Breast density and risk of breast cancer: masking and detection bias.

Breast density is associated with risk of breast cancer (BC) diagnosis, impacting risk prediction tools and patient notification policies. Density affects mammography sensitivity and may influence screening intensity. Therefore, the observed association between density and BC diagnosis may not reflect the relationship between density and disease risk. We investigate the association between breast density and BC risk using data sourced from 33,542 women in the Breast Cancer Surveillance Consortium, 2000-2018. We estimated mammogram sensitivity and rates of screening mammography among dense (BI-RADS c, d) and non-dense (BI-RADS a, b) breasts. We used Kaplan-Meier estimates to summarize the relative risks of BC diagnosis (RRdx) by density and fit a natural history model to estimate the relative risks of BC onset (RRonset) given density-specific sensitivities. RRdx for dense versus non-dense breasts was 1.80 (95% CI 1.46 to 2.57). Based on estimated screening sensitivities of 0.88 and .78 for non-dense and dense breasts, respectively, RRonset was 1.73 (95% CI 1.43 to 2.25). Sensitivity analyses suggested higher breast density is robustly associated with increased risk of BC onset, similar in magnitude to the increased risk of BC diagnosis. These finding support laws requiring notifications to women with dense breasts of their increased BC risk.

Estimation of Breast Cancer Overdiagnosis in a U.S. Breast Screening Cohort.

BACKGROUND: Mammography screening can lead to overdiagnosis-that is, screen-detected breast cancer that would not have caused symptoms or signs in the remaining lifetime. There is no consensus about the frequency of breast cancer overdiagnosis. OBJECTIVE: To estimate the rate of breast cancer overdiagnosis in contemporary mammography practice accounting for the detection of nonprogressive cancer. DESIGN: Bayesian inference of the natural history of breast cancer using individual screening and diagnosis records, allowing for nonprogressive preclinical cancer. Combination of fitted natural history model with life-table data to predict the rate of overdiagnosis among screen-detected cancer under biennial screening. SETTING: Breast Cancer Surveillance Consortium (BCSC) facilities. PARTICIPANTS: Women aged 50 to 74 years at first mammography screen between 2000 and 2018. MEASUREMENTS: Screening mammograms and screen-detected or interval breast cancer. RESULTS: The cohort included 35 986 women, 82 677 mammograms, and 718 breast cancer diagnoses. Among all preclinical cancer cases, 4.5% (95% uncertainty interval [UI], 0.1% to 14.8%) were estimated to be nonprogressive. In a program of biennial screening from age 50 to 74 years, 15.4% (UI, 9.4% to 26.5%) of screen-detected cancer cases were estimated to be overdiagnosed, with 6.1% (UI, 0.2% to 20.1%) due to detecting indolent preclinical cancer and 9.3% (UI, 5.5% to 13.5%) due to detecting progressive preclinical cancer in women who would have died of an unrelated cause before clinical diagnosis. LIMITATIONS: Exclusion of women with first mammography screen outside BCSC. CONCLUSION: On the basis of an authoritative U.S. population data set, the analysis projected that among biennially screened women aged 50 to 74 years, about 1 in 7 cases of screen-detected cancer is overdiagnosed. This information clarifies the risk for breast cancer overdiagnosis in contemporary screening practice and should facilitate shared and informed decision making about mammography screening. PRIMARY FUNDING SOURCE: National Cancer Institute.

Comparing Mammographic Density Assessed by Digital Breast Tomosynthesis or Digital Mammography: The Breast Cancer Surveillance Consortium.

Background Consistency in reporting Breast Imaging Reporting and Data System (BI-RADS) breast density on mammograms is important because breast density is used for breast cancer risk assessment and is reported directly to women and clinicians to inform decisions about supplemental screening. Purpose To assess the consistency of BI-RADS density reporting between digital breast tomosynthesis (DBT) and digital mammography (DM) and evaluate density as a breast cancer risk factor when assessed using DM versus DBT. Materials and Methods The Breast Cancer Surveillance Consortium is a prospective cohort study of women undergoing mammography with DM or DBT. This secondary analysis included women aged 40-79 years who underwent at least two screening mammography examinations less than 36 months apart. Percentage agreement and κ statistic were estimated for pairs of BI-RADS density assessments. Cox proportional hazards regression was used to calculate hazard ratios (HRs) of breast density as a risk factor for invasive breast cancer. Results A total of 403 326 pairs of mammograms from 342 149 women were evaluated. There were no significant differences in breast density assessment in pairs consisting of one DM and one DBT examination (57 516 of 74 729 [77%]; κ = 0.64), two DM examinations (238 678 of 301 743 [79%]; κ = 0.67), and two DBT examinations (20 763 of 26 854 [77%]; κ = 0.65). Results were similar when restricting the analyses to pairs read by the same radiologist. The breast cancer HRs for breast density were similar for DM and DBT (P = .45 for interaction). The HRs for density acquired using DM and DBT, respectively, were 0.55 (95% CI: 0.49, 0.63) and 0.37 (95% CI: 0.21, 0.66) for almost entirely fat, 1.47 (95% CI: 1.37, 1.58) and 1.36 (95% CI: 1.02, 1.82) for heterogeneously dense, and 1.72 (95% CI: 1.54, 1.93) and 2.05 (95% CI: 1.25, 3.36) for extremely dense breasts. Conclusion Radiologist reporting of Breast Imaging Reporting and Data System density obtained with digital breast tomosynthesis did not differ from that obtained with digital mammography. © RSNA, 2021 Online supplemental material is available for this article.

Validation of the breast cancer surveillance consortium model of breast cancer risk.

PURPOSE: In order to use a breast cancer prediction model in clinical practice to guide screening and prevention, it must be well calibrated and validated in samples independent from the one used for development. We assessed the accuracy of the breast cancer surveillance consortium (BCSC) model in a racially diverse population followed for up to 10 years. METHODS: The BCSC model combines breast density with other risk factors to estimate a woman's 5- and 10-year risk of invasive breast cancer. We validated the model in an independent cohort of 252,997 women in the Chicago area. We evaluated calibration using the ratio of expected to observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC). RESULTS: In an independent cohort of 252,997 women (median age 50 years, 26% non-Hispanic Black), the BCSC model was well calibrated (E/O = 0.94, 95% confidence interval [CI] 0.90-0.98), but underestimated the incidence of invasive breast cancer in younger women and in women with low mammographic density. The AUROC was 0.633, similar to that observed in prior validation studies. CONCLUSIONS: The BCSC model is a well-validated risk assessment tool for breast cancer that may be particularly useful when assessing the utility of supplemental screening in women with dense breasts.

Joint relative risks for estrogen receptor-positive breast cancer from a clinical model, polygenic risk score, and sex hormones.

BACKGROUND: Models that predict the risk of estrogen receptor (ER)-positive breast cancers may improve our ability to target chemoprevention. We investigated the contributions of sex hormones to the discrimination of the Breast Cancer Surveillance Consortium (BCSC) risk model and a polygenic risk score comprised of 83 single nucleotide polymorphisms. METHODS: We conducted a nested case-control study of 110 women with ER-positive breast cancers and 214 matched controls within a mammography screening cohort. Participants were postmenopausal and not on hormonal therapy. The associations of estradiol, estrone, testosterone, and sex hormone binding globulin with ER-positive breast cancer were evaluated using conditional logistic regression. We assessed the individual and combined discrimination of estradiol, the BCSC risk score, and polygenic risk score using the area under the receiver operating characteristic curve (AUROC). RESULTS: Of the sex hormones assessed, estradiol (OR 3.64, 95% CI 1.64-8.06 for top vs bottom quartile), and to a lesser degree estrone, was most strongly associated with ER-positive breast cancer in unadjusted analysis. The BCSC risk score (OR 1.32, 95% CI 1.00-1.75 per 1% increase) and polygenic risk score (OR 1.58, 95% CI 1.06-2.36 per standard deviation) were also associated with ER-positive cancers. A model containing the BCSC risk score, polygenic risk score, and estradiol levels showed good discrimination for ER-positive cancers (AUROC 0.72, 95% CI 0.65-0.79), representing a significant improvement over the BCSC risk score (AUROC 0.58, 95% CI 0.50-0.65). CONCLUSION: Adding estradiol and a polygenic risk score to a clinical risk model improves discrimination for postmenopausal ER-positive breast cancers.

Breast cancer risk prediction using a clinical risk model and polygenic risk score.

Breast cancer risk assessment can inform the use of screening and prevention modalities. We investigated the performance of the Breast Cancer Surveillance Consortium (BCSC) risk model in combination with a polygenic risk score (PRS) comprised of 83 single nucleotide polymorphisms identified from genome-wide association studies. We conducted a nested case-control study of 486 cases and 495 matched controls within a screening cohort. The PRS was calculated using a Bayesian approach. The contributions of the PRS and variables in the BCSC model to breast cancer risk were tested using conditional logistic regression. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (AUROC). Increasing quartiles of the PRS were positively associated with breast cancer risk, with OR 2.54 (95 % CI 1.69-3.82) for breast cancer in the highest versus lowest quartile. In a multivariable model, the PRS, family history, and breast density remained strong risk factors. The AUROC of the PRS was 0.60 (95 % CI 0.57-0.64), and an Asian-specific PRS had AUROC 0.64 (95 % CI 0.53-0.74). A combined model including the BCSC risk factors and PRS had better discrimination than the BCSC model (AUROC 0.65 versus 0.62, p = 0.01). The BCSC-PRS model classified 18 % of cases as high-risk (5-year risk ≥3 %), compared with 7 % using the BCSC model. The PRS improved discrimination of the BCSC risk model and classified more cases as high-risk. Further consideration of the PRS's role in decision-making around screening and prevention strategies is merited.

Misclassification of Breast Imaging Reporting and Data System (BI-RADS) Mammographic Density and Implications for Breast Density Reporting Legislation.

USA states have begun legislating mammographic breast density reporting to women, requiring that women undergoing screening mammography who have dense breast tissue (Breast Imaging Reporting and Data System [BI-RADS] density c or d) receive written notification of their breast density; however, the impact that misclassification of breast density will have on this reporting remains unclear. The aim of this study was to assess reproducibility of the four-category BI-RADS density measure and examine its relationship with a continuous measure of percent density. We enrolled 19 radiologists, experienced in breast imaging, from a single integrated health care system. Radiologists interpreted 341 screening mammograms at two points in time 6 months apart. We assessed intra- and interobserver agreement in radiologists'; interpretations of BI-RADS density and explored whether agreement depended upon radiologist characteristics. We examined the relationship between BI-RADS density and percent density in a subset of 282 examinations. Intraradiologist agreement was moderate to substantial, with kappa varying across radiologists from 0.50 to 0.81 (mean = 0.69, 95% CI [0.63, 0.73]). Intraradiologist agreement was higher for radiologists with ≥10 years experience interpreting mammograms (difference in mean kappa = 0.10, 95% CI [0.01, 0.24]). Interradiologist agreement varied widely across radiologist pairs from slight to substantial, with kappa ranging from 0.02 to 0.72 (mean = 0.46, 95% CI [0.36, 0.55]). Of 145 examinations interpreted as "nondense" (BI-RADS density a or b) by the majority of radiologists, 82.8% were interpreted as "dense" (BI-RADS density c or d) by at least one radiologist. Of 187 examinations interpreted as "dense" by the majority of radiologists, 47.1% were interpreted as "nondense" by at least one radiologist. While the examinations of almost half of the women in our study were interpreted clinically as having BI-RADS density c or d, only about 10% of examinations had percent density >50%. Our results suggest that breast density reporting based on a single BI-RADS density interpretation may be misleading due to high interradiologist variability and a lack of correspondence between BI-RADS density and percent density.

Extending the Breast Cancer Surveillance Consortium Model of Invasive Breast Cancer.

PURPOSE: We extended the Breast Cancer Surveillance Consortium (BCSC) version 2 (v2) model of invasive breast cancer risk to include BMI, extended family history of breast cancer, and age at first live birth (version 3 [v3]) to better inform appropriate breast cancer prevention therapies and risk-based screening. METHODS: We used Cox proportional hazards regression to estimate the age- and race- and ethnicity-specific relative hazards for family history of breast cancer, breast density, history of benign breast biopsy, BMI, and age at first live birth for invasive breast cancer in the BCSC cohort. We evaluated calibration using the ratio of expected-to-observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC). RESULTS: We analyzed data from 1,455,493 women age 35-79 years without a history of breast cancer. During a mean follow-up of 7.3 years, 30,266 women were diagnosed with invasive breast cancer. The BCSC v3 model had an E/O of 1.03 (95% CI, 1.01 to 1.04) and an AUROC of 0.646 for 5-year risk. Compared with the v2 model, discrimination of the v3 model improved most in Asian, White, and Black women. Among women with a BMI of 30.0-34.9 kg/m2, the true-positive rate in women with an estimated 5-year risk of 3% or higher increased from 10.0% (v2) to 19.8% (v3) and the improvement was greater among women with a BMI of ≥35 kg/m2 (7.6%-19.8%). CONCLUSION: The BCSC v3 model updates an already well-calibrated and validated breast cancer risk assessment tool to include additional important risk factors. The inclusion of BMI was associated with the largest improvement in estimated risk for individual women.

Geographic Access to Early Pregnancy Loss Management.

Early pregnancy loss (EPL) is common, but patients face barriers to the most effective medication (mifepristone followed by misoprostol) and procedural (uterine aspiration) management options. This cross-sectional geospatial analysis evaluated access in New Mexico to mifepristone and misoprostol and uterine aspiration in emergency departments (comprehensive) and to uterine aspiration anywhere in a hospital (aspiration) for EPL. Access was defined as a 60-minute car commute. We collected data from hospital key informants and public databases and performed logistical regression to evaluate associations between access and rurality, area deprivation, race, and ethnicity. Thirty-five of 42 (83.3%) hospitals responded between October 2020 and August 2021. Two hospitals (5.7%) provided comprehensive management; 24 (68.6%) provided aspiration. Rural and higher deprivation areas had statistically significantly lower adjusted odds ratios for comprehensive management (0.03-0.07 and 0.3-0.4, respectively) and aspiration (0.03-0.06 and 0.1-0.3, respectively) access. Mifepristone and uterine aspiration implementation would address disparate access to EPL treatment.

Reported mammographic density: film-screen versus digital acquisition.

PURPOSE: To test the hypothesis that American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) categories for breast density reported by radiologists are lower when digital mammography is used than those reported when film-screen (FS) mammography is used. MATERIALS AND METHODS: This study was institutional review board approved and HIPAA compliant. Demographic data, risk factors, and BI-RADS breast density categories were collected from five mammography registries that were part of the Breast Cancer Surveillance Consortium. Active, passive, or waiver of consent was obtained for all participants. Women aged 40 years and older who underwent at least two screening mammographic examinations less than 36 months apart between January 1, 2000, and December 31, 2009, were included. Women with prior breast cancer, augmentation, or use of agents known to affect density were excluded. The main sample included 89 639 women with both FS and digital mammograms. The comparison group included 259 046 women with two FS mammograms and 87 066 women with two digital mammograms. BI-RADS density was cross-tabulated according to the order in which the two types of mammogram were acquired and by the first versus second interpretation. RESULTS: Regardless of acquisition method, the percentage of women with a change in density from one reading to the next was similar. Breast density was lower in 19.8% of the women who underwent FS before digital mammography and 17.1% of those who underwent digital before FS mammography. Similarly, lower density classifications were reported on the basis of the second mammographic examination regardless of acquisition method (15.8%-19.8%). The percentage of agreement between density readings was similar regardless of mammographic types paired (67.3%-71.0%). CONCLUSION: The study results showed no difference in reported BI-RADS breast density categories according to acquisition method. Reported BI-RADS density categories may be useful in the development of breast cancer risk models in which FS, digital, or both acquisition methods are used.

Effects of blood meal source on blood consumption and reproductive success of cat fleas, Ctenocephalides felis.

Cat fleas, small blood-feeding ectoparasites that feed on humans and animals, cause discomfort through their bites, and can transmit numerous diseases to animals and humans. Traditionally, fleas have been reared for research on live animals, but this process requires animal handling permits, inflicts discomfort on animals, and requires money and time to maintain the host animals. Although artificial membrane-based feeding systems have been implemented, these methods are not sustainable in the long term because they result in lower blood consumption and egg production than those with rearing on live hosts. To maximize these parameters, we tested blood from four hosts to determine the most suitable blood, on the basis of blood consumption and egg production. We also tested the effects of adding the phagostimulant adenosine-5´-triphosphate to the blood to maximize blood consumption. In 48 hours, fleas fed dog blood consumed the most blood, averaging 9.5 µL per flea, whereas fleas fed on cow, cat, or human blood consumed 8.3 µL, 5.7 µL, or 5.2 µL, respectively. Addition of 0.01 M and 0.1 M adenosine-5´-triphosphate to dog and cow blood did not enhance blood consumption. In a 1-week feeding period, the total egg production was also greatest in fleas fed dog blood, with females producing 129.5 eggs, whereas females on cat, human, and cow blood produced 97.2, 83.0, and 70.7 eggs, respectively. The observed results in dog blood indicate an improvement over previously reported results in cat fleas fed with an artificial feeding system. Improving the sustainability of rearing cat flea colonies without feeding on live animals will enable more humane and convenient production of this pest for scientific research.

Sleeping behavior and associated factors during COVID-19 in students at a Hispanic serving institution in the US southwestern border region.

Sleep is responsible for maintenance and regulatory functions in human physiology. Insufficient sleep has been associated with cardiovascular disease, weight gain, obesity, inflammation, and morbidity. University students are at high risk under normal circumstances of stress and anxiety due to extracurricular demands, competing pressures on student time, long study hours, and financial concerns. The COVID 19 pandemic has disrupted normal college students' lives adding stresses such as lost jobs and family responsibilities such as serving as caregivers, which disproportionally affect minority and rural student. This study aimed to assess the correlation of sleep disorders in New Mexico State University students during COVID 19 with selected variates including base demographics (e.g., gender, age, etc.), lifestyle metrics (e.g., employment status, discipline, class, etc.), living arrangements (e.g., housing type, number of children, etc.), alcohol and tobacco use, vaccination status, family COVID status, and family vaccination status. Single- and multi-factor logistic regressions were performed to analyze the data on the students. Qualtrics software was used to collect data on demographics and sleep disorders. R software was used for data analysis. Correlations were found between sleeping less, sleeping more, and disturbed sleep among several covariate categories. For all three responses, being married (sleeping less: OR = 0.342, 95% CI = 0.181-0.642, sleeping more: OR = 0.265, 95% CI = 0.111-0.591; disturbed sleeping: OR = 0.345, 95% CI = 0.182-0.650), frequency of feeling sleepy-very often (OR = 16.87, 95% CI = 6.571-47.434; OR = 8.393, 95% CI = 3.086-25.298; OR = 13.611, 95% CI = 5.409-36.975) and change in diet- quality decreased (OR = 7.304, 95% CI = 3.615-15.270; OR = 5.250, 95% CI = 2.309-12.558; OR = 4.181, 95% CI = 2.145-8.359) were all significant correlated to change in sleeping behavior. Other correlations were found among covariates and sleep changes. Several covariates were determined to be correlated with the effect of COVID-19 on sleeping.

Differences in incidence, staging, and survival of urologic cancers in patients under 65 living in the US-Mexico border region.

Objectives: To describe and compare the incidence, stage at diagnosis, and survival for genitourinary cancers in the border regions and in Hispanic-Americans. Materials and methods: A population-based search was performed using the Surveillance, Epidemiology, and End Results Program 18 database and the Texas Cancer Registry from 2000 to 2017. Cox regression models were performed with adjusted for age, gender, race, cancer type, cancer stage, insurance status, and cause of death were used to compare cancer-specific survival. Results: A total of 63,236 kidney and renal pelvis, 38,398 bladder, 170,640 prostate, 24,313 testicular cancer cases were identified. Cancer-specific survival was found to be improved in Hispanic-Americans in kidney and renal pelvis (hazard ratio [HR], 0.903, 95% confidence interval [CI], 0.856-0.952, p = 0.0001), and bladder cancers (HR, 0.817, 95% CI, 0.743-0.898, p < 0.001), despite a more advanced stage at diagnosis in Hispanics with bladder cancer (p < 0.0074). Testicular cancer has a survival disadvantage for individuals living in the border region (HR, 1.315, 95% CI, 1.124-1.539, p = 0.0006). Conclusions: Disparities exist between Hispanic-Americans and Non-Hispanic White and also between individuals living in the border counties when compared to other regions. This is most significant in individuals with testicular cancer residing in the border region who demonstrate worse overall survival.

Prevalence of associated factors on depression during COVID 19 in students in a minority serving institution: A cross sectional study.

Introduction: The COVID-19 pandemic changed the learning style of university students in the US, affecting their mental health of students. This study aims to understand the factors that influenced depression during the COVID-19 pandemic in the New Mexico State University (NMSU) student population. Methods: A questionnaire assessing mental health and lifestyle factors was delivered to NMSU students by using QualtricsXM software. Depression was assessed using the Patient Health Questionnaire- 9 (PHQ-9); depression was defined as a score ≥10. Single and multifactor logistic regression was performed using R software. Results: This study determined that the prevalence of depression among female students was 72% and 56.30% among male students. Several covariates were significant for increased odds of depression in students, including decreased diet quality (OR: 5.126, 95% CI: 3.186-8.338), annual household income $10,000 - $20,000 (OR: 3.161, 95% CI: 1.444-7.423), increased alcohol consumption (OR: 2.362, 95% CI: 1.504-3.787), increased smoking (OR: 3.581, 95% CI:1.671-8.911), quarantining due to COVID (OR: 2.001, 95% CI: 1.348-2.976), and family member dying of COVID (OR: 1.916, 95% CI: 1.072-3.623). Covariates of being male (OR: 0.501, 95% CI: 0.324-0.776), married (OR: 0.499, 95% CI: 0.318-0.786), eating a balanced diet (OR: 0.472, 95% CI: 0.316-0.705), and sleeping 7-8 h per night (OR: 0.271, 95% CI: 0.175-0.417) were all protective factors for depression in NMSU students. Limitation: This is a cross-sectional study, and therefore, causation cannot be determined. Conclusion: Several factors regarding demographics, lifestyle, living arrangements, alcohol and tobacco use, sleeping behavior, family vaccination, and COVID status were significantly associated with depression in students during the COVID-19 pandemic.

Risk of cancer versus risk of cancer diagnosis? Accounting for diagnostic bias in predictions of breast cancer risk by race and ethnicity.

OBJECTIVES: Cancer risk prediction may be subject to detection bias if utilization of screening is related to cancer risk factors. We examine detection bias when predicting breast cancer risk by race/ethnicity. METHODS: We used screening and diagnosis histories from the Breast Cancer Surveillance Consortium to estimate risk of breast cancer onset and calculated relative risk of onset and diagnosis for each racial/ethnic group compared with non-Hispanic White women. RESULTS: Of 104,073 women aged 40-54 receiving their first screening mammogram at a Breast Cancer Surveillance Consortium facility between 2000 and 2018, 10.2% (n = 10,634) identified as Asian, 10.9% (n = 11,292) as Hispanic, and 8.4% (n = 8719) as non-Hispanic Black. Hispanic and non-Hispanic Black women had slightly lower screening frequencies but biopsy rates following a positive mammogram were similar across groups. Risk of cancer diagnosis was similar for non-Hispanic Black and White women (relative risk vs non-Hispanic White = 0.90, 95% CI 0.65 to 1.14) but was lower for Asian (relative risk = 0.70, 95% CI 0.56 to 0.97) and Hispanic women (relative risk = 0.82, 95% CI 0.62 to 1.08). Relative risks of disease onset were 0.78 (95% CI 0.68 to 0.88), 0.70 (95% CI 0.59 to 0.83), and 0.95 (95% CI 0.84 to 1.09) for Asian, Hispanic, and non-Hispanic Black women, respectively. CONCLUSIONS: Racial/ethnic differences in mammography and biopsy utilization did not induce substantial detection bias; relative risks of disease onset were similar to or modestly different than relative risks of diagnosis. Asian and Hispanic women have lower risks of developing breast cancer than non-Hispanic Black and White women, who have similar risks.

Test sensitivity in a prospective cancer screening program: A critique of a common proxy measure.

The true sensitivity of a cancer screening test, defined as the frequency with which the test returns a positive result if the cancer is present, is a key indicator of diagnostic performance. Given the challenges of directly assessing test sensitivity in a prospective screening program, proxy measures for true sensitivity are frequently reported. We call one such proxy empirical sensitivity, as it is given by the observed ratio of screen-detected cancers to the sum of screen-detected and interval cancers. In the setting of the canonical three-state Markov model for progression from preclinical onset to clinical diagnosis, we formulate a mathematical relationship for how empirical sensitivity varies with the screening interval and the mean preclinical sojourn time and identify conditions under which empirical sensitivity exceeds or falls short of true sensitivity. In particular, when the inter-screening interval is short relative to the mean sojourn time, empirical sensitivity tends to exceed true sensitivity, unless true sensitivity is high. The Breast Cancer Surveillance Consortium (BCSC) has reported an estimate of 0.87 for the empirical sensitivity of digital mammography. We show that this corresponds to a true sensitivity of 0.82 under a mean sojourn time of 3.6 years estimated based on breast cancer screening trials. However, the BCSC estimate of empirical sensitivity corresponds to even lower true sensitivity under more contemporary, longer estimates of mean sojourn time. Consistently applied nomenclature that distinguishes empirical sensitivity from true sensitivity is needed to ensure that published estimates of sensitivity from prospective screening studies are properly interpreted.

Cumulative 6-Year Risk of Screen-Detected Ductal Carcinoma In Situ by Screening Frequency.

Importance: 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. Objective: 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. Exposures: 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. Results: 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.

Reproducibility of BI-RADS breast density measures among community radiologists: a prospective cohort study.

Using data from the Vermont Breast Cancer Surveillance System (VBCSS), we studied the reproducibility of Breast Imaging Reporting and Data System (BI-RADS) breast density among community radiologists interpreting mammograms in a cohort of 11,755 postmenopausal women. Radiologists interpreting two or more film-screen screening or bilateral diagnostic mammograms for the same woman within a 3- to 24-month period during 1996-2006 were eligible. We observed moderate-to-substantial overall intra-rater agreement for use of BI-RADS breast density in clinical practice, with an overall intra-radiologist percent agreement of 77.2% (95% confidence interval (CI), 74.5-79.5%), an overall simple kappa of 0.58 (95% CI, 0.55-0.61), and an overall weighted kappa of 0.70 (95% CI, 0.68-0.73). Agreement exhibited by individual radiologists varied widely, with intra-radiologist percent agreement ranging from 62.1% to 87.4% and simple kappa ranging from 0.19 to 0.69 across individual radiologists. Our findings underscore the need for additional evaluation of the BI-RADS breast density categorization system in clinical practice.

Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population.

BACKGROUND: Estimating advanced breast cancer risk in women undergoing annual or biennial mammography could identify women who may benefit from less or more intensive screening. We developed an actionable model to predict cumulative 6-year advanced cancer (prognostic pathologic stage II or higher) risk according to screening interval. METHODS: We included 931 186 women aged 40-74 years in the Breast Cancer Surveillance Consortium undergoing 2 542 382 annual (prior mammogram within 11-18 months) or 752 049 biennial (prior within 19-30 months) screening mammograms. The prediction model includes age, race and ethnicity, body mass index, breast density, family history of breast cancer, and prior breast biopsy subdivided by menopausal status and screening interval. We used fivefold cross-validation to internally validate model performance. We defined higher than 95th percentile as high risk (>0.658%), higher than 75th percentile to 95th or less percentile as intermediate risk (0.380%-0.658%), and 75th or less percentile as low to average risk (<0.380%). RESULTS: Obesity, high breast density, and proliferative disease with atypia were strongly associated with advanced cancer. The model is well calibrated and has an area under the receiver operating characteristics curve of 0.682 (95% confidence interval = 0.670 to 0.694). Based on women's predicted advanced cancer risk under annual and biennial screening, 69.1% had low or average risk regardless of screening interval, 12.4% intermediate risk with biennial screening and average risk with annual screening, and 17.4% intermediate or high risk regardless of screening interval. CONCLUSION: Most women have low or average advanced cancer risk and can undergo biennial screening. Intermediate-risk women may consider annual screening, and high-risk women may consider supplemental imaging in addition to annual screening.

Multilevel Factors Associated With Time to Biopsy After Abnormal Screening Mammography Results by Race and Ethnicity.

Importance: Diagnostic delays in breast cancer detection may be associated with later-stage disease and higher anxiety, but data on multilevel factors associated with diagnostic delay are limited. Objective: To evaluate individual-, neighborhood-, and health care-level factors associated with differences in time from abnormal screening to biopsy among racial and ethnic groups. Design, Setting, and Participants: This prospective cohort study used data from women aged 40 to 79 years who had abnormal results in screening mammograms conducted in 109 imaging facilities across 6 US states between 2009 and 2019. Data were analyzed from February 21 to November 4, 2021. Exposures: Individual-level factors included self-reported race and ethnicity, age, family history of breast cancer, breast density, previous breast biopsy, and time since last mammogram; neighborhood-level factors included geocoded education and income based on residential zip codes and rurality; and health care-level factors included mammogram modality, screening facility academic affiliation, and facility onsite biopsy service availability. Data were also assessed by examination year. Main Outcome and Measures: The main outcome was unadjusted and adjusted relative risk (RR) of no biopsy within 30, 60, and 90 days using sequential log-binomial regression models. A secondary outcome was unadjusted and adjusted median time to biopsy using accelerated failure time models. Results: A total of 45 186 women (median [IQR] age at screening, 56 [48-65] years) with 46 185 screening mammograms with abnormal results were included. Of screening mammograms with abnormal results recommended for biopsy, 15 969 (34.6%) were not resolved within 30 days, 7493 (16.2%) were not resolved within 60 days, and 5634 (12.2%) were not resolved within 90 days. Compared with White women, there was increased risk of no biopsy within 30 and 60 days for Asian (30 days: RR, 1.66; 95% CI, 1.31-2.10; 60 days: RR, 1.58; 95% CI, 1.15-2.18), Black (30 days: RR, 1.52; 95% CI, 1.30-1.78; 60 days: 1.39; 95% CI, 1.22-1.60), and Hispanic (30 days: RR, 1.50; 95% CI, 1.24-1.81; 60 days: 1.38; 95% CI, 1.11-1.71) women; however, the unadjusted risk of no biopsy within 90 days only persisted significantly for Black women (RR, 1.28; 95% CI, 1.11-1.47). Sequential adjustment for selected individual-, neighborhood-, and health care-level factors, exclusive of screening facility, did not substantially change the risk of no biopsy within 90 days for Black women (RR, 1.27; 95% CI, 1.12-1.44). After additionally adjusting for screening facility, the increased risk for Black women persisted but showed a modest decrease (RR, 1.20; 95% CI, 1.08-1.34). Conclusions and Relevance: In this cohort study involving a diverse cohort of US women recommended for biopsy after abnormal results on screening mammography, Black women were the most likely to experience delays to diagnostic resolution after adjusting for multilevel factors. These results suggest that adjustment for multilevel factors did not entirely account for differences in time to breast biopsy, but unmeasured factors, such as systemic racism and other health care system factors, may impact timely diagnosis.