Molecular Breast Imaging Biopsy with a Dual-Detector System.

Purpose To develop a molecular breast imaging (MBI)-guided biopsy system using dual-detector MBI and to perform initial testing in participants. Materials and Methods The Stereo Navigator MBI Accessory biopsy system comprises a lower detector, upper fenestrated compression paddle, and upper detector. The upper detector retracts, allowing craniocaudal, oblique, or medial or lateral biopsy approaches. The compression paddle allows insertion of a needle guide and needle. Lesion depth is calculated by triangulation of lesion location on the upper detector at 0° and 15° and relative lesion activity on upper and lower detectors. In a prospective study (July 2022-June 2023), participants with Breast Imaging Reporting and Data System category 2, 3, 4, or 5 breast lesions underwent MBI-guided biopsy. After injection of 740 MBq technetium 99m sestamibi, craniocaudal and mediolateral oblique MBI (2-minute acquisition per view) confirmed lesion visualization. A region of interest over the lesion permitted depth calculation in the system software. Upper detector retraction allowed biopsy device placement. Specimen images were obtained on the retracted upper detector, confirming sampling of the target. Results Of 21 participants enrolled (mean age, 50.6 years ± 10.1 [SD]; 21 [100%] women), 17 underwent MBI-guided biopsy with concordant pathology. No lesion was observed at the time of biopsy in four participants. Average lesion size was 17 mm (range, 6-38 mm). Average procedure time, including preprocedure imaging, was 55 minutes ± 13 (range, 38-90 minutes). Pathology results included invasive ductal carcinoma (n = 1), fibroadenoma (n = 4), pseudoangiomatous stromal hyperplasia (n = 6), and fibrocystic changes (n = 6). Conclusion MBI-guided biopsy using a dual-head system with retractable upper detector head was feasible, well tolerated, and efficient. Keywords: Breast Biopsy, Molecular Breast Imaging, Image-guided Biopsy, Molecular Breast Imaging-guided Biopsy, Breast Cancer Clinical trial registration no. NCT06058650 © RSNA, 2024.

A Survey of Patient Experience During Molecular Breast Imaging.

Molecular breast imaging (MBI) is one of several options available to patients seeking supplemental screening due to mammographically dense breasts. Patient experience during MBI may influence willingness to undergo the test but has yet to be formally assessed. We aimed to assess patient comfort level during MBI, to compare MBI comfort with mammography comfort, to identify factors associated with MBI discomfort, and to evaluate patients' willingness to return for future MBI. Methods: A 10-question survey was sent by e-mail to patients undergoing MBI between August and December 2022 to obtain quantitative assessments and qualitative opinions about MBI. Results: Of 561 invited patients, 209 (37%) completed the survey and provided study consent. Their average age was 60.1 y (range, 40-81 y). Of the 209 responders, 202 (97%) were presenting for screening MBI, 195 (94%) had dense breasts, and 46 (22%) had a personal history of breast cancer. The average rating of MBI comfort was 2.9 (SD, 1.5; median, 3.0) on a 7-point scale (1 indicating extremely comfortable and 7 indicating extremely uncomfortable). The rating distribution was as follows: 140 (67%) comfortable (rating, 1-3); 24 (12%) neither comfortable nor uncomfortable (rating, 4); and 45 (22%) uncomfortable (rating, 5 or 6). No responders gave a 7 rating. The most frequently mentioned sources of discomfort included breast compression (n = 16), back or neck discomfort (n = 14), and maintaining position during the examination (n = 14). MBI comfort was associated with responder age (74% ≥55 y old were comfortable, versus 53% <55 y old [P = 0.003]) and history of MBI (71% with prior MBI were comfortable, versus 61% having a first MBI [P = 0.006]). Of 208 responders with a prior mammogram, 148 (71%) said MBI is more comfortable than mammography (a significant majority [P < 0.001]). Of 202 responders to the question of whether they were willing to return for a future MBI, 196 (97%) were willing. A notable factor in positive patient experience was interaction with the MBI nuclear medicine technologist. Conclusion: Most responders thought MBI to be a comfortable examination and more comfortable than mammography. Patient experience during MBI may be improved by ensuring back support and soliciting patient feedback at the time of positioning and throughout the examination. Methods under study to reduce imaging time may be most important for improving patient experience.

A Review of Theranostics: Perspectives on Emerging Approaches and Clinical Advancements.

Theranostics is the combination of two approaches-diagnostics and therapeutics-applied for decades in cancer imaging using radiopharmaceuticals or paired radiopharmaceuticals to image and selectively treat various cancers. The clinical use of theranostics has increased in recent years, with U.S. Food and Drug Administration (FDA) approval of lutetium 177 (177Lu) tetraazacyclododecane tetraacetic acid octreotate (DOTATATE) and 177Lu-prostate-specific membrane antigen vector-based radionuclide therapies. The field of theranostics has imminent potential for emerging clinical applications. This article reviews critical areas of active clinical advancement in theranostics, including forthcoming clinical trials advancing FDA-approved and emerging radiopharmaceuticals, approaches to dosimetry calculations, imaging of different radionuclide therapies, expanded indications for currently used theranostic agents to treat a broader array of cancers, and emerging ideas in the field. Keywords: Molecular Imaging, Molecular Imaging-Cancer, Molecular Imaging-Clinical Translation, Molecular Imaging-Target Development, PET/CT, SPECT/CT, Radionuclide Therapy, Dosimetry, Oncology, Radiobiology © RSNA, 2023.

Impact of Artificial Intelligence System and Volumetric Density on Risk Prediction of Interval, Screen-Detected, and Advanced Breast Cancer.

PURPOSE: Artificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval cancers is unknown. METHODS: We identified 2,412 women with invasive breast cancer and 4,995 controls matched on age, race, and date of mammogram, from two US mammography cohorts, who had two-dimensional full-field digital mammograms performed 2-5.5 years before cancer diagnosis. We assessed Breast Imaging Reporting and Data System density, an AI malignancy score (1-10), and volumetric density measures. We used conditional logistic regression to estimate odds ratios (ORs), 95% CIs, adjusted for age and BMI, and C-statistics (AUC) to describe the association of AI score with invasive cancer and its contribution to models with breast density measures. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance. RESULTS: On mammograms between 2-5.5 years prior to cancer, a one unit increase in AI score was associated with 20% greater odds of invasive breast cancer (OR, 1.20; 95% CI, 1.17 to 1.22; AUC, 0.63; 95% CI, 0.62 to 0.64) and was similarly predictive of interval (OR, 1.20; 95% CI, 1.13 to 1.27; AUC, 0.63) and advanced cancers (OR, 1.23; 95% CI, 1.16 to 1.31; AUC, 0.64) and in dense (OR, 1.18; 95% CI, 1.15 to 1.22; AUC, 0.66) breasts. AI score improved prediction of all cancer types in models with density measures (PLRT values < .001); discrimination improved for advanced cancer (ie, AUC for dense volume increased from 0.624 to 0.679, Δ AUC 0.065, P = .01) but did not reach statistical significance for interval cancer. CONCLUSION: AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, in particular, advanced cancer.

Breast Cancer Mode of Detection in a Population-Based Cohort.

OBJECTIVE: To evaluate how breast cancers come to clinical attention (mode of detection [MOD]) in a population-based cohort, determine the relative frequency of different MODs, and characterize patient and tumor characteristics associated with MOD. PATIENTS AND METHODS: We used the Rochester Epidemiology Project to identify women ages 40 to 75 years with a first-time diagnosis of breast cancer from May 9, 2017, to May 9, 2019 (n=500) in a 9-county region in Minnesota. We conducted a retrospective medical record review to ascertain the relative frequency of MODs, evaluating differences between screening mammography vs all other MODs by breast density and cancer characteristics. Multiple logistic regression was conducted to examine the likelihood of MOD for breast density and stage of disease. RESULTS: In our population-based cohort, 162 of 500 breast cancers (32.4%) were detected by MODs other than screening mammography, including 124 (24.8%) self-detected cancers. Compared with women with mammography-detected cancers, those with MODs other than screening mammography were more frequently younger than 50 years of age (P=.004) and had higher-grade tumors (P=.007), higher number of positive lymph nodes (P<.001), and larger tumor size (P<.001). Relative to women with mammography-detected cancers, those with MODs other than screening mammography were more likely to have dense breasts (odds ratio, 1.87; 95% CI, 1.20 to 2.92; P=.006) and advanced cancer at diagnosis (odds ratio, 3.58; 95% CI, 2.29 to 5.58; P<.001). CONCLUSION: One-third of all breast cancers in this population were detected by MODs other than screening mammography. Increased likelihood of nonmammographic MODs was observed among women with dense breasts and advanced cancer.

PPV of the Molecular Breast Imaging Lexicon.

BACKGROUND. Molecular breast imaging (MBI) is used for various breast imaging indications. An MBI lexicon has been developed, although the likelihood of malignancy of the lexicon descriptors has not been assessed to our knowledge. OBJECTIVE. The purpose of this article was to evaluate the PPV for malignancy of the MBI lexicon imaging descriptors. METHODS. This retrospective study included MBI examinations performed from August 1, 2005, through August 31, 2017, that were positive (BI-RADS analogous categories 0, 3, 4, 5, or 6) according to the clinical report and had an available reference standard. Examinations were performed using dual-detector cadmium zinc telluride MBI systems after injection of 99mTc sestamibi. Category 3 lesions had pathologic correlation, at least 2 years of imaging follow-up, or final resolution on follow-up imaging as category 1 or 2; category 4 and 5 lesions had pathologic correlation. MBI examinations were reviewed by one of two radiologists to assess lesions on the basis of the published MBI lexicon for type (mass vs nonmass uptake), distribution (if nonmass uptake), uptake intensity, and number of MBI views on which the lesion was seen. PPV for malignancy was summarized. RESULTS. The analysis included 643 lesions (479 benign, 164 malignant; 83 mass, 560 nonmass uptake) in 509 patients (median age, 56 years). PPV was 73.5% (61/83) for masses and 18.4% (103/560) for nonmass uptake. Among the nonmass uptake lesions, PPV was 36.2% (17/47) for segmental, 20.1% (77/384) for focal, 30.8% (4/13) for diffuse, and 4.3% (5/116) for regional or multiple regional distribution. PPV was 5.3% (5/94) for one view, 15.2% (32/210) for two views, 14.6% (13/89) for three views, and 45.4% (113/249) for four views showing the lesion. PPV was 14.0% (43/307) for mild, 22.4% (51/228) for moderate, and 64.8% (70/108) for marked uptake intensity. CONCLUSION. The MBI lexicon lesion descriptors are associated with likelihood of malignancy. PPV was higher for masses, lesions seen on multiple MBI views, and lesions with marked uptake intensity. Among nonmass uptake lesions, PPV was highest for those with segmental distribution. CLINICAL IMPACT. Insight into the likelihood of malignancy associated with the MBI lexicon descriptors can inform radiologists' interpretations and guide potential future incorporation of the MBI lexicon into the ACR BI-RADS Atlas.

Updates in Molecular Breast Imaging.

Molecular breast imaging (MBI) is a nuclear medicine study performed with dedicated gamma camera systems optimized to image the uptake of Tc-99m sestamibi in the breast. MBI provides a relatively low-cost and simple functional breast imaging method that can identify breast cancers obscured by dense fibroglandular tissue on mammography. Recent studies have also found that background levels of uptake in benign dense tissue may provide breast cancer risk information. This article discusses the latest updates in MBI technology, recent evidence supporting its clinical use, and work in progress that may aid in wider adoption of MBI.

Comparison of breast density assessments between synthesized C-View™ & intelligent 2D™ mammography.

OBJECTIVE: To compare breast density assessments between C-View™ and Intelligent 2D™, different generations of synthesized mammography (SM) from Hologic. METHODS: In this retrospective study, we identified a subset of females between March 2017 and December 2019 who underwent screening digital breast tomosynthesis (DBT) with C-View followed by DBT with Intelligent 2D. Clinical Breast Imaging Reporting and Database System breast density was obtained along with volumetric breast density measures (including density grade, breast volume, percentage volumetric density, dense volume) using VolparaTM. Differences in density measures by type of synthesized image were calculated using the pairwise t-test or McNemar's test, as appropriate. RESULTS: 67 patients (avg age 62.7; range 40-84) were included with an average of 13.3 months between the two exams. No difference was found in Breast Imaging Reporting and Database System density between the SM reconstructions (p = 0.74). Similarly, there was no difference in VolparaTM mean density grade (p = 0.71), mean breast volume (p = 0.48), mean dense volume (p = 0.43) or mean percent volumetric density (p = 0.12) between the exams. CONCLUSION: We found no significant differences in clinical and automated breast density assessments between these two versions of SM. ADVANCES IN KNOWLEDGE: Lack of differences in density estimates between the two SM reconstructions is important as density assignment impacts risk stratification and adjunct screening recommendations.

Mammographic Variation Measures, Breast Density, and Breast Cancer Risk.

OBJECTIVE. Our previous work showed that variation measures, which represent breast architecture derived from mammograms, were significantly associated with breast cancer. For replication purposes, we examined the association of three variation measures (variation [V], which is measured in the image domain, and P1 and p1 [a normalized version of P1], which are derived from restricted regions in the Fourier domain) with breast cancer risk in an independent population. We also compared these measures to volumetric density measures (volumetric percent density [VPD] and dense volume [DV]) from a commercial product. MATERIALS AND METHODS. We examined 514 patients with breast cancer and 1377 control patients from a screening practice who were matched for age, date of examination, mammography unit, facility, and state of residence. Spearman rank-order correlation was used to evaluate the monotonic association between measures. Breast cancer associations were estimated using conditional logistic regression, after adjustment for age and body mass index. Odds ratios were calculated per SD increment in mammographic measure. RESULTS. These variation measures were strongly correlated with VPD (correlation, 0.68-0.80) but not with DV (correlation, 0.31-0.48). Similar to previous findings, all variation measures were significantly associated with breast cancer (odds ratio per SD: 1.30 [95% CI, 1.16-1.46] for V, 1.55 [95% CI, 1.35-1.77] for P1, and 1.51 [95% CI, 1.33-1.72] for p1). Associations of volumetric density measures with breast cancer were similar (odds ratio per SD: 1.54 [95% CI, 1.33-1.78] for VPD and 1.34 [95% CI, 1.20-1.50] for DV). When DV was included with each variation measure in the same model, all measures retained significance. CONCLUSION. Variation measures were significantly associated with breast cancer risk (comparable to the volumetric density measures) but were independent of the DV.

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.

123I Scan With Whole-Body Retention Measurement at 48 Hours for Simplified Dosimetry Before 131I Treatment of Metastatic Thyroid Cancer.

ABSTRACT: A previously published model (Atkins) allows for calculation of 131I maximum tolerated activity on the basis of 48-hour whole-body retention of 131I on a pretherapy diagnostic scan. Our practice uses iodine 123I for diagnostic imaging of metastatic thyroid cancer for staging before 131I therapy, with images typically acquired 24 hours after administration of the radiopharmaceutical. We explored the feasibility of an additional 123I whole-body scan and retention measurement at 48 hours, with application of the model to estimate maximum tolerated activity of radioiodine before 131I treatment of metastatic thyroid cancer.

Screening Algorithms in Dense Breasts: AJR Expert Panel Narrative Review.

Screening mammography reduces breast cancer mortality; however, when used to examine women with dense breasts, its performance and resulting benefits are reduced. Increased breast density is an independent risk factor for breast cancer. Digital breast tomosynthesis (DBT), ultrasound (US), molecular breast imaging (MBI), MRI, and contrast-enhanced mammography (CEM) each have shown improved cancer detection in dense breasts when compared with 2D digital mammography (DM). DBT is the preferred mammographic technique for producing a simultaneous reduction in recalls (i.e., additional imaging). US further increases cancer detection after DM or DBT and reduces interval cancers (cancers detected in the interval between recommended screening examinations), but it also produces substantial additional false-positive findings. MBI improves cancer detection with an effective radiation dose that is approximately fourfold that of DM or DBT but is still within accepted limits. MRI provides the greatest increase in cancer detection and reduces interval cancers and late-stage disease; abbreviated techniques will reduce cost and improve availability. CEM appears to offer performance similar to that of MRI, but further validation is needed. Dense breast notification will soon be a national standard; therefore, understanding the performance of mammography and supplemental modalities is necessary to optimize screening for women with dense breasts.

Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction.

Background The associations of density measures from the publicly available Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software with breast cancer have primarily focused on estimates from the contralateral breast at the time of diagnosis. Purpose To evaluate LIBRA measures on mammograms obtained before breast cancer diagnosis and compare their performance to established density measures. Materials and Methods For this retrospective case-control study, full-field digital mammograms in for-processing (raw) and for-presentation (processed) formats were obtained (March 2008 to December 2011) in women who developed breast cancer an average of 2 years later and in age-matched control patients. LIBRA measures included absolute dense area and area percent density (PD) from both image formats. For comparison, dense area and PD were assessed by using the research software (Cumulus), and volumetric PD (VPD) and absolute dense volume were estimated with a commercially available software (Volpara). Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression (odds ratios [ORs] and 95% confidence intervals [CIs]) was performed to examine the associations of density measures with breast cancer by adjusting for age and body mass index. Results Evaluated were 437 women diagnosed with breast cancer (median age, 62 years ± 17 [standard deviation]) and 1225 matched control patients (median age, 61 years ± 16). LIBRA PD showed strong correlations with Cumulus PD (r = 0.77-0.84) and Volpara VPD (r = 0.85-0.90) (P < .001 for both). For LIBRA, the strongest breast cancer association was observed for PD from processed images (OR, 1.3; 95% CI: 1.1, 1.5), although the PD association from raw images was not significantly different (OR, 1.2; 95% CI: 1.1, 1.4; P = .25). Slightly stronger breast cancer associations were seen for Cumulus PD (OR, 1.5; 95% CI: 1.3, 1.8; processed images; P = .01) and Volpara VPD (OR, 1.4; 95% CI: 1.2, 1.7; raw images; P = .004) compared with LIBRA measures. Conclusion Automated density measures provided by the Laboratory for Individualized Breast Radiodensity Assessment from raw and processed mammograms correlated with established area and volumetric density measures and showed comparable breast cancer associations. © RSNA, 2020 Online supplemental material is available for this article.

Breast Density Awareness, Knowledge, and Attitudes Among US Women: National Survey Results Across 5 Years.

PURPOSE: To assess changes in breast density (BD) awareness, knowledge, and attitudes among US women over a period of 5 years. METHODS: Using a probability-based web panel representative of the US population, we administered an identical BD survey in 2012 and 2017 to women aged 40 to 74 years. RESULTS: In 2017, 65.8% had heard of BD (versus 57.5% in 2012; P = .0002). BD awareness in both 2012 and 2017 was significantly associated with race, income, and education. Among women aware of BD in 2017, 76.5% had knowledge of BD's relationship to masking (versus 71.5% in 2012; P = .04); 65.5% had knowledge of BD's relationship to cancer risk (versus 58.5%; P = .009); and 47.3% had discussed BD with a provider (versus 43.1% in 2012; P = .13). After multivariable adjustment, residence in a state with BD legislation was associated in 2017 with knowledge of BD's relationship to risk but not to masking. Most women wanted to know their BD (62.5% in 2017 versus 59.8% in 2012; P = .46); this information was anticipated to cause anxiety in 44.8% (versus 44.9% in 2012; P = .96); confusion in 35.9% (versus 43.0%; P = .002); and feeling informed in 89.7% (versus 90.4%; P = .64). Over three-quarters supported federal BD legislation in both surveys. Response rate to the 2017 survey was 55% (1,502 of 2,730) versus 65% (1,506 of 2,311) in 2012. CONCLUSION: Although BD awareness has increased, important disparities persist. Knowledge of BD's impact on risk has increased; knowledge about masking and BD discussions with providers have not. Most women want to know their BD, would not feel anxious or confused as a result of knowing, and would feel empowered to make decisions. The federal BD notification legislation presents an opportunity to improve awareness and knowledge and encourage BD conversations with providers.

Dose Reduction in Molecular Breast Imaging With a New Image-Processing Algorithm.

OBJECTIVE. The purpose of this study was to determine whether application of a proprietary image-processing algorithm would allow a reduction in the necessary administered activity for molecular breast imaging (MBI) examinations. MATERIALS AND METHODS. Images from standard-dose MBI examinations (300 MBq 99mTc-sestamibi) of 50 subjects were analyzed. The images were acquired in dynamic mode and showed at least one breast lesion. Half-dose MBI examinations were simulated by summing one-half of the dynamic frames and were processed with the algorithm under study in both a default and a preferred filter mode. Two breast radiologists independently completed a set of two-alternative forced-choice tasks to compare lesion conspicuity on standard-dose images, half-dose images, and the algorithm-processed half-dose images in both modes. RESULTS. Relative to the standard-dose images, the half-dose images were preferred in 4, the default-filtered half-dose images in 50, and preferred-filtered half-dose images in 76 of 100 readings. Compared with standard-dose images, in terms of lesion conspicuity, the half-dose images were rated better in 2, equivalent in 6, and poorer in 92 of 100 readings. The default-filtered half-dose images were rated better, equivalent, or poorer in 13, 73, and 14 of 100 readings. The preferred-filtered half-dose images were rated as better, equivalent, or poorer in 55, 34, and 11 of 100 readings. CONCLUSION. Compared with that on standard-dose images, lesion conspicuity on images obtained with the algorithm and acquired at one-half the standard dose was equivalent or better without compromise of image quality. The algorithm can also be used to decrease imaging time with a resulting increase in patient comfort and throughput.

Longitudinal Changes in Volumetric Breast Density in Healthy Women across the Menopausal Transition.

BACKGROUND: Mammographic breast density declines during menopause. We assessed changes in volumetric breast density across the menopausal transition and factors that influence these changes. METHODS: Women without a history of breast cancer, who had full field digital mammograms during both pre- and postmenopausal periods, at least 2 years apart, were sampled from four facilities within the San Francisco Mammography Registry from 2007 to 2013. Dense breast volume (DV) was assessed using Volpara on mammograms across the time period. Annualized change in DV from pre- to postmenopause was estimated using linear mixed models adjusted for covariates and per-woman random effects. Multiplicative interactions were evaluated between premenopausal risk factors and time to determine whether these covariates modified the annualized changes. RESULTS: Among the 2,586 eligible women, 1,802 had one premenopausal and one postmenopausal mammogram, 628 had an additional perimenopausal mammogram, and 156 had two perimenopausal mammograms. Women experienced an annualized decrease in DV [-2.2 cm3 (95% confidence interval, -2.7 to -1.7)] over the menopausal transition. Declines were greater among women with a premenopausal DV above the median (54 cm3) versus below (DV, -3.5 cm3 vs. -1.0 cm3; P < 0.0001). Other breast cancer risk factors, including race, body mass index, family history, alcohol, and postmenopausal hormone therapy, had no effect on change in DV over the menopausal transition. CONCLUSIONS: High premenopausal DV was a strong predictor of greater reductions in DV across the menopausal transition. IMPACT: We found that few factors other than premenopausal density influence changes in DV across the menopausal transition, limiting targeted prevention efforts.

Molecular breast imaging detected invasive lobular carcinoma in dense breasts: A case report.

This case highlights the role of molecular breast imaging (MBI) in evaluating persistent clinical concerns after a negative diagnostic mammogram and ultrasound. MBI is especially useful in the diagnosis of invasive lobular carcinoma due to its occult nature on conventional imaging modalities.

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.

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.