Background At least nineteen states have laws that require telling women with dense breasts and a negative screening mammogram to consider supplemental screening. The most readily available supplemental screening modality is ultrasound, yet little is known about its effectiveness. Objective To evaluate the benefits, harms, and cost-effectiveness of supplemental ultrasound screening for women with dense breasts. Design Comparative modeling with 3 validated simulation models. Data Sources Surveillance, Epidemiology, and End Results Program; Breast Cancer Surveillance Consortium; the medical literature. Target Population A contemporary cohort of women eligible for routine screening. Time Horizon Lifetime. Perspective Payer. Interventions Supplemental ultrasound screening for women with dense breasts following a negative screening mammogram. Outcome Measures Breast cancer deaths averted, quality-adjusted life years (QALYs) gained, false positive ultrasound biopsy recommendations, costs, costs per QALY gained. Results of Base-Case Analysis Supplemental ultrasound screening after a negative mammogram for women aged 50–74 with heterogeneously or extremely dense breasts averted 0.36 additional breast cancer deaths (range across models: 0.14–0.75), gained 1.7 QALYs (0.9–4.7), and resulted in 354 false-positive ultrasound biopsy recommendations (345–421) per 1000 women with dense breasts compared with biennial screening by mammography alone. The cost-effectiveness ratio was $325,000 per QALY gained ($112,000-$766,000). Restricting supplemental ultrasound screening to women with extremely dense breasts cost $246,000 per QALY gained ($74,000-$535,000). Results of Sensitivity Analysis The conclusions were not sensitive to ultrasound performance characteristics, screening frequency, or starting age. Limitations Provider costs for coordinating supplemental ultrasound were not considered. Conclusions Supplemental ultrasound screening for women with dense breasts undergoing screening mammography would substantially increase costs while producing relatively small benefits in breast cancer deaths averted and QALYs gained. Primary Funding Source National Institutes of Health

The transition to digital breast cancer screening in the United States increased total costs for small added health benefits. The value of digital mammography screening among women aged 40 to 49 years depends on women's preferences regarding false positives.

Purpose To evaluate the comparative effectiveness of combined biennial digital mammography and tomosynthesis screening, compared to biennial digital mammography screening alone, among women with dense breasts. Materials and Methods We used an established, discrete-event breast cancer simulation model to estimate the comparative clinical effectiveness and cost-effectiveness of biennial screening with both digital mammography and tomosynthesis versus digital mammography alone among U.S. women ages 50–74 years with dense breasts from a federal payer perspective and a lifetime horizon. We estimated input values for test performance, costs, and health state utilities from the National Cancer Institute’s Breast Cancer Surveillance Consortium, Medicare reimbursement rates, and the medical literature. We performed sensitivity analyses to determine the implications of varying key model parameters, including combined screening sensitivity and specificity, transient utility decrement of diagnostic work-up, and the additional cost of tomosynthesis. Results For our base case analysis, the incremental cost per quality-adjusted life year (QALY) gained by adding tomosynthesis to digital mammography screening was $53,893. An additional 0.5 deaths were averted and 405 false-positives were avoided per 1,000 women after 12 rounds of screening. Combined screening remained cost-effective (less than $100,000 per QALY gained) over a wide range of incremental improvements in test performance. Overall, cost-effectiveness was most sensitive to the additional cost of tomosynthesis. Conclusion Biennial combined digital mammography and tomosynthesis screening for U.S. women aged 50–74 years with dense breasts is likely to be cost-effective if priced appropriately (≤ $226 combined exams versus $139 for digital mammography alone) and if reported interpretive performance metrics of improved specificity with tomosynthesis are met in routine practice.

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.

Background Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration procedure, which involves evaluating large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Methods Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We develop an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs, therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using previously developed University of Wisconsin Breast Cancer Simulation Model (UWBCS). Results In a recent study, calibration of the UWBCS required the evaluation of 378,000 input parameter combinations to build a race-specific model and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378,000 combinations. Conclusion Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration.

Breast cancer, the leading cause of cancer death for women, can be detected at earlier stages through mammography screening. Therefore, most developed countries implemented population‐based mammography screening programs. However, cost of mammography and limited resources in terms of number of trained personnel and diagnostic machines prevent mammography screening to be adopted by many other countries. In fact, even in resource‐rich countries, there is a growing concern about cost of mammography screening. In this study, we investigate the optimal allocation of limited mammography resources to screen a population. We propose a constrained partially observable Markov decision process (CPOMDP) model that maximizes total expected quality‐adjusted life years of the patients when they are allowed only a limited number of mammography screenings. We use a variable resolution grid‐based approximation scheme to convert the CPOMDP model into a mixed‐integer linear program and conduct several numerical experiments using breast cancer epidemiology data. We observe that as mammography screening capacity decreases, patients in the 40–49 age group should be given the least priority with respect to screening. We further find that efficient allocation of available resources between patients with different risk levels leads to significant quality‐adjusted life year gains, especially for the patients with higher breast cancer risk.

Stereotactic radiosurgery (SRS) is an effective technique to treat brain metastasis for which several inverse planning methods may be appropriate. We compare three different optimization models for segment duration optimization in SRS using Leksell Gamma Knife Icon (Elekta, Stockholm, Sweden). We investigate (1) a linear programming approach, (2) a piecewise quadratic penalty approach, and (3) an unconstrained convex moment-based penalty approach. We examine the performances of these approaches using anonymized data from 14 previously treated cases. In addition, we investigate the important modeling question of selecting weights for the objective functions where we use a simulated annealing algorithm to determine these weights for each model. The inverse plans obtained via optimization models are compared against each other and against the clinical plans. The three inverse planning models can all yield optimal treatment plans in a reasonable amount of time and the treatment plans obtained by these models meet or exceed clinical guidelines while displaying high conformity.

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