Theodora M. Ripping

Overdiagnosis by mammographic screening for breast cancer studied in birth cohorts in the Netherlands

A drawback of early detection of breast cancer through mammographic screening is the diagnosis of breast cancers that would never have become clinically detected. This phenomenon, called overdiagnosis, is ideally quantified from the breast cancer incidence of screened and unscreened cohorts of women with follow‐up until death. Such cohorts do not exist, requiring other methods to estimate overdiagnosis. We are the first to quantify overdiagnosis from invasive breast cancer and ductal carcinoma in situ (DCIS) in birth cohorts using an age‐period‐cohort ‐model (APC‐model) including variables for the initial and subsequent screening rounds and a 5‐year period after leaving screening. Data on the female population and breast cancer incidence were obtained from Statistics Netherlands, “Stichting Medische registratie” and the Dutch Cancer Registry for women aged 0–99 years. Data on screening participation was obtained from the five regional screening organizations. Overdiagnosis was calculated from the excess breast cancer incidence in the screened group divided by the breast cancer incidence in presence of screening for women aged 20–99 years (population perspective) and for women in the screened‐age range (individual perspective). Overdiagnosis of invasive breast cancer was 11% from the population perspective and 17% from the invited women perspective in birth cohorts screened from age 49 to 74. For invasive breast cancer and DCIS together, overdiagnosis was 14% from population perspective and 22% from invited women perspective. A major strength of an APC‐model including the different phases of screening is that it allows to estimate overdiagnosis in birth cohorts, thereby preventing overestimation.

Breast cancer diagnosis and death in the Netherlands: a changing burden

BACKGROUND Lifetime risks are often used in communications on cancer to the general public. The most-cited estimate for breast cancer risk (1 in 8 women), however, appears to be outdated. Here we describe the breast cancer burden in the Netherlands over time by means of lifetime and age-conditional risks. The aim is to identify changes in absolute risk of primary breast cancer diagnosis and death. METHODS Data on breast cancer incidence, mortality and size of the female population were retrieved from the Netherlands Cancer Registry and Statistics Netherlands. Lifetime and age-conditional risks were calculated for 1990, 2000 and 2010 using the life-table method (DevCan software). RESULTS The lifetime risk of developing breast cancer (ductal carcinoma in situ and invasive) in 1990, 2000 and 2010 was estimated at 10.8 (1 in 9.3 women), 13.5 (1 in 7.4) and 15.2% (1 in 6.6), respectively. Most women were still diagnosed after the age of 50, with the highest risk between 60 and 70 years in 2010. The lifetime risk of breast cancer death was 3.8% (1 in 27) in 2010, which is lower than in 1990 (4.5%; 1 in 22) and 2000 (4.2%; 1 in 24). CONCLUSION Breast cancer risk has increased to 1 in 6.6 women being diagnosed during their lifetime (invasive cancer only: 1 in 7.4), whereas risk of breast cancer death has decreased from 1 in 22 to 1 in 27 women. To keep cancer management and prevention up-to-date, it remains important to closely monitor the ever-changing breast cancer burden.

Breast cancer screening effect across breast density strata: A case–control study

Breast cancer screening is known to reduce breast cancer mortality. A high breast density may affect this reduction. We assessed the effect of screening on breast cancer mortality in women with dense and fatty breasts separately. Analyses were performed within the Nijmegen (Dutch) screening programme (1975–2008), which invites women (aged 50–74 years) biennially. Performance measures were determined. Furthermore, a case–control study was performed for women having dense and women having fatty breasts. Breast density was assessed visually with a dichotomized Wolfe scale. Breast density data were available for cases. The prevalence of dense breasts among controls was estimated with age‐specific rates from the general population. Sensitivity analyses were performed on these estimates. Screening performance was better in the fatty than in the dense group (sensitivity 75.7% vs 57.8%). The mortality reduction appeared to be smaller for women with dense breasts, with an odds ratio (OR) of 0.87 (95% CI 0.52–1.45) in the dense and 0.59 (95% CI 0.44–0.79) in the fatty group. We can conclude that high density results in lower screening performance and appears to be associated with a smaller mortality reduction. Breast density is thus a likely candidate for risk‐stratified screening. More research is needed on the association between density and screening harms.

Quantifying Overdiagnosis in Cancer Screening: A Systematic Review to Evaluate the Methodology

Background Overdiagnosis is the main harm of cancer screening programs but is difficult to quantify. This review aims to evaluate existing approaches to estimate the magnitude of overdiagnosis in cancer screening in order to gain insight into the strengths and limitations of these approaches and to provide researchers with guidance to obtain reliable estimates of overdiagnosis in cancer screening. Methods A systematic review was done of primary research studies in PubMed that were published before January 1, 2016, and quantified overdiagnosis in breast cancer screening. The studies meeting inclusion criteria were then categorized by their methods to adjust for lead time and to obtain an unscreened reference population. For each approach, we provide an overview of the data required, assumptions made, limitations, and strengths. Results A total of 442 studies were identified in the initial search. Forty studies met the inclusion criteria for the qualitative review. We grouped the approaches to adjust for lead time in two main categories: the lead time approach and the excess incidence approach. The lead time approach was further subdivided into the mean lead time approach, lead time distribution approach, and natural history modeling. The excess incidence approach was subdivided into the cumulative incidence approach and early vs late-stage cancer approach. The approaches used to obtain an unscreened reference population were grouped into the following categories: control group of a randomized controlled trial, nonattenders, control region, extrapolation of a prescreening trend, uninvited groups, adjustment for the effect of screening, and natural history modeling. Conclusions Each approach to adjust for lead time and obtain an unscreened reference population has its own strengths and limitations, which should be taken into consideration when estimating overdiagnosis.

Immediate and delayed effects of mammographic screening on breast cancer mortality and incidence in birth cohorts

Background:Trend studies investigating the impact of mammographic screening usually display age-specific mortality and incidence rates over time, resulting in an underestimate of the benefit of screening, that is, mortality reduction, and an overestimate of its major harmful effect, that is, overdiagnosis. This study proposes a more appropriate way of analysing trends.Methods:Breast cancer mortality (1950–2009) and incidence data (1975–2009) were obtained from Statistics Netherlands, ‘Stg. Medische registratie’ and the National Cancer Registry in the Netherlands for women aged 25–85 years. Data were visualised in age–birth cohort and age–period figures.Results:Birth cohorts invited to participate in the mammographic screening programme showed a deflection in the breast cancer mortality rates within the first 5 years after invitation. Thereafter, the mortality rate increased, although less rapidly than in uninvited birth cohorts. Furthermore, invited birth cohorts showed a sharp increase in invasive breast cancer incidence rate during the first 5 years of invitation, followed by a moderate increase during the following screening years and a decline after passing the upper age limit.Conclusion:When applying a trend study to estimate the impact of mammographic screening, we recommend using a birth cohort approach.

Overdiagnosis in cancer screening: the need for a standardized denominator

It is widely accepted that overdiagnosis is a major harm of screening, but its extent is still topic of controversy. This is partly the result of incomparable overdiagnosis estimates in scientific literature, as a variety of denominators are used to calculate the percentage of overdiagnosis in cancer screening. We propose to use the following denominator to calculate the percentage of overdiagnosis: ‘all cancers detected during the screening period, both interval and screen-detected, in participants of a screening programme’. This denominator is more appropriate than existing denominators because it presents overdiagnosis as a real percentage, is unaffected by attendance percentages, is applicable to all observational study designs, and can be easily recalculated to absolute numbers. This denominator can be widely applied and increases comparability between overdiagnosis estimates, which is needed to correctly present the balance between the benefits and harms of screening.

Extrapolation of pre-screening trends: Impact of assumptions on overdiagnosis estimates by mammographic screening

BACKGROUND Overdiagnosis by mammographic screening is defined as the excess in breast cancer incidence in the presence of screening compared to the incidence in the absence of screening. The latter is often estimated by extrapolating the pre-screening incidence trend. The aim of this theoretical study is to investigate the impact of assumptions in extrapolating the pre-screening incidence trend of invasive breast cancer on the estimated percentage of overdiagnosis. METHODS We extracted data on invasive breast cancer incidence and person-years by calendar year (1975-2009) and 5-year age groups (0-85 years) from Dutch databases. Different combinations of assumptions for extrapolating the pre-screening period were investigated, such as variations in the type of regression model, end of the pre-screening period, screened age range, post-screening age range and adjustment for a trend in women <45. This resulted in 69,120 estimates of the percentage of overdiagnosis, i.e. excess cancer incidence in the presence of screening as a proportion of the number of screen-detected and interval cancers. RESULTS Most overdiagnosis percentages are overestimated because of inadequate adjustment for lead time. The overdiagnosis estimates range between -7.1% and 65.1%, with a median of 33.6%. The choice of pre-screening period has the largest influence on the estimated percentage of overdiagnosis: the median estimate is 17.1% for extrapolations using 1975-1986 as the pre-screening period and 44.7% for extrapolations using 1975-1988 as the pre-screening period. CONCLUSION The results of this theoretical study most likely cover the true overdiagnosis estimate, which is unknown, and may not necessarily represent the median overdiagnosis estimate. This study shows that overdiagnosis estimates heavily depend on the assumptions made in extrapolating the incidence in the pre-screening period, especially on the choice of the pre-screening period. These limitations should be acknowledged when adopting this approach to estimate overdiagnosis.

Towards personalized screening: Cumulative risk of breast cancer screening outcomes in women with and without a first-degree relative with a history of breast cancer.

Several reviews have estimated the balance of benefits and harms of mammographic screening in the general population. The balance may, however, differ between individuals with and without family history. Therefore, our aim is to assess the cumulative risk of screening outcomes; screen‐detected breast cancer, interval cancer, and false‐positive results, in women screenees aged 50–75 and 40–75, with and without a first‐degree relative with a history of breast cancer at the start of screening. Data on screening attendance, recall and breast cancer detection were collected for each woman living in Nijmegen (The Netherlands) since 1975. We used a discrete time survival model to calculate the cumulative probability of each major screening outcome over 19 screening rounds. Women with a family history of breast cancer had a higher risk of all screening outcomes. For women screened from age 50–75, the cumulative risk of screen‐detected breast cancer, interval cancer and false‐positive results were 9.0, 4.4 and 11.1% for women with a family history and 6.3, 2.7 and 7.3% for women without a family history, respectively. The results for women 40–75 followed the same pattern for women screened 50–75 for cancer outcomes, but were almost doubled for false‐positive results. To conclude, women with a first‐degree relative with a history of breast cancer are more likely to experience benefits and harms of screening than women without a family history. To complete the balance and provide risk‐based screening recommendations, the breast cancer mortality reduction and overdiagnosis should be estimated for family history subgroups.

Statistical Methods for Estimating the Cumulative Risk of Screening Mammography Outcomes

Background: This study illustrates alternative statistical methods for estimating cumulative risk of screening mammography outcomes in longitudinal studies. Methods: Data from the US Breast Cancer Surveillance Consortium (BCSC) and the Nijmegen Breast Cancer Screening Program in the Netherlands were used to compare four statistical approaches to estimating cumulative risk. We estimated cumulative risk of false-positive recall and screen-detected cancer after 10 screening rounds using data from 242,835 women ages 40 to 74 years screened at the BCSC facilities in 1993–2012 and from 17,297 women ages 50 to 74 years screened in Nijmegen in 1990–2012. Results: In the BCSC cohort, a censoring bias model estimated bounds of 53.8% to 59.3% for false-positive recall and 2.4% to 7.6% for screen-detected cancer, assuming 10% increased or decreased risk among women screened for one additional round. In the Nijmegen cohort, false-positive recall appeared to be associated with subsequent discontinuation of screening leading to overestimation of risk of a false-positive recall based on adjusted discrete-time survival models. Bounds estimated by the censoring bias model were 11.0% to 19.9% for false-positive recall and 4.2% to 9.7% for screen-detected cancer. Conclusion: Choice of statistical methodology can substantially affect cumulative risk estimates. The censoring bias model is appropriate under a variety of censoring mechanisms and provides bounds for cumulative risk estimates under varying degrees of dependent censoring. Impact: This article illustrates statistical methods for estimating cumulative risks of cancer screening outcomes, which will be increasingly important as screening test recommendations proliferate. Cancer Epidemiol Biomarkers Prev; 25(3); 513–20. ©2015 AACR.

Weighing the Benefits and Harms

Abstract Breast cancer screening aims to reduce mortality, but the screening process introduces harm as well as benefit. Over the past several decades, mammographic breast cancer screening has been the subject of controversy with questions focusing on whether the benefits outweigh the harms. In light of this debate, this chapter evaluates evidence reviews from North America and Europe that have been used to guide decision-making or have served as the basis for recommendations for screening mammography. It provides a detailed explanation of methodological differences between the reviews that partly explain variations in their conclusions and provides recommendations to those designing and applying harm/benefit balance sheets. The chapter concludes with a discussion of some future challenges, such as the introduction of new screening modalities and personalized screening, which are likely to affect the balance of benefits and harms.Breast cancer screening aims to reduce mortality, but the screening process introduces harm as well as benefit. Over the past several decades, mammographic breast cancer screening has been the subject of controversy with questions focusing on whether the benefits outweigh the harms. In light of this debate, this chapter evaluates evidence reviews from North America and Europe that have been used to guide decision-making or have served as the basis for recommendations for screening mammography. It provides a detailed explanation of methodological differences between the reviews that partly explain variations in their conclusions and provides recommendations to those designing and applying harm/benefit balance sheets. The chapter concludes with a discussion of some future challenges, such as the introduction of new screening modalities and personalized screening, which are likely to affect the balance of benefits and harms.