Does cumulative risk of screen-detected ductal carcinoma in situ (DCIS) vary according to mammography screening interval and clinical risk factors?
For this cohort study, a well-calibrated model was developed to predict cumulative 6-year risk of screen-detected DCIS in 916 931 women. Compared with women undergoing biennial mammography, those undergoing annual mammography had a 40% to 45% higher 6-year cumulative risk of screen-detected DCIS, whereas those undergoing triennial mammography had lower risk.
This risk model provides estimates of the 6-year probability of screen-detected DCIS and can inform discussions of screening benefits and harms for those considering a screening interval other than biennial.
Detection of ductal carcinoma in situ (DCIS) by mammography screening is a controversial outcome with potential benefits and harms. The association of mammography screening interval and woman’s risk factors with the likelihood of DCIS detection after multiple screening rounds is poorly understood.
To develop a 6-year risk prediction model for screen-detected DCIS according to mammography screening interval and women’s risk factors.
Design, Setting, and Participants
This Breast Cancer Surveillance Consortium cohort study assessed women aged 40 to 74 years undergoing mammography screening (digital mammography or digital breast tomosynthesis) from January 1, 2005, to December 31, 2020, at breast imaging facilities within 6 geographically diverse registries of the consortium. Data were analyzed between February and June 2022.
Screening interval (annual, biennial, or triennial), age, menopausal status, race and ethnicity, family history of breast cancer, benign breast biopsy history, breast density, body mass index, age at first birth, and false-positive mammography history.
Main Outcomes and Measures
Screen-detected DCIS defined as a DCIS diagnosis within 12 months after a positive screening mammography result, with no concurrent invasive disease.
A total of 916 931 women (median [IQR] age at baseline, 54 [46-62] years; 12% Asian, 9% Black, 5% Hispanic/Latina, 69% White, 2% other or multiple races, and 4% missing) met the eligibility criteria, with 3757 screen-detected DCIS diagnoses. Screening round–specific risk estimates from multivariable logistic regression were well calibrated (expected-observed ratio, 1.00; 95% CI, 0.97-1.03) with a cross-validated area under the receiver operating characteristic curve of 0.639 (95% CI, 0.630-0.648). Cumulative 6-year risk of screen-detected DCIS estimated from screening round–specific risk estimates, accounting for competing risks of death and invasive cancer, varied widely by all included risk factors. Cumulative 6-year screen-detected DCIS risk increased with age and shorter screening interval. Among women aged 40 to 49 years, the mean 6-year screen-detected DCIS risk was 0.30% (IQR, 0.21%-0.37%) for annual screening, 0.21% (IQR, 0.14%-0.26%) for biennial screening, and 0.17% (IQR, 0.12%-0.22%) for triennial screening. Among women aged 70 to 74 years, the mean cumulative risks were 0.58% (IQR, 0.41%-0.69%) after 6 annual screens, 0.40% (IQR, 0.28%-0.48%) for 3 biennial screens, and 0.33% (IQR, 0.23%-0.39%) after 2 triennial screens.
Conclusions and Relevance
In this cohort study, 6-year screen-detected DCIS risk was higher with annual screening compared with biennial or triennial screening intervals. Estimates from the prediction model, along with risk estimates of other screening benefits and harms, could help inform policy makers’ discussions of screening strategies.
Detection of ductal carcinoma in situ (DCIS) is a controversial outcome of mammography screening. The incidence of DCIS increased markedly in the US with the widespread adoption of screening mammography,1,2 and more than 30% of screen-detected breast cancers are DCIS.3 Because DCIS is a nonobligate precursor to invasive breast cancer, the detection and treatment of DCIS may reduce the risk of subsequent invasive disease,4,5 yet there is concern that a substantial fraction of DCIS may never lead to invasive cancer if left untreated.2,6,7 Overdiagnosis is challenging to estimate8,9 but has influenced national breast cancer screening recommendations as a potential harm of breast cancer screening.10,11
The US Preventive Services Task Force and American Cancer Society recommendations include elements of individual informed decision-making regarding breast cancer screening strategies, including whether to start screening before the age of 50 years and whether screens should be performed annually or biennially. Aggregate data on mammography screening benefits and harms7,12,13 and individual-level breast cancer risk prediction models14 are available to inform these decisions, yet few models provide individual-level predictions of mammography screening outcomes. Models were recently published for cumulative 6-year risk of advanced (prognostic stage II or higher) breast cancer and cumulative 10-year risk of a false-positive mammography result based on mammography screening frequency and readily available clinical risk factors.15,16 Prediction models for screen-detected DCIS would further inform screening decisions and guidelines.
The purpose of this study is to examine DCIS detection rates according to mammography screening interval and clinical risk factors and develop a risk prediction model to estimate the cumulative 6-year risk of screen-detected DCIS. We used a 6-year horizon to enable comparison of outcomes for 6 annual, 3 biennial, and 2 triennial screening rounds.
For this cohort study, we used observational clinical data from 6 breast imaging registries within the Breast Cancer Surveillance Consortium (BCSC): the Carolina Mammography Registry, the Kaiser Permanente Washington Registry, the New Hampshire Mammography Network, the Vermont Breast Cancer Surveillance System, the San Francisco Mammography Registry, and the Metropolitan Chicago Breast Cancer Registry. Each registry prospectively collects clinical data on women undergoing breast imaging from participating radiology facilities within its catchment area. The registries and a central statistical coordinating center received institutional review board approval from their respective institutions for active or passive consenting processes or a waiver of consent to enroll participants, link data, and perform analyses. Identifiable data are collected by each registry. Limited data sets (containing dates and residential zip codes but no other direct identifiers) are sent to the BCSC Statistical Coordinating Center for pooling and statistical analysis. All procedures were Health Insurance Portability and Accountability Act compliant, and registries and the statistical coordinating center received a federal certificate of confidentiality for the identities of women, physicians, and facilities. The study followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines17 for reporting results from cohort studies and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guidelines for development of the risk prediction model.
Women aged 40 to 74 years undergoing mammography screening (digital mammography or digital breast tomosynthesis) from January 1, 2005, to December 31, 2020, were eligible for inclusion. We excluded women with a prior history of breast cancer (invasive or DCIS), lobular carcinoma in situ, or mastectomy. Screening mammograms were identified based on the radiologist’s clinical indication for the examination. To reflect women who were routinely screened and evaluate the screening interval, we restricted the study to screening mammograms among women who underwent mammography within the prior 42 months (corresponding to the upper limit of our triennial screening interval definition). Thus, a woman’s first mammogram was not included. We also excluded mammography screening that was unilateral, was preceded by mammography within the prior 9 months, was followed by screening ultrasonography within 3 months, or occurred 12 months before or after screening magnetic resonance imaging. At least 1 year of follow-up for complete capture of cancer diagnoses was required.
Participating radiology facilities provide imaging modality, examination indication, breast density, and assessment data to BCSC registries using standard nomenclature from the Breast Imaging Reporting and Data System (BI-RADS).18 Demographic and risk factor information is self-reported or extracted from electronic medical records. The BCSC registries ascertain breast cancer diagnoses and tumor characteristics by linking women to pathology databases; regional Surveillance, Epidemiology, and End Results programs; and state tumor registries. Deaths are obtained by linking to state death records.
Outcome and Predictor Definitions
Screen-detected DCIS was defined as a DCIS diagnosis within 12 months after a screening mammogram with a positive final assessment (BI-RADS category 3, 4, or 5), with no invasive breast cancer diagnosis.12 We evaluated rates of screen-detected DCIS in relation to mammography screening interval, mammography screening modality (digital mammography vs digital breast tomosynthesis [DBT]), and 9 clinical breast cancer risk factors: age, menopausal status, first-degree family history of breast cancer, history of benign breast biopsy, BI-RADS breast density,18 body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), age at first birth, history of false-positive screening mammography results in the previous 5 years, and race and ethnicity. Screening interval for each mammogram was defined based on the time since the woman’s prior mammogram (annual: 11-18 months; biennial: 19-30 months; and triennial: 31-42 months). Breast density is categorized by radiologists during clinical interpretation as almost entirely fatty, scattered fibroglandular densities, heterogeneously dense, or extremely dense.18 Postmenopausal women were those with both ovaries removed, in whom menstruation had stopped naturally, who were currently receiving postmenopausal hormone therapy, or who were 60 years or older. Premenopausal women were those who reported menstruating within the last 180 days, who used oral contraceptives, or who were younger than 45 years. History of benign breast biopsy was defined based on diagnoses abstracted from clinical pathology reports. We grouped prior benign diagnoses based on the highest grade as proliferative with atypia greater than proliferative without atypia greater than nonproliferative using published taxonomy19-22 or as unknown if a woman reported a prior biopsy with no available BCSC pathology result. Self-reported race and ethnicity were included as a social construct that could potentially capture differences in screen-detected DCIS risk due to social determinants of health, including inequities in access to high-quality screening and diagnostic services, and were categorized as Hispanic/Latina and for non-Hispanic/Latina as Asian, Black, White, or other or multiple races (including American Indian or Alaska Native, Native Hawaiian or Pacific Islander, and self-reported other race).
Analyses were conducted between February and June 2022. The screening mammogram was the unit of analysis. We estimated absolute screen-detected DCIS risk after 1 round of screening using multivariable logistic regression, including screening interval, modality, age (linear and quadratic, centered at 55 years), calendar year of screen (linear and quadratic, centered at January 31, 2020), menopausal status, first-degree breast cancer family history, benign biopsy history, BMI (categorical), breast density, age at first live birth (categorical), prior false-positive mammography result, and race and ethnicity. Before model fitting, 20 imputed values for each missing variable were generated using multiple imputation via chained equations (eMethods and eTable 5 in Supplement 1).23 For each covariate combination, risk scores from a single screening round were estimated by averaging over the 20 risk scores estimated in fitted logistic regression models from each imputed data set. We evaluated interactions of risk factors with age, age squared, and menopausal status and retained those that were statistically significant at a 2-sided P < .05 on type 3 tests; these interactions included those between linear age and BMI, linear age and prior false-positive mammography results, and menopausal status and BMI. We also tested interactions between each risk factor and screening interval; none were significant at P < .05 and thus were not included in the model. Mammography modality (digital mammography vs DBT) was not associated with DCIS detection and was omitted from the final model. Model calibration was estimated as the ratio of expected to observed number (E/O ratio) of screen-detected DCIS, both overall and within predicted risk decile groups. Model discriminatory accuracy was summarized using the area under the receiver operating characteristic curve (AUC). To internally validate the model, we compared the AUC from the model fit using the full data to the AUC from a model fit using 5-fold cross-validation, and the difference between them (optimism) was 0.004. To account for this small overfitting, the AUC and 95% CI were adjusted by subtracting the optimism from the estimates obtained from the full data.
The cumulative screen-detected DCIS risks after hypothetical repeat screening patterns consisting of 6 annual, 3 biennial, or 2 triennial screens occurring at 12-, 24-, or 36-month intervals, respectively, were estimated using a discrete-time survival model based on the fitted logistic regression models for 1 round of screening while accounting for competing risks of death or invasive cancer within 1 year after annual screening, 2 years after biennial screening and 3 years after triennial screening.24 A 6-year horizon enables comparison of outcomes for 6 annual, 3 biennial, or 2 triennial screening rounds. Mean predicted 6-year cumulative risks and IQRs for different screening intervals were estimated in a standardized population; the weights of the study population were adjusted to reflect the US female population based on age, race and ethnicity, and family history of breast cancer.25,26 The cumulative 6-year risk of screen-detected DCIS was categorized into 5 risk levels (high, >95th percentile; intermediate, 75th-95th percentile; average, 25th-75th percentile; low, 5th-25th percentile; and very low, ≤5th percentile) adjusted by US population weights and standardized to the same population for different screening intervals. Data were analyzed using R software, version 4.0.4 (R Foundation for Statistical Computing) and SAS software, version 9.4 (SAS Institute Inc). Two-sided α = .05 was used to determine statistical significance. The eMethods in Supplement 1 provide additional statistical methods details.
A total of 2 320 016 annual, 681 983 biennial, and 199 058 triennial mammograms in 916 931 women (median [IQR] age at baseline, 54 [46-62] years) were included, with 3757 screen-detected DCIS diagnoses. Overall, the distribution of self-reported race and ethnicity was 12% Asian, 9% Black, 5% Hispanic/Latina, 69% White, 2% other or multiple races, and 4% missing. The screening interval was shorter among women who were older, who were White, and who had a first-degree family history of breast cancer, prior benign biopsy, normal BMI, or history of false-positive mammography results (Table 1).
In multivariable-adjusted analyses of a single screening round, DCIS detection was more likely with longer screening interval (biennial vs annual screening: odds ratio [OR], 1.43; 95% CI, 1.33-1.55; triennial vs annual screening: OR, 1.83; 95% CI, 1.63-2.05) (Table 2). Detection of DCIS was more common among women who had a first-degree family history of breast cancer, were nulliparous or 30 years or older at first live birth, had a prior benign breast biopsy, or reported Asian race (Table 2). Breast density was more strongly associated with DCIS detection among younger women, whereas prior false-positive mammography results were more strongly associated with DCIS detection among older women (Table 3). The positive association of BMI with DCIS detection was limited to postmenopausal women (Table 3). Detection of DCIS did not vary according to mammography modality (OR, 1.00; 95% CI, 0.89-1.12 for DBT vs digital mammography).
Overall, 11.2% of annual screeners had high 6-year risk of screen-detected DCIS compared with 2.7% among biennial screeners and 1.1% among triennial screeners (Table 4). Women aged 40 to 49 years had the lowest proportion in the intermediate or high-risk groups, whereas women aged 70 to 74 years had the highest proportion.
The model predicting DCIS detection at a single screening round was well calibrated, with an E/O ratio of 1.00 (95% CI, 0.97-1.03) and little deviation from unity across all deciles of predicted risk (eFigure in Supplement 1). The adjusted AUC for predicting DCIS detection was 0.639 (95% CI, 0.630-0.648).
Mean cumulative 6-year risk of screen-detected DCIS was higher with increasing age and shorter screening interval (Figure; eTables 1-4 in Supplement 1). Among women aged 40 to 49 years, the mean 6-year screen-detected DCIS risk was 0.30% (IQR, 0.21%-0.37%) for annual screening, 0.21% (IQR, 0.14%-0.26%) for biennial screening, and 0.17% (IQR, 0.12%-0.22%) for triennial screening. For women aged 70 to 74 years, the mean cumulative risks were 0.58% (IQR, 0.41%-0.69%) after 6 annual screens, 0.40% (IQR, 0.28%-0.48%) after 3 biennial screens, and 0.33% (IQR, 0.23%-0.39%) after 2 triennial screens.
eTables 1 through 4 in Supplement 1 list the mean cumulative 6-year risks of screen-detected DCIS by decade of age according to women’s risk factors and screening interval. For example, the 6-year risk of DCIS detection for women aged 50 to 59 years undergoing annual screening ranged from 0.34% (IQR, 0.24%-0.41%) for women with no prior benign breast biopsy to 1.11% (IQR, 0.80%-1.35%) for women with a history of proliferative benign breast disease with atypia, whereas the risk was 0.24% (IQR, 0.17%-0.29%) for women with no prior benign breast biopsy and 0.76% (IQR, 0.55%-0.93%) for women with a history of proliferative benign breast disease with atypia who underwent biennial screening.
The results of this cohort study suggest that DCIS detection rates on mammography screening vary by screening interval and clinical risk factors. Cumulative risk of screen-detected DCIS after 6 years of annual screening is substantially higher than for women undergoing 3 biennial screens. Age, first-degree family history of breast cancer, and history of benign breast biopsy are particularly strong risk factors for screen-detected DCIS. Breast density is a strong risk factor among younger women, and history of false-positive mammography results and obesity are strong risk factors among older women. Our risk prediction model integrates screening interval and individual risk factors to estimate the probability of screen-detected DCIS. These risk estimates can be used by policy makers in conjunction with estimates of other breast cancer screening outcomes (such as cumulative risk of false-positive mammography results and advanced cancer) when evaluating the balance of screening benefits and harms by screening interval.15,16
Ductal carcinoma in situ currently makes up more than 30% of screen-detected breast cancer in the US.27 Although the goal of breast cancer screening is early detection, screening recommendations from the US Preventive Services Task Force and the American Cancer Society acknowledge concerns about overdiagnosis and overtreatment of DCIS.10,11 Ductal carcinoma in situ is considered a nonobligate precursor of invasive breast cancer.28 Given the potential for subsequent invasive cancer and the current inability to reliably distinguish high-risk from indolent DCIS, treatment guidelines for DCIS recommend breast-conserving surgery and consideration of radiation therapy and endocrine therapy.29 Locoregional therapy reduces the risk of subsequent invasive breast cancer but has not been shown to influence overall survival or breast cancer–specific survival.30-35 Given the morbidity of DCIS treatments and evolving biological models of DCIS progression,28 many scientists have called for reconsideration of how DCIS is managed,36-38 and trials of active surveillance for low-grade DCIS are ongoing.39-41
Consistent with the recently published model of cumulative advanced breast cancer risk,15 we estimated 6-year risk of screen-detected DCIS to inform decision-making about mammography screening strategies. Previous studies13,15,27,42 have identified risk groups that can undergo biennial screening with little adverse change in risk of advanced cancer or life-years gained compared with annual mammography. Our results indicate that women who have low advanced cancer risk with biennial screening (eg, women with healthy weight and nondense breasts)15 would also experience reduced cumulative DCIS detection with a biennial vs annual screening interval. Of note, risk of screen-detected DCIS on a single screening round was higher with increasing time since last mammography, reflecting the longer interval for DCIS to emerge. However, the probability of screen-detected DCIS for biennial mammography is only 40% to 45% higher than annual mammography; similarly, the probability of screen-detected DCIS for triennial mammography is less than 3 times that of annual mammography. Consequently, cumulative DCIS risk after 6 years of screening is substantially lower for women undergoing 2 triennial or 3 biennial screens compared with 6 annual screens.
Our results do not directly provide new insights into the natural history of DCIS. Potential advantages of increased DCIS detection could include lower-interval invasive breast cancer rates.5 Annual screening may offer the opportunity to detect DCIS that has a short sojourn time.43 However, simulation modeling suggests that increased detection of DCIS with more frequent screening corresponds to increased overdiagnosis,44 and population-based data show that large increases in DCIS incidence do not lead to a reduction in early-stage invasive cancer incidence or mortality.45 Thus, uncertainty exists regarding whether screen-detected DCIS is a potential screening harm or benefit. Physicians referring women for screening may wish to consider advanced cancer risk as the primary outcome influencing screening frequency and supplemental imaging.15 Our results could be used to estimate the effect of the chosen screening strategy on the risk of DCIS detection and are relevant for policy makers considering a wide range of outcomes associated with different population-level screening strategies.10
Our study results are consistent with an extensive literature demonstrating that benign breast disease history, family history of breast cancer, breast density, BMI, and age at first live birth are associated with overall DCIS risk.46-49 To our knowledge, our study is the first to evaluate the history of false-positive mammography results in relation to future DCIS risk, although prior studies50,51 have identified false-positive mammography as a risk factor for breast cancer overall (invasive or DCIS). Our study provides new insights regarding interactions between age and breast density and false-positive mammography results in relation to risk of screen-detected DCIS. We also observed that the risk of screen-detected DCIS was higher among Asian women and lower among Hispanic/Latina women compared with White women. Reasons for these differences require further exploration.
Prior studies52-56 have demonstrated increases in overall or invasive breast cancer detection with DBT, but few have directly assessed DCIS detection. A meta-analysis57 of 4 European prospective, observational studies found that DCIS detection was higher on DBT vs digital mammography, whereas a large US-based observational study58 and a European randomized clinical trial59 both observed no difference in DCIS detection by modality. Our study found no difference in DCIS detection rate on DBT vs digital mammography after adjustment for other factors. Differences in study populations (eg, age and breast density), European vs US radiologist practices, the proportion of prevalent vs incident screening examinations, and covariate adjustments could contribute to the observed differences across studies.
Strengths and Limitations
This study has several strengths, including the large, diverse, population-based sample and the prospective collection of risk factor information. However, as with any observational study, some limitations exist. Residual confounding could still impact differences in risk estimates by screening interval. Data on menopausal status and BMI were missing for a substantial fraction of examinations. We used multiple imputation to avoid bias that would have resulted from exclusion of examinations with incomplete data.60 We did not examine DCIS rates by nuclear grade, which correlates with risk of subsequent invasive breast cancer.61 We used cross-validation to assess the accuracy of our model. The AUC optimism and SEs for the risk factor ORs did not account for the process of selecting interactions for inclusion in the model and as a result may be underestimated. External validation is needed to evaluate model performance in other populations.61
In summary, the results of this cohort study suggest wide variation in the probability of DCIS detection according to screening interval and clinical risk factors. Our risk model permits estimation of the probability of screen-detected DCIS during a 6-year time horizon according to mammography screening frequency and women’s risk factors. Our findings can be used by policy makers assessing the balance of benefits and harms of different screening strategies, in conjunction with existing risk models for other screening outcomes, such as advanced cancers and false-positive mammography results.15,16
Accepted for Publication: December 15, 2022.
Published: February 20, 2023. doi:10.1001/jamanetworkopen.2023.0166
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2023 Sprague BL et al. JAMA Network Open.
Corresponding Author: Brian L. Sprague, PhD, Office of Health Promotion Research, University of Vermont, UVM Medical Center Building, Room 4425m, 1 S Prospect St, Burlington, VT 05405 (email@example.com).
Author Contributions: Drs Miglioretti and Chen had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Sprague, Miglioretti, Kerlikowske.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Sprague, Chen, Kerlikowske.
Critical revision of the manuscript for important intellectual content: Chen, Miglioretti, Gard, Tice, Hubbard, Aiello Bowles, Kaufman, Kerlikowske.
Statistical analysis: Chen, Miglioretti, Gard, Hubbard.
Obtained funding: Sprague, Miglioretti, Aiello Bowles, Kerlikowske.
Administrative, technical, or material support: Kerlikowske.
Conflict of Interest Disclosures: Dr Gard reported receiving personal fees from Kaiser Permanente Washington Health Research Institute for provision of statistical consulting services to the statistical coordinating center of the Breast Cancer Surveillance Consortium (BCSC) during the conduct of the study and outside the submitted work. Dr Hubbard reported receiving grants from Pfizer, Merck, and Johnson & Johnson outside the submitted work. No other disclosures were reported.
Funding/Support: This work was supported by grant P01CA154292 from the National Institutes of Health. Data collection for this research was additionally supported by the BCSC with funding from grant U54CA163303 from the National Cancer Institute, Patient-Centered Outcomes Research Institute (PCORI) Program Award PCS-1504-30370, and grant R01 HS018366-01A1 from the Agency for Healthcare Research and Quality. Dr Sprague’s effort was also supported by grant P20GM103644 from the National Institute of General Medical Sciences. Ms Aiello Bowles’ effort was also supported by grant R50CA211115 from the National Cancer Institute. The collection of cancer and vital status data was supported in part by several state public health departments and cancer registries throughout the US.
Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Disclaimer: The statements presented in this work are solely the responsibility of the authors and do not necessarily represent the official views of PCORI, its board of governors or methodology committee, the National Cancer Institute, or the National Institutes of Health.
Data Sharing Statement: See Supplement 2.
Additional Contributions: We thank the participating women, mammography facilities, and radiologists for the data they have provided for this study. You can learn more about the BCSC at https://www.bcsc-research.org/.
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