Stories in the public media that 30 to 50% of screen-detected breast cancers are overdiagnosed dissuade women from being screened because overdiagnosed cancers would never result in death if undetected yet do result in unnecessary treatment. However, such concerns are unwarranted because the frequency of overdiagnosis, when properly calculated, is only 0 to 5%. In the previous issue of Breast Cancer Research, Duffy and Parmar report that accurate estimation of the rate of overdiagnosis recognizes the effect of lead time on detection rates and the consequent requirement for an adequate number of years of follow-up. These indispensable elements were absent from highly publicized studies that overestimated the frequency of overdiagnosis.

Rationale and objectives

The purpose of this study was to compare the precision of mammographic breast density measurement using radiologist reader assessment, histogram threshold segmentation, fuzzy C-mean segmentation, and spectral material decomposition.

Materials and methods

Spectral mammography images from a total of 92 consecutive asymptomatic women (aged 50-69 years) who presented for annual screening mammography were retrospectively analyzed for this study. Breast density was estimated using 10 radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and spectral material decomposition. The breast density correlation between left and right breasts was used to assess the precision of these techniques to measure breast composition relative to dual-energy material decomposition.

Results

In comparison to the other techniques, the results of breast density measurements using dual-energy material decomposition showed the highest correlation. The relative standard error of estimate for breast density measurements from left and right breasts using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual-energy material decomposition was calculated to be 1.95, 2.87, 2.07, and 1.00, respectively.

Conclusions

The results indicate that the precision of dual-energy material decomposition was approximately factor of two higher than the other techniques with regard to better correlation of breast density measurements from right and left breasts.

Purpose

Mammographic density has been shown to be an indicator of breast cancer risk and also reduces the sensitivity of screening mammography. Currently, there is no accepted standard for measuring breast density. Dual energy mammography has been proposed as a technique for accurate measurement of breast density. The purpose of this study is to validate its accuracy in postmortem breasts and compare it with other existing techniques.

Methods

Forty postmortem breasts were imaged using a dual energy mammography system. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Dual energy decomposition was applied after scatter correction to calculate breast density. Breast density was also estimated using radiologist reader assessment, standard histogram thresholding and a fuzzy C-mean algorithm. Chemical analysis was used as the reference standard to assess the accuracy of different techniques to measure breast composition.

Results

Breast density measurements using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual energy were in good agreement with the measured fibroglandular volume fraction using chemical analysis. The standard error estimates using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean, and dual energy were 9.9%, 8.6%, 7.2%, and 4.7%, respectively.

Conclusions

The results indicate that dual energy mammography can be used to accurately measure breast density. The variability in breast density estimation using dual energy mammography was lower than reader assessment rankings, standard histogram thresholding, and fuzzy C-mean algorithm. Improved quantification of breast density is expected to further enhance its utility as a risk factor for breast cancer.

This review article explores the issue of overdiagnosis in screening mammography. Overdiagnosis is the screen detection of a breast cancer, histologically confirmed, that might not otherwise become clinically apparent during the lifetime of the patient. While screening mammography is an imperfect tool, it remains the best tool we have to diagnose breast cancer early, before a patient is symptomatic and at a time when chances of survival and options for treatment are most favorable. In 2015, an estimated 231,840 new cases of breast cancer (excluding ductal carcinoma in situ) will be diagnosed in the United States, and some 40,290 women will die. Despite these data, screening mammography for women ages 40-69 has contributed to a substantial reduction in breast cancer mortality, and organized screening programs have led to a shift from late-stage diagnosis to early-stage detection. Current estimates of overdiagnosis in screening mammography vary widely, from 0% to upwards of 30% of diagnosed cancers. This range reflects the fact that measuring overdiagnosis is not a straightforward calculation, but usually one based on different sets of assumptions and often biased by methodological flaws. The recent development of tomosynthesis, which creates high-resolution, three-dimensional images, has increased breast cancer detection while reducing false recalls. Because the greatest harm of overdiagnosis is overtreatment, the key goal should not be less diagnosis but better treatment decision tools. (Population Health Management 2015;18:S3-S11).

Purpose

To develop criteria to identify thresholds for the minimally acceptable performance of physicians interpreting diagnostic mammography studies.

Materials and methods

In an institutional review board-approved HIPAA-compliant study, an Angoff approach was used to set criteria for identifying minimally acceptable interpretive performance for both workup after abnormal screening examinations and workup of a breast lump. Normative data from the Breast Cancer Surveillance Consortium (BCSC) was used to help the expert radiologist identify the impact of cut points. Simulations, also using data from the BCSC, were used to estimate the expected clinical impact from the recommended performance thresholds.

Results

Final cut points for workup of abnormal screening examinations were as follows: sensitivity, less than 80%; specificity, less than 80% or greater than 95%; abnormal interpretation rate, less than 8% or greater than 25%; positive predictive value (PPV) of biopsy recommendation (PPV2), less than 15% or greater than 40%; PPV of biopsy performed (PPV3), less than 20% or greater than 45%; and cancer diagnosis rate, less than 20 per 1000 interpretations. Final cut points for workup of a breast lump were as follows: sensitivity, less than 85%; specificity, less than 83% or greater than 95%; abnormal interpretation rate, less than 10% or greater than 25%; PPV2, less than 25% or greater than 50%; PPV3, less than 30% or greater than 55%; and cancer diagnosis rate, less than 40 per 1000 interpretations. If underperforming physicians moved into the acceptable range after remedial training, the expected result would be (a) diagnosis of an additional 86 cancers per 100,000 women undergoing workup after screening examinations, with a reduction in the number of false-positive examinations by 1067 per 100,000 women undergoing this workup, and (b) diagnosis of an additional 335 cancers per 100,000 women undergoing workup of a breast lump, with a reduction in the number of false-positive examinations by 634 per 100,000 women undergoing this workup.

Conclusion

Interpreting physicians who fall outside one or more of the identified cut points should be reviewed in the context of an overall assessment of all their performance measures and their specific practice setting to determine if remedial training is indicated.

Rationale and objectives

The aim of this study was to describe the impact of a tailored Web-based educational program designed to reduce excessive screening mammography recall.

Materials and methods

Radiologists enrolled in one of four mammography registries in the United States were invited to take part and were randomly assigned to receive the intervention or to serve as controls. The controls were offered the intervention at the end of the study, and data collection included an assessment of their clinical practice as well. The intervention provided each radiologist with individual audit data for his or her sensitivity, specificity, recall rate, positive predictive value, and cancer detection rate compared to national benchmarks and peer comparisons for the same measures; profiled breast cancer risk in each radiologist's respective patient populations to illustrate how low breast cancer risk is in population-based settings; and evaluated the possible impact of medical malpractice concerns on recall rates. Participants' recall rates from actual practice were evaluated for three time periods: the 9 months before the intervention was delivered to the intervention group (baseline period), the 9 months between the intervention and control groups (T1), and the 9 months after completion of the intervention by the controls (T2). Logistic regression models examining the probability that a mammogram was recalled included indication of intervention versus control and time period (baseline, T1, and T2). Interactions between the groups and time period were also included to determine if the association between time period and the probability of a positive result differed across groups.

Results

Thirty-one radiologists who completed the continuing medical education intervention were included in the adjusted model comparing radiologists in the intervention group (n = 22) to radiologists who completed the intervention in the control group (n = 9). At T1, the intervention group had 12% higher odds of positive mammographic results compared to the controls, after controlling for baseline (odds ratio, 1.12; 95% confidence interval, 1.00-1.27; P = .0569). At T2, a similar association was found, but it was not statistically significant (odds ratio, 1.10; 95% confidence interval, 0.96 to 1.25). No associations were found among radiologists in the control group when comparing those who completed the continuing medical education intervention (n = 9) to those who did not (n = 10). In addition, no associations were found between time period and recall rate among radiologists who set realistic goals.

Conclusions

This study resulted in a null effect, which may indicate that a single 1-hour intervention is not adequate to change excessive recall among radiologists who undertook the intervention being tested.

Objective

Using a combination of performance measures, we updated previously proposed criteria for identifying physicians whose performance interpreting screening mammography may indicate suboptimal interpretation skills.

Materials and methods

In this study, six expert breast imagers used a method based on the Angoff approach to update criteria for acceptable mammography performance on the basis of two sets of combined performance measures: set 1, sensitivity and specificity for facilities with complete capture of false-negative cancers; and set 2, cancer detection rate (CDR), recall rate, and positive predictive value of a recall (PPV1) for facilities that cannot capture false-negative cancers but have reliable cancer follow-up information for positive mammography results. Decisions were informed by normative data from the Breast Cancer Surveillance Consortium (BCSC).

Results

Updated combined ranges for acceptable sensitivity and specificity of screening mammography are sensitivity≥80% and specificity≥85% or sensitivity 75-79% and specificity 88-97%. Updated ranges for CDR, recall rate, and PPV1 are: CDR≥6 per 1000, recall rate 3-20%, and any PPV1; CDR 4-6 per 1000, recall rate 3-15%, and PPV1≥3%; or CDR 2.5-4.0 per 1000, recall rate 5-12%, and PPV1 3-8%. Using the original criteria, 51% of BCSC radiologists had acceptable sensitivity and specificity; 40% had acceptable CDR, recall rate, and PPV1. Using the combined criteria, 69% had acceptable sensitivity and specificity and 62% had acceptable CDR, recall rate, and PPV1.

Conclusion

The combined criteria improve previous criteria by considering the interrelationships of multiple performance measures and broaden the acceptable performance ranges compared with previous criteria based on individual measures.

OBJECTIVE: To determine the early impact of the COVID-19 pandemic on breast imaging centers in California and Texas and compare regional differences. METHODS: An 11-item survey was emailed to American College of Radiology accredited breast imaging facilities in California and Texas in August 2020. A question subset addressed March-April government restrictions on elective services (during the shutdown and after reopening). Comparisons were made between states with chi-square and Fishers tests, and timeframes with McNemars and paired t-tests. RESULTS: There were 54 respondents (54/240, 23%, 26 California, 28 Texas). Imaging volumes fell during the shutdown and remained below pre-pandemic levels after reopening, with reduction in screening greatest (ultrasound 12% of baseline, mammography 13%, MRI 23%), followed by diagnostic MRI (43%), procedures (44%), and diagnostics (45%). California reported higher volumes during the shutdown (procedures, MRI) and after reopening (diagnostics, procedures, MRI) versus Texas (P = 0.001-0.02). Most screened patients (52/54, 96% symptoms and 42/54, 78% temperatures), and 100% (53/53) modified check-in and check-out. Reading rooms or physician work were altered for social distancing (31/54, 57%). Physician mask (45/48, 94%), gown (15/48, 31%), eyewear (22/48, 46%), and face shield (22/48, 46%) use during procedures increased after reopening versus pre-pandemic (P < 0.001-0.03). Physician (47/54, 87%) and staff (45/53, 85%) financial impacts were common, but none reported terminations. CONCLUSION: Breast imaging volumes during the early pandemic fell more severely in Texas than in California. Safety measures and financial impacts on physicians and staff were similar in both states.

AIMS

This study aimed to evaluate the influence of hormonal receptor and Ki-67 proliferation marker in predicting MRI accuracy of measuring residual tumor size in HER2 negative breast cancer receiving neoadjuvant chemotherapy (NAC).

METHODS

Fifty-four women were studied. Patients received AC and/or taxane-based regimens. The accuracy of MR determined clinical complete response (CCR) was compared to pathological complete response (pCR). The size of detectable residual tumor on MRI was correlated with pathology-diagnosed tumor size using Pearson’s correlation.

RESULTS

MRI correctly diagnosed 16 of the 17 pCR patients. There were 8 false negative diagnoses, 7 hormonal receptor (HR) positive and one HR negative. The overall sensitivity, specificity, and accuracy of MRI were 78%, 94%, and 83%, respectively. The positive predictive value was 97% and the negative predictive value was 67%. For MRI-pathology tumor size correlation, HR negative cancers showed a higher correlation (R=0.79) than HR positive cancers (R= 0.58). A worse MRI-pathology size discrepancy was found in HR positive cancer than in HR negative cancer (1.6±2.8 cm vs. 0.56±0.9 cm, p=0.05). Tumors with a low Ki-67 proliferation (<40%) showed a larger size discrepancy than those with a high Ki-67 proliferation (≥40%) (1.2±2.0 cm vs. 0.4±0.8 cm p=0.05).

CONCLUSIONS

The results showed that the diagnostic performance of MRI for breast cancer undergoing NAC is associated with molecular biomarker profile. Among HER2 negative tumors, the accuracy of MRI was worse in HR positive than negative cancers, and also worse in low proliferative than high proliferative tumors. These findings may help in surgical planning.

Background

Breast density is an important risk factor for breast cancer and is known to be associated with characteristics such as age, race, and hormone levels; however, it is unclear what factors contribute to changes in breast density in postmenopausal women over time. Understanding factors associated with density changes may enable a better understanding of breast cancer risk and facilitate potential strategies for prevention.

Methods

This study investigated potential associations between personal factors and changes in mammographic density in a cohort of 3,392 postmenopausal women with no personal history of breast cancer between 2011 and 2017. Self-reported information on demographics, breast and reproductive history, and lifestyle factors, including body mass index (BMI), alcohol intake, smoking, and physical activity, was collected by an electronic intake form, and breast imaging reporting and database system (BI-RADS) mammographic density scores were obtained from electronic medical records. Factors associated with a longitudinal increase or decrease in mammographic density were identified using Fisher's exact test and multivariate conditional logistic regression.

Results

7.9% of women exhibited a longitudinal decrease in mammographic density, 6.7% exhibited an increase, and 85.4% exhibited no change. Longitudinal changes in mammographic density were correlated with age, race/ethnicity, and age at menopause in the univariate analysis. In the multivariate analysis, Asian women were more likely to exhibit a longitudinal increase in mammographic density and less likely to exhibit a decrease compared to White women. On the other hand, obese women were less likely to exhibit an increase and more likely to exhibit a decrease compared to normal weight women. Women who underwent menopause at age 55 years or older were less likely to exhibit a decrease in mammographic density compared to women who underwent menopause at a younger age. Besides obesity, lifestyle factors (alcohol intake, smoking, and physical activity) were not associated with longitudinal changes in mammographic density.

Conclusions

The associations we observed between Asian race/obesity and longitudinal changes in BI-RADS density in postmenopausal women are paradoxical in that breast cancer risk is lower in Asian women and higher in obese women. However, the association between later age at menopause and a decreased likelihood of decreasing in BI-RADS density over time is consistent with later age at menopause being a risk factor for breast cancer and suggests a potential relationship between greater cumulative lifetime estrogen exposure and relative stability in breast density after menopause. Our findings support the complexity of the relationships between breast density, BMI, hormone exposure, and breast cancer risk.