Background

One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined whether two BI-RADS density measures improve the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared with one measure.

Methods

We included 722,654 women of ages 35 to 74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000-2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death.

Results

The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC, 0.640 vs. 0.635). Of 18.6% of women (134,404 of 722,654) who decreased density categories, 15.4% (20,741 of 134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model.

Conclusion

The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density.

Impact

A two-density model should be considered for women whose density decreases when calculating breast cancer risk.

Background

Established mammography screening performance metrics use the initial screening mammography assessment because they were developed for radiologist performance auditing, yet these metrics are frequently used to inform health policy and screening decision making. The authors have developed new performance metrics based on the final assessment that consider the entire screening episode, including diagnostic workup.

Methods

The authors used data from 2,512,577 screening episodes during 2005-2017 at 146 facilities in the United States participating in the Breast Cancer Surveillance Consortium. Screening performance metrics based on the final assessment of the screening episode were compared with conventional metrics defined with the initial assessment. Results were also stratified by breast density and breast cancer risk.

Results

The cancer detection rates were similar for the final assessment (4.1 per 1000; 95% confidence interval [CI], 3.8-4.3 per 1000) and the initial assessment (4.1 per 1000; 95% CI, 3.9-4.3 per 1000). The interval cancer rate was 12% higher when it was based on the final assessment (0.77 per 1000; 95% CI, 0.71-0.83 per 1000) versus the initial assessment (0.69 per 1000; 95% CI, 0.64-0.74 per 1000), and this resulted in a modest difference in sensitivity (84.1% [95% CI, 83.0%-85.1%] vs 85.7% [95% CI, 84.8%-86.6%], respectively). Absolute differences in the interval cancer rate between final and initial assessments increased with breast density and breast cancer risk (eg, a difference of 0.29 per 1000 for women with extremely dense breasts and a 5-year risk >2.49%).

Conclusions

Established screening performance metrics underestimate the interval cancer rate of a mammography screening episode, particularly for women with dense breasts or an elevated breast cancer risk. Women, clinicians, policymakers, and researchers should use final-assessment performance metrics to support informed screening decisions.

Background: The utility of incorporating detailed family history into breast cancer risk prediction hinges on its independent contribution to breast cancer risk. We evaluated associations between detailed family history and breast cancer risk while accounting for breast density.Methods: We followed 222,019 participants ages 35 to 74 in the Breast Cancer Surveillance Consortium, of whom 2,456 developed invasive breast cancer. We calculated standardized breast cancer risks within joint strata of breast density and simple (1^st-degree female relative) or detailed (first-degree, second-degree, or first- and second-degree female relative) breast cancer family history. We fit log-binomial models to estimate age-specific breast cancer associations for simple and detailed family history, accounting for breast density.Results: Simple first-degree family history was associated with increased breast cancer risk compared with no first-degree history [Risk ratio (RR), 1.5; 95% confidence interval (CI), 1.0-2.1 at age 40; RR, 1.5; 95% CI, 1.3-1.7 at age 50; RR, 1.4; 95% CI, 1.2-1.6 at age 60; RR, 1.3; 95% CI, 1.1-1.5 at age 70). Breast cancer associations with detailed family history were strongest for women with first- and second-degree family history compared with no history (RR, 1.9; 95% CI, 1.1-3.2 at age 40); this association weakened in higher age groups (RR, 1.2; 95% CI, 0.88-1.5 at age 70). Associations did not change substantially when adjusted for breast density.Conclusions: Even with adjustment for breast density, a history of breast cancer in both first- and second-degree relatives is more strongly associated with breast cancer than simple first-degree family history.Impact: Future efforts to improve breast cancer risk prediction models should evaluate detailed family history as a risk factor. Cancer Epidemiol Biomarkers Prev; 26(6); 938-44. ©2017 AACR.

Importance

Many established breast cancer risk factors are used in clinical risk prediction models, although the proportion of breast cancers explained by these factors is unknown.

Objective

To determine the population-attributable risk proportion (PARP) for breast cancer associated with clinical breast cancer risk factors among premenopausal and postmenopausal women.

Design, setting, and participants

Case-control study with 1:10 matching on age, year of risk factor assessment, and Breast Cancer Surveillance Consortium (BCSC) registry. Risk factor data were collected prospectively from January 1, 1996, through October 31, 2012, from BCSC community-based breast imaging facilities. A total of 18 437 women with invasive breast cancer or ductal carcinoma in situ were enrolled as cases and matched to 184 309 women without breast cancer, with a total of 58 146 premenopausal and 144 600 postmenopausal women enrolled in the study.

Exposures

Breast Imaging Reporting and Data System (BI-RADS) breast density (heterogeneously or extremely dense vs scattered fibroglandular densities), first-degree family history of breast cancer, body mass index (>25 vs 18.5-25), history of benign breast biopsy, and nulliparity or age at first birth (≥30 years vs <30 years).

Main outcomes and measures

Population-attributable risk proportion of breast cancer.

Results

Of the 18 437 women with breast cancer, the mean (SD) age was 46.3 (3.7) years among premenopausal women and 61.7 (7.2) years among the postmenopausal women. Overall, 4747 (89.8%) premenopausal and 12 502 (95.1%) postmenopausal women with breast cancer had at least 1 breast cancer risk factor. The combined PARP of all risk factors was 52.7% (95% CI, 49.1%-56.3%) among premenopausal women and 54.7% (95% CI, 46.5%-54.7%) among postmenopausal women. Breast density was the most prevalent risk factor for both premenopausal and postmenopausal women and had the largest effect on the PARP; 39.3% (95% CI, 36.6%-42.0%) of premenopausal and 26.2% (95% CI, 24.4%-28.0%) of postmenopausal breast cancers could potentially be averted if all women with heterogeneously or extremely dense breasts shifted to scattered fibroglandular breast density. Among postmenopausal women, 22.8% (95% CI, 18.3%-27.3%) of breast cancers could potentially be averted if all overweight and obese women attained a body mass index of less than 25.

Conclusions and relevance

Most women with breast cancer have at least 1 breast cancer risk factor routinely documented at the time of mammography, and more than half of premenopausal and postmenopausal breast cancers are explained by these factors. These easily assessed risk factors should be incorporated into risk prediction models to stratify breast cancer risk and promote risk-based screening and targeted prevention efforts.

Background

Advanced breast cancer is an outcome used to evaluate screening effectiveness. The advanced cancer definition resulting in the best discrimination of breast cancer death has not been studied in a breast imaging population.

Methods

A total of 52 496 women aged 40-79 years participating in the Breast Cancer Surveillance Consortium diagnosed with invasive cancer were staged using the 8th edition of American Joint Committee on Cancer (AJCC) anatomic and prognostic pathologic systems and Tomosynthesis Mammographic Imaging Screening Trial (TMIST) tumor categories. We calculated the area under the receiver operating characteristic curve for predicting 5-year breast cancer death and the sensitivity and specificity for predicting 5-year breast cancer death for 3 advanced cancer classifications: anatomic stage IIB or higher, prognostic pathologic stage IIA or higher, and TMIST advanced cancer.

Results

The area under the receiver operating characteristic curves for predicting 5-year breast cancer death for AJCC anatomic stage, AJCC prognostic pathologic stage, and TMIST tumor categories were 0.826 (95% confidence interval [CI] = 0.817 to 0.835), 0.856 (95% CI = 0.846 to 0.866), and 0.789 (95% CI = 0.780 to 0.797), respectively. AJCC prognostic pathologic stage had statistically significantly better discrimination than AJCC anatomic stage (difference = 0.030, bootstrap 95% CI = 0.024 to 0.037) and TMIST tumor categories (difference = 0.067, bootstrap 95% CI = 0.059 to 0.075). The sensitivity and specificity for predicting 5-year breast cancer death for AJCC anatomic stage IIB or higher, AJCC prognostic pathologic stage IIA or higher, and TMIST advanced cancer were 72.6%, 76.7%, and 96.1%; and 78.9%, 81.6%, and 41.1%, respectively.

Conclusions

Defining advanced cancer as AJCC prognostic pathologic stage IIA or higher most accurately predicts breast cancer death. Use of this definition by investigators will facilitate comparing breast cancer screening effectiveness studies.

Background

Twenty-one states have laws requiring that women be notified if they have dense breasts and that they be advised to discuss supplemental imaging with their provider.

Objective

To better direct discussions of supplemental imaging by determining which combinations of breast cancer risk and Breast Imaging Reporting and Data System (BI-RADS) breast density categories are associated with high interval cancer rates.

Design

Prospective cohort.

Setting

Breast Cancer Surveillance Consortium (BCSC) breast imaging facilities.

Patients

365,426 women aged 40 to 74 years who had 831,455 digital screening mammography examinations.

Measurements

BI-RADS breast density, BCSC 5-year breast cancer risk, and interval cancer rate (invasive cancer ≤12 months after a normal mammography result) per 1000 mammography examinations. High interval cancer rate was defined as more than 1 case per 1000 examinations.

Results

High interval cancer rates were observed for women with 5-year risk of 1.67% or greater and extremely dense breasts or 5-year risk of 2.50% or greater and heterogeneously dense breasts (24% of all women with dense breasts). The interval rate of advanced-stage disease was highest (>0.4 case per 1000 examinations) among women with 5-year risk of 2.50% or greater and heterogeneously or extremely dense breasts (21% of all women with dense breasts). Five-year risk was low to average (0% to 1.66%) for 51.0% of women with heterogeneously dense breasts and 52.5% with extremely dense breasts, with interval cancer rates of 0.58 to 0.63 and 0.72 to 0.89 case per 1000 examinations, respectively.

Limitation

The benefit of supplemental imaging was not assessed.

Conclusion

Breast density should not be the sole criterion for deciding whether supplemental imaging is justified because not all women with dense breasts have high interval cancer rates. BCSC 5-year risk combined with BI-RADS breast density can identify women at high risk for interval cancer to inform patient-provider discussions about alternative screening strategies.

Primary funding source

National Cancer Institute.

Objective

The purpose of this study was to determine whether pathologic findings of screen-detected and interval cancers differ for digital versus film mammography.

Materials and methods

Breast Cancer Surveillance Consortium data from 2003-2011 on 3,021,515 screening mammograms (40.3% digital, 59.7% film) of women 40-89 years old were reviewed. Cancers were considered screen detected if diagnosed within 12 months of an examination with positive findings and interval if diagnosed within 12 months of an examination with negative findings. Tumor characteristics for screen-detected and interval cancers were compared for digital versus film mammography by use of logistic regression models to estimate the odds ratio and 95% CI with adjustment for age, race and ethnicity, hormone therapy use, screening interval, examination year, and registry. Generalized estimating equations were used to account for correlation within facilities.

Results

Among 15,729 breast cancers, 85.3% were screen detected and 14.7% were interval. Digital and film mammography had similar rates of screen-detected (4.47 vs 4.42 per 1000 examinations) and interval (0.73 vs 0.79 per 1000 examinations) cancers for digital versus film. In adjusted analyses, interval cancers diagnosed after digital examinations with negative findings were less likely to be American Joint Committee on Cancer stage IIB or higher (odds ratio, 0.69; 95% CI, 0.52-0.93), have positive nodal status (odds ratio, 0.78; 95% CI, 0.64-0.95), or be estrogen receptor negative (odds ratio, 0.71; 95% CI, 0.56-0.91) than were interval cancers diagnosed after a film examination with negative findings.

Conclusion

Screen-detected cancers diagnosed after digital and film mammography had similar rates of unfavorable tumor characteristics. Interval-detected cancers diagnosed after a digital examination were less likely to have unfavorable tumor features than those diagnosed after film mammography, but the absolute differences were small.

Women with lobular carcinoma in situ (LCIS), atypical lobular hyperplasia (ALH), atypical ductal hyperplasia (ADH), or atypical hyperplasia (AH) are at increased breast cancer (BC) risk. We investigated the accuracy and outcomes of mammography screening in women with histology-proven LCIS, ALH, ADH, or AH history who had screening through Breast Cancer Surveillance Consortium-affiliated mammography facilities. Screens from two cohorts, defined by LCIS/ALH or ADH/AH history, were compared to two cohorts without such history mammogram-matched for age-group, breast density, family history, screen-year, and mammography registry. Overall 359 BCs (277 invasive BC) occurred within 1 year from screening among 52,380 screens. In the LCIS/ALH cohort [versus comparator screens] cancer incidence rates, cancer detection rates (CDR), and interval cancer rates (ICR) were significantly higher (all P < 0.001); although ICR was 4.4/1,000 screens [versus 0.9/1,000; P < 0.001] the proportion that were interval cancers did not differ between compared cohorts (P = 0.43); screening sensitivity was 76.1 % [versus 82.3 %; P = 0.43], however, specificity was significantly lower at 85.1 % [versus 90.7 %; P < 0.0001]. In the ADH/AH cohort [versus comparator] cancer rates and CDR were significantly higher (P < 0.001); although ICR was 2.6/1,000 screens [versus 0.9/1,000; P = 0.002] the proportion that were interval cancers did not differ between cohorts (P = 0.74); screening sensitivity was 81.0 % [versus 82.6 %; P = 0.74] and specificity was lower at 86.2 % [versus 90.2 %; P < 0.0001]. Mammography screening sensitivity in LCIS/ALH and ADH/AH cohorts did not significantly differ from that of matched screens, however, specificity was lower, and ICRs were higher (reflecting underlying cancer rates). Adjunct screening may be of value in these women if it reduces ICR without substantially reducing specificity.

Background There is growing evidence that digital breast tomosynthesis (DBT) results in lower recall rates and higher cancer detection rates when compared with digital mammography. However, whether DBT interpretative performance changes with experience (learning curve effect) is unknown. Purpose To evaluate screening DBT performance by cumulative DBT volume within 2 years after adoption relative to digital mammography (DM) performance 1 year before DBT adoption. Materials and Methods This prospective study included 106 126 DBT and 221 248 DM examinations in 271 362 women (mean age, 57.5 years) from 2010 to 2017 that were interpreted by 104 radiologists from 53 facilities in the Breast Cancer Surveillance Consortium. Conditional logistic regression was used to estimate within-radiologist effects of increasing cumulative DBT volume on recall and cancer detection rates relative to DM and was adjusted for examination-level characteristics. Changes were also evaluated by subspecialty and breast density. Results Before DBT adoption, DM recall rate was 10.4% (95% confidence interval [CI]: 9.5%, 11.4%) and cancer detection rate was 4.0 per 1000 screenings (95% CI: 3.6 per 1000 screenings, 4.5 per 1000 screenings); after DBT adoption, DBT recall rate was lower (9.4%; 95% CI: 8.2%, 10.6%; P = .02) and cancer detection rate was similar (4.6 per 1000 screenings; 95% CI: 4.0 per 1000 screenings, 5.2 per 1000 screenings; P = .12). Relative to DM, DBT recall rate decreased for a cumulative DBT volume of fewer than 400 studies (odds ratio [OR] = 0.83; 95% CI: 0.78, 0.89) and remained lower as volume increased (400-799 studies, OR = 0.8 [95% CI: 0.75, 0.85]; 800-1199 studies, OR = 0.81 [95% CI: 0.76, 0.87]; 1200-1599 studies, OR = 0.78 [95% CI: 0.73, 0.84]; 1600-2000 studies, OR = 0.81 [95% CI: 0.75, 0.88]; P < .001). Improvements were sustained for breast imaging subspecialists (OR range, 0.67-0.85; P < .02) and readers who were not breast imaging specialists (OR range, 0.80-0.85; P < .001). Recall rates decreased more in women with nondense breasts (OR range, 0.68-0.76; P < .001) than in those with dense breasts (OR range, 0.86-0.90; P ≤ .05; P interaction < .001). Cancer detection rates for DM and DBT were similar, regardless of DBT volume (P ≥ .10). Conclusion Early performance improvements after digital breast tomosynthesis (DBT) adoption were sustained regardless of DBT volume, radiologist subspecialty, or breast density. © RSNA, 2019 See also the editorial by Hooley in this issue.