The balance between the benefits and harms of imaging-based cancer screening continues to be an area of controversy and widespread media attention. Of the potential harms, overdiagnosis from screening is likely the most elusive in estimating and quantifying. This article describes the major methodological issues with recently reported estimates of overdiagnosis that are based on excess cancer incidence, and suggests that modeling focused on tumor lead-time can serve as a complementary method for excess incidence-based overdiagnosis estimates. Radiologists should be conversant on the topic of overdiagnosis and understand the limitations of different methods used to estimate its magnitude.
Introduction
With recent updates to both the American Cancer Society and the U.S. Preventive Services Task Force mammography recommendations, the balance between the benefits and harms of routine cancer screening is again in the media spotlight . Whereas stakeholders can easily grasp notions of benefits such as decreased mortality and morbidity and harms such as false-positive tests and unnecessary biopsies, one of the more abstract and confusing factor for patients, physicians, radiologists, and policymakers is overdiagnosis. Simply mentioning this potential harm without a sense for its magnitude leaves patients and physicians without actionable information to use in shared decision-making, something recommended by both the American Cancer Society and the U.S. Preventive Services Task Force.
Overdiagnosis can be defined as a screen-detected cancer that would not have become clinically significant during the patient’s lifetime. Although the definition is simple, its measurement is quite complex. Because all screen-detected cancers are treated under current standard of care, whether a case has been overdiagnosed or not cannot be directly observed. Instead, the magnitude of overdiagnosis can only be estimated with different techniques requiring varying assumptions. Not surprisingly, estimates for breast cancer overdiagnosis vary over a wide range in the medical literature, and are as low as 5% and as high as 42% . Some of this variability may be due to the use of different denominators or references (eg, only cases detected by screening or all cancer cases) . However, much of the reason for variability lies in the methodologies used for estimation.
In this article, we examine two major methodologies used to estimate cancer overdiagnosis in the context of breast cancer screening. We will describe why a commonly used approach, based on excess incidence (EI) under screening, is prone to overestimation. We will also describe how an alternative approach based on estimation of the lead-time (LT), although imperfect, can provide useful complementary information. In reviewing the validity of these approaches, we hope to better elucidate the assumptions that generate discrepant overdiagnosis estimates. We conclude that refinements are needed in current approaches to estimation if we are to provide information that can properly inform shared decision-making for cancer screening.
The Counterfactual Incidence Problem
There has been extensive media coverage regarding breast cancer overdiagnosis based on the EI approach . At first glance, the use of EI for estimating overdiagnosis is certainly appealing given its seeming directness and simplicity. Using the EI approach, breast cancer incidence trends with and without screening are compared to provide estimates of cancers that would not have presented clinically in the absence of screening. Unfortunately, this direct method of estimating overdiagnosis has multiple limitations that call into question its validity.
The most obvious limitation with the EI approach is that once screening is started, it is not possible to observe the true baseline incidence in the absence of screening. In other words, the counterfactual incidence without screening is never directly measurable. Instead, because all cancers are treated after detection, the cancer incidence without screening has to be imputed or extrapolated in some fashion. Studies using the EI approach have attempted to compensate for the lack of data on the true counterfactual incidence using ad hoc corrections or extrapolations.
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The Ecological Fallacy Problem
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The Insufficient Follow-up Problem
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The Trial Design Problem
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An Alternative LT Approach
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The Progressive Disease Assumption
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Modeling as a Complement to the EI Approach
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Conclusion
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