Background Controversy persists about optimal mammography screening strategies. Objective To evaluate mammography strategies considering screening and treatment advances. Design Collaboration of six simulation models. Data Sources National data on incidence, risk, breast density, digital mammography performance, treatment effects, and other-cause mortality. Target Population An average-risk cohort. Time Horizon Lifetime. Perspective Societal. Interventions Mammograms from age 40, 45 or 50 to 74 at annual or biennial intervals, or annually from 40 or 45 to 49 then biennially to 74, assuming 100% screening and treatment adherence. Outcome Measures Screening benefits (vs. no screening) include percent breast cancer mortality reduction, deaths averted, and life-years gained. Harms include number of mammograms, false-positives, benign biopsies, and overdiagnosis. Results for Average-Risk Women Biennial strategies maintain 79.8%-81.3% (range across strategies and models: 68.3–98.9%) of annual screening benefits with almost half the false-positives and fewer overdiagnoses. Screening biennially from ages 50–74 achieves a median 25.8% (range: 24.1%-31.8%) breast cancer mortality reduction; annual screening from ages 40–74 years reduces mortality an additional 12.0% (range: 5.7%-17.2%) vs. no screening, but yields 1988 more false-positives and 7 more overdiagnoses per 1000 women screened. Annual screening from ages 50–74 had similar benefits as other strategies but more harms, so would not be recommended. Sub-population Results Annual screening starting at age 40 for women who have a two- to four-fold increase in risk has a similar balance of harms and benefits as biennial screening of average-risk women from 50–74. Limitations We do not consider other imaging technologies, polygenic risk, or non-adherence. Conclusion These results suggest that biennial screening is efficient for average-risk groups, but decisions on strategies depend on the weight given to the balance of harms and benefits. Primary Funding Source National Institutes of Health

In this simulation modeling study that projected trends in breast cancer mortality rates among US women, decreases in overall breast cancer mortality from 2000 to 2012 were associated with advances in screening and in adjuvant therapy, although the associations varied by breast cancer molecular subtype.

Background Biennial screening is generally recommended for average-risk women aged 50–74 years, but tailored screening may provide greater benefits. Objective To estimate outcomes for varying screening intervals after age 50 based on breast density and risk. Design Collaborative simulation modeling using national incidence, breast density, and screening performance data. Setting U.S. population. Patients Women ages ≥50 with combinations of breast density and relative risk (RR: 1.0, 1.3, 2.0, 4.0). Interventions Annual, biennial, or triennial digital mammography screening from age 50 to 74 (versus no screening) and age 65 to 74 (versus biennial 50–64). Measurements Lifetime breast cancer deaths, life expectancy and quality-adjusted life years (QALYs), false-positives, benign biopsies, overdiagnoses, cost-effectiveness and ratio of false-positives to breast cancer deaths averted. Results Screening benefits and overdiagnosis increase with breast density and risk. False-positives and benign biopsies decrease with increasing risk. Among women with fatty or scattered fibroglandular breast density and RR=1.0–1.3, breast cancer deaths averted were similar for triennial versus biennial screening for both age groups (medians: age 50–74, 3.4–5.1 vs. 4.1–6.5; age 65–74, 1.5–2.1 vs. 1.8–2.6). Breast cancer deaths averted increased with annual versus biennial screening for ages 50–74 years with all levels of breast density and RR=4.0, and ages 65–74 years with heterogeneously or extremely dense breasts and RR=4.0, but harms were almost 2-fold higher. Triennial screening for average-risk and annual screening for highest-risk subgroups cost <$100,000 per QALY gained. Limitations Models did not consider ages <50, RR< 1, or other imaging modalities. Conclusions Average-risk/low-breast density women undergoing triennial screening and higher-risk/high-breast density women receiving annual screening will maintain a similar or better balance of benefits and harms compared to biennial screening of average-risk women. Primary Funding Source National Cancer Institute

As advances in risk assessment facilitate identification of women with increased risk of ER-negative breast cancer, additional mortality reductions could be realized through more frequent targeted screening, provided these benefits are balanced against screening harms.

Background The coronavirus disease 2019 (COVID-19) pandemic has disrupted breast cancer control through short-term declines in screening and delays in diagnosis and treatments. We projected the impact of COVID-19 on future breast cancer mortality between 2020 and 2030. Methods Three established Cancer Intervention and Surveillance Modeling Network breast cancer models modeled reductions in mammography screening use, delays in symptomatic cancer diagnosis, and reduced use of chemotherapy for women with early-stage disease for the first 6 months of the pandemic with return to prepandemic patterns after that time. Sensitivity analyses were performed to determine the effect of key model parameters, including the duration of the pandemic impact. Results By 2030, the models project 950 (model range = 860-1297) cumulative excess breast cancer deaths related to reduced screening, 1314 (model range = 266-1325) associated with delayed diagnosis of symptomatic cases, and 151 (model range = 146-207) associated with reduced chemotherapy use in women with hormone positive, early-stage cancer. Jointly, 2487 (model range = 1713-2575) excess breast cancer deaths were estimated, representing a 0.52% (model range = 0.36%-0.56%) cumulative increase over breast cancer deaths expected by 2030 in the absence of the pandemic’s disruptions. Sensitivity analyses indicated that the breast cancer mortality impact would be approximately double if the modeled pandemic effects on screening, symptomatic diagnosis, and chemotherapy extended for 12 months. Conclusions Initial pandemic-related disruptions in breast cancer care will have a small long-term cumulative impact on breast cancer mortality. Continued efforts to ensure prompt return to screening and minimize delays in evaluation of symptomatic women can largely mitigate the effects of the initial pandemic-associated disruptions.

The University of Wisconsin Breast Cancer Epidemiology Simulation Model (UWBCS), also referred to as Model W, is a discrete-event microsimulation model that uses a systems engineering approach to replicate breast cancer epidemiology in the US over time. This population-based model simulates the lifetimes of individual women through 4 main model components: breast cancer natural history, detection, treatment, and mortality. A key feature of the UWBCS is that, in addition to specifying a population distribution in tumor growth rates, the model allows for heterogeneity in tumor behavior, with some tumors having limited malignant potential (i.e., would never become fatal in a woman's lifetime if left untreated) and some tumors being very aggressive based on metastatic spread early in their onset. The model is calibrated to Surveillance, Epidemiology, and End Results (SEER) breast cancer incidence and mortality data from 1975 to 2010, and cross-validated against data from the Wisconsin cancer reporting system. The UWBCS model generates detailed outputs including underlying disease states and observed clinical outcomes by age and calendar year, as well as costs, resource usage, and quality of life associated with screening and treatment. The UWBCS has been recently updated to account for differences in breast cancer detection, treatment, and survival by molecular subtypes (defined by ER/HER2 status), to reflect the recent advances in screening and treatment, and to consider a range of breast cancer risk factors, including breast density, race, body-mass-index, and the use of postmenopausal hormone therapy. Therefore, the model can evaluate novel screening strategies, such as risk-based screening, and can assess breast cancer outcomes by breast cancer molecular subtype. In this article, we describe the most up-to-date version of the UWBCS.

Patients who receive an abnormal cancer screening result require follow-up for diagnostic testing, but the time to follow-up varies across patients and practices. We used a simulation study to estimate the change in lifetime screening benefits when time to follow-up for breast, cervical, and colorectal cancers was increased. Estimates were based on four independently developed microsimulation models that each simulated the life course of adults eligible for breast (women ages 50-74 years), cervical (women ages 21-65 years), or colorectal (adults ages 50-75 years) cancer screening. We assumed screening based on biennial mammography for breast cancer, triennial Papanicolaou testing for cervical cancer, and annual fecal immunochemical testing for colorectal cancer. For each cancer type, we simulated diagnostic testing immediately and at 3, 6, and 12 months after an abnormal screening exam. We found declines in screening benefit with longer times to diagnostic testing, particularly for breast cancer screening. Compared to immediate diagnostic testing, testing at 3 months resulted in reduced screening benefit, with fewer undiscounted life years gained per 1,000 screened (breast: 17.3%, cervical: 0.8%, colorectal: 2.0% and 2.7%, from two colorectal cancer models), fewer cancers prevented (cervical: 1.4% fewer, colorectal: 0.5% and 1.7% fewer, respectively), and, for breast and colorectal cancer, a less favorable stage distribution. Longer times to diagnostic testing after an abnormal screening test can decrease screening effectiveness, but the impact varies substantially by cancer type. Understanding the impact of time to diagnostic testing on screening effectiveness can help inform quality improvement efforts. .

We suggest that Pro12Ala polymorphism of the PPAR-gamma gene may be a modifier of insulin resistance in women with PCOS.