Whaley, Dana H.

In women with dense breasts, adding molecular breast imaging to digital breast tomosynthesis (DBT) increased both overall and invasive cancer detection rates, while modestly raising the recall rate compared with DBT alone.

Ultrasound‐guided breast biopsies can be challenging to perform, especially when the target is adjacent to the nipple, skin, or implant or when the target is small and in very posterior, dense fibroglandular tissue. Oftentimes, a slightly modified approach can result in a diagnostic biopsy specimen with minimal complications. After a brief review of basic techniques for ultrasound‐guided breast biopsies that includes a review of conventional breast biopsy devices, a presentation of procedural modifications and techniques to consider for more challenging cases is described. In particular, novel open‐trough and tandem‐needle techniques are detailed. Several cases using these techniques are then presented.

Abstract 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.

Abstract Background High mammographic breast density is the primary reason for missed cancers or delayed detection on mammography, and is associated with a higher rate of advanced and interval cancers which increase breast cancer mortality. Digital breast tomosynthesis (DBT) has been shown to reduce false positive findings relative to 2D mammography but does not eliminate the potential for tumor masking in dense breasts due to the similar x-ray attenuation characteristics of tumors and normal fibroglandular tissue. Molecular Breast Imaging (MBI) performed with a dedicated gamma camera to detect functional uptake of a radiotracer, Tc-99m sestamibi, has been shown to reveal breast cancers obscured by density on mammography. In single-institution studies, adding MBI to 2D mammography in women with dense breasts detected an additional 5 to10 invasive cancers per 1000 screened, with modest increases in recall rate (6 to 8%) at a lower cost-per-cancer detected than mammography alone. Despite this promising evidence, the lack of multicenter trial data has limited wider acceptance. Also, MBI has yet to be compared to DBT, which in some centers has replaced 2D mammography screening. We present interim results from a multicenter trial comparing cancer detection rate of DBT and MBI in screening of women with mammographically dense breasts. Methods In this ongoing, prospective, multicenter clinical trial, asymptomatic women aged 40-75 years with dense breasts on prior mammogram and no prior history of supplemental screening are invited to undergo two annual rounds of concurrent DBT and MBI. MBI is performed with injection of 300 MBq Tc-99m sestamibi with a dual-head semiconductor-based gamma camera. Screening tests are interpreted independently. Here, preliminary cancer detection rates (cancers per 1000 women screened), recall rates, and biopsy rates of DBT and MBI for initial screening are reported. Results In 537 women out of a planned 3000 who have completed the first round of screening, 7 cancers were detected: one by DBT only and 6 by MBI only, giving cancer detection rates of 1.9 for DBT vs. 11.2 for MBI and incremental cancer detection rate of 9.3 for MBI. The one DBT-only cancer was a node-negative 0.8 cm invasive lobular carcinoma. All 6 cancers detected by MBI were invasive; 5 of 6 were node negative (median size 1.0 cm; range 0.6 to 2.6 cm). Recall rate was 11% (60/537) for DBT alone; 16% (84/537) for MBI alone, and 21% (115/537) for the combination. Biopsy was prompted by DBT in 13 patients (PPV 8% [1/13]); by MBI in 23 patients (PPV 26% [6/23]); and by the combination of modalities in 33 (PPV 21% [7/33]). Conclusion These preliminary results demonstrate that MBI detects invasive breast cancers occult on DBT in dense breasts. Data from a second screening round will allow calculation of sensitivity and specificity, and determination of the impact of screening MBI in reducing advanced (> 2 cm) and interval cancers. Additional planned analyses will evaluate a denoising algorithm for further reduction in MBI radiation dose to match that of DBT. Citation Format: Rhodes D, Hunt K, Conners A, Zingula S, Whaley D, Ellis R, Gasal Spilde J, Mehta R, Polley M-Y, O'Connor M, Hruska C. Molecular breast imaging and tomosynthesis to eliminate the reservoir of undetected cancer in dense breasts: The Density MATTERS trial [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr PD4-05.

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.

Abstract Purpose: Volumetric breast density and body mass index (BMI) are increasingly used for breast cancer risk stratification. We examine if the effect of volumetric breast density on breast cancer risk increases with increasing BMI. Methods: Participants were from two case-control studies within mammography practices, the San Francisco Mammography Registry and the Mayo Clinic Rochester, Breast Screening Practice. Breast cancers were included if diagnosed between January 2007 and 2015 and with a screening full-field digital mammogram at least 6 months prior to diagnosis; the earliest mammogram within 5 years of diagnosis was selected. Up to three controls were matched to each case on age, race, date, mammography machine, and state of residence. Volumetric percent density (VPD), dense volume (DV) and non-dense volume (NDV) were measured using VolparaTM software. Breast cancer risk was assessed using logistic regression stratified by menopause status and adjusted for matching factors, family history of breast cancer, parity/age at first birth, and postmenopausal hormone therapy. Models for DV were additionally adjusted for NDV, and NDV models for DV. Multiplicative interactions were fit between BMI categories [<25 (underweight), 25-30 (normal weight) and >30 (overweight/obese) kg/m2] and VPD, DV, and NDV, and trend tests assessed for increasing odds ratios (OR) with increasing BMI. Results: A total of 781 premenopausal and 1850 postmenopausal breast cancers and 3535 controls were included in the analysis. Median age of premenopausal women was 45 years (IQR: 6.1) and 63.3 years (IQR: 14) for postmenopausal women. Cases vs. controls had greater VPD and DV for both premenopausal (VPD: 14.9% vs. 12.0%, DV: 74.1 cm3 vs. 64.4 cm3) and postmenopausal women (VPD: 6.8% vs. 6.1%, DV: 53.4 cm3 vs. 48.0 cm3)(all p's<0.001). Trends between increasing BMI and VPD were evident for both pre (ptrend=0.0007) and postmenopausal (ptrend=0.0005) women. Among premenopausal women, the odds ratio (OR) for breast cancer associated with a 10% increase in VPD was of 1.39, 2.19 and 2.88 for BMI <25, 25-30 and >30 kg/m2 (p-trend=0.0007), respectively. For DV, OR's were 1.39, 1.33 and 1.51 for a 1 SD increase in DV, respectively, though the interaction with DV was not significant (ptrend=0.68). Among postmenopausal women, a 10% increase in VPD was associated with OR's of 1.35, 2.03, 3.6 for BMI <25, 25-29, >30-kg/m2 (ptrend =0.0001), respectively, and 1.31, 1.34 and 1.65 for a 1 SD increase in DV (ptrend =0.01), respectively. Associations between NDV and breast cancer risk did not differ by BMI category for premenopausal (ptrend =0.52) or postmenopausal (ptrend =0.07) women. Conclusions: The effect of VPD on breast cancer risk is strongest in overweight/obese women. As volumetric breast density and BMI are commonly used in clinical risk stratification, these differences in risk have high clinical relevance for informing prevention decisions. Citation Format: Natalie J. Engmann, Christopher G. Scott, Matthew R. Jensen, Stacey J. Winham, Lin Ma, Kathleen R. Brandt, Amir Mahmoudzadeh, Serghei Malkov, Dana Whaley, Carrie Hruska, Fang Fang Wu, Diana L. Miglioretti, Aaron D. Norman, John Heine, John Shepherd, Vernon S. Pankratz, Celine M. Vachon, Karla Kerlikowske. Overweight and obese women with high volumetric breast density at high breast cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3226.

Abstract Background: Reductions in breast density with tamoxifen and aromatase inhibitors may be an intermediate marker of treatment response. We compare changes in volumetric breast density among breast cancer cases using tamoxifen or aromatase inhibitors (AI) to untreated women without breast cancer. Methods: Breast cancer cases with a digital mammogram prior to diagnosis and after initiation of tamoxifen (n = 366) or AI (n = 403) and a sample of controls (n = 2170) were identified from the Mayo Clinic Mammography Practice and San Francisco Mammography Registry. Volumetric percent density (VPD) and dense breast volume (DV) were measured using Volpara (Matakina Technology) and Quantra (Hologic) software. Linear regression estimated the effect of treatment on annualized changes in density. Results: Premenopausal women using tamoxifen experienced annualized declines in VPD of 1.17% to 1.70% compared with 0.30% to 0.56% for controls and declines in DV of 7.43 to 15.13 cm3 compared with 0.28 to 0.63 cm3 in controls, for Volpara and Quantra, respectively. The greatest reductions were observed among women with ≥10% baseline density. Postmenopausal AI users had greater declines in VPD than controls (Volpara P = 0.02; Quantra P = 0.03), and reductions were greatest among women with ≥10% baseline density. Declines in VPD among postmenopausal women using tamoxifen were only statistically greater than controls when measured with Quantra. Conclusions: Automated software can detect volumetric breast density changes among women on tamoxifen and AI. Impact: If declines in volumetric density predict breast cancer outcomes, these measures may be used as interim prognostic indicators. Cancer Epidemiol Biomarkers Prev; 26(6); 930–7. ©2017 AACR.

Abstract Breast density represents the stromal and epithelial tissue in the breast and is a strong risk factor for breast cancer. Reductions in breast density attributable to tamoxifen (TAM) and aromatase inhibitors (AI) may be associated with reduced risk of first primary and subsequent breast cancer. Studies assessing breast density changes have principally used two-dimensional measures. We assess changes in breast density following initiation of TAM and AI using two automated volumetric density measures that have shown strong associations to breast cancer risk. Breast cancer cases with a full field digital mammogram (FFDM) prior to diagnosis (index mammogram) and after initiation of TAM (n = 379) or AI (n = 425) were identified from the Mayo Clinic Mammography Practice and the San Francisco Mammography Registry. Volumetric percent density (VPD) and dense volume (DV) were measured on 4-view FFDM using VolparaTM (Matakina Technology) and QuantraTM (Hologic) automated software. We used linear regression to assess the effect of treatment on mean annualized change in VPD and DV (change from index to last mammogram divided by time interval) for each software type, adjusting for age, body mass index (BMI) and density at index mammogram. The median time between index mammogram and cancer diagnosis was 0.6 months (IQR: 0.2, 2.2) and median time between index and last mammogram was 3 years (IQR: 2.0, 3.9). Women on TAM were younger, had lower BMI and higher baseline VPD and DV relative to women on AI (Table). An annual decrease in VPD and DV was observed with both TAM and AI. Both Volpara and Quantra estimated a similar magnitude of change in VPD in women on TAM and AI, and a greater change in DV with TAM. Our findings suggest that both Volpara and Quantra can assess volumetric changes in breast density among women on hormone therapy. If declines in volumetric density correlate with a reduction in breast cancer risk, these automated measures could be used in clinical practice to assess response to therapy. Annualized changes in volumetric breast density estimated by linear regression.Tamoxifen (n = 379)Aromatase Inhibitors (n = 425)Baseline Median (IQR)Annualized Change (95% CI)*Baseline Median (IQR)Annualized Change (95% CI)*Age at Diagnosis50.0 (45.0, 60.0)–63.0 (58.0, 71.0)–Body Mass Index (BMI)23.6 (21.5, 26.8)–25.7 (22.7, 29.9)–Time Interval¥3.0 (2.1, 3.9)–3.0 (2.1, 3.9)–VolparaPercent Density (VPD,%)11.6 (6.8, 18.8)-0.17 (-0.27, -0.10)7.2 (5.0, 11.0)-0.19 (-0.29, -0.12)Dense Volume (DV, cm3)64.7 (45.4, 90.9)-0.90 (-1.45, -0.48)51.9 (38.9, 69.9)-0.52 (-0.93, -0.23)QuantraPercent Density (VPD,%)14.5 (9.2, 20.2)-0.42 (-0.59, -0.28)9.9 (7.1, 14.5)-0.38 (-0.54, -0.25)Dense Volume (DV, cm3)94.0 (58.0, 144.0)-2.20 (-3.52, -1.19)80.0 (49.0, 128.0)-0.95 (-1.85, -0.35)IQR = Interquartile range ¥ Median number of years between index mammogram and last mammogram post-initiation of therapy. *Annualized change estimated as change from index to last mammogram divided by time interval and adjusted for study site, age at diagnosis, BMI and density at index mammogram. Citation Format: Natalie J. Engmann, Celine M. Vachon, Christopher G. Scott, Matthew R. Jensen, Lin Ma, Kathleen R. Brandt, Amir P. Mahmoudzadeh, Serghei Malkov, Dana H. Whaley, Carrie B. Hruska, Fang F. Wu, Stacey J. Winham, Diana L. Miglioretti, Aaron D. Norman, John J. Heine, John Shepherd, V Shane Pankratz, Karla Kerlikowske. Longitudinal changes in volumetric breast density with adjuvant endocrine therapy among women with breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3424.

Abstract Purpose: Mammographic breast density is an established and strong risk factor for breast cancer. Recently, a number of common genetic susceptibility loci have been identified as risk factors for breast cancer. We present the first report on the relative contribution of 76 validated breast cancer susceptibility loci, in the context of a polygenic risk score (PRS), to the breast density and breast cancer association. We also examine whether the PRS improves prediction of the Breast Cancer Surveillance Consortium (BCSC) 5-year risk model above and beyond breast density and clinical factors included in the model. Methods: The study population included 1643 cases and 2397 controls from three independent epidemiologic studies: the Mayo Mammography Health Study (MMHS), the Mayo Clinic Breast Cancer Study (MCBCS), and the Bavarian Breast Cancer Cases and Control Study (BBCC). Data collected on patients in each of the studies included clinical risk factor, BI-RADS breast density [a) almost entirely fat; b) scattered fibroglandular densities; c) heterogeneously dense; and d) extremely dense] and genotypes for the 76 breast cancer susceptibility loci known at the time of the study. We formed a PRS from the reported per-SNP odds ratios (OR) for 76 known breast cancer susceptibility loci, and evaluated whether BI-RADS density and the PRS were independent risk factors for breast cancer, when adjusted for age, 1/BMI and study. We also incorporated the PRS (OR) into the BCSC 5-year risk model and estimated 5-year risk for the MMHS nested case-control study of 339 invasive cases, 765 controls. Results: BI-RADS density (p<0.0001) and PRS (p<0.0001) were independent risk factors for breast cancer that together showed greater discrimination of risk (AUC=0.69) than density (AUC=0.66; ΔAUC=0.029) or PRS score alone (AUC=0.68; ΔAUC=0.013; p<0.001). Relative to those with scattered fibroglandular densities and average (2nd quartile) PRS, women with extremely dense breasts and in the highest PRS quartile, had a 2.7 fold (95%CI: 1.7-4.1) increased risk of breast cancer, while those with fatty breasts and in the lowest PRS quartile had a reduced risk (OR=0.30, 95%CI: 0.18-0.51). Incorporation of the PRS into the BCSC risk model improved discrimination (ΔAUC=0.031, p=0.001), for a net reclassification improvement of 20% (95%CI: 11%-28%), split equally among cases (9%) and controls (11%). Conclusion: BI-RADS density and the PRS are both important risk factors for breast cancer that can be included in breast cancer risk models to improve prediction of this disease. Using these models to identify high and low-risk risk groups will facilitate improved tailored screening and primary prevention interventions. Citation Format: Celine M. Vachon, V. Shane Pankratz, Christopher G. Scott, Lothar Haeberle, Elad Ziv, Matthew R. Jensen, Kathleen R. Brandt, Dana H. Whaley, Janet E. Olson, Katharina Heusinger, Carolin C. Hack, Sebastian M. Jud, Matthias W. Beckmann, Jeffrey A. Tice, Kristen S. Purrington, Thomas A. Sellers, Karla Kerlikowske, Peter A. Fasching, Fergus J. Couch. The contribution of common breast cancer susceptibility loci to the breast density and breast cancer association and the Breast Cancer Surveillance Consortium (BCSC) risk model. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 940. doi:10.1158/1538-7445.AM2014-940