Personalized Screening for Breast Cancer: A Wolf in Sheep s Clothing?

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1 Women s Imaging Commentary Feig Personalized Screening for Breast Cancer Women s Imaging Commentary Stephen A. Feig 1 Feig SA Keywords: breast cancer, risk-based screening DOI: /AJR Received July 24, 2015; accepted after revision August 13, Department of Radiological Sciences, University of California Irvine Medical Center, 101 City Dr S, Orange, CA Address correspondence to S. A. Feig (sfeig@uci.edu). AJR 2015; 205: X/15/ American Roentgen Ray Society Personalized Screening for Breast Cancer: A Wolf in Sheep s Clothing? R isk-based screening has different meanings to different people. For radiologists, risk-based screening means additional screening beyond the basic American College of Radiology (ACR), Society of Breast Imaging (SBI), and American Cancer Society (ACS) recommendations for annual mammography beginning at 40 years old and continuing as long as a woman is in generally good health and has a remaining life expectancy of at least 5 7 years and no comorbid conditions [1 3]. ACS recommends supplementary annual screening with breast MRI, in addition to mammography, for high-risk women, such as those with a lifetime risk estimate of 20 25% or greater, BRCA1 or BRCA2 mutation (or both), or a first-degree relative of a known BRCA1 or BRCA2 mutation carrier. ACS found insufficient evidence to recommend either for or against annual MRI for women having a 15 20% lifetime risk, such as those with a personal history of breast or ovarian cancer or biopsy-proven atypical ductal hyperplasia (ADH) or atypical lobular hyperplasia (ALH) [4]. Beyond guidelines for average-risk women, ACR and SBI have stipulated ages earlier than 40 years to begin annual mammography for some high-risk women, such as those with a BRCA1 or BRCA2 mutation (or both) or a first-degree relative positive for either mutation, those having a mother or sister with premenopausal breast cancer, or those with a personal history of biopsy-proven ductal carcinoma in situ (DCIS) or ADH [1]. Combined screening with both mammography and ultrasound has been shown to increase cancer detection rates by 30% over those with mammography alone in highrisk women [5, 6]. Some have recommended supplementary screening with ultrasound for women with heterogeneously or extremely dense breasts if they are in the 11 19% lifetime risk group [7]. ACR and SBI do not currently advocate ultrasound screening because of the low biopsy positive predictive value of 8 9%, its operator dependence, the lengthy examination time for handheld ultrasound, and the limited availability of breast ultrasound technologists [1]. However, ACR and SBI guidelines state that adjunctive screening ultrasound may be considered for women having dense breasts [1]. Automated ultrasound systems reduce both examination time and operator dependence and seem to yield detection rates similar to handheld ultrasound; however, recall rates and falsepositive biopsy rates remain high [8]. Do Proposals for Personalized (Restricted) Screening Make Sense? In contrast to the ACR and SBI approaches to supplementary screening for women at high risk or having dense breasts where mammography is less sensitive, others have proposed less frequent screening or no screening for women who have average density breasts, are at average risk status, are years old, or are older than 74 years old. The objectives of this definition of risk-based screening, which advocates refer to as personalized screening, is to reduce costs, recall rates, and false-positive biopsies. For example, Schousboe et al. [9] have advocated not screening women years old in the absence of a family history of breast cancer or a history of previous breast biopsy and not screening women years old more frequently than every 3 4 years except for density categories 3 and 4. Van Ravesteyn et al. [10] have proposed that women years old should be screened every other year if their risk is twice that of women years old and every year only if their risk is four times that of women years old. Because younger women have lower breast cancer incidence rates, these protocols attempt to balance the AJR:205, December

2 Feig cancer detection rates of these two age groups by applying restrictive criteria to most younger women. To these investigators, personalized screening implies a reduction in screening frequency and a constriction in the span of ages screened from those specified by ACS guidelines and ACR and SBI guidelines. The recommendations of Schousboe et al. [9], van Ravestyn et al. [10], and others [11 15] are similar to those of the 2009 U.S. Preventive Service Task Force [16 18]. Because these proposals for personalized screening have not been evaluated through prospective trials, their effectiveness can be estimated only indirectly through previously published retrospective studies. Benefits for women who adhere to annual screening recommended by ACS, ACR, and SBI would be substantially higher than those who follow the more restrictive U.S. Preventive Service Task Force guidelines according to Hendrick and Helvie [19]. Using Cancer Intervention and Surveillance Modeling Network (CISNET) modeling, Hendrick and Helvie calculated that annual screening of all U.S. women years would save 71% more lives than biennial screening of women years old as recommended by the U.S. Preventive Service Task Force. For all U.S. women, annual screening beginning at 40 years old, assuming 100% compliance, would save 99,829 more lives than biennial screening from 50 to 74 years old as recommended by U.S. Preventive Service Task Force [19]. For women years old, annual screening would result in a 39.6% mortality reduction as compared with a 23.6% reduction if U.S. Preventive Service Task Force guidelines were followed [19]. The concept of restricted screening for women at supposedly lower risk may appeal to clinicians because they know that personalized treatment tailored to the molecular characteristics of an individual tumor will be more effective. Tumor histology and molecular markers are being increasingly used to successfully select chemotherapeutic and hormonal agents and to alter such treatments on the basis of changes in markers, as well as dynamic and morphologic changes on MRI studies. However, there are many reasons why this personalized approach may not be applicable to triage asymptomatic women for reduced screening or no screening. Evaluation of protocols for such personalized screening will be far less reliable than evaluation of responses to personalized treatment protocols. After a tumor is diagnosed, it can be completely analyzed in the pathology laboratory. However, there is no way to predict molecular characteristics of undetected tumors on the basis of a woman s risk factors. For example, there is no known relationship of breast cancer growth rates to risk status among women in the same age group, and there is no evidence that breast cancer growth rates vary according to family history, prior breast biopsy, or breast density [20, 21]. Incorporation of breast density into risk models has only a modest ability to determine who will develop breast cancer and who will not [11]. The main reason to screen low- and average-risk women less frequently would seem to be financial rather than medical [12]. It is ironic that many personalized screening protocols advocate less frequent screenings of women 50 years old and younger, because it has been well established that breast cancers grow faster among women years old [22 26]. The 30% decreased breast cancer mortality in the United States since 1990 is likely due to increased utilization of screening; improvements in mammographic technique, such as digital mammography; and improvements in chemotherapy, radiation therapy, and surgery. These treatments are most effective when breast cancers are detected earlier; conversely, they are less effective when breast cancers are not found until they have obtained larger sizes or reached later stages because women are not being screened often enough or at all. As will be shown in this article, given that the vast majority of women with breast cancer are not at elevated risk, limiting screening to only high-risk women would miss 75% of breast cancers. Also, given that breast density is not a major risk factor, exclusion of women with nondense breasts from annual screening would result in lost opportunities for early detection. For most women, personalized screening is a euphemism for an unwarranted restriction of preventive health care. It is not currently feasible to detect most breast cancers while screening only a small fraction of women. Single-nucleotide polymorphisms (SNPs) are genetic variants in the loci of genes. Use of multiple genetic markers such as SNPs, along with breast density and family history, does not now have sufficient discriminatory power to avoid missing substantial numbers of cancers. Increased multimodality screening for high-risk women will be productive, but only a minority of breast cancers occur in that smaller population. Breast Cancer Risk Absolute risk refers to the likelihood that a woman will develop breast cancer over a given period of time. For example, the average woman has a 14.4% lifetime risk (approximately 1/7) of developing breast cancer from birth to an arbitrarily defined endpoint of 85 years. Absolute risk can also be estimated for those with any given risk factor or combination of risk factors or for any period of time such as the next 5, 10, or 20 years. As another example, the absolute risk of breast cancer for women having the BRCA1 mutation is 18% by 39 years old, 59% by 49 years old, and 65% by 70 years old [27]. Relative risk is the risk of developing breast cancer for women with a risk factor compared with the risk of developing breast cancer for women without any known risk factor. Relative risks for the same risk factor may vary according to patient age and with respect to development of DCIS or invasive cancer. As examples, women having a personal history of several types of biopsy-proven diagnoses are at increased relative risk for breast cancer during their lifetime. These include ALH, with three times the risk; ADH, with four to five times the risk; and lobular carcinoma in situ (LCIS), with 8 12 times the risk, compared to the risk among women do not have these conditions [28 30]. Family history of breast cancer also confers elevated risk. The relative risk associated with a maternal diagnosis earlier than 50 years old is 2.4 times and for having a sister diagnosed earlier than 50 years old is 3.2 times the risk among women without such family history. The relative risks for having these same relatives diagnosed after 50 years old would be somewhat lower [31]. The relative risk is 2.9 times for a woman with two or more family members with breast cancer and 3.9 times for those having three or more family members with breast cancer, compared to the risk among women without such family history [27, 31]. The relative risk for a second subsequent primary breast cancer in a woman with a personal history of breast cancer is three to four times, compared to the risk among women without personal history of breast cancer [32, 33]. Despite these welldocumented associations of family history and breast cancer risk, most women with breast cancer do not have any prior family history of breast cancer. Other factors influence risk to a much smaller degree than those already discussed. The relative risk for women who experienced 1366 AJR:205, December 2015

3 Personalized Screening for Breast Cancer menarche earlier than 12 years old is times that for women who experienced menarche at 14 years old or older [34, 35]. The relative risk of breast cancer for women who experienced menopause at 55 years old or older is approximately 2.0 times that for women who experienced menopause at 45 years old or younger [34]. Current users of oral contraceptives have a relative risk of 1.3 times that of women who have never used oral contraceptives [31]. Women who have used postmenopausal hormones for 5 years or longer have a relative risk of 1.4 times that for women who have never taken postmenopausal hormones [31]. Selective Screening: Retrospective Studies Many retrospective studies have been performed over the past 40 years to determine whether selective screening of women at higher risk might identify the majority of new breast cancers while reducing the number of women being screened. Using data from the Health Insurance Plan (HIP) project, in which women in New York City were screened with mammography and clinical examination during the 1960s, Shapiro et al. [36, 37] found that women having three or more major and minor risk factors accounted for only 33% of breast cancer cases and women with fewer than three risk factors accounted for 67% of breast cancer cases in the first 5 years of entry into the study. Using data from a program in Philadelphia where women were screened with mammography and clinical examination in the 1970s, Solin et al. [38] found that only 15% of women with screendetected breast cancer had a mother or sister with breast cancer and that only 31.7% of women with screen-detected breast cancer had any family history of breast cancer. In the Edinburgh, Scotland, trial of screening mammography conducted in the 1980s, 70% of cancers detected on the first screening round would have been missed if women had been selected for screening on the basis of a mother or sister with breast cancer, a history of a prior breast biopsy, or menopausal status [39, 40]. In the Utrecht, Netherlands, Diagnostisch Onderzoek Mammacarcinoom (DOM) project, De Waard et al. [41] found that only 37% of women with screen-detected breast cancer had a sister or mother with a history of breast cancer, nulliparity, later age at first birth, obesity, or a higher-risk Wolfe parenchymal pattern. In Florence, Italy, Paci et al. [42] found that selection of 87% of the population for screening would be required in order to detect 95% of cancers. Among women years old whose breast cancers were detected at a community breast practice in Rochester, NY, Destounis et al. [43] found that 61% of patients with screen-detected breast cancer had no family history of breast cancer. Among breast cancers detected at screening of women years old at Cornell Medical Center in New York City, only 8% had a first-degree relative with breast cancer [44]. The most recent retrospective study by Keedy et al. [45], from the University of California, San Francisco, which appears in this issue of AJR, is consistent with these earlier studies. Among women years old with breast cancer detected by screening mammography, 88% had no strong family history of breast cancer (one or more first-degree relative) [45]. Breast Density as a Risk Factor The role of breast density as a risk factor for subsequent breast cancer was first proposed by Wolfe [46] in One limitation of his density classification was moderate intraobserver variability. Percent cutoff for sheetlike density, his highest-risk dense parenchyma pattern, was never defined. Other experts such as Egan and Mosteller [47] claimed that the reported excess risks were mainly owing to cancers that were masked by dense tissue on the initial study and subsequently emerged on later examinations. After Wolfe s [46] initial studies, other investigators have used a simpler, more reproducible system for breast density classification that is based on estimates for percentage of breast volume containing dense tissue. These studies confirmed a statistically significant increase in relative risk for progressively increasing categories of breast density [48 55]. However, this excess risk was smaller than that predicted by Wolfe. Boyd et al. [49] studied the relative risk for breasts having more than 75% density and found that the relative risk was 17.8 times at less than 12 months after a screening examination with negative findings but markedly declined to 3.5 times on subsequent screening, compared with the risk for breasts having less than 10% density. That extremely important finding indicates that the effect of density on masking is far greater than the effect on risk. On the basis of this observation, they advised the development of digital mammography and supplementary screening techniques such as ultrasound and MRI for women having dense breasts [49]. The 4th edition of ACR BI-RADS provided a breast density classification system in which a radiologist should estimate the percentage of volumetric density as almost entirely fatty (0 24%), scattered areas of fibroglandular density (25 50%), heterogeneously dense (51 75%), and extremely dense (> 75%) [56]. Data from the Breast Cancer Surveillance Consortium indicate that radiologists across the United States have distributed 10%, 40%, 40%, and 10% of all interpreted mammograms into each of these respective categories [57]. The most frequently described relative risks for ACR volumetric density categories 1, 2, 3, and 4 are 0.5, 1.0, 1.5, and 2.0, respectively [50 55]. Thus, the relative risk for an average breast would be ( ) / 2 = The relative risk for a woman having scattered densities versus the average breast would be 1.0 / 1.25 = 0.8. The relative risk for a heterogeneously dense breast versus an average breast would be only 1.5 / 1.25 = 1.2. The relative risk for extremely dense versus average would be 2.0 / 1.25 = 1.6. The relative risk for an extremely dense breast versus a fatty breast would be 2.0 / 0.5 = 4.0. Relative risks of 1.2 and 1.6 in the population of women with dense breasts are not high enough to exclude or restrict screening of the remaining 50% of the population who have nondense breasts (fatty and scattered densities), especially because there is no evidence that breast cancer growth rates in breasts that contain less than 50% dense tissue are low enough to allow longer screening intervals. Moreover, radiologist assessment of density patterns is subjective. There is overlap between the appearance of adjacent patterns, such as scattered densities versus heterogeneous densities. Thus, there is moderate intra- and interobserver variability [58, 59]. In summary, there is insufficient evidence to screen women with nondense breasts less frequently. The relative number of breast cancers in each of the four density categories can be estimated by multiplying the percentage of women in each category by the respective relative risk. For example, = 0.05; = 0.4; = 0.6; and = 0.2. Dividing each by their summation of 1.25 indicates the following: 4.0% of breast cancers occur in fatty breasts; 32.0% of breast cancers occur in breasts with scattered densities; 48.0% of breast cancers occur in heterogeneously dense breasts; and 16.0% of breast cancers occur in extremely dense breasts. Thus, the proposal by Schous- AJR:205, December

4 Feig boe et al. [9] that women years old with nondense breasts should not be screened more frequently than every 3 4 years could hinder the detection of 32.0% of all cancers in that age group. Risk Assessment Models Various risk prediction models have been developed to inform patients about their individual risk. These models have been used in helping patients and their physicians make decisions regarding chemoprevention with tamoxifen and raloxifene, prophylactic mastectomy, supplementary screening with breast MRI, lifestyle modification such as exercise and weight loss, and dietary changes to reduce their risk, as well as the need for genetic testing for BRCA1 and BRCA2 mutations. Risk models are based on multiple factors including age and family history. However, risk models have not yet been able to identify groups of women who can safely avoid compliance with ACS screening mammography guidelines. The Gail model is the most commonly used risk model and was first described in 1989 [60]. The Gail model incorporates six breast cancer risk factors: patient age, age at menarche, age at first live birth, personal history of breast biopsy regardless of results, biopsyproven ADH, and breast cancer in a first-degree relative and race or ethnicity. The Gail model is the only risk model that has been validated with three large population databases [61 63]. The modified Gail model (also known as the National Cancer Institute Gail model) differs from the original Gail model in that it predicts risk for invasive cancer only, rather than for both invasive and in situ breast cancers [62]. The Breast Cancer Surveillance Consortium breast cancer risk model, which includes a risk calculator, represents the most recent version of the Gail model, incorporating mammographic density, and is available to patients and providers on the World Wide Web [64]. However, addition of density information into the Gail model may allow only modestly increased predictive accuracy [11]. Moreover, mammography reports from 2014 and later may no longer contain the same volumetric density estimates needed for the Gail model. The current (5th) edition of BI-RADS, published in 2013, replaces the former quartilebased volumetric breast density categories 1, 2, 3, and 4 with nonvolumetric categories a, b, c, and d to indicate the radiologist s subjective estimate of the relative possibility that a lesion could be obscured by dense tissue. The radiologist is also encouraged to report the location of the dense tissue (e.g., in the upper outer quadrants). Thus, the current BI-RADS categories provide more clinically useful information regarding whether a lesion might be obscured on a mammogram but less quantitative information regarding breast density as a risk factor for an individual patient. The Claus model contains a more detailed family history than the Gail model [65]. The Gail model includes first-degree relatives with breast cancer but not their age at onset. The Claus model also includes second-degree relatives plus the age at onset of breast cancer for all relatives. Unlike the Gail model, the Claus model does not input age at menarche, age at first live birth, or a history of breast biopsies and ADH. The Claus model has never been validated in an independent study [66, 67]. The BRCAPro model is used to estimate the likelihood of a BRCA1 or BRCA2 mutation to determine if further clinical testing for either or both or these mutations is warranted [68]. This model includes patient age, first- and second-degree relatives and their age at onset of breast cancer (as with the Claus model), as well as family history of bilateral breast cancer, ovarian cancer, and male breast cancer. However, because this model does not incorporate factors such as history of breast biopsies, ADH, LCIS, and ADH, it will underestimate the risk of breast cancer for most women. The Jonker model is very similar to the BR- CAPro model, also including the hypothetical BRCAu gene, but does not incorporate data on breast biopsies or hormonal or reproductive factors (or both) [69]. The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model assesses the likelihood of BRCA1 and BRCA2 mutations, as well as the multiplicative effects of multiple genes. This model includes information on first-, second-, and third-degree relatives, age at onset, bilateral breast cancer, ovarian cancer, and male breast cancer [70]. The Tyrer-Cuzick (International Breast Cancer Intervention Studies [IBIS]) model includes the most comprehensive set of variables of any model. It includes almost every risk factor for any other model but does not include information on breast cancer in thirddegree relatives or male relatives [71]. Comparative Accuracy of Risk Models Use of different risk models to assess a patient with the same risk factors indicates moderate intermodel predictive variability [4]. The Gail, BRCAPro, Claus, and Jonker models underestimate risk, whereas the Tyrer-Cuzick (IBIS) and BOADICEA models produce higher, more accurate estimates [72, 73]. It has been suggested that addition of mammographic density data might improve the accuracy of models that incorporate clinical factors [74]. However, there are several reasons to believe that this may be easier said than done. Radiologist assessment of breast density is partly subjective and variable [58, 59]. There is considerable overlap in radiologist assignment of mammograms to scattered densities versus heterogeneously dense categories, which together comprise 80% of all mammograms. Risk for each of these differs only slightly from average risk (0.8 and 1.2 times, respectively). Although risk for extremely dense breasts is four times that for fatty breasts, only 10% of mammograms fall into this density category. There are inherent limitations in estimating 3D volumetric density by use of 2D mammograms [75]. Objective physical methods for estimating density such as dual-energy absorption might be more accurate, but results among different methods vary and need to be assessed through long-term follow-up for breast cancer incidence versus density [76]. Although BRCA1 and BRCA2 mutations are associated with an extremely high risk of breast cancer, they occur in less than 1% of the population and account for only 2% of all breast cancers [77, 78]. By comparison, SNPs are genetic variants in the loci of genes and are much more common than BRCA1 and BRCA2 mutations, but each SNP is associated with only a slightly increased risk of breast cancer. Many of these susceptibility loci have been discovered, and many more are expected to be identified [79 83]. One anticipated result of this research might be to ultimately incorporate these loci into risk models to improve the accuracy for risk prediction. However, even the most optimistic prediction would be that even if all SNPs were identified, it would still be necessary to screen the majority of women with mammography to detect an acceptable proportion of breast cancers. To triage women with SNPs to determine which women might safely be excluded from screening might be extremely complex, prohibitively expensive, and only marginally beneficial. The accuracy of such genetic testing is unclear, and there might be variability among different laboratories. There are insufficient data to 1368 AJR:205, December 2015

5 Personalized Screening for Breast Cancer support the incorporation of SNPs into current screening practice. Conclusion Current ACR and SBI guidelines and ACS guidelines recommend that all women begin annual mammography screening at no later than 40 years old and continue as long as they have generally good health and a life expectancy of 5 years or longer. These guidelines also recommend that supplementary screening with breast MRI be considered for women with a 15 20% or higher lifetime risk and that certain high-risk women may begin annual screening mammography at earlier than 40 years old. Thus, for radiologists and others who follow these guidelines, risk-based screening implies that high-risk women should receive additional screening beyond the basic screening recommendations for all other women. However, the terms risk-based screening and personalized screening have recently taken on a totally different meaning for some health care planners, who advocate less frequent than annual screening or even no screening at all for women at supposedly lower risk (e.g., less than the risk for the average 50-year-old woman). The problem with this approach is that retrospective studies of screened women have shown that most breast cancers are detected in women with no major risk factors such as close family history of breast cancer or personal history of biopsyproven ADH, ALH, or LCIS. Because breast cancer risk models such as the Gail or Tyrer- Cuzick (IBIS) models are based on a mathematic compilation of a woman s major and minor risk factors, it is unlikely that such models could do much better at effectively excluding some women from screening than use of risk factors alone or in combination. Although BRCA1 and BRCA2 mutations convey an extremely high risk, they account for no more than 2% of all breast cancers. Women having extremely dense breasts have a risk that is 1.6 the average risk, but they represent only 10% of the population. Women with scattered densities have 0.8 times the average risk, and those with heterogeneously dense breasts have 1.2 times the average risk. Thus, recommending different screening frequencies to each of these groups would have only a slight effect on detection rates, especially because distinction of each group at the margins is somewhat arbitrary. The cost and complexity of testing all women for multiple SNPs and newer genetic risk factors is currently uncertain. Because screening detection rates are very low, the number of women needed for evaluation of a personalized screening protocol is far greater than the number needed for a personalized treatment trial. The results from a personalized treatment trial should be initially apparent within months, whereas the outcomes from a personalized screening trial may be unclear for years, especially if the trial involves subgroup analysis. It would be unfortunate if the concept of personalized medical care were inappropriately applied to restrict access to screening; although personalized medical care has been used successfully in treating breast cancer patients, personalized screening could thus be harmful. Admittedly, personalized screening will yield higher detection rates than current ACS recommendations for annual screening starting at 40 years old because high-risk women will have a higher prevalence of cancer than will women with no known risk factors. The cost-effectiveness of restricted screening requires a formal economic analysis of health care costs and outcomes of restricted versus nonrestricted screening approaches. How can proposals for personalized screening be safely and reliably evaluated? Prospective trials that compare clinical outcomes of supposedly lower-risk women who are provided only limited screening with outcomes of similar women who are screened annually may not be able to identify and treat some breast cancers until they have progressed. Instead, it would be preferable to first perform retrospective studies in which all women undergo newer methods of genetic testing, quantitative measurement of breast density, and complete documentation or conventional risk factors. Unless all women are screened according to ACS guidelines and ACR and SBI guidelines with a sufficiently long follow-up, subgroups for less frequent screening cannot be safely identified. Until that is successfully completed, personalized screening recommendations could remain a wolf in sheep s clothing. References 1. Lee CH, Dershaw D, Kopans D, et al. 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