Challenges and Opportunities in the Implementation of Risk-Based Screening for Breast Cancer

Plain Language Summary

There is widespread interest in trying to change the interval at which women undergo breast cancer screening based on their actual risk of breast cancer. This risk-based screening may improve the proportions of breast cancer detected early and possibly reduce the number of women who need to be screened with reduction in the associated harms of overdiagnosis. Risk factors are already used to determine screening: for example, age is used to determine who is eligible for screening and age of onset of screening may be reduced and screening frequency maybe increased in women with a family history. Women with BRCA1 or BRCA2 mutations are screened using MRI in addition to mammography because of the potential for missing cancers and the high risk of interval cancers in mutation positive women. The question arises whether such risk-adapted screening can improve the ability to detect breast cancers whilst reducing the number of women that are unnecessarily recalled for harmless changes on the mammogram. In general, incorporation of additional risk factors such as hormonal factors, the density of breast tissue on the mammogram, and genetic factors to age and family history improves the predictive accuracy of risk estimates. Additional screening modalities such as ultrasound, MRI, and digital breast tomosynthesis may increase detection rates and reduce cancers occurring between screens. However, it is not clear how these modalities and additional risk factors may be applied in breast screening and national programs, and whether this will be beneficial to the women screened. Here we discuss some of the challenges and potential opportunities of attempting to introduce risk-adapted screening.

Introduction

The use of mammography is first identified on PubMed in 1939 and population screening first referred to in 1968. The introduction of population-based screening programs occurred in the late 1980s after the results of a number of important randomized controlled trials showed a reduction in mortality from 2-yearly mammography in women particularly over 50 years of age. Most national screening programs and screening guidelines recommend annual or biennial two-view mammography, which has transitioned between 2005 and 2015 from film-screen to full field digital mammography. The UK opted for a screening interval of 3 years based on recommendations made in the Forrest Report in 1986. The original aim was to offer mammography screening to all women aged 50–64 years of age. Women were invited dependent on their locality between ages 49 and 53 years (screening vans travel around each screening area) and then offered screening on a 3-yearly basis. The upper age limit was extended to 69 years in 2000 and there is currently an age extension trial evaluating the effectiveness of an additional screen aged 47–49 years and another aged 71–73 years compared with the standard screening. Although currently screening ends at 70 (or 73 in age extension) women may self-refer for screening beyond this age. Most other countries with screening programs, including Scandinavia and the Netherlands, offer 2-yearly screening based on a period below the 30-month lead time. Countries with no screening programs, such as the USA, recommend annual screening in their guidelines.

Over the past two decades, deaths from breast cancer in most westernized countries have fallen by 30–40% in women <70 years. Some of this reduction has been attributed to mammographic screening. It is, however, difficult to distinguish the potential benefits of mammography from more widespread use of adjuvant systemic therapies and general improvements in overall care. A recent review of the UK National Health Service Breast Screening Programme (NHSBSP) estimated that it reduced breast cancer mortality by 20%, thus saving 1300 lives annually. The challenge is to improve the effectiveness of mammographic screening further. One approach is to assess the value of screening based on risk, where women at high risk are screened more frequently and earlier than those at low risk.

Improving Screening by Basing its Application on Risk

One hypothesis, which remains to be tested is that screening based on risk will lead to improvements in breast cancer mortality. Screening is more efficient in subgroups with higher disease prevalence. Risk-based screening may increase benefits and decrease harms and costs associated with screening Risk may be used to adapt several aspects of screening strategies. First, women at high risk may benefit from starting screening at an earlier age and/or stopping screening at a later age. Second, women at high risk of a true interval cancer (ie, a fast growing tumor that develops and becomes symptomatic in between screens) may benefit from more frequent screening, and women at low risk or with slow growing tumors (eg, older women and women with fatty breasts) may avoid the harms of screening by being screened less frequently. Third, women at high risk of a missed cancer on mammography (eg, women at high risk and dense breast tissue) may benefit from supplemental screening with other modalities, such as tomosynthesis, ultrasound, or MRI. Last, women at high risk of a screening harm from mammography such as a false-positive result or radiation-induced breast cancer, may benefit from an alternative screening modality such as tomosynthesis for reducing false-positive recalls and ultrasound or MRI for avoiding ionizing radiation. These are separate but highly interrelated issues. For example, breast density increases risk of breast cancer but may also decrease the sensitivity for mammography, indicating a potential need for alternative or supplemental screening modalities. Family history in both first and to a lesser extent second degree relatives increases risk of breast cancer but is also associated with a greater incidence of interval cancers compared with women without a family history. The UK National Health Service Breast Screening Programme (NHSBSP) three-yearly screening may result in more interval cancers than programs that screen more frequently. There is evidence that women at very high risk of breast cancer are more likely to develop interval cancers and reduction of the screening interval or supplemental screening in this poor prognosis group may be indicated, particularly as the cancers that do develop in women at very high risk tend to have faster doubling times than those in the general population. The Swedish two-county study showed that women with a positive family history of breast cancer were more likely to develop breast cancer between their biennial screening examinations than similar women with negative family histories.

Risk Factor-Adapted Mammographic Screening

Screening is already based on risk since age is a major breast cancer risk factor and all national screening programs and screening guidelines recommend starting screening at an age when breast cancer becomes more common. Some countries recommend younger women be screened more frequently given evidence they have more aggressive tumors. However a very recent paper suggests that menopausal status might be a better factor than age when considering screening interval. Premenopausal women were more likely to be diagnosed with later stage disease with poorer prognostic characteristics when screened biennially versus annually. However, there was no difference for postmenopausal women. And no differences within age group. Biologically, this might make sense if hormones affects aggressiveness.

In some countries, the age of commencing screening is lower and screening more frequent when another major risk factor is taken into account, namely a family history of breast cancer, since the disease arises at a younger age and often more rapidly in this group. These two risk factors are important since they are used as criteria to improve the utility and cost of screening. However, women may have other risk factors which may add to family risk or be present in the absence of a family history and the question is whether these other factors improve risk estimation and, in turn, whether their application will result in further reductions of breast cancer mortality.

Women with a family history may be referred to genetics services by their family doctor. However, factors other than age and family history are either not well known by women or their family doctor to affect risk, such as age of first pregnancy, or are occult, such as mammographic density and single nucleotide polymorphisms (SNPs), and require further investigation for their estimation. Thus further questions arise concerning the number of risk factors we need to know in order to determine risk as precisely as possible, how this information may be collected, and how such information may be given to women and applied in national screening programs and screening guidelines.

How Many Risk Factors?

If risk factor directed screening were tested in randomized trials versus standard screening, decisions would need to be made concerning the optimal methods for estimation of breast cancer risk. There may have to be a balance between what is desirable versus what is possible. A relatively simple approach is to ask women at screening for information concerning age, family history, and other factors such as age of first pregnancy. More difficult approaches include determination of mammographic density and measuring breast cancer risk SNPs and possibly adding this information to standard risk factors.

Combining Standard Risk Factors: Risk Models

Breast cancer risk is usually assessed using models which include family history of breast cancer and possibly other cancers (eg, ovarian), reproductive and hormonal history (age at first full term pregnancy, menarche, and menopause, and postmenopausal hormone therapy use), height and weight, and history of prior benign breast biopsies and their histology. Models generally perform well for predicting risk in a population (eg, 1 in 4 lifetime risk) but not for individual risk (“am I the one to develop cancer or one of the three who will not”). For screening on a population basis this risk stratification is probably already sufficient. The Gail, BCSC, and Tyrer-Cuzick models may be used to determine general risk whereas others, such as Claus, BRCAPRO, the Manchester Score, and BOADICEA, are currently used to predict the presence of mutations in high risk genes, such as BRCA1 and BRCA2 .

In the US, the Gail model is widely used. It is based on age, number of first degree relatives with breast cancer, number of surgical breast biopsies, and reproductive factors as above. In a comparison of models using UK data, it was demonstrated that for women with a family history of breast cancer, the Tyrer-Cuzick model performed better than the Gail model; similar results were reported in studies in the US. The Tyrer-Cuzick model has a more extensive familial component, and includes HRT use, height and weight, and the histology of surgical biopsies. The BCSC risk model also has better discriminatory accuracy that the Gail model. It includes breast density, age, race/ethnicity, first degree family history, and history of breast biopsy and was recently updated to include type of benign breast disease. It has better discriminatory accuracy than the Gail model. Given it only includes five risk factors, it is easy to use in clinical practice.

Adding Mammographic Density to Standard Measures

Mammographic breast density is one of the strongest risk factors for breast cancer increasing risk by 4–6 fold between the bottom and top 20% of mammographic density in the population suggesting that density should be incorporated into risk models. Density also has a substantial heritable component. Some data indicate that stratification of screening frequency by risk may be more effective and likely to be cost effective if density is included (see also chapter: Screening for Breast Cancer in Women with Dense Breasts ).

There is evidence that incorporating density into the standard models can increase the model’s predictive values when added to the Gail model irrespective of the method of density estimation (BIRADS, percent density, and volumetric assessment ) and to the BCSC model. In the PROCAS (Predicting the Risk Of Cancer At Screening) study, percentage density was estimated by two radiologists for each of >50,000 women in the Manchester NHSBSP using a visual analog scale (VAS) and adjusted for age and body mass index. Fig. 7.1 shows a distribution of the odds ratio for the Tyrer-Cuzick model alone, adjusted visual density, and the combination of the two. The value of such analyses may be assessed by the increase in the proportion of women appearing in the high and low risk groups. Density modestly improved on the T-C model. However, when the two methods were combined, the proportion of women in the moderate risk group (5–8% 10-year risk) increased from 8.6% using the TC model to 12.3% when the T-C results were combined with adjusted visual density. In the high risk group (>8% 10-year risk) the proportion increased from 1.2% to 2.7% ( Fig. 7.1 ).

In the PROCAS study density was also estimated using volumetric assessment of density (see below). The value of this method is that it may be estimated automatically making it more transferable to national screening programs.

The overall spread of risks can be seen in Fig. 7.2 from a combination of Gail and Tyrer-Cuzick incorporating mammographic density.