Variation in tumor natural history contributes to racial disparities in breast cancer stage at diagnosis

The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review

OBJECTIVE

Assumptions on the natural history of ductal carcinoma in situ (DCIS) are necessary to accurately model it and estimate overdiagnosis. To improve current estimates of overdiagnosis (0-91%), the purpose of this review was to identify and analyse assumptions made in modelling studies on the natural history of DCIS in women.

METHODS

A systematic review of English full-text articles using PubMed, Embase, and Web of Science was conducted up to February 6, 2023. Eligibility and all assessments were done independently by two reviewers. Risk of bias and quality assessments were performed. Discrepancies were resolved by consensus. Reader agreement was quantified with Cohen's kappa. Data extraction was performed with three forms on study characteristics, model assessment, and tumour progression.

RESULTS

Thirty models were distinguished. The most important assumptions regarding the natural history of DCIS were addition of non-progressive DCIS of 20-100%, classification of DCIS into three grades, where high grade DCIS had an increased chance of progression to invasive breast cancer (IBC), and regression possibilities of 1-4%, depending on age and grade. Other identified risk factors of progression of DCIS to IBC were younger age, birth cohort, larger tumour size, and individual risk.

CONCLUSION

To accurately model the natural history of DCIS, aspects to consider are DCIS grades, non-progressive DCIS (9-80%), regression from DCIS to no cancer (below 10%), and use of well-established risk factors for progression probabilities (age). Improved knowledge on key factors to consider when studying DCIS can improve estimates of overdiagnosis and optimization of screening.

Breast cancer diagnosis in Inner-City African American and Hispanic women: The importance of early screening

PURPOSE

To evaluate the diagnosis of breast cancer in inner-city African-American and Hispanic women under age 50 to support the importance of screening in this population.

METHODS

This retrospective chart review included women newly diagnosed with breast cancer from 1/1/2015 to 1/1/2019 in a city hospital mainly serving minority patients. Chi-square and Fisher's exact tests were used for analysis.

RESULTS

In this cohort of 108 newly diagnosed African-American (63%) and Hispanic (31%) women, 60/108 (56%) presented with a site of palpable concern for diagnostic workup, and the remaining were diagnosed via asymptomatic screening. Women ages 30-49 were significantly more likely to present with a site of palpable concern when compared to women ages 50-69 (68% vs. 44%, p = 0.045). Additionally, women ages 30-49 were more likely to have triple-negative breast cancer (TNBC) than women ages 50-69 (20% vs. 10%, p = 0.222). However, women ages 30-49 were less likely to have prior mammogram than women ages 50-69 (24% vs. 46%, p = 0.062).

CONCLUSION

African-American and Hispanic women ages 30-49 were more likely to present with a site of palpable concern and TNBC than those ages 50-69. However, these young minority women ages 30-49 were less likely to have prior screening mammograms when compared to those ages 50-69. Our data highlights the importance of starting screening mammography no later than age 40 in African-American and Hispanic women. In addition, these women should have risk assessment for breast cancer no later than age 30 and be screened appropriately.

Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts

Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.

Since 2000, our research teams have used computer models of breast cancer to address important clinical and policy-relevant questions as part of the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET). Our 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to represent the burden of breast cancer. We have used our models to investigate questions related to breast cancer biology, compare strategies to improve the balance of benefits and harms of screening mammography, and support insights into the delivery of care by modeling outcomes following clinical decisions about breast cancer treatment. Moving forward, our research will continue to use systems modeling to address issues related to reducing the burden of breast cancer including modeling structural inequities affecting racial disparities. Our future work will also leverage lessons from engaging multidisciplinary scientific teams, expand efforts to share modeling resources with other researchers, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.

Protective Innate Immune Variants in Racial/Ethnic Disparities of Breast and Prostate Cancer

Individuals of African descent are disproportionately affected by specific complex diseases, such as breast and prostate cancer, which are driven by both biological and nonbiological factors. In the case of breast cancer, there is clear evidence that psychosocial factors (environment, socioeconomic status, health behaviors, etc.) have a strong influence on racial disparities. However, even after controlling for these factors, overall phenotypic differences in breast cancer pathology remain among groups of individuals who vary by geographic ancestry. There is a growing appreciation that chronic/reoccurring inflammation, primarily driven by mechanisms of innate immunity, contributes to core functions associated with cancer progression. Germline mutations in innate immune genes that have been retained in the human genome offer enhanced protection against environmental pathogens, and protective innate immune variants against specific pathogens are enriched among populations whose ancestors were heavily exposed to those pathogens. Consequently, it is predicted that racial/ethnic differences in innate immune programs will translate into ethnic differences in both pro- and antitumor immunity, tumor progression, and prognosis, leading to the current phenomenon of racial/ethnic disparities in cancer. This review explores examples of protective innate immune genetic variants that are (i) distributed disproportionately among racial populations and (ii) associated with racial/ethnic disparities of breast and prostate cancer.

Risk factors for estrogen receptor positive ductal carcinoma in situ of the breast in African American women

Background

Compared to U.S. white women, African American women are more likely to die from ductal carcinoma in situ (DCIS). Elucidation of risk factors for DCIS in African American women may provide opportunities for risk reduction.

Methods

We used data from three epidemiologic studies in the African American Breast Cancer Epidemiology and Risk Consortium to study risk factors for estrogen receptor (ER) positive DCIS (488 cases; 13,830 controls). Results were compared to associations observed for ER+ invasive breast cancer (n = 2,099).

Results

First degree family history of breast cancer was associated with increased risk of ER+ DCIS [odds ratio (OR): 1.69, 95% confidence interval (CI): 1.31, 2.17]. Oral contraceptive use within the past 10 years (vs. never) was also associated with increased risk (OR: 1.43, 95%CI: 1.03, 1.97), as was late age at first birth (≥25 years vs. <20 years) (OR: 1.26, 95%CI: 0.96, 1.67). Risk was reduced in women with older age at menarche (≥15 years vs. <11 years) (OR: 0.62, 95%CI: 0.42, 0.93) and higher body mass index (BMI) in early adulthood (≥25 vs. <20 kg/m² at age 18 or 21) (OR: 0.75, 95%CI: 0.55, 1.01). There was a positive association of recent BMI with risk in postmenopausal women only. In general, associations of risk factors for ER+ DCIS were similar in magnitude and direction to those for invasive ER+ breast cancer.

Conclusions

Our findings suggest that most risk factors for invasive ER+ breast cancer are also associated with increased risk of ER+ DCIS among African American women.

•

Few studies of risk factors for ductal carcinoma in situ (DCIS) have evaluated associations for African American women.

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We analyzed data from the African American African American Breast Cancer Epidemiology and Risk (AMBER) Consortium.

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Family history of breast cancer, reproductive factors, and anthropometric factors were associated with risk of ER+ DCIS.

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In general, risk factor associations for ER+ DCIS were similar to those for ER+ invasive breast cancer.

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Our findings support a common etiology and pathogenesis between ER+ DICS and ER+ invasive cancer in African American women.

Assessing the Value of Time Series Real-World and Clinical Trial Data vs. Baseline-Only Data in Predicting Responses to Pregabalin Therapy for Patients with Painful Diabetic Peripheral Neuropathy

BACKGROUND AND OBJECTIVE

Treatment challenges necessitate new approaches to customize care to individual patient needs. Integrating data from randomized controlled trials and observational studies may reduce potential covariate biases, yielding information to improve treatment outcomes. The objective of this study was to predict pregabalin responses, in individuals with painful diabetic peripheral neuropathy, by examining time series data (lagged inputs) collected after treatment initiation vs. baseline using microsimulation.

METHODS

The platform simulated pregabalin-treated patients to estimate hypothetical future pain responses over 6 weeks based on six distinct time series regressions with lagged variables as inputs (hereafter termed "time series regressions"). Data were from three randomized controlled trials (N = 398) and an observational study (N = 3159). Regressions were derived after performing a hierarchical cluster analysis with a matched patient dataset from coarsened exact matching. Regressions were validated using unmatched (observational study vs. randomized controlled trial) patients. Predictive implications (of 6-week outcomes) were compared using only baseline vs. 1- to 2-week prior data.

RESULTS

Time series regressions for pain performed well (adjusted R² 0.85-0.91; root mean square error 0.53-0.57); those with only baseline data performed less well (adjusted R² 0.13-0.44; root mean square error 1.11-1.40). Simulated patient distributions yielded positive predictive values for > 50% pain score improvements from baseline for the six clusters (287-777 patients each; range 0.87-0.98).

CONCLUSIONS

Effective prediction of pregabalin response for painful diabetic peripheral neuropathy was accomplished through combining cluster analyses, coarsened exact matching, and time series regressions, reflecting distinct patterns of baseline and "on-treatment" variables. These results advance the understanding of microsimulation to predict patient treatment responses through integration and inter-relationships of multiple, complex, and time-dependent characteristics.

Vitamin D Status and Association of VDR Genetic Polymorphism to Risk of Breast Cancer in Ethiopia

Emerging evidence associates vitamin D deficiency and vitamin D receptor (VDR) genetic variations with risk for breast cancer. This study investigated the prevalence of vitamin D deficiency and its association with tumor characteristics and the implications of VDR genetic variations for risk of breast cancer in Ethiopia. This unmatched case–control study involved 392 female breast cancer patients and 193 controls. The plasma 25-hydroxyvitamin D (25(OH)D₃) level was quantified in chemotherapy-naïve (N = 112) and tamoxifen-treated patients (N = 89). Genotyping for the VDR common variant alleles rs7975232 (ApaI), rs2228570 (FokI), and rs731236 (TaqI) was done. Eighty-six percent of the patients were vitamin D deficient (<50 nmol/L). Chemotherapy-naïve breast cancer patients had a higher prevalence of vitamin D deficiency (91.9% vs. 78.3%) compared to the tamoxifen-treated group (p < 0.001). The prevalence of severe vitamin D deficiency (<25 nmol/L) was significantly higher in chemotherapy-naïve (41.1%) than tamoxifen-treated (11.2%) patients. Vitamin D deficiency was not significantly associated with tumor characteristics or VDR genotype. The rs2228570 GG genotype was associated with increased risk of breast cancer (OR = 1.44, 95% confidence interval = 1.01−2.06). Our result indicates that rs2228570 might be a moderate risk factor for breast cancer development in the Ethiopian population. The high prevalence of severe vitamin D deficiency in treatment-naïve breast cancer patients indicates the need for nutritional supplementation of vitamin D at the time of chemotherapy initiation.

The effect of mammography screening regimen on incidence-based breast cancer mortality

OBJECTIVES

Incidence-based mortality quantifies the distribution of cancer deaths and life-years lost, according to age at detection. We investigated the temporal distribution of the disease burden, and the effect of starting and stopping ages and interval between screening mammography examinations, on incidence-based mortality.

METHODS

Incidence-based mortality was estimated using an established breast cancer simulation model, adapted and validated to simulate breast cancer incidence, screening performance, and delivery of therapies in Canada. Ten strategies were examined, with varying starting age (40 or 50), stopping age (69 or 74), and interval (1, 2, 3 years), and "No Screening." Life-years lost were computed as the difference between model predicted time of breast cancer death and that estimated from life tables.

RESULTS

Without screening, 70% of the burden in terms of breast cancer deaths extends between ages 45 and 75. The mean of the distribution of ages of detection of breast cancers that will be fatal in an unscreened population is 61.8 years, while the mean age of detection weighted by the number of life-years lost is 55, a downward shift of 6.8 years. Similarly, the mean age of detection for the distribution of life-years gained through screening is lower than that for breast cancer deaths averted.

CONCLUSION

Incidence-based mortality predictions from modeling elucidate the age dependence of the breast cancer burden and can provide guidance for optimizing the timing of screening regimens to achieve maximal impact. Of the regimens studied, the greatest lifesaving effect was achieved with annual screening beginning at age 40.

The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update

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.

Literature review of data-based models for identification of factors associated with racial disparities in breast cancer mortality

In the United States, early detection methods have contributed to the reduction of overall breast cancer mortality but this pattern has not been observed uniformly across all racial groups. A vast body of research literature shows a set of health care, socio-economic, biological, physical, and behavioural factors influencing the mortality disparity. In this paper, we review the modelling frameworks, statistical tests, and databases used in understanding influential factors, and we discuss the factors documented in the modelling literature. Our findings suggest that disparities research relies on conventional modelling and statistical tools for quantitative analysis, and there exist opportunities to implement data-based modelling frameworks for (1) exploring mechanisms triggering disparities, (2) increasing the collection of behavioural data, and (3) monitoring factors associated with the mortality disparity across time.

Effect of Time to Diagnostic Testing for Breast, Cervical, and Colorectal Cancer Screening Abnormalities on Screening Efficacy: A Modeling Study

Background: 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.Methods: 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.Results: 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.Conclusions: Longer times to diagnostic testing after an abnormal screening test can decrease screening effectiveness, but the impact varies substantially by cancer type.Impact: Understanding the impact of time to diagnostic testing on screening effectiveness can help inform quality improvement efforts. Cancer Epidemiol Biomarkers Prev; 27(2); 158-64. ©2017 AACR.

Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening

PURPOSE

Due to limitations in the ability to identify non-progressive disease, ductal carcinoma in situ (DCIS) is usually managed similarly to localized invasive breast cancer. We used simulation modeling to evaluate the potential impact of a hypothetical test that identifies non-progressive DCIS.

METHODS

A discrete-event model simulated a cohort of U.S. women undergoing digital screening mammography. All women diagnosed with DCIS underwent the hypothetical DCIS prognostic test. Women with test results indicating progressive DCIS received standard breast cancer treatment and a decrement to quality of life corresponding to the treatment. If the DCIS test indicated non-progressive DCIS, no treatment was received and women continued routine annual surveillance mammography. A range of test performance characteristics and prevalence of non-progressive disease were simulated. Analysis compared discounted quality-adjusted life years (QALYs) and costs for test scenarios to base-case scenarios without the test.

RESULTS

Compared to the base case, a perfect prognostic test resulted in a 40% decrease in treatment costs, from $13,321 to $8005 USD per DCIS case. A perfect test produced 0.04 additional QALYs (16 days) for women diagnosed with DCIS, added to the base case of 5.88 QALYs per DCIS case. The results were sensitive to the performance characteristics of the prognostic test, the proportion of DCIS cases that were non-progressive in the model, and the frequency of mammography screening in the population.

CONCLUSION

A prognostic test that identifies non-progressive DCIS would substantially reduce treatment costs but result in only modest improvements in quality of life when averaged over all DCIS cases.

Mammography decision making: Trends and predictors of provider communication in the Health Information National Trends Survey, 2011 to 2014

BACKGROUND

In 2009, the US Preventive Services Task Force recommended that the decision to initiate screening mammography before age 50 years should be individualized. Herein, the authors examined whether health care providers are communicating regarding mammography decision making with women and whether communication is associated with screening behavior.

METHODS

Data were drawn from the 2011 to 2014 Health Information National Trends Survey (HINTS). A total of 5915 female respondents aged ≥ 40 years who responded to the following question were included: "Has a doctor or other health professional ever told you that you could choose whether or not to have a mammogram?" We used logistic regression to generate odds ratios (ORs) and 95% confidence intervals (95% CIs) for predictors of provider communication and assessed whether provider communication was associated with mammography in the previous 2 years overall and stratified by age.

RESULTS

Fewer than 50% of the women reported provider communication regarding mammogram choice. Women who reported provider communication were not found to be more likely to report no mammogram within the past 2 years (OR, 1.07; 95% CI, 0.87-1.31) compared with those who did not. When stratified by 10-year age group, provider communication was associated with a higher likelihood of no mammogram only among women age ≥70 years (OR, 1.64; 95% CI, 1.15-2.34), and was associated with a lower likelihood of no mammogram only among women aged 40 to 49 years (OR, 0.63; 95% CI, 0.43-0.92).

CONCLUSIONS

Between 2011 and 2014, less than one-half of women received communication regarding mammogram choice despite recommendations from the US Preventive Services Task Force. Provider communication regarding mammogram choice can influence screening behavior, particularly for younger and older women. Cancer 2017;123:401-409. © 2016 American Cancer Society.

The INTRABEAM® Photon Radiotherapy System for the adjuvant treatment of early breast cancer: a systematic review and economic evaluation

BACKGROUND

Initial treatment for early breast cancer is usually either breast-conserving surgery (BCS) or mastectomy. After BCS, whole-breast external beam radiotherapy (WB-EBRT) is the standard of care. A potential alternative to post-operative WB-EBRT is intraoperative radiation therapy delivered by the INTRABEAM(®) Photon Radiotherapy System (Carl Zeiss, Oberkochen, Germany) to the tissue adjacent to the resection cavity at the time of surgery.

OBJECTIVE

To assess the clinical effectiveness and cost-effectiveness of INTRABEAM for the adjuvant treatment of early breast cancer during surgical removal of the tumour.

DATA SOURCES

Electronic bibliographic databases, including MEDLINE, EMBASE and The Cochrane Library, were searched from inception to March 2014 for English-language articles. Bibliographies of articles, systematic reviews, clinical guidelines and the manufacturer's submission were also searched. The advisory group was contacted to identify additional evidence.

METHODS

Systematic reviews of clinical effectiveness, health-related quality of life and cost-effectiveness were conducted. Two reviewers independently screened titles and abstracts for eligibility. Inclusion criteria were applied to full texts of retrieved papers by one reviewer and checked by a second reviewer. Data extraction and quality assessment were undertaken by one reviewer and checked by a second reviewer, and differences in opinion were resolved through discussion at each stage. Clinical effectiveness studies were included if they were carried out in patients with early operable breast cancer. The intervention was the INTRABEAM system, which was compared with WB-EBRT, and study designs were randomised controlled trials (RCTs). Controlled clinical trials could be considered if data from available RCTs were incomplete (e.g. absence of data on outcomes of interest). A cost-utility decision-analytic model was developed to estimate the costs, benefits and cost-effectiveness of INTRABEAM compared with WB-EBRT for early operable breast cancer.

RESULTS

One non-inferiority RCT, TARGeted Intraoperative radioTherapy Alone (TARGIT-A), met the inclusion criteria for the review. The review found that local recurrence was slightly higher following INTRABEAM than WB-EBRT, but the difference did not exceed the 2.5% non-inferiority margin providing INTRABEAM was given at the same time as BCS. Overall survival was similar with both treatments. Statistically significant differences in complications were found for the occurrence of wound seroma requiring more than three aspirations (more frequent in the INTRABEAM group) and for a Radiation Therapy Oncology Group toxicity score of grade 3 or 4 (less frequent in the INTRABEAM group). Cost-effectiveness base-case analysis indicates that INTRABEAM is less expensive but also less effective than WB-EBRT because it is associated with lower total costs but fewer total quality-adjusted life-years gained. However, sensitivity analyses identified four model parameters that can cause a switch in the treatment option that is considered cost-effective.

LIMITATIONS

The base-case result from the model is subject to uncertainty because the disease progression parameters are largely drawn from the single available RCT. The RCT median follow-up of 2 years 5 months may be inadequate, particularly as the number of participants with local recurrence is low. The model is particularly sensitive to this parameter.

CONCLUSIONS AND IMPLICATIONS

A significant investment in INTRABEAM equipment and staff training (clinical and non-clinical) would be required to make this technology available across the NHS. Longer-term follow-up data from the TARGIT-A trial and analysis of registry data are required as results are currently based on a small number of events and economic modelling results are uncertain.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42013006720.

FUNDING

The National Institute for Health Research Health Technology Assessment programme. Note that the economic model associated with this document is protected by intellectual property rights, which are owned by the University of Southampton. Anyone wishing to modify, adapt, translate, reverse engineer, decompile, dismantle or create derivative work based on the economic model must first seek the agreement of the property owners.

Cancer Models and Real-world Data: Better Together

Decision-analytic models are increasingly used to inform health policy decisions. These models synthesize available data on disease burden and intervention effectiveness to project estimates of the long-term consequences of care, which are often absent when clinical or policy decisions must be made. While models have been influential in informing US cancer screening guidelines under ideal conditions, incorporating detailed data on real-world screening practice has been limited given the complexity of screening processes and behaviors throughout diverse health delivery systems in the United States. We describe the synergies that exist between decision-analytic models and health care utilization data that are increasingly accessible through research networks that assemble data from the growing number of electronic medical record systems. In particular, we present opportunities to enrich cancer screening models by grounding analyses in real-world data with the goals of projecting the harms and benefits of current screening practices, evaluating the value of existing and new technologies, and identifying the weakest links in the cancer screening process where efforts for improvement may be most productively focused. We highlight the example of the National Cancer Institute-funded consortium Population-based Research Optimizing Screening through Personalized Regimens (PROSPR), a collaboration to harmonize and analyze screening process and outcomes data on breast, colorectal, and cervical cancers across seven research centers. The pairing of models with such data can create more robust models to not only better inform policy but also inform health care systems about best approaches to improve the provision of cancer screening in the United States.

What Predicts an Advanced-Stage Diagnosis of Breast Cancer? Sorting Out the Influence of Method of Detection, Access to Care, and Biologic Factors

BACKGROUND

Multiple studies have yielded important findings regarding the determinants of an advanced-stage diagnosis of breast cancer. We seek to advance this line of inquiry through a broadened conceptual framework and accompanying statistical modeling strategy that recognize the dual importance of access-to-care and biologic factors on stage.

METHODS

The Centers for Disease Control and Prevention-sponsored Breast and Prostate Cancer Data Quality and Patterns of Care Study yielded a seven-state, cancer registry-derived population-based sample of 9,142 women diagnosed with a first primary in situ or invasive breast cancer in 2004. The likelihood of advanced-stage cancer (American Joint Committee on Cancer IIIB, IIIC, or IV) was investigated through multivariable regression modeling, with base-case analyses using the method of instrumental variables (IV) to detect and correct for possible selection bias. The robustness of base-case findings was examined through extensive sensitivity analyses.

RESULTS

Advanced-stage disease was negatively associated with detection by mammography (P < 0.001) and with age < 50 (P < 0.001), and positively related to black race (P = 0.07), not being privately insured [Medicaid (P = 0.01), Medicare (P = 0.04), uninsured (P = 0.07)], being single (P = 0.06), body mass index > 40 (P = 0.001), a HER2 type tumor (P < 0.001), and tumor grade not well differentiated (P < 0.001). This IV model detected and adjusted for significant selection effects associated with method of detection (P = 0.02). Sensitivity analyses generally supported these base-case results.

CONCLUSIONS

Through our comprehensive modeling strategy and sensitivity analyses, we provide new estimates of the magnitude and robustness of the determinants of advanced-stage breast cancer.

IMPACT

Statistical approaches frequently used to address observational data biases in treatment-outcome studies can be applied similarly in analyses of the determinants of stage at diagnosis. Cancer Epidemiol Biomarkers Prev; 25(4); 613-23. ©2016 AACR.

Using Active Learning for Speeding up Calibration in Simulation Models

BACKGROUND

Most cancer simulation models include unobservable parameters that determine 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 a lengthy calibration procedure, which involves evaluating a 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 developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the 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.

Comparison of the Genomic Landscape Between Primary Breast Cancer in African American Versus White Women and the Association of Racial Differences With Tumor Recurrence

PURPOSE

African American women are more likely to die as a result of breast cancer than white women. The influence of somatic genomic profiles on this racial disparity is unclear. We aimed to compare the racial distribution of tumor genomic characteristics and breast cancer recurrence.

METHODS

We assessed white and African American women with stage I to III breast cancer diagnosed from 1988 to 2013 and primary tumors submitted to The Cancer Genome Atlas from 2010 to 2014. We used Cox proportional hazards models to evaluate the association of race and genetic traits with tumor recurrence.

RESULTS

We investigated exome sequencing and gene expression data in 663 and 711 white and 105 and 159 African American women, respectively. African Americans had more TP53 mutations (42.9% v 27.6%; P = .003) and fewer PIK3CA mutations (20.0% v 33.9%; P = .008). Intratumor genetic heterogeneity was greater in African American than white tumors overall by 5.1 units (95% CI, 2.4 to 7.7) and within triple-negative tumors by 4.1 units (95% CI, 1.4 to 6.8). African Americans had more basal tumors by the 50-gene set predictor using the predication analysis of microarray method (PAM50; 39.0% v 18.6%; P < .001) and fewer PAM50 luminal A tumors (17.0% v 34.7%; P < .001). Among triple-negative subtypes, African Americans had more basal-like 1 and mesenchymal stem-like tumors. African Americans had a higher risk of tumor recurrence than whites (hazard ratio, 2.22; 95% CI, 1.05 to 4.67). Racial differences in TP53 mutation, PAM50 basal subtype, and triple-negative tumor prevalence but not intratumor genetic heterogeneity influenced the magnitude and significance of the racial disparity in tumor recurrence.

CONCLUSION

African Americans had greater intratumor genetic heterogeneity and more basal gene expression tumors, even within triple-negative breast cancer. This pattern suggests more aggressive tumor biology in African Americans than whites, which could contribute to racial disparity in breast cancer outcome.

Racial differences in breast cancer, cardiovascular disease, and all-cause mortality among women with ductal carcinoma in situ of the breast

Ductal carcinoma in situ (DCIS) of the breast represents 15-20% of new breast cancer diagnoses in the US annually. However, long-term competing risks of mortality, as well as racial differences in outcomes among US women with DCIS, are unknown. Case data from the years 1978-2010 were obtained using SEER*Stat software available through the National Cancer Institute from the 2010 SEER registries. Included were all women aged 40 and over with newly diagnosed DCIS. There were 67,514 women in the analysis, including 54,518 white women and 6,113 black women. A total of 12,173 deaths were observed over 607,287 person-years of follow-up. The 20-year cumulative incidence of all-cause death among women with DCIS was 39.6% (CI 38.9-40.3). The corresponding 20-year rates for breast cancer death and CVD death were 3.2% (CI 3.0-3.4) and 13.2% (CI 12.8-13.7), respectively. Black women with DCIS had a higher risk of death compared to white women, with these hazard ratios elevated throughout the entire study period. For example, between 1990 and 2010, black women had a higher risk of all-cause death (HR 3.06, CI 2.39-3.91), breast cancer death (HR 5.78, CI 3.16-10.57), and CVD death (HR 6.43, CI 3.61-11.45) compared to white women diagnosed between 50 and 59 years of age. The risk of all-cause and CVD death was greater than breast cancer death among women diagnosed with DCIS over 20 years. Black women had higher risks of dying from all-causes compared to white women. These differences persisted into the modern treatment era.

Breast cancer screening, area deprivation, and later-stage breast cancer in Appalachia: does geography matter?

OBJECTIVE

To model the relationship of an area-based measure of a breast cancer screening and geographic area deprivation on the incidence of later stage breast cancer (LSBC) across a diverse region of Appalachia.

DATA SOURCE

Central cancer registry data (2006-2008) from three Appalachian states were linked to Medicare claims and census data.

STUDY DESIGN

Exploratory spatial analysis preceded the statistical model based on negative binomial regression to model predictors and effect modification by geographic subregions.

PRINCIPAL FINDINGS

Exploratory spatial analysis revealed geographically varying effects of area deprivation and screening on LSBC. In the negative binomial regression model, predictors of LSBC included receipt of screening, area deprivation, supply of mammography centers, and female population aged>75 years. The most deprived counties had a 3.31 times greater rate of LSBC compared to the least deprived. Effect of screening on LSBC was significantly stronger in northern Appalachia than elsewhere in the study region, found mostly for high-population counties.

CONCLUSIONS

Breast cancer screening and area deprivation are strongly associated with disparity in LBSC in Appalachia. The presence of geographically varying predictors of later stage tumors in Appalachia suggests the importance of place-based health care access and risk.