The Short-Term Effect of Weight Loss Surgery on Volumetric Breast Density and Fibroglandular Volume.

PURPOSE: Obesity and breast density are both associated with an increased risk of breast cancer and are potentially modifiable. Weight loss surgery (WLS) causes a significant reduction in the amount of body fat and a decrease in breast cancer risk. The effect of WLS on breast density and its components has not been documented. Here, we analyze the impact of WLS on volumetric breast density (VBD) and on each of its components (fibroglandular volume and breast volume) by using three-dimensional methods. MATERIALS AND METHODS: Fibroglandular volume, breast volume, and their ratio, the VBD, were calculated from mammograms before and after WLS by using Volpara™ automated software. RESULTS: For the 80 women included, average body mass index decreased from 46.0 ± 7.22 to 33.7 ± 7.06 kg/m2. Mammograms were performed on average 11.6 ± 9.4 months before and 10.1 ± 7 months after WLS. There was a significant reduction in average breast volume (39.4 % decrease) and average fibroglandular volume (15.5 % decrease), and thus, the average VBD increased from 5.15 to 7.87 % (p < 1 × 10-9) after WLS. When stratified by menopausal status and diabetic status, VBD increased significantly in all groups but only perimenopausal and postmenopausal women and non-diabetics experienced a significant reduction in fibroglandular volume. CONCLUSIONS: Breast volume and fibroglandular volume decreased, and VBD increased following WLS, with the most significant change observed in postmenopausal women and non-diabetics. Further studies are warranted to determine how physical and biological alterations in breast density components after WLS may impact breast cancer risk.

Characterizing the Mammography Technologist Workforce in North Carolina.

PURPOSE: Mammography technologists' level of training, years of experience, and feedback on technique may play an important role in the breast-cancer screening process. However, information on the mammography technologist workforce is scant. METHODS: In 2013, we conducted a survey mailed to 912 mammography technologists working in 224 facilities certified by the Mammography Quality Standards Act in North Carolina. Using standard survey methodology, we developed and implemented a questionnaire on the education and training, work experiences, and workplace interactions of mammography technologists. We aggregated responses using survey weights to account for nonresponse. We describe and compare lead (administrative responsibilities) and nonlead (supervised by another technologist) mammography technologist characteristics, testing for differences, using t-tests and χ(2) analysis. RESULTS: A total of 433 mammography technologists responded (survey response rate = 47.5%; 95% confidence interval [CI]: 44.2%-50.7%), including 128 lead and 305 nonlead technologists. Most mammography technologists were non-Hispanic, white women; their average age was 48 years. Approximately 93% of lead and nonlead technologists had mammography-specific training, but <4% had sonography certification, and 3% had MRI certification. Lead technologists reported more years of experience performing screening mammography (P = .02) and film mammography (P = .03), more administrative hours (P < .0001), and more workplace autonomy (P = .002) than nonlead technologists. Nonlead technologists were more likely to report performing diagnostic mammograms (P = .0004) or other breast imaging (P = .001), discuss image quality with a peer (P = .013), and have frequent face-to-face interaction with radiologists (P = .03). CONCLUSIONS: Our findings offer insights into mammography technologists' training and work experiences, highlighting variability in characteristics of lead versus nonlead technologists.

Performance of digital screening mammography in a population-based cohort of black and white women.

PURPOSE: There is scarce information on whether digital screening mammography performance differs between black and white women. METHODS: We examined 256,470 digital screening mammograms performed from 2005 to 2010 among 31,654 black and 133,152 white Carolina Mammography Registry participants aged ≥40 years. We compared recall rate, sensitivity, specificity, and positive predictive value (PPV1) between black and white women, adjusting for potential confounders using random effects logistic regression. RESULTS: Breast cancer was diagnosed in 276 black and 1,095 white women. Recall rates were similar for blacks and whites (8.6 vs. 8.5 %), as were sensitivity (83.7 vs. 82.4 %), specificity (91.8 vs. 91.9 %), and PPV1 (4.8 vs. 5.3 %) (all p values >0.05). Stratified and adjusted models showed similar results. Despite comparable mammography performance, tumors diagnosed in black women were more commonly poorly differentiated and hormone receptor negative. CONCLUSION: Equivalent performance of digital screening mammography by race suggests that efforts to understand tumor disparities should focus on etiologic factors that influence tumor biology.

Do mammographic technologists affect radiologists' diagnostic mammography interpretative performance?

OBJECTIVE: The purpose of this study was to determine whether the technologist has an effect on the radiologists' interpretative performance of diagnostic mammography. MATERIALS AND METHODS: Using data from a community-based mammography registry from 1994 to 2009, we identified 162,755 diagnostic mammograms interpreted by 286 radiologists and performed by 303 mammographic technologists. We calculated sensitivity, false-positive rate, and positive predictive value (PPV) of the recommendation for biopsy from mammography for examinations performed (i.e., images acquired) by each mammographic technologist, separately for conventional (film-screen) and digital modalities. We assessed the variability of these performance measures among mammographic technologists, using mixed effects logistic regression and taking into account the clustering of examinations within women, radiologists, and radiology practices. RESULTS: Among the 291 technologists performing conventional examinations, mean sensitivity of the examinations performed was 83.0% (95% CI, 80.8-85.2%), mean false-positive rate was 8.5% (95% CI, 8.0-9.0%), and mean PPV of the recommendation for biopsy from mammography was 27.1% (95% CI, 24.8-29.4%). For the 45 technologists performing digital examinations, mean sensitivity of the examinations they performed was 79.6% (95% CI, 73.1-86.2%), mean false-positive rate was 8.8% (95% CI, 7.5-10.0%), and mean PPV of the recommendation for biopsy from mammography was 23.6% (95% CI, 18.8-28.4%). We found significant variation by technologist in the sensitivity, false-positive rate, and PPV of the recommendation for biopsy from mammography for conventional but not digital mammography (p < 0.0001 for all three interpretive performance measures). CONCLUSION: Our results suggest that the technologist has an influence on radiologists' interpretive performance for diagnostic conventional but not digital mammography. Future studies should examine why this difference between modalities exists and determine if similar patterns are observed for screening mammography.

The influence of mammographic technologists on radiologists' ability to interpret screening mammograms in community practice.

RATIONALE AND OBJECTIVES: To determine whether the mammographic technologist has an effect on the radiologists' interpretative performance of screening mammography in community practice. MATERIALS AND METHODS: In this institutional review board-approved retrospective cohort study, we included Carolina Mammography Registry data from 372 radiologists and 356 mammographic technologists from 1994 to 2009 who performed 1,003,276 screening mammograms. Measures of interpretative performance (recall rate, sensitivity, specificity, positive predictive value [PPV1], and cancer detection rate [CDR]) were ascertained prospectively with cancer outcomes collected from the state cancer registry and pathology reports. To determine if the mammographic technologist influenced the radiologists' performance, we used mixed effects logistic regression models, including a radiologist-specific random effect and taking into account the clustering of examinations across women, separately for screen-film mammography (SFM) and full-field digital mammography (FFDM). RESULTS: Of the 356 mammographic technologists included, 343 performed 889,347 SFM examinations, 51 performed 113,929 FFDM examinations, and 38 performed both SFM and FFDM examinations. A total of 4328 cancers were reported for SFM and 564 cancers for FFDM. The technologists had a statistically significant effect on the radiologists' recall rate, sensitivity, specificity, and CDR for both SFM and FFDM (P values <.01). For PPV1, variability by technologist was observed for SFM (P value <.0001) but not for FFDM (P value = .088). CONCLUSIONS: The interpretative performance of radiologists in screening mammography varies substantially by the technologist performing the examination. Additional studies should aim to identify technologist characteristics that may explain this variation.

Effect of observing change from comparison mammograms on performance of screening mammography in a large community-based population.

PURPOSE: To evaluate the effect of comparison mammograms on accuracy, sensitivity, specificity, positive predictive value (PPV(1)), and cancer detection rate (CDR) of screening mammography to determine the role played by identification of change on comparison mammograms. MATERIALS AND METHODS: This HIPAA-compliant and institutional review board-approved prospective study was performed with waiver of patient informed consent. A total of 1,157,980 screening mammograms obtained between 1994 and 2008 in 435,183 women aged at least 40 years were included. Radiologists recorded presence of comparison mammograms and change, if seen. Women were followed for 1 year to monitor cancer occurrence. Performance measurements were calculated for screening with comparison mammograms versus screening without comparison mammograms and for screening with comparison mammograms that showed a change versus screening with comparison mammograms that did not show a change while controlling for age, breast density, and data clustering. RESULTS: Comparison mammograms were available in 93% of examinations. For screening with comparison mammograms versus screening without comparison mammograms, CDR per 1000 women was 3.7 versus 7.1; recall rate, 6.9% versus 14.9%; sensitivity, 78.9% versus 87.4%; specificity, 93.5% versus 85.7%; and PPV(1), 5.4% versus 4.8%. For screening with comparison mammograms that showed a change versus screening with comparison mammograms that did not show a change, CDR per 1000 women was 25.4 versus 0.8; recall rate, 41.4% versus 2.0%; sensitivity, 96.6% versus 43.5%; specificity, 60.4% versus 98.1%; and PPV(1), 6.0% versus 3.9%. Detected cancers with change were 21.1% ductal carcinoma in situ and 78.9% invasive carcinoma. Detected cancers with no change were 19.3% ductal carcinoma in situ and 80.7% invasive carcinoma. CONCLUSION: Performance is affected when change from comparison mammograms is noted. Without change, sensitivity is low and specificity is high. With change, sensitivity is high, with a high false-positive rate (low specificity). Further work is needed to appreciate changes that might indicate cancer and to identify changes that are likely not indicative of cancer.