Many women undergoing breast cancer screening and diagnostic imaging are diagnosed with high-risk benign breast lesions. There is clinical uncertainty as to how aggressively to manage these cases with surgical excision, chemoprevention, and surveillance imaging. The VBCSS and their research partners at the Breast Cancer Surveillance Consortium (BCSC), are excited to develop and validate new statistical risk models for short and long-term breast cancer risk among women diagnosed with high-risk breast lesions.  This will inform clinical practice and national guidelines for identifying cases that require aggressive interventions while sparing others from unnecessary side effects. The VBCSS is using existing clinical data from our repository, as well as data collected by the five other sites across the U.S. that make up the Breast Cancer Surveillance Consortium (BCSC), to carry out this study.

The VBCSS is excited to be partnering with both our national colleagues, the Carolina Mammography Registry, the Kaiser Permanente Washington Breast Cancer Surveillance Registry, the San Francisco Mammography Registry, the Sacramento Area Breast Imaging Registry, and the New Hampshire Mammography Network, as well as UVM researchers Donald Weaver, MD, Thomas Ahern, PhD, Hannah Perry, MD, and Michelle Sowden, DO on this collaborative project! 

This project is funded by grant R01 CA282725 from the National Cancer Institute (NCI).

This multi-site study led by the University of Washington, the VBCSS, and collaborators from the Breast Cancer Surveillance Consortium (BCSC), has three aims. The first is to evaluate the performance of five commercially available AI technologies for automated breast cancer screening in a diverse and generalizable screening cohort. To accomplish this, the VBCSS will use previously collected data as well as breast images provided to the us from the University of Vermont Medical Center Breast Imaging Department, along with six other geographically diverse breast imaging registries' data and breast images to externally validate the commercially available AI technologies. Key investigators on the UVM team include Dr. Brian Sprague and Dr. Hannah Perry.

The second aim of this study is to use multi-level analyses to identify targeted approaches for both improving AI performance and incorporating AI into radiologists' clinical workflow.  We will evaluate the performance of five commercially available AI technologies based on woman-, exam-, tumor, and radiologist-level characteristics to inform future targeted algorithm training and refinement efforts in a continuous feedback loop. Then we will develop and explore targeted approaches for improving clinical workflow efficiency, including use of AI technologies as standalone tools to safely triage negative exams.

The third and final aim of this project is to determine the long-term benefits, harms, and costs of widespread AI-driven breast cancer screening. Using an established Cancer Intervention and Surveillance Modeling Network (CISNET) simulation model, we will compare population-level, long-term benefits, harms, and costs associated with widespread translation of the most up-to-date AI technologies for screening both as a standalone tool and as a second reader to radiologists in the U.S. screening population.

Access publications associated with this Population-Based Evaluation of Artificial Intelligence (AI) for Mammography study.

This project is funded by grant R01 CA262023 from the National Institutes of Health (NIH).

The long-term goal of the Northern New England Clinical and Translational Research (NNE-CTR) Network, composed of MaineHealth (MH), the University of Vermont (UVM), and the University of Southern Maine (USM), is to sustain a clinical and translational research infrastructure that supports improvements in community health for inhabitants in the IDeA states of Maine, New Hampshire, and Vermont.  The Vermont Breast Cancer Surveillance System (VBCSS) is a UVM-based research program that addresses a wide spectrum of questions in rural breast cancer control, including prevention, detection, survivorship, and health equity, through a breast imaging data registry that is a partnership between UVM, academic and community-based radiology practices, and the Vermont Department of Health.  The VBCSS is one of the six founding member registries within the Breast Cancer Surveillance Consortium (BCSC), which pools data nationally to develop novel breast cancer risk prediction models, evaluate the performance of breast cancer screening and diagnosis modalities in clinical practice, evaluate novel artificial intelligence approaches in breast imaging, and advance equitable risk-based breast cancer screening and surveillance in community practice.

In this supplement application we seek to enhance the institutional data science capacity within the NNE-CTR at the University of Vermont, with a specific goal to rebuild and strengthen the VBCSS to modernize its data systems, expand its data collection using state-of-the-art technologies and novel data sources, and develop improved data sharing technologies that in total will significantly advance the scope of research accomplished by the VBCSS and NNE-CTR investigators.  With this expanded NNE-CTR data science capacity we will also support the development of other existing and new cancer-related data resources at the University of Vermont.  In particular, we will link the VBCSS data repository to Vermont's all-payer claims database (VHCURES). This will enable us to address new research questions in breast cancer related to the decline in mammography utilization observed in the state over the past decade.

Access publications associated with this Enhancing NNE-CTR Data Science Capacity to Support Clinical Informatics Applications in Cancer Control Research study.

This project is funded by grant U54 GM115516 from the National Institute of General Medical Sciences (NIGMS).

Breast cancer remains the second leading cause of cancer death in United States women, with racial and ethnic disparities in breast cancer stage at diagnosis, rates of second breast cancers, and mortality. Our program follows the premise that screening and surveillance are most effective and equitable when all women have access to high-quality risk assessment and breast imaging, and when screening and surveillance strategies are targeted to clinically meaningful outcomes. There are three parts to this study. 

Project 1

The aim of this project is to develop equitable advanced breast cancer risk models that incorporate imaging features, artificial intelligence (AI) algorithms, and clinical factors. As well as compare the benefits and harms of targeted screening frequency and supplemental MRI based on advanced cancer risk.

Project 2

This project takes a multilevel approach to identify woman-, neighborhood-, radiologist-, and facility-level factors that drive inequities in breast cancer screening performance and outcomes, and to explore whether targeted AI use and other interventions can improve population outcomes with attention to health equity.

Project 3

The last piece of this study focuses on improving surveillance imaging in breast cancer survivors through equitably predicting women at high risk of a surveillance failure (i.e. interval 2nd breast cancer), improving surveillance performance through AI, and examining social determinants of health as multilevel drivers of surveillance failures and targets for future interventions.

To accomplish these aims the VBCSS, in collaboration with the Breast Cancer Surveillance Consortium (BCSC) and Cancer Intervention and Surveillance Modeling Network (CISNET) computer simulation models will perform secondary analyses of data from breast imaging registries. The VBCSS will extract limited data sets from our registry to be pooled with data from our collaborators.

Access publications associated with this Advancing Equitable Risk-based Breast Cancer Screening and Surveillance in Community Practice study.

This project is funded by grant P01 CA154292 from the National Cancer Institute (NCI).

Researchers in the Department of Health and Human Services (DHHS), National Institutes of Health (NIH), National Cancer Institute (NCI), Division of Cancer Epidemiology and Genetics (DCEG), Integrative Tumor Epidemiology Branch (ITEB) conducted the study “The Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project” over 10 years ago in collaboration with the VBCSS at the University of Vermont. With the goal of investigating the molecular pathology and biology of mammographic density and other breast cancer risk factors, this molecular epidemiological study enrolled women aged 40-65 years who underwent diagnostic, image-guided breast biopsy following an abnormal breast imaging exam between October 2007 and June 2010. The study collected data, mammograms, and biospecimens, including breast biopsies, during study enrollment.

During the last 10 years, there has been an extensive detailed characterization of the mammograms and breast biopsies. This follow-up study seeks to perform a transcriptomic and microenvironmental characterization of benign breast lesions in relation to breast cancer risk factors, risk, and progression. The objective of this study is to expand the BREAST Stamp Project dataset through two main ways:

1. Retrieval of updated, coded 10-year follow-up breast cancer diagnosis data for all women who met study eligibility criteria during the BREAST Stamp Project enrollment period (Oct 1, 2007 - June 30, 2010).
2. Retrieval of coded breast tumor blocks from all Stamp participants who were originally diagnosed with a benign lesion and subsequently developed DCIS or invasive breast cancer over the course of the 10-year follow-up period.

We will examine gene expression and molecular signatures in benign lesions associated with 10-year breast cancer risk as well as investigate conserved and differential molecular and microenvironmental features between precursor lesions and their matched tumors.

Access publications associated with this The BREAST Stamp Follow-up study.

This project is funded by contract 6648-00-S021 from the National Cancer Institute (NCI).

This study, led by collaborators at University of Wisconsin-Madison, is designed to develop new statistical approaches to improve the accuracy of breast cancer screening performance reports for radiology facilities and radiologists. Radiology facility breast cancer screening performance reports typically only include cancer diagnosis data available from the facility at which the screening mammography was performed. However, some women will seek diagnostic care at other healthcare facilities and may be diagnosed with cancer at other healthcare facilities. These cases “lost to follow-up” bias the screening performance report.

We will test a range of analytic approaches that account for this incomplete cancer capture and can be applied to data readily available at screening centers. We will develop and test new approaches, using existing data from the University of Wisconsin-Madison, University of California-Davis, and the University of Vermont Breast Cancer Surveillance System. UVM will participate in the development of the statistical approaches, including testing of the algorithms on data from the Vermont Breast Cancer Surveillance System.

This project is funded by grant AWD00001647 from the American Cancer Society (ACS).

While breast cancer mortality has decreased in the U.S. over the last two decades, reductions have not been experienced equally by all women. Black women as well as women with lower income, less insurance and rural residence, experience lower survival rates, more advanced breast cancers at diagnosis, and less access to high quality care after screening. Less is known about disparities during the diagnostic work-up period.  Women must navigate multiple steps after an abnormal screening mammogram, such as additional imaging and biopsies before receiving a definitive diagnosis. More than 12 million women in the U.S. must undergo additional diagnostic tests annually. Failure to receive timely, quality diagnostic work-up may result in delayed diagnosis, more invasive procedures, delayed treatment, and fewer treatment options. Women of minority race/ethnicity, lower education, lower income, rural residence, and the underinsured - may encounter more diagnostic obstacles than non-vulnerable women, such as difficulty accessing standard of-care imaging (e.g., ultrasound, image-guided biopsy) at their usual place of service. These diagnostic disparities are associated with longer delays to surgery and treatment, and worse overall outcomes. Providing equitable, timely access to quality diagnostic work-up can reduce these breast cancer disparities.

The goal of this study, led by Christoph Lee at the University of Washington, in collaboration with the VBCSS and the Breast Cancer Surveillance Consortium (BCSC) is to identify the woman-, residential-, exam-, provider-, and practice-level factors that affect diagnostic outcomes and that may serve as targets for interventions aimed at mitigating disparities. We hypothesize that vulnerable women have less access to diagnostic imaging technologies, experience more diagnostic failures and management delays, and that practice-level determinants play a significant role in diagnostic disparities.

This study has three aims - 

1. Identify vulnerable women who have less access to and use of diagnostic technologies. We will identify specific groups of vulnerable women with less access to and use of standard-of-care and advanced diagnostic imaging technologies and determine the multi-level factors that contribute to these disparities.
2. Determine woman-, exam-, provider-, and practice-level factors that affect diagnostic outcomes of vulnerable vs. non-vulnerable women. We will characterize differences in clinically significant diagnostic outcomes and use multi-level modeling to identify health determinants that mediate these disparities.
3. Determine woman-, exam-, provider-, and practice-level factors that affect timeliness of diagnostic evaluation among vulnerable vs. non-vulnerable women. Informed by Aims 1 and 2, we will identify points of delay along the diagnostic evaluation continuum that can serve as quality-of-care indicators of disparities between vulnerable vs. non-vulnerable women and targets for future interventions.

These aims will be carried out using existing data from the Breast Cancer Surveillance Consortium (BCSC) which consists of national experts in breast cancer epidemiology, biostatistics, data science, medicine and radiology. The BCSC represents the largest longitudinal U.S. breast cancer imaging data resource that is representative of the general U.S. population by race/ethnicity. With data already collected for over 13 million exams, 5.5 million of which were performed among vulnerable women, we are well-positioned to address disparities in breast cancer diagnosis and management.

Access publications associated with this Racial and Socioeconomic Disparities in Breast Cancer Diagnostic Work Up and Outcomes study.

This project is funded by grant R01 CA266377 from the National Cancer Institute (NCI).

Breast density is a risk factor for developing breast cancer and decreases the accuracy of screening mammography. An estimated 27 million women aged 40-74 in the U.S. have dense breasts and they experience elevated rates of advanced stage breast cancer diagnoses associated with poor outcomes. In the absence of screening guidelines for women with dense breasts, there has been a dramatic increase in use of supplemental ultrasound screening, which is widely available and has low direct medical costs. Early studies of supplemental ultrasound performance suggest increased cancer detection but high rates of false positive exams leading to unnecessary biopsies.

The United States Preventive Services Task Force has called for studies that evaluate the impact of supplemental ultrasound screening on meaningful clinical outcomes, such as advanced cancer rates, to inform screening guidelines for women with dense breasts. We recently demonstrated that mammography screening failure rates (i.e., advanced cancers and interval cancers after a normal mammogram) among women with dense breasts vary widely according to clinical risk factors.

We are assessing supplemental ultrasound screening performance within a new risk-based framework. We hypothesize that supplemental ultrasound screening targeted to the subset of women with dense breasts at high risk of mammography screening failures will yield a favorable benefit-to-harm profile. We are using data from more than 100,000 screening ultrasound exams and 2 million mammography screening exams collected via the Breast Cancer Surveillance Consortium (BCSC) to carry out the following aims. 

1. We are examining the test performance of supplemental screening ultrasound according to technique (handheld vs. automated) and type of primary screening (digital mammography vs. digital breast tomosynthesis).
2. We will also evaluate supplemental screening ultrasound outcomes across levels of risk for mammography screening failures. These results will be used as inputs in two simulation models from the Cancer Intervention and Surveillance Modeling Network (CISNET).
3.  We will evaluate the long-term benefits, harms, and costs of supplemental ultrasound strategies targeted to women at high risk of mammography screening failures.

Our study is the largest evaluation of supplemental ultrasound and the first to evaluate rates of interval and advanced cancers according to risk of mammography screening failures. Our results will provide urgently needed, actionable evidence for women, healthcare providers, and guideline-makers evaluating screening options for women with dense breasts. This evidence will support effective supplemental screening strategies that reduce the burden of breast cancer among women for whom mammography screening is not adequate, while minimizing potential harms.

Access publications associated with this Identifying Effective Risk-Based Supplemental Ultrasound Screening Strategies for Women with Dense Breasts study.

This project is funded by grant R01 CA248068 from the National Cancer Institute (NCI).

The VBCSS collaborates with the  Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Working Group (BWG) to conduct innovative modeling research focused on new precision oncology paradigms that are expected to re-define breast cancer control best practices.  The VBCSS provides data inputs to the simulation models via our participation in the Breast Cancer Surveillance Consortium (BCSC), and VBCSS investigators Dr. Brian Sprague and Dr. Donald Weaver provide scientific expertise in breast cancer screening and diagnosis to the CISNET modeling teams. Significant topics are selected where modeling is suited to fill evidence gaps and facilitate clinical and policy translation. Unique components of our approach include modeling of absolute risk of disease accounting for multiple risk factors, addressing important comorbidities—specifically type 2 diabetes—that affect both disease risk and survival, exploring emerging biomarker-based approaches for screening, providing guidance regarding precision systemic treatments and their impact on quality of life in survivors, and investigating race disparities.

The aims of this study include:

1. Evaluate the impact of new precision screening approaches.
2. Evaluate the impact of precision treatment paradigms in the adjuvant, neo-adjuvant, and metastatic setting.
3. Synthesize the first two aims to quantify the contributions of precision screening and precision treatment to US breast cancer mortality reductions.
4. Provide evidence to guide interventions to reduce race disparities by quantifying multiple risk, screening, treatment, and survival factors that impact disparities.

Each aim includes three or more model groups selected for their unique structure and includes outside collaborators and junior investigators. The models will share common inputs and provide a standard set of outcomes for benefits, harms, and costs. Continuously funded for the past 19 years, the modeling teams have published 204 research papers informing public health policy decisions and trained 13 junior investigators.

For this study the BWG is partnering with the American Cancer Society (ACS), the American College of Radiology (ACR), the American Society of Clinical Oncology (ASC), and the Breast Cancer Surveillance Consortium (BCSC), among others. The experienced Coordinating Center is providing the infrastructure to support the project goals including resource sharing and model accessibility. This exceptional environment provides unprecedented synergy and leveraging of resources to address new research questions and support career development that would not otherwise be possible. Overall, this research is advancing modeling research and helping guide breast cancer control policy. 

The CISNET BWG has recently helped inform the United States Preventative Task Force USPSTF) updated recommendations for women to begin biennial breast cancer screening at age 40. You can access the full findings in this JAMA article.

Access publications associated with this Cancer Intervention and Surveillance Modeling Network Breast Working Group study.

This project is funded by grant U01 CA253911 by the National Cancer Institute (NCI).