrend < 0.001); -15.9 ± 5.4 and -26.6 ± 5.0 cm(2) of subcutaneous adipose tissue (linear Ptrend < 0.001); and -6.6 ± 1.9 and -5.0 ± 1.9 cm(2) visceral adipose tissue (nonlinear Ptrend = 0.037). For each -1 cm(2) reduction in visceral adipose tissue, BPE decreased by -3.43 ± 1.34 cm(2) (P = 0.010) and explained 9.7% of the variability in BPE. Changes in other aforementioned body composition outcomes did not significantly correlate with changes in BPE. These mechanistic data support observational evidence that shifting energetic homeostasis through exercise may alter the risk of developing breast cancer. Additional adequately powered studies are needed to confirm and expand upon our findings that changes in body composition are associated with changes in BPE. Cancer Prev Res; 9(7); 581-8. ©2016 AACR.

Effect of menstrual cycle phase on background parenchymal uptake at molecular breast imaging

RATIONALE AND OBJECTIVES

The level of Tc-99m sestamibi uptake within normal fibroglandular tissue on molecular breast imaging (MBI), termed background parenchymal uptake (BPU), has been anecdotally observed to fluctuate with menstrual cycle. Our objective was to assess the impact of menstrual cycle phase on BPU appearance.

MATERIALS AND METHODS

Premenopausal volunteers who reported regular menstrual cycles and no exogenous hormone use were recruited to undergo serial MBI examinations during the follicular and luteal phase. A study radiologist, blinded to cycle phase, categorized BPU as photopenic, minimal mild, moderate, or marked. Change in BPU with cycle phase was determined, as well as correlations of BPU with mammographic density and hormone levels.

RESULTS

In 42 analyzable participants, high BPU (moderate or marked) was observed more often in luteal phase compared to follicular (P = .016). BPU did not change with phase in 30 of 42 participants (71%) and increased in the luteal phase compared to follicular in 12 (29%). High BPU was more frequent in dense breasts compared to nondense breasts at both the luteal (58% [15 of 26] vs. 13% [2 of 16], P = .004) and follicular phases (35% [9 of 26] vs. 6% [1 of 16], P = .061). Spearman correlation coefficients did not show any correlation of BPU with hormone levels measured at either cycle phase and suggested a weak correlation between change in BPU and changes in estrone and estradiol between phases.

CONCLUSIONS

We observed variable effects of menstrual cycle on BPU among our cohort of premenopausal women; however, when high BPU was observed, it was most frequently seen during the luteal phase compared to follicular phase and in women with dense breasts compared to nondense breasts.

Sex Differences Research, Precision Medicine, and the Future of Women's Health

The National Institutes of Health's (NIH) commitment to improving health outcomes for women and men through rigorous science has been compromised by the lack of basic science evidence obtained from female animals. To correct this limitation, in June 2015 the NIH announced expectations that "sex," as a biological variable, be included into research design and analysis in studies of vertebrate animals and humans (NOT-OD-15-102). Scientists must take the responsibility to implement this directive. However, in doing so, there is a risk that attention could be restricted to only studies of direct comparison between female/women and male/men. By contrast, understanding how sex influences health and disease needs to take a programmatic approach that includes the study of sex-specific conditions. A programmatic approach will assure the advancement of knowledge to improve women's health.

Current and Future Methods for Measuring Breast Density: A Brief Comparative Review

Breast density is one of the strongest predictors of breast cancer risk. Women with the densest breasts are 4 to 6 times more likely to develop cancer compared with those with the lowest densities. Breast density is generally assessed using mammographic imaging; however, this approach has limitations. Magnetic resonance imaging and ultrasound tomography are some alternative imaging modalities that can aid mammography in patient screening and the measurement of breast density. As breast density becomes more commonly discussed, knowledge of the advantages and limitations of breast density as a marker of risk will become more critical. This review article discusses the relationship between breast density and breast cancer risk, lists the benefits and drawbacks of using multiple different imaging modalities to measure density and briefly discusses how breast density will be applied to aid in breast cancer prevention and treatment.