Estimating Breast Cancer Survival by Molecular Subtype in the Absence of Screening and Adjuvant Treatment
MEDICAL DECISION MAKING
2018; 38: 32S–43S
As molecular subtyping of breast cancer influences clinical management, the evaluation of screening and adjuvant treatment interventions at the population level needs to account for molecular subtyping. Performing such analyses are challenging because molecular subtype-specific, long-term outcomes are not readily accessible; these markers were not historically recorded in tumor registries. We present a modeling approach to estimate historical survival outcomes by estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status.Our approach leverages a simulation model of breast cancer outcomes and integrates data from two sources: the Surveillance Epidemiology and End Results (SEER) databases and the Breast Cancer Surveillance Consortium (BCSC). We not only produce ER- and HER2-specific estimates of breast cancer survival in the absence of screening and adjuvant treatment but we also estimate mean tumor volume doubling time (TVDT) and mean mammographic detection threshold by ER/HER2-status.In general, we found that tumors with ER-negative and HER2-positive status are associated with more aggressive growth, have lower TVDTs, are harder to detect by mammography, and have worse survival outcomes in the absence of screening and adjuvant treatment. Our estimates have been used as inputs into model-based analyses that evaluate the effects of screening and adjuvant treatment interventions on population outcomes by ER and HER2 status developed by the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group. In addition, our estimates enable a re-assessment of historical trends in breast cancer incidence and mortality in terms of contemporary molecular tumor characteristics.Our approach can be generalized beyond breast cancer and to more complex molecular profiles.
View details for PubMedID 29554464
A Molecular Subtype-Specific Stochastic Simulation Model of US Breast Cancer Incidence, Survival, and Mortality Trends from 1975 to 2010
MEDICAL DECISION MAKING
2018; 38: 89S–98S
We present a Monte Carlo simulation model that reproduces US invasive breast cancer incidence and mortality trends from 1975 to 2010 as a function of screening and adjuvant treatment. This model was developed for multiple purposes, including to quantify the impact of screening and adjuvant therapy on past and current trends, predict future trends, and evaluate potential outcomes under hypothetical screening and treatment interventions. The model first generates the life histories of individual breast cancer patients by determining the patient's age, tumor size, estrogen receptor (ER) status, human epidermal growth factor 2 (HER2) status, SEER (Surveillance, Epidemiology, and End Results) historic stage, detection mode at time of detection, preclinical tumor course, and death age and cause of death (breast cancer v. other causes). The model incorporates common inputs used by the Cancer Intervention and Surveillance Modeling Network (CISNET), including the dissemination patterns for screening mammography, breast cancer survival in the absence of adjuvant therapy, dissemination and efficacy of treatment by ER and HER2 status, and death from causes other than breast cancer. In this article, predicted mortality outcomes are compared assuming proportional v. nonproportional hazards effects of treatment on breast cancer survival. We found that the proportional hazards treatment effects are sufficient for ER-negative disease. However, for ER-positive disease, the treatment effects appear to be higher during the early years following diagnosis and then diminish over time. Using nonproportional hazards effects for ER-positive cases, the predicted breast cancer mortality rates closely match the SEER mortality trends from 1975 to 2010, particularly after 1995. Our work indicates that population-level simulation modeling may have a broader role in assessing the time dependence of treatment effects.
View details for PubMedID 29554473
Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology
MEDICAL DECISION MAKING
2018; 38: 112S–125S
Collaborative modeling has been used to estimate the impact of potential cancer screening strategies worldwide. A necessary step in the interpretation of collaborative cancer screening model results is to understand how model structure and model assumptions influence cancer incidence and mortality predictions. In this study, we examined the relative contributions of the pre-clinical duration of breast cancer, the sensitivity of screening, and the improvement in prognosis associated with treatment of screen-detected cases to the breast cancer incidence and mortality predictions of 5 Cancer Intervention and Surveillance Modeling Network (CISNET) models.To tease out the impact of model structure and assumptions on model predictions, the Maximum Clinical Incidence Reduction (MCLIR) method compares changes in the number of breast cancers diagnosed due to clinical symptoms and cancer mortality between 4 simplified scenarios: 1) no-screening; 2) one-time perfect screening exam, which detects all existing cancers and perfect treatment (i.e., cure) of all screen-detected cancers; 3) one-time digital mammogram and perfect treatment of all screen-detected cancers; and 4) one-time digital mammogram and current guideline-concordant treatment of all screen-detected cancers.The 5 models predicted a large range in maximum clinical incidence (19% to 71%) and in breast cancer mortality reduction (33% to 67%) from a one-time perfect screening test and perfect treatment. In this perfect scenario, the models with assumptions of tumor inception before it is first detectable by mammography predicted substantially higher incidence and mortality reductions than models with assumptions of tumor onset at the start of a cancer's screen-detectable phase. The range across models in breast cancer clinical incidence (11% to 24%) and mortality reduction (8% to 18%) from a one-time digital mammogram at age 62 y with observed sensitivity and current guideline-concordant treatment was considerably smaller than achievable under perfect conditions.The timing of tumor inception and its effect on the length of the pre-clinical phase of breast cancer had a substantial impact on the grouping of models based on their predictions for clinical incidence and breast cancer mortality reduction. This key finding about the timing of tumor inception will be included in future CISNET breast analyses to enhance model transparency. The MCLIR approach should aid in the interpretation of variations in model results and could be adopted in other disease screening settings to enhance model transparency.
View details for PubMedID 29554471
Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012.
2018; 319 (2): 154–64
Given recent advances in screening mammography and adjuvant therapy (treatment), quantifying their separate and combined effects on US breast cancer mortality reductions by molecular subtype could guide future decisions to reduce disease burden.To evaluate the contributions associated with screening and treatment to breast cancer mortality reductions by molecular subtype based on estrogen-receptor (ER) and human epidermal growth factor receptor 2 (ERBB2, formerly HER2 or HER2/neu).Six Cancer Intervention and Surveillance Network (CISNET) models simulated US breast cancer mortality from 2000 to 2012 using national data on plain-film and digital mammography patterns and performance, dissemination and efficacy of ER/ERBB2-specific treatment, and competing mortality. Multiple US birth cohorts were simulated.Screening mammography and treatment.The models compared age-adjusted, overall, and ER/ERBB2-specific breast cancer mortality rates from 2000 to 2012 for women aged 30 to 79 years relative to the estimated mortality rate in the absence of screening and treatment (baseline rate); mortality reductions were apportioned to screening and treatment.In 2000, the estimated reduction in overall breast cancer mortality rate was 37% (model range, 27%-42%) relative to the estimated baseline rate in 2000 of 64 deaths (model range, 56-73) per 100 000 women: 44% (model range, 35%-60%) of this reduction was associated with screening and 56% (model range, 40%-65%) with treatment. In 2012, the estimated reduction in overall breast cancer mortality rate was 49% (model range, 39%-58%) relative to the estimated baseline rate in 2012 of 63 deaths (model range, 54-73) per 100 000 women: 37% (model range, 26%-51%) of this reduction was associated with screening and 63% (model range, 49%-74%) with treatment. Of the 63% associated with treatment, 31% (model range, 22%-37%) was associated with chemotherapy, 27% (model range, 18%-36%) with hormone therapy, and 4% (model range, 1%-6%) with trastuzumab. The estimated relative contributions associated with screening vs treatment varied by molecular subtype: for ER+/ERBB2-, 36% (model range, 24%-50%) vs 64% (model range, 50%-76%); for ER+/ERBB2+, 31% (model range, 23%-41%) vs 69% (model range, 59%-77%); for ER-/ERBB2+, 40% (model range, 34%-47%) vs 60% (model range, 53%-66%); and for ER-/ERBB2-, 48% (model range, 38%-57%) vs 52% (model range, 44%-62%).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.
View details for PubMedID 29318276
Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling.
Medical decision making : an international journal of the Society for Medical Decision Making
2018; 38 (1_suppl): 9S–23S
Since their inception in 2000, the Cancer Intervention and Surveillance Network (CISNET) breast cancer models have collaborated to use a nationally representative core of common input parameters to represent key components of breast cancer control in each model. Employment of common inputs permits greater ability to compare model output than when each model begins with different input parameters. The use of common inputs also enhances inferences about the results, and provides a range of reasonable results based on variations in model structure, assumptions, and methods of use of the input values. The common input data are updated for each analysis to ensure that they reflect the most current practice and knowledge about breast cancer. The common core of parameters includes population rates of births and deaths; age- and cohort-specific temporal rates of breast cancer incidence in the absence of screening and treatment; effects of risk factors on incidence trends; dissemination of plain film and digital mammography; screening test performance characteristics; stage or size distribution of screen-, interval-, and clinically- detected tumors by age; the joint distribution of ER/HER2 by age and stage; survival in the absence of screening and treatment by stage and molecular subtype; age-, stage-, and molecular subtype-specific therapy; dissemination and effectiveness of therapies over time; and competing non-breast cancer mortality.In this paper, we summarize the methods and results for the common input values presently used in the CISNET breast cancer models, note assumptions made because of unobservable phenomena and/or unavailable data, and highlight plans for the development of future parameters.These data are intended to enhance the transparency of the breast CISNET models.
View details for PubMedID 29554466
Collaborative Modeling of the Benefits and Harms Associated With Different US Breast Cancer Screening Strategies
ANNALS OF INTERNAL MEDICINE
2016; 164 (4): 215-?
Controversy persists about optimal mammography screening strategies.To evaluate screening outcomes, taking into account advances in mammography and treatment of breast cancer.Collaboration of 6 simulation models using national data on incidence, digital mammography performance, treatment effects, and other-cause mortality.United States.Average-risk U.S. female population and subgroups with varying risk, breast density, or comorbidity.Eight strategies differing by age at which screening starts (40, 45, or 50 years) and screening interval (annual, biennial, and hybrid [annual for women in their 40s and biennial thereafter]). All strategies assumed 100% adherence and stopped at age 74 years.Benefits (breast cancer-specific mortality reduction, breast cancer deaths averted, life-years, and quality-adjusted life-years); number of mammograms used; harms (false-positive results, benign biopsies, and overdiagnosis); and ratios of harms (or use) and benefits (efficiency) per 1000 screens.Biennial strategies were consistently the most efficient for average-risk women. Biennial screening from age 50 to 74 years avoided a median of 7 breast cancer deaths versus no screening; annual screening from age 40 to 74 years avoided an additional 3 deaths, but yielded 1988 more false-positive results and 11 more overdiagnoses per 1000 women screened. Annual screening from age 50 to 74 years was inefficient (similar benefits, but more harms than other strategies). For groups with a 2- to 4-fold increased risk, annual screening from age 40 years had similar harms and benefits as screening average-risk women biennially from 50 to 74 years. For groups with moderate or severe comorbidity, screening could stop at age 66 to 68 years.Other imaging technologies, polygenic risk, and nonadherence were not considered.Biennial screening for breast cancer is efficient for average-risk populations. Decisions about starting ages and intervals will depend on population characteristics and the decision makers' weight given to the harms and benefits of screening.National Institutes of Health.
View details for DOI 10.7326/M15-1536
View details for Web of Science ID 000370135300012
View details for PubMedID 26756606
Effects of screening and systemic adjuvant therapy on ER-specific US breast cancer mortality.
Journal of the National Cancer Institute
2014; 106 (11)
Molecular characterization of breast cancer allows subtype-directed interventions. Estrogen receptor (ER) is the longest-established molecular marker.We used six established population models with ER-specific input parameters on age-specific incidence, disease natural history, mammography characteristics, and treatment effects to quantify the impact of screening and adjuvant therapy on age-adjusted US breast cancer mortality by ER status from 1975 to 2000. Outcomes included stage-shifts and absolute and relative reductions in mortality; sensitivity analyses evaluated the impact of varying screening frequency or accuracy.In the year 2000, actual screening and adjuvant treatment reduced breast cancer mortality by a median of 17 per 100000 women (model range = 13-21) and 5 per 100000 women (model range = 3-6) for ER-positive and ER-negative cases, respectively, relative to no screening and no adjuvant treatment. For ER-positive cases, adjuvant treatment made a higher relative contribution to breast cancer mortality reduction than screening, whereas for ER-negative cases the relative contributions were similar for screening and adjuvant treatment. ER-negative cases were less likely to be screen-detected than ER-positive cases (35.1% vs 51.2%), but when screen-detected yielded a greater survival gain (five-year breast cancer survival = 35.6% vs 30.7%). Screening biennially would have captured a lower proportion of mortality reduction than annual screening for ER-negative vs ER-positive cases (model range = 80.2%-87.8% vs 85.7%-96.5%).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.
View details for DOI 10.1093/jnci/dju289
View details for PubMedID 25255803
- Effects of Screening and Systemic Adjuvant Therapy on ER-Specific US Breast Cancer Mortality JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE 2014; 106 (11)
Feasibility evaluation of an online tool to guide decisions for BRCA1/2 mutation carriers
2013; 12 (1): 65-73
Women with BRCA1 or BRCA2 (BRCA1/2) mutations face difficult decisions about managing their high risks of breast and ovarian cancer. We developed an online tool to guide decisions about cancer risk reduction (available at: http://brcatool.stanford.edu ), and recruited patients and clinicians to test its feasibility. We developed questionnaires for women with BRCA1/2 mutations and clinicians involved in their care, incorporating the System Usability Scale (SUS) and the Center for Healthcare Evaluation Provider Satisfaction Questionnaire (CHCE-PSQ). We enrolled BRCA1/2 mutation carriers who were seen by local physicians or participating in a national advocacy organization, and we enrolled clinicians practicing at Stanford University and in the surrounding community. Forty BRCA1/2 mutation carriers and 16 clinicians participated. Both groups found the tool easy to use, with SUS scores of 82.5-85 on a scale of 1-100; we did not observe differences according to patient age or gene mutation. General satisfaction was high, with a mean score of 4.28 (standard deviation (SD) 0.96) for patients, and 4.38 (SD 0.89) for clinicians, on a scale of 1-5. Most patients (77.5 %) were comfortable using the tool at home. Both patients and clinicians agreed that the decision tool could improve patient-doctor encounters (mean scores 4.50 and 4.69, on a 1-5 scale). Patients and health care providers rated the decision tool highly on measures of usability and clinical relevance. These results will guide a larger study of the tool's impact on clinical decisions.
View details for DOI 10.1007/s10689-012-9577-8
View details for PubMedID 23086584
- Algorithms for the generalized weighted frequency assignment problem COMPUTERS & OPERATIONS RESEARCH 2012; 39 (12): 3256-3266
A Simulation Model to Predict the Impact of Prophylactic Surgery and Screening on the Life Expectancy of BRCA1 and BRCA2 Mutation Carriers
CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION
2012; 21 (7): 1066-1077
Women with inherited mutations in the BRCA1 or BRCA2 (BRCA1/2) genes are recommended to undergo a number of intensive cancer risk-reducing strategies, including prophylactic mastectomy, prophylactic oophorectomy, and screening. We estimate the impact of different risk-reducing options at various ages on life expectancy.We apply our previously developed Monte Carlo simulation model of screening and prophylactic surgery in BRCA1/2 mutation carriers. Here, we present the mathematical formulation to compute age-specific breast cancer incidence in the absence of prophylactic oophorectomy, which is an input to the simulation model, and provide sensitivity analysis on related model parameters.The greatest gains in life expectancy result from conducting prophylactic mastectomy and prophylactic oophorectomy immediately after BRCA1/2 mutation testing; these gains vary with age at testing, from 6.8 to 10.3 years for BRCA1 and 3.4 to 4.4 years for BRCA2 mutation carriers. Life expectancy gains from delaying prophylactic surgery by 5 to 10 years range from 1 to 9.9 years for BRCA1 and 0.5 to 4.2 years for BRCA2 mutation carriers. Adding annual breast screening provides gains of 2.0 to 9.9 years for BRCA1 and 1.5 to 4.3 years for BRCA2. Results were most sensitive to variations in our assumptions about the magnitude and duration of breast cancer risk reduction due to prophylactic oophorectomy.Life expectancy gains depend on the type of BRCA mutation and age at interventions. Sensitivity analysis identifies the degree of breast cancer risk reduction due to prophylactic oophorectomy as a key determinant of life expectancy gain.Further study of the impact of prophylactic oophorectomy on breast cancer risk in BRCA1/2 mutation carriers is warranted.
View details for DOI 10.1158/1055-9965.EPI-12-0149
View details for PubMedID 22556274
Online Tool to Guide Decisions for BRCA1/2 Mutation Carriers
JOURNAL OF CLINICAL ONCOLOGY
2012; 30 (5): 497-506
Women with BRCA1 or BRCA2 (BRCA1/2) mutations must choose between prophylactic surgeries and screening to manage their high risks of breast and ovarian cancer, comparing options in terms of cancer incidence, survival, and quality of life. A clinical decision tool could guide these complex choices.We built a Monte Carlo model for BRCA1/2 mutation carriers, simulating breast screening with annual mammography plus magnetic resonance imaging (MRI) from ages 25 to 69 years and prophylactic mastectomy (PM) and/or prophylactic oophorectomy (PO) at various ages. Modeled outcomes were cancer incidence, tumor features that shape treatment recommendations, overall survival, and cause-specific mortality. We adapted the model into an online tool to support shared decision making.We compared strategies on cancer incidence and survival to age 70 years; for example, PO plus PM at age 25 years optimizes both outcomes (incidence, 4% to 11%; survival, 80% to 83%), whereas PO at age 40 years plus MRI screening offers less effective prevention, yet similar survival (incidence, 36% to 57%; survival, 74% to 80%). To characterize patients' treatment and survivorship experiences, we reported the tumor features and treatments associated with risk-reducing interventions; for example, in most BRCA2 mutation carriers (81%), MRI screening diagnoses stage I, hormone receptor-positive breast cancers, which may not require chemotherapy.Cancer risk-reducing options for BRCA1/2 mutation carriers vary in their impact on cancer incidence, recommended treatments, quality of life, and survival. To guide decisions informed by multiple health outcomes, we provide an online tool for joint use by patients with their physicians (http://brcatool.stanford.edu).
View details for DOI 10.1200/JCO.2011.38.6060
View details for PubMedID 22231042
- Bayesian Forecasting of Spare Parts Using Simulation SERVICES PARTS MANAGEMENT: DEMAND FORECASTING AND INVENTORY CONTROL 2011: 105–23