Miglioretti, Diana L.

10500 Background: Inherited pathogenic variants in ATM, CHEK2, and PALB2 confer moderate to high risks of breast cancer. The optimal approach to screening in these women has not been established. Methods: We used two simulation models from the Cancer Intervention and Surveillance Modeling Network (CISNET) and data from the Cancer Risk Estimates Related to Susceptibility consortium (CARRIERS) to project lifetime breast cancer incidence and mortality in ATM, CHEK2, and PALB2 carriers. We simulated screening with annual mammography from ages 40-74 alone and with annual magnetic resonance imaging (MRI) starting at ages 40, 35, 30, and 25. Joint and separate mammography and MRI screening performance was based on published literature. Lifetime outcomes per 1,000 women were reported as means and ranges across both models. Results: Estimated risk of breast cancer by age 80 was 22% (21-23%) for ATM, 28% (26-30%) for CHEK2, and 40% (38-42%) for PALB2. Screening with MRI and mammography reduced breast cancer mortality by 52-60% across variants (Table). Compared to no screening, starting MRI at age 30 increased life years (LY)/1000 women by 501 (478-523) in ATM, 620 (587-652) in CHEK2, and 1,025 (998-1,051) in PALB2. Starting MRI at age 25 versus 30 gained 9-12 LY/1000 women with 517-518 additional false positive screens and 197-198 benign biopsies. Conclusions: For women with ATM, CHEK2, and PALB2 pathogenic variants, breast cancer screening with MRI and mammography halves breast cancer mortality. These mortality benefits are similar to those for MRI screening for BRCA1/2 mutation carriers and should inform practice guidelines.[Table: see text]

Abstract Small pulmonary nodules are most often managed by surveillance imaging with computed tomography (CT) of the chest, but the optimal frequency and duration of surveillance are unknown. The Watch the Spot Trial is a multicenter, pragmatic, comparative-effectiveness trial with cluster randomization by hospital or health system that compares more- versus less-intensive strategies for active surveillance of small pulmonary nodules. The study plans to enroll approximately 35,200 patients with a small pulmonary nodule that is newly detected on chest CT imaging, either incidentally or by screening. Study protocols for more- and less-intensive surveillance were adapted from published guidelines. The primary outcome is the percentage of cancerous nodules that progress beyond American Joint Committee on Cancer seventh edition stage T1a. Secondary outcomes include patient-reported anxiety and emotional distress, nodule-related health care use, radiation exposure, and adherence with the assigned surveillance protocol. Distinctive aspects of the trial include: 1) the pragmatic integration of study procedures into existing clinical workflow; 2) the use of cluster randomization by hospital or health system; 3) the implementation and evaluation of a system-level intervention for protocol-based care; 4) the use of highly efficient, technology-enabled methods to identify and (passively) enroll participants; 5) reliance on data collected as part of routine clinical care, including data from electronic health records and state cancer registries; 6) linkage with state cancer registries for complete ascertainment of the primary study outcome; and 7) intensive engagement with a diverse group of patient and nonpatient stakeholders in the design and execution of the study.

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 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.