Abstract
Interval breast cancers (those diagnosed between recommended mammography screens) generally have poorer outcomes and are more common among women with dense breasts. We aimed to develop a risk model for interval breast cancer. We conducted a nested case–control study within the Melbourne Collaborative Cohort Study involving 168 interval breast cancer patients and 498 matched control subjects. We measured breast density using the CUMULUS software. We recorded first‐degree family history by questionnaire, measured body mass index (BMI) and calculated age‐adjusted breast tissue aging, a novel measure of exposure to estrogen and progesterone based on the Pike model. We fitted conditional logistic regression to estimate odds ratio (OR) or odds ratio per adjusted standard deviation (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). The stronger risk associations were for unadjusted percent breast density (OPERA = 1.99; AUC = 0.66), more so after adjusting for age and BMI (OPERA = 2.26; AUC = 0.70), and for family history (OR = 2.70; AUC = 0.56). When the latter two factors and their multiplicative interactions with age‐adjusted breast tissue aging (p = 0.01 and 0.02, respectively) were fitted, the AUC was 0.73 (95% CI 0.69–0.77), equivalent to a ninefold interquartile risk ratio. In summary, compared with using dense breasts alone, risk discrimination for interval breast cancers could be doubled by instead using breast density, BMI, family history and hormonal exposure. This would also give women with dense breasts, and their physicians, more information about the major consequence of having dense breasts—an increased risk of developing an interval breast cancer.
Highlights
Interval breast cancers are diagnosed after a negative screen but before the recommended screen
To better inform women of the consequences of their breast density, we aimed to develop a risk model for interval breast cancer based on breast density and other breast cancer risk factors by conducting a matched case–control study nested within a prospective cohort
We found that the ability to predict interval breast cancers might be almost doubled by considering more than unadjusted breast density dichotomized about its median, similar to how dense breasts are defined using Breast Imaging Reporting and Data System (BI-RADS)
Summary
Interval breast cancers are diagnosed after a negative screen but before the recommended screen. Interval cancers are important for many reasons, not the least because women diagnosed with interval cancers have poor outcomes.[1] Compared with screen-detected breast cancers, interval breast cancers are generally larger and have a more aggressive phenotype.[2] Interval cancers have an incidence of 10–20 per 10,000 women attending two-yearly mammographic screening[3] and represent 20–30% of all breast cancers diagnosed in those women.[4]. A risk model for interval cancers would be important for breast cancer control. Women could be triaged according to their risk for tailored screening,[5] a concept being tested by the Women
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