Insights Into Breast Cancer Screening: A Computer Simulation of Two Contemporary Screening Strategies

Carter, Kimbroe J.; Castro, Frank; Morcos, Roy N.

doi:10.2214/ajr.17.18484

Cited by 6 publications

(5 citation statements)

References 32 publications

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“…Carter et al. 27 used a Monte Carlo computer simulation to study the balance of benefits and harms of mammographic breast cancer screening for average-risk women, comparing two different screening strategies (mixed annual-biennial screening guidelines vs. biennial guidelines). A discrete-event simulation model was developed by Comas et al.…”

Section: Discussionmentioning

confidence: 99%

Budget impact analysis of breast cancer screening in Italy: The role of new technologies

Foglia

Marinelli

Garagiola

et al. 2020

Health Serv Manage Res

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Although mammography screening significantly reduces breast cancer mortality, women could present different morphological characteristics that do not allow the correct vision of their breasts and the detection of cancer, resulting in a delay of diagnosis and an increase in the risk of mortality. The present study aims at analyzing potential areas of improvement of the current screening programs and then hypothesizing alternative technologies to use within the diagnostic phase, from an economic point of view. A Budget Impact Analysis approach was implemented, considering the Italian National Healthcare Service perspective, and representing the healthcare expenditure evolution, over three years. In the Budget Impact Analysis model, two distinct phases of the screening programs were considered: (1) the screening/diagnosis phase and (2) the phase related to cancer care and treatments of patients. The results provide clinicians and policy makers with a rational method to forecast economic resources in the screening programs in a general context of limited resources. In particular, results of the Budget Impact Analysis showed that, while the introduction of the ABUS InveniaTM technology into the screening programs would lead to an increase in the screening phase expenditure, it would generate an economic advantage related to the patients treatment and care.

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Section: Discussionmentioning

confidence: 99%

Budget impact analysis of breast cancer screening in Italy: The role of new technologies

Foglia

Marinelli

Garagiola

et al. 2020

Health Serv Manage Res

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show abstract

“…The study uses a published Monte Carlo Markov natural history model for breast cancer, calibrated using statistics from the Surveillance, Epidemiology, and End Results Program (SEER), the American Cancer Society and the National Vital Statistics System [14,[19][20][21]. In the model, a woman begins at birth with no cancer [14]. Breast cancer arises with a first malignant cell and follows Gompertzian growth with disease progression dependent on tumor volume [22][23][24].…”

Section: Plos Onementioning

confidence: 99%

“…It is assumed that during the study, women are 100 percent compliant with their assigned screening strategy. Sensitivity of mammography increases with tumor diameter, starting at 0.2 cm, and is assumed higher for post-menopausal women [14]. Stopping ages for the ACS guideline rule, 10-year or less life-expectancy, were calculated using life table data [14].…”

Section: Plos Onementioning

confidence: 99%

“…Sensitivity of mammography increases with tumor diameter, starting at 0.2 cm, and is assumed higher for post-menopausal women [14]. Stopping ages for the ACS guideline rule, 10-year or less life-expectancy, were calculated using life table data [14]. Tumor detection results from either mammography screening or from clinical signs and symptoms.…”

Section: Plos Onementioning

confidence: 99%

“…However, a computer model of breast cancer's natural history can evaluate these factors and permits insights not possible in clinical practice. A published model of breast cancer screening [14] is adapted to measure screen-PLL.…”

Section: Introductionmentioning

confidence: 99%

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A method for evaluating breast cancer screening strategies using screen-preventable loss of life

2020

Self Cite

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The objective of this study is to describe how screen-preventable loss of life (screen-PLL) can be used to analyze the distribution of life savings with mammographic screening. The determination of screen-PLL with mammography is possible using a natural history model of breast cancer that simulates clinical and pathologic events of this disease. This investigation uses a Monte Carlo Markov model with data from the Surveillance, Epidemiology, and End Results Program; American Cancer Society; and National Vital Statistics System. Populations of one million women per screening strategy are simulated over a lifetime with mammographic screening based on current guidelines of the American Cancer Society (ACS), United States Preventive Services Task Force (USPSTF), triennial screening from age 50–70, and no screening. Screen-PLL curves are generated and show guideline performance over a lifetime. The screen-PLL curve with no screening is determined by tumor discovery through clinical awareness and has the highest values of screen-PLL. The ACS and USPSTF strategies demonstrate screen-PLL curves favoring the elderly. The curve for triennial screening is more uniform than the ACS or USPSTF curves but could be improved by adding screen(s) at either end of the 50–70 age range. This study introduces the use of screen-PLL as a tool to improve the understanding of screening guidelines and allowing a more balanced allocation of life savings across an aging population. The method presented shows how screen-PLL can be used to analyze and potentially improve breast cancer screening guidelines.

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Overdiagnosis Should Not Be a Factor in Formulating Age-of-Onset and Screening-Interval Guidelines

Joe

2018

American Journal of Roentgenology

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Insights Into Breast Cancer Screening: A Computer Simulation of Two Contemporary Screening Strategies

Cited by 6 publications

References 32 publications

Budget impact analysis of breast cancer screening in Italy: The role of new technologies

Budget impact analysis of breast cancer screening in Italy: The role of new technologies

A method for evaluating breast cancer screening strategies using screen-preventable loss of life

Overdiagnosis Should Not Be a Factor in Formulating Age-of-Onset and Screening-Interval Guidelines

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