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What if the age at which a woman started regular mammographic screening depended on her risk, not age?

In this issue of Cancer, Durham and colleagues address the following question: what if the age at which a woman started regular mammographic screening depended on her risk and not only on her age? Given that risk depends on family history, the authors considered the age of starting risk‐based screening as a mathematical function of the youngest age at diagnosis of breast cancer in a first‐ degree relative. Some national agencies, such as the National Institute for Health and Care Excellence in the United Kingdom, recommend such an approach; we call this approach 1. In the United States, some agencies apparently recommend that regular screening commence for women at a fixed interval of 10 years earlier than the age at which their relative was diagnosed (see references 12 and 13 in Durham et al.). We call this approach 2. Durham and colleagues used a large prospective study to estimate the age at which a woman is at the population average risk for a 50‐year‐old woman as a function of the (youngest) age at diagnosis of her relative. They chose this age threshold because in the United States and many other countries, the recommended age for commencing regular mammographic screening for breast cancer is 50 years. Findings are presented in their Figure 1, which plots the estimated 5‐year incidence of breast cancer as a continuous function of a woman’s age for different groups of women defined by their breast cancer family history. A horizontal line is drawn for a cumulative incidence of 15 per 1000 women, the average 5‐year risk for a 50‐year‐old woman in this cohort. Within an insert of Durham et al’s Figure 1, the ages at which these lines cross the threshold for a 5‐year risk of 1.5% and, therefore, the ages at which regular screening would be recommended under approach 1 are listed. We present these data in Table 1, where we also have calculated the difference between the age at diagnosis of the relative and the age at screening under approach 2. Figure 1 plots these two variables and includes a horizontal line for the difference being 10 years (approach 2). The relationship is not a constant, and it is not linear. Figure 1 clearly shows that the data presented by Durham and colleagues do not support approach 2, whereby a fixed age difference applies regardless of the age at diagnosis of the relative. In particular, the difference of 10 years applies only if the relative was diagnosed at the age of 40 years. With the same level of evidence, Durham and colleagues have also shown that if the relative is diagnosed at a younger age, the difference is smaller, whereas if the relative is diagnosed when she is older than 50 years, the difference is increasingly larger. The difference increases almost exponentially as the age at diagnosis of the relative increases. Durham and colleagues focused on women with a relative diagnosed at or before the age of 45 years, and they suggest that these women might want to consider initiating screening 5–8 years earlier than their relative’s age at diagnosis. On the basis of the data that they have presented, we think their conclusion that the screening age be younger by a particular amount for women with a particular family history can be widened to cover all women who have a first‐degree relative diagnosed before the age of 70 years, except that the age difference depends on the age at diagnosis of the relative (see Figure 1). This nice example of how evidence can be assembled to provide advice on how screening can be tailored to risk, in this case on the basis of the first‐degree family history alone, could be expanded to other risk factors and potentially provide stronger risk prediction. In practice, there is a very important tradeoff between the strength and accuracy of risk prediction and the cost and practicalities of data collection. The data might be uninformative or even biased if they are collected without attention to quality control and validation. For example, for women with a family history, their risk can be better predicted by using information from multiple generations (including ages at diagnosis). Using the unique Prospective Family Study Cohort study, Terry and colleagues showed that the web‐