Vachon, CM

PURPOSE This national study was undertaken to determine screening mammography rates among transgender and gender diverse (TGD) individuals. METHODS TGD individuals and cisgender women who qualified to receive screening mammography based on American College of Radiology guidelines were identified in the longitudinal OptumLabs Data Warehouse. The primary end point was the percentage of patients who had high rates of screening (>75% recommended screenings). Multivariable analyses were conducted in a matched population of transgender men to cisgender women to identify and adjust for factors associated with high adherence. Individuals were matched at a 1:4 ratio (transgender men: cisgender women) on age, race/ethnicity, year of enrollment in their health care plan, and duration of follow-up. RESULTS This study focused on 10,478 TGD individuals (3,778 transgender men; 1,294 transgender women; 5,406 with gender dysphoria not meeting other TGD criteria, NMOT) and 6,218,369 cisgender women. For transgender men, transgender women, individuals with gender dysphoria NMOT, and cisgender women, high mammography rates were observed in 41.1% (95% CI, 39.5 to 42.6), 7.4% (95% CI, 6.0 to 8.8), 11.9% (95% CI, 11.0 to 12.7), and 38.3% (95% CI, 38.3 to 38.4), respectively ( P < .0001). When transgender men were compared with 15,112 matched cisgender women, an age-based differential association was observed between sex and high mammography rates (interaction P = .0003); compared with cisgender women, transgender men appeared more likely to manifest high rates of screening at age 40-49 years (odds ratios [ORs], 1.38 [95% CI, 1.28 to 1.50]; P < .0001) and 50-59 years (OR, 1.57 [95% CI, 1.35 to 1.81]; P < .0001) but not in the older age groups. CONCLUSION The relatively high rates of screening mammography in cisgender women and transgender men suggest clinicians are offering mammography to individuals who are at average or high breast cancer risk, as compared with cisgender women, and not to transgender women at low risk.

Abstract Background There is growing interest in artificial intelligence (AI) models for predicting future breast cancer. We performed a systematic review of studies of mammography-based AI models for future breast cancer risk prediction to summarize current evidence, identify knowledge gaps, and inform future research directions. Methods We searched 6 databases for studies from January 1, 2012, to February 28, 2025, that evaluated mammography-based AI models for future breast cancer risk prediction. We extracted study design, participants’ race and ethnicity, geographic origin, mammogram type, vendor, prediction time frame, breast cancer type predicted, external validation, and exclusion of cancers diagnosed on the index screening mammogram. Areas under the receiver operating curve (AUCs) were summarized overall and by study characteristics. Results A total of 41 studies met our inclusion criteria. All studies were retrospective, and most used 2D mammograms (n = 37 studies) acquired using Hologic equipment (n = 25) and performed in the United States (n = 17); White, non-Hispanic women were most represented. Nearly all (n = 40) studies assessed discrimination performance with a median AUC of 0.71 for no longer than 2-year risk prediction, 0.72 for 3-4 years, and 0.71 for 5 years or more prediction. Median AUC was 0.75 for studies including index cancers vs 0.68 when excluded. Six studies reported model calibration performance ranging from good to overestimation of risk. Conclusion Future studies should evaluate models using digital breast tomosynthesis, examine performance for aggressive or advanced breast cancer, include diverse populations, and evaluate both discrimination and model calibration. Prospective evaluations are needed to determine the clinical utility of mammography-based AI models for personalized risk-based breast cancer screening before implementation.

Abstract Background: Mammography (digital 2D or digital 3D/tomosynthesis) is the cornerstone of current screening strategies for breast cancer, but new approaches are needed to further reduce the proportion of cancers diagnosed at advanced stages and more effectively identify those women in need of additional testing and biopsies. Circulating cell-free nucleic acids (cfNAs) shed from tumors, isolated from peripheral blood, and analyzed with ultra-deep and broad sequencing of cancer-associated genes, have great potential for early cancer detection. The ultimate goal is to develop blood cfNA cancer screening tests for use in conjunction with established risk factors and/or radiographic features for improved cancer detection. Development of these tests requires large, well-annotated cohorts of asymptomatic participants with adequate volumes of prediagnostic blood. The STRIVE Study cohort was recently established to train and validate cfNA-based tests for early detection of breast cancer. Eligibility criteria and trial design: The STRIVE Study is a new prospective, multi-ethnic mammography cohort that will recruit 120,000 subjects from 15+ US breast cancer screening centers (including Mayo Clinic and Sutter Health sites). Eligibility criteria require only that a participant has a scheduled routine screening mammogram at a participating center and has not received a biopsy prior to the research blood draw. Participants are recruited within 28 days of screening mammography (digital or tomosynthesis), consent electronically, provide blood samples, and complete an on-line risk factor questionnaire. Participants will be followed for all cancer diagnoses, cancer recurrences, and death for at least 5 years. Pertinent medical record information, imaging findings (including breast density), and follow-up data will be transferred electronically to a central database throughout the study period. Additional blood samples will be collected from participants with abnormal mammogram results, or who are diagnosed with cancer, to document and better understand the evolution of cfNA signals. Recruitment began in February 2017. Primary Aims: To train and validate a cfNA blood-based test to identify breast cancer overall in a cohort of women undergoing screening mammography. Statistical Methods: The study will be divided into a training phase (1/3 of participants) and an independent clinical validation phase (remaining 2/3 of participants). In the training phase, statistical machine learning techniques will be used to develop algorithms incorporating cfNA signals, clinical characteristics, or radiological features. In the validation phase, the prespecified locked algorithm developed from the training phase will be clinically validated in an independent group of women. Contact information for people with a specific interest in the trial: Additional details regarding the STRIVE Study are available on the ClinicalTrials.gov website (NCT03085888). For site-specific questions, please call 844-366-9738 for the Mayo Clinic and 1-855-578-7483 for Sutter Health. Citation Format: Liu MC, Cummings S, Vachon CM, Kerlikowske K, Couch FJ, Morris EA, Olson JE, Polley EC, Conners AL, Ellis RL, Patel B, Maimone IV S, Zhang N, Hamilton S, Clarke CA, Allen BA, Maddala T, Hartman A-R. Development of cell-free nucleic acid-based tests for early detection of breast cancer: The STRIVE study [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr OT3-02-01.

Abstract Background Women with atypical hyperplasia (AH) are at an approximately four-fold increased risk of subsequent breast cancer (BC). Mammographic breast density (MBD) is a well-established risk factor for BC, but its contribution to BC risk in women with AH remains an open question. We previously reported no association between MBD [measured by Wolfe's parenchymal pattern (PP)] and BC risk in a cohort of 147 women with AH. Here, we present results in an expanded cohort of 459 women diagnosed with AH between 1985 and 2001. Methods The Mayo Clinic Benign Breast Disease Cohort includes 13,485 women who had benign core and/or excisional biopsy 1967-2001. Biopsy tissues were reviewed by our study pathologist to determine presence of AH. MBD was available from clinical records starting in 1985, coded as PP (the standard for 1985-1996) or BI-RADS (1997-2001) density criteria. The original four-level PP (N1-fatty, P1-ductal prominence &lt;25% of breast, P2-ductal prominence &gt;25%, DY-dysplasia) and BI-RADS (fatty, scattered densities, heterogeneously dense, extremely dense) measures were re-categorized as low, moderate or high MBD by combining the middle two categories for each. BC events and clinical information were obtained by questionnaires, medical records and the Mayo Clinic Tumor Registry. Women were followed from benign biopsy to date of BC, death or last contact. Standardized incidence ratios (SIRs) were generated overall and within subgroups defined by density measure (PP vs. BI-RADS), number of atypical foci, and BMI by dividing the observed number of BCs by population-based expected values. Cox regression was used to estimate MBD hazard ratios after adjustment for demographic and clinical variables. Results Of the 551 women diagnosed with AH between 1985 and 2001, 459 (83%) had MBD data within 1 year prior to biopsy. Of these, 68 (15%) had low, 221 (48%) had moderate, and 170 (37%) had high MBD, respectively. Over a median follow-up of 11.7 years, 80 BCs were observed. SIRs for breast cancer did not differ significantly across density categories, overall or within any subgroups examined (see Table). Cox regression adjusting for age, BMI and density measure (PP vs. BI-RADS) also failed to identify an association with MBD (p=0.55). Low MBDModerate MBDHigh MBD N / BCsSIR (95% CI)N / BCsSIR (95% CI)N / BCsSIR (95% CI)P-valueOverall68/123.5 (1.8,6.1)221/393.6 (2.5,4.9)170/293.4 (2.3,4.8)0.97MBD Measure PP59/113.6 (1.8,6.5)85/153.0 (1.7,5.0)130/243.3 (2.2,5.0)0.87BI-RADS9/12.7 (0.1,14.7)136/244.0 (2.6,6.0)40/53.4 (1.1,7.9)0.90No. Atypical Foci 147/62.3 (0.9,5.1)123/182.8 (1.7,4.4)96/163.5 (2.0,5.8)0.63214/47.5 (2.0,19.1)58/134.8 (2.6,8.3)41/62.5 (0.9,5.5)0.213+7/26.7 (0.8,24.0)40/84.4 (1.9,8.6)33/74.0 (1.0,8.2)0.83BMI &lt;2525/21.5 (0.2,5.4)75/184.9 (2.9,7.7)101/173.4 (2.0,5.5)0.1625-2919/54.9 (1.6,11.3)68/92.8 (1.3,5.2)36/42.1 (0.6,5.4)0.4530+23/54.8 (1.6,11.2)76/123.0 (1.6,5.3)32/74.0 (1.6,8.2)0.67SIRs compare observed numer of BCs to expected using Iowa SEER data. Analyses account for the effects of age and calendar period. P-value is test of heterogeneity in SIRs across columns. Conclusions We found no evidence of an association between MBD and subsequent BC in women with AH. Citation Format: Vierkant RA, Degnim AC, Hartmann LC, Frank RD, Radisky DC, Visscher DW, Frost MH, Winham SJ, Ghosh K, Vachon CM. No evidence of association between mammographic breast density and risk of breast cancer in women with atypical hyperplasia. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-09-05.

Abstract Background: Chemotherapy-related amenorrhea (CRA) impacts quality of life and reproductive options, and may play a role in treatment decision-making for young breast cancer patients. Although age, increasing doses of alkylating agents, and tamoxifen are known to increase the risk of chemotherapy-related amenorrhea (CRA), it is difficult to predict which women will develop CRA in order to inform fertility preservation and treatment decisions. We sought to investigate whether single nucleotide polymorphisms (SNPs) that have been associated with a younger age at natural menopause and ovarian reserve in non-oncologic populations were predictive of CRA in two large German clinical trials that administered gonadotoxic chemotherapy. Methods: A total of 1322 premenopausal participants under age 50 who enrolled in SUCCESS B (a phase 3 trial of FEC-docetaxel-trastuzumab +/- gemcitabine for high risk early stage breast cancer) or SUCCESS C (a phase 3 trial of FEC-docetaxel-cyclophosphamide vs. docetaxel-cyclophosphamide that also tested the efficacy of a weight loss intervention in early stage breast cancer patients) were eligible for this study. We excluded patients who were receiving ovarian suppression. On these trials, menstrual status was self-reported as amenorrheic or not every three months after chemotherapy. Bloods were stored and patients provided consent for DNA analysis. We genotyped nine candidate SNPs in six genes (rs2002555 and rs11170547 in AMHR2, rs6166 in FSHR, rs10852344 &lt;60kb from TNFRSF17/RUNDC2A/ GSPT1, rs12461110 in NLRP11, rs1801133 in MTHFR, rs2066470 in MTHRF, rs3810682 in BMP15, and rs615942 in COASY), and examined their association with CRA at 12-month and 24-month time points using logistic regression adjusted for age, BMI, and tamoxifen. Results: Of the 554 eligible women with SNP data and menstrual data at 12 months, 70% reported CRA; of the 458 eligible women with SNP data and menstrual data at 24 months, 69% reported CRA. The two SNPs in AMHR2 showed suggestive associations with CRA at 12 and 24 months. [Final results pending]. Conclusions: Genetic variation in AMHR2 may be associated with CRA. Additional research will be needed to validate these findings in other breast cancer survivors and to identify additional SNPs that may be associated with CRA. Together with other clinical factors, genetic variation may improve our ability to predict who will experience CRA, and may inform reproductive and treatment decision-making in cancer patients. Citation Format: Ruddy KJ, Rack B, Schwitulla J, Lambrechts D, Haeberle L, Schramm A, Trapp E, Scholz C, Beutler AS, Ginsburg E, Couch F, Partridge AH, Wang L, Weinshilboum RM, Janni W, Vachon C, Fasching P. Genetic predictors of chemotherapy-related amenorrhea. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P4-10-07.

Abstract Background: Optimal early detection and prevention strategies for breast cancer are predicated on our ability to accurately identify women at increased risk for disease. Current breast cancer risk prediction models rely on clinical and epidemiologic features. As demonstrated with other cancers, such as those of the cervix and colon, cancer risk assessment is enhanced when the tissue at risk is examined for evidence of premalignant abnormalities. Here, we present a multivariate model for individualized breast cancer risk prediction that incorporates tissue features identified from biopsies from women with benign breast disease (BBD). Methods: We used our cohort of more than 9000 women diagnosed with BBD at the Mayo Clinic from 1967–1991, and identified a nested case:control series of 377 women who developed cancer (cases) matched with 734 women who did not (controls). We identified a set of variables that either alone or in two-way interactions contributed to a multivariable risk prediction model. We combined this relative risk model with the baseline incidence of breast cancer and the baseline hazard of death from our full BBD cohort to develop a risk prediction tool that computes absolute risk estimates for breast cancer. We calculated 5-year risk predictions for these cases and controls using our model and using the Gail model and compared c-statistics from these risk predictions. We validated our model using an independent set of 378 breast cancer cases and 728 matched controls drawn from our BBD cohort. Results: Cases and controls in the model development data set differed significantly on a number of features when examined individually. Those included in a multivariable relative risk model for breast cancer among women with BBD are shown below. After including interactions between lobular involution, and histologic impression, with age at first live birth/number of children, and degree of involution with sclerosing adenosis, we devised a model that predicts the absolute risk of breast cancer. Predicted five-year risks in the model development series, we observed a c-statistic of 0.66, higher than the c-statistic of 0.61 for the Gail model. With risk prediction estimates from our validation series of cases and matched controls, the c-statistic of our model decreased to 0.59 and the c-statistic for the Gail model was 0.55. Conclusion: We have identified an initial risk prediction model for breast cancer in women with BBD. The identified model includes basic demographic information as well as a number of histologic features of the tissue samples. Its initial performance is promising, providing improved discrimination over the Gail model in our replication data set. Further work is needed to identify features which might provide additional predictive information for breast cancer risk. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-12-03.

Abstract BACKGROUND: Mammographic percent dense area, the percent ratio of dense to total breast area in a mammogram, is one of the strongest measures of a woman's risk of breast cancer. However, systematic differences have been observed between readers and mammography technologies (film and digital) that could cause clinically inconsistent associations with risk. The purpose of this study was to evaluate inter- and intra-reproducibility of percent dense area between readers and between film and digital technologies. METHODS: One hundred digitized film mammograms were randomly selected with 25 films in each of quartile of percent density and read by two readers at two different sites (Mayo Clinic and UCSF). The readers had extensive experience and were also jointly trained at university of Toronto using Cumulus software. After training, all films were read twice with at least one year between duplicate readings. The Mayo clinic reading used Cumulus while UCSF used custom semiautomatic software to estimate total and dense tissue area. In addition, digitized films and unprocessed full field digital mammograms of the same women were assessed by one reader. The time between the film and digital acquisitions ranged from nine to twenty-four months. Interclass correlation coefficient (ICC) was calculated for each comparison. RESULTS: The intra- and inter-observer ICCs, consistency for film images, were 0.96 (UCSF) and 0.97 (Mayo), and 0.96 (UCSF vs. Mayo). We found ICC between film and digital mammograms for percent dense area was 0.88. The digital mammogram had 9% significantly higher total breast area and 5% significantly lower percent density area compared to film. CONCLUSIONS: Similarly trained readers had a high reproducibility regardless of the software used. Our results suggest centralized reader training should enable pooling of film breast density results from different clinics. However, pooling film and digital results would need careful calibration due to lower measured percent dense areas than on film. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P5-08-03.

Abstract Background: Mammographic density, the variation in fat, epithelial and stromal tissues seen on screening mammography, is a strong risk factor for breast cancer and can be modified by hormonal agents. Changes in density from tamoxifen or postmenopausal hormone (PMH) use are associated with risk, suggesting that density may be a surrogate marker of therapeutic efficacy. Aromatase inhibitors (AIs) are given as adjuvant therapy in hormone receptor positive postmenopausal breast cancer and are known to decrease levels of estrone and estradiol in both serum and breast tissue. Our goal here was to examine the influence of AIs on mammographic density in women with early breast cancer. Methods: We conducted a case-control study of postmenopausal breast cancer patients initiating adjuvant AI therapy (anastrozole or exemestane) on protocols NCIC CTG MA27, NCCTG N063I and MC (Mayo Clinic) 0532. Eligibility included; an intact contralateral breast with no prior surgery; a screening mammogram within twelve months before AI initiation and at 9-15 months on therapy; no prior endocrine therapy and informed consent. Controls were sampled from the Mayo Mammography Health Study, a cohort of 19,924 receiving screening mammography at the Mayo Clinic, and matched to cases on age, prior PMH use, baseline body mass index (BMI) and interval between mammograms. Pre-treatment and on-study mammograms for cases (corresponding mammograms for controls) were digitized. Change in percent density was estimated on the craniocaudal view of the non-cancerous breast using two methods: a subjective assessment of change by an expert radiologist (within 5%; 5-10% increase, 10-25% increase, 25%+ increase, 5-10% decrease, 10-25% decrease and 25%+ decrease) and a quantitative assessment of absolute change using a computer-assisted thresholding program (Cumulus). Analyses compared magnitude of change in density by both the subjective and quantitative methods between cases and matched controls. Results: 574 pairs were eligible for analyses (MA27-505 cases; N063I-12 cases; MC0532-57 cases). Characteristics of the two groups are shown in the table below. Using either density estimation method, there was a greater decrease in density among women on AI therapy vs. matched controls. In 33% (95% CI: 29-37%) of pairs, there was at least a one greater category decrease for the case relative to her control by subjective estimation. In 14% (95% CI: 11-18%) of the pairs, there was at least a 5% greater decrease for the case relative to her control by quantitative estimation. Data will be available according to AI class (non-steroidal versus steroidal) in November. Conclusions: In the largest report to date to examine the influence of AI therapy on mammographic density, we provide evidence that AI is associated with decreases in density in a small subgroup of women. We are currently examining factors that influence these AI-associated decreases in density and whether these differences are unique to one class of AI. (Supported in part by NIH grants P50CA116201, U01GM61388, U10CA77202, U10CA25224) Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P2-09-03.

Abstract Abstract #62 Background: Breast cancer is the leading cause of cancer deaths in younger women (25 to 49 years of age). Young women with breast cancer also have worse overall survival and increased risk of recurrence compared to older women with breast cancer. Innovative approaches to understanding risk factors and tissue characteristics for the younger population can improve understanding of breast cancer etiology and enhance risk-stratification for these women. This study was aimed at examining breast cancer risk factors among young women (&lt;50 years) with BBD. Materials and Methods: Utilizing the Mayo Clinic Surgical and Pathology Indices, women ages 18 to 85 who had benign excisional breast biopsy between January 1, 1967 and December 31, 1991 were identified. The diagnosis of breast cancer served as the study endpoint and was determined using the Mayo medical record and questionnaire information from study participants. The breast pathologist, blinded to the initial diagnosis and clinical outcome, performed pathology review. BBD was classified as non-proliferative disease (NPD), proliferative disease without atypia (PDWA), or atypical hyperplasia (AH). Age-related lobular involution (reduction in number and size of acini per lobule) was classified as none-0%, partial- 1 to74%, or complete- &gt;75% involution. Relative risk (RR) was estimated by comparing the number of observed breast cancers with the number expected, based on breast cancer rates in the Iowa Surveillance, Epidemiology, and End Results registry. Results: Of the 9376 women in the Mayo BBD cohort, 4460 women were aged &lt;50 years at BBD diagnosis and formed the study cohort. The mean age at BBD diagnosis was 39.4 (+ 8.3) years. With a median follow-up of 20 years, 326 breast cancer cases were identified. The histologic findings were NPD in 72% of women, PDWA in 26%, and AH in 2%. The relative risk of breast cancer for the overall cohort of young women with BBD was 1.5 (95% CI [1.4, 1.7]). The relative risk among those with AH was 6.9 (95% CI [4.6, 10.1), compared with a RR of 2.0 (95% CI [1.7, 2.4]) for PDWA, and RR of 1.2 (95% CI [1.0, 1.4]) for NPD. Risk was associated with extent of lobular involution (RR for no involution was 1.7 (95%CI [1.4, 2.1]); partial involution 1.4 (95%CI [1.2, 1.7]); complete involution 0.7 (95%CI [0.3, 1.4]). Family history was available for 83% of the cohort and RR was 2.2 (95% CI [1.7, 2.8]) for women with strong family history and was 1.3 (95% CI [1.1, 1.6]) for women with no family history. Discussion: Young women with BBD are at increased risk of breast cancer. Risk is high in women with atypical hyperplasia, and those with a family history of breast cancer. Lobular involution is associated with reduced breast cancer risk in this population, suggesting a role in modifying breast cancer risk. These findings suggest the need for further research in this population, along with tissue-based studies to examine the processes leading to breast cancer, and enable identification of those women at highest risk. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 62.

Abstract Abstract #4037 Background: Mammographic breast density is a strong and consistent risk factor for breast cancer. Women with dense tissue in over 60 to 75% of the breast are at 4 to 6 times greater risk than women with little or no density. However, the biology of breast density is not well understood and studies at the tissue level are limited. We present the first report of a tissue-based study of breast density, sampling mammographically dense and non-dense areas of the breast of healthy women with the goal of examining histologic correlates.&#x2028; Materials and Methods: Healthy women volunteers aged 40 to 85 years, with no breast symptoms, normal screening mammogram within 6 months, and no personal history of breast cancer were considered eligible. Women with breast-related symptoms, those on endocrine therapy, or with bleeding tendencies were excluded from participation. Sixty consecutive eligible women were enrolled in the study- results are based on the first 34 participants. Eligible women underwent an ultrasound-guided, core-needle breast biopsy of mammographically dense and non-dense areas of the breast, performed by a single experienced radiologist. Quantitative assessment of breast tissue from dense and non-dense areas was performed for epithelium, stroma, and fat. In addition, we assessed the difference in the extent of lobular involution (complete, partial and none) between dense and non-dense areas. We used means and SD to describe distributions in each tissue type. Signed rank test was used to assess within woman differences between dense and non-dense tissue.&#x2028; Results: The mean age of the participants was 50.1 years (range 40 to 79 years). The histological findings are outlined below.&#x2028; &#x2028; Dense tissue is comprised of increased stroma and epithelium and decreased fat, consistent with prior reports on breast density. The finding of 'no involution' was greater in dense tissue compared to non-dense tissue (24% versus 8.8%); non-dense tissue had greater proportion of complete involution compared to dense tissue (82.5% versus 35.2%).&#x2028; Discussion: This report is the first to quantify histology in tissue sampled from dense and non-dense regions of the healthy breast. We find a dramatic difference in tissue composition between dense and non-dense tissue in the breast. Future studies using this tissue to assess molecular markers will further help to characterize pathways through which density may increase breast cancer risk. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 4037.