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PurposeTo examine whether interpersonal aspects of patient-clinician interactions, such as patient-perceived medical discrimination, clinician mistrust, and treatment decision-making contribute to racial/ethnic/educational disparities in breast cancer care.MethodsA telephone interview was administered to 542 Asian/Pacific Islander (API), Black, Hispanic, and White women identified through the Greater Bay Area Cancer Registry, ages 20 and older diagnosed with a first primary invasive breast cancer. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were calculated from logistic regression models that assessed associations between race/ethnicity/education, medical discrimination, clinician mistrust, and treatment decision-making with concordance to breast cancer treatment guidelines (guideline-concordant treatment) and perceived quality of care (pQoC).ResultsApproximately three-quarters of women received treatment that was guideline-concordant (76.6%) and reported that their breast cancer care was excellent (72.1%). Non-college-educated Black women had lower odds of guideline-concordant care (aOR (CI) = 0.29 (0.12-0.67)) vs. college-educated White women. Odds of excellent pQoC were lower among the following: college-educated Hispanic women (aOR (CI) = 0.09 (0.02-0.47)) and API women regardless of education (aORs ≤ 0.50) vs. college-educated White women, women reporting low and moderate levels of discrimination (aORs ≤ 0.44) vs. none, and women reporting any clinician mistrust (aOR (CI) = 0.50 (0.29-0.88)) vs. none. Disparities in guideline-concordant care and pQoC persisted after controlling for medical discrimination, clinician mistrust, and decision-making.ConclusionsInterpersonal aspects of the patient-clinician interaction had an impact on pQoC but not receipt of guideline-concordant treatment and did not explain disparities in either outcome.Implications for cancer survivorsAlthough breast cancer survivors' interpersonal interactions with clinicians did not influence receipt of appropriate treatment, intervention strategies to improve patient-clinician relations may help attenuate disparities in survivors' pQoC.

PurposeThis study aims to determine whether radiologists who perform well in screening also perform well in interpreting diagnostic mammography.Materials and methodsWe evaluated the accuracy of 468 radiologists interpreting 2,234,947 screening and 196,164 diagnostic mammograms. Adjusting for site, radiologist, and patient characteristics, we identified radiologists with performance in the highest tertile and compared to those with lower performance.ResultsA moderate correlation was noted for radiologists' accuracy when interpreting screening versus their accuracy on diagnostic examinations: sensitivity (rspearman=0.51, 95% CI: 0.22, 0.80; P=.0006) and specificity (rspearman=0.40, 95% CI: 0.30, 0.49; P

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.

PurposeTo examine radiologists' screening performance in relation to the number of diagnostic work-ups performed after abnormal findings are discovered at screening mammography by the same radiologist or by different radiologists.Materials and methodsIn an institutional review board-approved HIPAA-compliant study, the authors linked 651 671 screening mammograms interpreted from 2002 to 2006 by 96 radiologists in the Breast Cancer Surveillance Consortium to cancer registries (standard of reference) to evaluate the performance of screening mammography (sensitivity, false-positive rate [ FPR false-positive rate ], and cancer detection rate [ CDR cancer detection rate ]). Logistic regression was used to assess the association between the volume of recalled screening mammograms ("own" mammograms, where the radiologist who interpreted the diagnostic image was the same radiologist who had interpreted the screening image, and "any" mammograms, where the radiologist who interpreted the diagnostic image may or may not have been the radiologist who interpreted the screening image) and screening performance and whether the association between total annual volume and performance differed according to the volume of diagnostic work-up.ResultsAnnually, 38% of radiologists performed the diagnostic work-up for 25 or fewer of their own recalled screening mammograms, 24% performed the work-up for 0-50, and 39% performed the work-up for more than 50. For the work-up of recalled screening mammograms from any radiologist, 24% of radiologists performed the work-up for 0-50 mammograms, 32% performed the work-up for 51-125, and 44% performed the work-up for more than 125. With increasing numbers of radiologist work-ups for their own recalled mammograms, the sensitivity (P = .039), FPR false-positive rate (P = .004), and CDR cancer detection rate (P < .001) of screening mammography increased, yielding a stepped increase in women recalled per cancer detected from 17.4 for 25 or fewer mammograms to 24.6 for more than 50 mammograms. Increases in work-ups for any radiologist yielded significant increases in FPR false-positive rate (P = .011) and CDR cancer detection rate (P = .001) and a nonsignificant increase in sensitivity (P = .15). Radiologists with a lower annual volume of any work-ups had consistently lower FPR false-positive rate , sensitivity, and CDR cancer detection rate at all annual interpretive volumes.ConclusionThese findings support the hypothesis that radiologists may improve their screening performance by performing the diagnostic work-up for their own recalled screening mammograms and directly receiving feedback afforded by means of the outcomes associated with their initial decision to recall. Arranging for radiologists to work up a minimum number of their own recalled cases could improve screening performance but would need systems to facilitate this workflow.

BackgroundWomen with elevated mammographic density have an increased risk of developing breast cancer. However, among women diagnosed with breast cancer, it is unclear whether higher density portends reduced survival, independent of other factors.MethodsWe evaluated relationships between mammographic density and risk of death from breast cancer and all causes within the US Breast Cancer Surveillance Consortium. We studied 9232 women diagnosed with primary invasive breast carcinoma during 1996-2005, with a mean follow-up of 6.6 years. Mammographic density was assessed using the Breast Imaging Reporting and Data System (BI-RADS) density classification. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by Cox proportional hazards regression; women with scattered fibroglandular densities (BI-RADS 2) were the referent group. All statistical tests were two-sided.ResultsA total of 1795 women died, of whom 889 died of breast cancer. In multivariable analyses (adjusted for site, age at and year of diagnosis, American Joint Committee on Cancer stage, body mass index, mode of detection, treatment, and income), high density (BI-RADS 4) was not related to risk of death from breast cancer (HR = 0.92, 95% CI = 0.71 to 1.19) or death from all causes (HR = 0.83, 95% CI = 0.68 to 1.02). Analyses stratified by stage and other prognostic factors yielded similar results, except for an increased risk of breast cancer death among women with low density (BI-RADS 1) who were either obese (HR = 2.02, 95% CI = 1.37 to 2.97) or had tumors of at least 2.0 cm (HR = 1.55, 95% CI = 1.14 to 2.09).ConclusionsHigh mammographic breast density was not associated with risk of death from breast cancer or death from any cause after accounting for other patient and tumor characteristics. Thus, risk factors for the development of breast cancer may not necessarily be the same as factors influencing the risk of death after breast cancer has developed.

BackgroundIt is not well understood whether breast density is a marker of cumulative exposure to estrogen or a marker of recent exposure to estrogen. The authors examined the relationship between bone mineral density (BMD; a marker of lifetime estrogen exposure) and breast density.MethodsThe authors conducted a cross-sectional analysis among 1800 postmenopausal women > or = 54 years. BMD data were taken from two population-based studies conducted in 1992-1993 (n = 1055) and in 1998-1999 (n = 753). The authors linked BMD data with breast density information collected as part of a mammography screening program. They used linear regression to evaluate the density relationship, adjusted for age, hormone therapy use, body mass index (BMI), and reproductive covariates.ResultsThere was a small but significant negative association between BMD and breast density. The negative correlation between density measures was not explained by hormone therapy or age, and BMI was the only covariate that notably influenced the relationship. Stratification by BMI only revealed the negative correlation between bone and breast densities in women with normal BMI. There was no relationship in overweight or obese women. The same relationship was seen for all women who had never used hormone therapy, but it was not significant once stratified by BMI.ConclusionsBMD and breast density were not positively associated although both are independently associated with estrogen exposure. It is likely that unique organ responses obscure the relationship between the two as indicators of cumulative estrogen exposure.

A study used a large, geographically diverse group of patients to assess the effect of computer-aided detection on the performance of screening mammography in community-based settings. Results showed that the use of computer-aided detection was associated with reduced accuracy of interpretation of screening mammograms, but the increased rate of biopsy with the use of such detection was not clearly associated with improved detection of invasive breast cancer.

Objective. To understand mammographers' perception of individual women's breast cancer risk. Materials and Methods. Radiologists interpreting screening mammography examinations completed a mailed survey consisting of questions pertaining to demographic and [...]

The relationship between the use of antibiotics and the risk of breast cancer was studied. The study reveals that the use of antibiotics is associated with increased risk of incident and fatal breast cancer.

Breast implants may make breast cancer diagnosis by mammography more difficult, according to a study of approximately 1 million women. Among women with breast implants, mammography only identified 45% of the women who had breast cancer. Among women without breast implants, mammography identified 67% of those who had breast cancer. Breast implants do not make breast cancer worse.