Patterns of Surveillance Advanced Imaging and Serum Tumor Biomarker Testing Following Launch of the Choosing Wisely Initiative

BACKGROUND

The purpose of this study was to assess advanced imaging (bone scan, CT, or PET/CT) and serum tumor biomarker use in asymptomatic breast cancer survivors during the surveillance period.

PATIENTS AND METHODS

Cancer registry records for 2,923 women diagnosed with primary breast cancer in Washington State between January 1, 2007, and December 31, 2014, were linked with claims data from 2 regional commercial insurance plans. Clinical data including demographic and tumor characteristics were collected. Evaluation and management codes from claims data were used to determine advanced imaging and serum tumor biomarker testing during the peridiagnostic and surveillance phases of care. Multivariable logistic regression models were used to identify clinical factors and patterns of peridiagnostic imaging and biomarker testing associated with surveillance advanced imaging.

RESULTS

Of 2,923 eligible women, 16.5% (n=480) underwent surveillance advanced imaging and 31.8% (n=930) received surveillance serum tumor biomarker testing. Compared with women diagnosed before the launch of the Choosing Wisely campaign in 2012, later diagnosis was associated with lower use of surveillance advanced imaging (odds ratio [OR], 0.68; 95% CI, 0.52-0.89). Factors significantly associated with use of surveillance advanced imaging included increasing disease stage (stage III: OR, 3.65; 95% CI, 2.48-5.38), peridiagnostic advanced imaging use (OR, 1.76; 95% CI, 1.33-2.31), and peridiagnostic serum tumor biomarker testing (OR, 1.35; 95% CI, 1.01-1.80).

CONCLUSIONS

Although use of surveillance advanced imaging in asymptomatic breast cancer survivors has declined since the launch of the Choosing Wisely campaign, frequent use of surveillance serum tumor biomarker testing remains prevalent, representing a potential target for further efforts to reduce low-value practices.

The Value of Patient and Tumor Factors in Predicting Preoperative Breast MRI Outcomes

Purpose

To identify patient and tumor features that predict true-positive, false-positive, and negative breast preoperative MRI outcomes.

Materials and Methods

Using a breast MRI database from a large regional cancer center, the authors retrospectively identified all women with unilateral breast cancer who underwent preoperative MRI from January 2005 to February 2015. A total of 1396 women with complete data were included. Patient features (ie, age, breast density) and index tumor features (ie, type, grade, hormone receptor, human epidermal growth factor receptor type 2/neu, Ki-67) were extracted and compared with preoperative MRI outcomes (ie, true positive, false positive, negative) using univariate (ie, Fisher exact) and multivariate machine learning approaches (ie, least absolute shrinkage and selection operator, AutoPrognosis). Overall prediction performance was summarized using the area under the receiver operating characteristic curve (AUC), calculated using internal validation techniques (bootstrap and cross-validation) to account for model training.

Results

At the examination level, 181 additional cancers were identified among 1396 total preoperative MRI examinations (median patient age, 56 years; range, 25-94 years), resulting in a positive predictive value for biopsy of 43% (181 true-positive findings of 419 core-needle biopsies). In univariate analysis, no patient or tumor feature was associated with a true-positive outcome (P > .05), although greater mammographic density (P = .022) and younger age (< 50 years, P = .025) were associated with false-positive examinations. Machine learning approaches provided weak performance for predicting true-positive, false-positive, and negative examinations (AUC range, 0.50-0.57).

Conclusion

Commonly used patient and tumor factors driving expert opinion for the use of preoperative MRI provide limited predictive value for determining preoperative MRI outcomes in women. Supplemental material is available for this article. © RSNA, 2020See also the commentary by Grimm in this issue.

Screening Performance of Digital Breast Tomosynthesis vs Digital Mammography in Community Practice by Patient Age, Screening Round, and Breast Density

Importance

Digital mammography (DM) and digital breast tomosynthesis (DBT) are used for routine breast cancer screening. There is minimal evidence on performance outcomes by age, screening round, and breast density in community practice.

Objective

To compare DM vs DBT performance by age, baseline vs subsequent screening round, and breast density category.

Design, Setting, and Participants

This comparative effectiveness study assessed 1 584 079 screening examinations of women aged 40 to 79 years without prior history of breast cancer, mastectomy, or breast augmentation undergoing screening mammography at 46 participating Breast Cancer Surveillance Consortium facilities from January 2010 to April 2018.

Exposures

Age, Breast Imaging Reporting and Data System breast density category, screening round, and modality.

Main Outcomes and Measures

Absolute rates and relative risks (RRs) of screening recall and cancer detection.

Results

Of 1 273 492 DM and 310 587 DBT examinations analyzed, 1 028 891 examinations (65.0%) were of white non-Hispanic women; 399 952 women (25.2%) were younger than 50 years; and 671 136 women (42.4%) had heterogeneously dense or extremely dense breasts. Adjusted differences in DM vs DBT performance were largest on baseline examinations: for example, per 1000 baseline examinations in women ages 50 to 59, recall rates decreased from 241 examinations for DM to 204 examinations for DBT (RR, 0.84; 95% CI, 0.73-0.98), and cancer detection rates increased from 5.9 with DM to 8.8 with DBT (RR, 1.50; 95% CI, 1.10-2.08). On subsequent examinations, women aged 40 to 79 years with heterogeneously dense breasts had improved recall rates and improved cancer detection with DBT. For example, per 1000 examinations in women aged 50 to 59 years, the number of recall examinations decreased from 102 with DM to 93 with DBT (RR, 0.91; 95% CI, 0.84-0.98), and cancer detection increased from 3.7 with DM to 5.3 with DBT (RR, 1.42; 95% CI, 1.23-1.64). Women aged 50 to 79 years with scattered fibroglandular density also had improved recall and cancer detection rates with DBT. Women aged 40 to 49 years with scattered fibroglandular density and women aged 50 to 79 years with almost entirely fatty breasts benefited from improved recall rates without change in cancer detection rates. No improvements in recall or cancer detection rates were observed in women with extremely dense breasts on subsequent examinations for any age group.

Conclusions and Relevance

This study found that improvements in recall and cancer detection rates with DBT were greatest on baseline mammograms. On subsequent screening mammograms, the benefits of DBT varied by age and breast density. Women with extremely dense breasts did not benefit from improved recall or cancer detection with DBT on subsequent screening rounds.

Facility Variability in Examination Indication Among Women With Prior Breast Cancer: Implications and the Need for Standardization

OBJECTIVE

We sought to identify and characterize examinations in women with a personal history of breast cancer likely performed for asymptomatic surveillance.

METHODS

We included surveillance mammograms (1997-2017) in asymptomatic women with a personal history of breast cancer diagnosed at age ≥18 years (1996-2016) from 103 Breast Cancer Surveillance Consortium facilities. We examined facility-level variability in examination indication. We modeled the relative risk (RR) and 95% confidence intervals (CIs) at the examination level of a (1) nonscreening indication and (2) surveillance interval ≤9 months using Poisson regression with fixed effects for facility, stage, diagnosis age, surgery, examination year, and time since diagnosis.

RESULTS

Among 244,855 surveillance mammograms, 69.5% were coded with a screening indication, 12.7% short-interval follow-up, and 15.3% as evaluation of a breast problem. Within a facility, the proportion of examinations with a screening indication ranged from 6% to 100% (median 86%, interquartile range 79%-92%). Facilities varied the most for examinations in the first 5 years after diagnosis, with 39.4% of surveillance mammograms having a nonscreening indication. Within a facility, breast conserving surgery compared with mastectomy (RR = 1.64; 95% CI = 1.60-1.68) and less time since diagnosis (1 year versus 5 years; RR = 1.69; 95% CI = 1.66-1.72; 3 years versus 5 years = 1.20; 95% CI = 1.18-1.23) were strongly associated with a nonscreening indication with similar results for ≤9-month surveillance interval. Screening indication and >9-month surveillance intervals were more common in more recent years.

CONCLUSION

Variability in surveillance indications across facilities in the United States supports including indications beyond screening in studies evaluating surveillance mammography effectiveness and demonstrates the need for standardization.

Provider preferences for attributes of artificial intelligence in breast cancer screening: A discrete choice experiment

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Background: One of the emerging medical applications of artificial intelligence (AI) is the interpretation of mammograms for breast cancer screening. It is uncertain what attributes would result in acceptance of AI for breast cancer screening (AI BCS) among ordering clinicians. Methods: We performed qualitative interviews to identify the most important attributes of AI BCS for ordering clinicians. We then invited US-based primary care providers (PCPs) to participate in a discrete choice experiment (DCE). The experiment featured 15 choices between radiologist alone and two AI BCS alternatives where respondents traded better metrics on some attributes for worse metrics on others. Responses were analyzed using a mixed logit model adjusting for preference heterogeneity to determine the probability of recommending AI BCS. Results: In qualitative interviews, the six most important attributes to PCPs were AI sensitivity, specificity, radiologist involvement, understandability of AI decision-making, supporting evidence, and diversity of training data. Forty PCPs completed the DCE. Sensitivity was the most important attribute: a 4 percentage point improvement in sensitivity over the average radiologist increased the probability of recommending AI by 0.41 (95% confidence interval (CI), 0.38-0.42). Specificity was approximately half as important. Respondents were indifferent to whether radiologists confirmed all or only screens likely to be abnormal. However, no radiologist involvement reduced the probability of recommendation by 0.31 (95% CI, 0.29-0.31). An AI developed using data from diverse populations increased the probability of recommendation by 0.38 (95% CI, 0.36-0.39). Lastly, an AI that is transparent in the rationale for its decisions increased the probability of recommendation by 0.41 (95% CI, 0.39-0.41). Conclusions: PCPs prefer AI BCS that improves sensitivity versus specificity, and involves radiologists in the confirmation of abnormal screens. Improving sensitivity alone, however, will likely not be sufficient to support widespread PCP acceptance – algorithms will need to be developed with diverse data and more transparent explanations of their decisions.

New mammography screening performance metrics based on the entire screening episode

BACKGROUND

Established mammography screening performance metrics use the initial screening mammography assessment because they were developed for radiologist performance auditing, yet these metrics are frequently used to inform health policy and screening decision making. The authors have developed new performance metrics based on the final assessment that consider the entire screening episode, including diagnostic workup.

METHODS

The authors used data from 2,512,577 screening episodes during 2005-2017 at 146 facilities in the United States participating in the Breast Cancer Surveillance Consortium. Screening performance metrics based on the final assessment of the screening episode were compared with conventional metrics defined with the initial assessment. Results were also stratified by breast density and breast cancer risk.

RESULTS

The cancer detection rates were similar for the final assessment (4.1 per 1000; 95% confidence interval [CI], 3.8-4.3 per 1000) and the initial assessment (4.1 per 1000; 95% CI, 3.9-4.3 per 1000). The interval cancer rate was 12% higher when it was based on the final assessment (0.77 per 1000; 95% CI, 0.71-0.83 per 1000) versus the initial assessment (0.69 per 1000; 95% CI, 0.64-0.74 per 1000), and this resulted in a modest difference in sensitivity (84.1% [95% CI, 83.0%-85.1%] vs 85.7% [95% CI, 84.8%-86.6%], respectively). Absolute differences in the interval cancer rate between final and initial assessments increased with breast density and breast cancer risk (eg, a difference of 0.29 per 1000 for women with extremely dense breasts and a 5-year risk >2.49%).

CONCLUSIONS

Established screening performance metrics underestimate the interval cancer rate of a mammography screening episode, particularly for women with dense breasts or an elevated breast cancer risk. Women, clinicians, policymakers, and researchers should use final-assessment performance metrics to support informed screening decisions.

The Role of Social Determinants of Health in Self-Reported Access to Health Care Among Women Undergoing Screening Mammography

Background: Social determinants of health (SDOH) contribute to health care disparities, with social and economic barriers often leading to difficulties in obtaining necessary care. We evaluated barriers to receiving health care, focusing on caretaker responsibilities, health insurance and cost, and transportation. Materials and Methods: We included women ages ≥40 years receiving screening mammography across three Breast Cancer Surveillance Consortium registries from 2012 to 2017. Women self-reported social and financial barriers to receiving health care in the 12 months before their screening mammogram. We evaluated woman- and census-based community-level factors associated with reporting a barrier using multivariate logistic regression. We assessed interaction with urban versus nonurban residence using Wald tests. Results: Among 393,430 women, 3.6% reported a barrier with a higher proportion in urban versus nonurban settings (3.9% [n = 11,977] vs. 2.2% [n = 1,655], respectively; p < 0.001). Among women reporting a barrier, health care cost and/or no insurance was the most common (49.3%), and no transportation was the least common (7.8%). Compared with white women, odds of reporting barriers were higher among black (adjusted odds ratio [aOR] = 1.30, 95% confidence interval [CI]: 1.16-1.44), Hispanic (aOR = 1.66, 95% CI: 1.53-1.80), and other race (aOR = 1.84, 95% CI: 1.65-2.04) women. Barriers were less likely in women with higher median household income (aOR = 0.69, 95% CI: 0.61-0.79) or higher average health insurance costs (aOR = 0.85, 95% CI: 0.74-0.98), but were more likely in high diversity index areas (aOR = 1.28, 95% CI: 1.11-1.48). Conclusions: Social and financial barriers exist based on race/ethnicity and SDOH related to income, insurance costs, and place of residence among women undergoing screening mammography. Breast imaging facilities could utilize information on these barriers to improve biennial screening adherence or ensure that women with abnormal findings obtain appropriate follow-up care through targeted interventions.

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Importance

Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives.

Objective

To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms.

Design, Setting, and Participants

In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016.

Main Outcomes and Measurements

Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated.

Results

Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity.

Conclusions and Relevance

While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation.

Assessment of Radiologist Performance in Breast Cancer Screening Using Digital Breast Tomosynthesis vs Digital Mammography

Importance

Many US radiologists have screening mammography recall rates above the expert-recommended threshold of 12%. The influence of digital breast tomosynthesis (DBT) on the distribution of radiologist recall rates is uncertain.

Objective

To evaluate radiologists' recall and cancer detection rates before and after beginning interpretation of DBT examinations.

Design, Setting, and Participants

This cohort study included 198 radiologists from 104 radiology facilities in the Breast Cancer Surveillance Consortium who interpreted 251 384 DBT and 2 000 681 digital mammography (DM) screening examinations from 2009 to 2017, including 126 radiologists (63.6%) who interpreted DBT examinations during the study period and 72 (36.4%) who exclusively interpreted DM examinations (to adjust for secular trends). Data were analyzed from April 2018 to July 2019.

Exposures

Digital breast tomosynthesis and DM screening examinations.

Main Outcomes and Measures

Recall rate and cancer detection rate.

Results

A total of 198 radiologists interpreted 2 252 065 DM and DBT examinations (2 000 681 [88.8%] DM examinations; 251 384 [11.2%] DBT examinations; 710 934 patients [31.6%] aged 50-59 years; 1 448 981 [64.3%] non-Hispanic white). Among the 126 radiologists (63.6%) who interpreted DBT examinations, 83 (65.9%) had unadjusted DM recall rates of no more than 12% before using DBT, with a median (interquartile range) recall rate of 10.0% (7.5%-13.0%). On DBT examinations, 96 (76.2%) had an unadjusted recall rate of no more than 12%, with a median (interquartile range) recall rate of 8.8% (6.3%-11.3%). A secular trend in recall rate was observed, with the multivariable-adjusted risk of recall on screening examinations declining by 1.2% (95% CI, 0.9%-1.5%) per year. After adjusting for examination characteristics and secular trends, recall rates were 15% lower on DBT examinations compared with DM examinations interpreted before DBT use (relative risk, 0.85; 95% CI, 0.83-0.87). Adjusted recall rates were significantly lower on DBT examinations compared with DM examinations interpreted before DBT use for 45 radiologists (35.7%) and significantly higher for 18 (14.3%); 63 (50.0%) had no statistically significant change. The unadjusted cancer detection rate on DBT was 5.3 per 1000 examinations (95% CI, 5.0-5.7 per 1000 examinations) compared with 4.7 per 1000 examinations (95% CI, 4.6-4.8 per 1000 examinations) on DM examinations interpreted before DM use (multivariable-adjusted risk ratio, 1.21; 95% CI, 1.11-1.33).

Conclusions and Relevance

In this study, DBT was associated with an overall decrease in recall rate and an increase in cancer detection rate. However, our results indicated that there is wide variability among radiologists, including a subset of radiologists who experienced increased recall rates on DBT examinations. Radiology practices should audit radiologist DBT screening performance and consider additional DBT training for radiologists whose performance does not improve as expected.

Physician Ordering of Screening Ultrasound: National Rates and Association With State-Level Breast Density Reporting Laws

PURPOSE

To describe factors associated with screening ultrasound ordering and determine whether adoption of state-level breast density reporting laws was associated with changes in ordering rates.

MATERIALS AND METHODS

We performed a cohort study using National Ambulatory Medical Care Survey data for 2007 to 2015. We included preventive office visits for women aged 40 to 74 years without breast symptoms and signs or additional reasons requiring ultrasound ordering. Multivariate logistic regression was used to identify changes in ultrasound ordering rates pre- versus post-state-level density reporting laws, accounting for patient-, physician-, and practice-level characteristics. Analyses were weighted to account for the multistage probability sampling design of National Ambulatory Medical Care Survey.

RESULTS

Our sample included 12,787 visits over the 9-year study period. Overall, 28.9% (3,370 of 12,787) of women underwent a breast examination and 22.1% (2,442 of 12,787) had a screening mammogram ordered. Only 3.3% (379 of 12,787) had screening ultrasound ordered. Screening ultrasounds were ordered more frequently for younger women (rate ratio [RR] 0.8 per 10-year increase in age, 95% confidence interval [CI]: 0.6-0.9, P = .003) and at urban practices (RR 2.3, 95% CI: 1.1-5.0, P = .028), and less frequently in practices with computer reminders for ordering screening tests (RR 0.6, 95% CI: 0.3-0.9, P = .024). In multivariate analyses, the rate of ultrasound ordering did not change after adoption of density notification laws (RR 0.7, 95% CI: 0.3-2.0, P = .57).

CONCLUSION

The rate of screening ultrasound ordering remains low over time. There was no observed association between adoption of state-level density reporting laws and overall changes in ultrasound ordering.

Trends in Clinical Breast Density Assessment From the Breast Cancer Surveillance Consortium

Changes to mammography practice, including revised Breast Imaging Reporting and Data System (BI-RADS) density classification guidelines and implementation of digital breast tomosynthesis (DBT), may impact clinical breast density assessment. We investigated temporal trends in clinical breast density assessment among 2 990 291 digital mammography (DM) screens and 221 063 DBT screens interpreted by 722 radiologists from 144 facilities in the Breast Cancer Surveillance Consortium. After age-standardization, 46.3% (95% CI = 44.1% to 48.6%) of DM screens were assessed as dense (heterogeneously/extremely dense) during the BI-RADS 4th edition era (2005-2013), compared to 46.5% (95% CI = 43.8% to 49.1%) during the 5th edition era (2014-2016) (P = .93 from two-sided generalized score test). Among DBT screens in the BI-RADS 5th edition era, 45.8% (95% CI = 42.0% to 49.7%) were assessed as dense (P = .77 from two-sided generalized score test) compared to 46.5% (95% CI = 43.8% to 49.1%) dense on DM in BI-RADS 5th edition era. Results were similar when examining all four density categories and age subgroups. Clinicians, researchers, and policymakers may reasonably expect stable density distributions across screened populations despite changes to the BI-RADS guidelines and implementation of DBT.

Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI's potential in breast screening practice

INTRODUCTION

Various factors are driving interest in the application of artificial intelligence (AI) for breast cancer (BC) detection, but it is unclear whether the evidence warrants large-scale use in population-based screening.

AREAS COVERED

We performed a scoping review, a structured evidence synthesis describing a broad research field, to summarize knowledge on AI evaluated for BC detection and to assess AI's readiness for adoption in BC screening. Studies were predominantly small retrospective studies based on highly selected image datasets that contained a high proportion of cancers (median BC proportion in datasets 26.5%), and used heterogeneous techniques to develop AI models; the range of estimated AUC (area under ROC curve) for AI models was 69.2-97.8% (median AUC 88.2%). We identified various methodologic limitations including use of non-representative imaging data for model training, limited validation in external datasets, potential bias in training data, and few comparative data for AI versus radiologists' interpretation of mammography screening.

EXPERT OPINION

Although contemporary AI models have reported generally good accuracy for BC detection, methodological concerns, and evidence gaps exist that limit translation into clinical BC screening settings. These should be addressed in parallel to advancing AI techniques to render AI transferable to large-scale population-based screening.

Double reading of automated breast ultrasound with digital mammography or digital breast tomosynthesis for breast cancer screening

PURPOSE

To evaluate the impact of double reading automated breast ultrasound (ABUS) when added to full field digital mammography (FFDM) or digital breast tomosynthesis (DBT) for breast cancer screening.

METHODS

From April 2014 to June 2015, 124 women with dense breasts and intermediate to high breast cancer risk were recruited for screening with FFDM, DBT, and ABUS. Readers used FFDM and DBT in clinical practice and received ABUS training prior to study initiation. FFDM or DBT were first interpreted alone by two independent readers and then with ABUS. All recalled women underwent diagnostic workup with at least one year of follow-up. Recall rates were compared using the sign test; differences in outcomes were evaluated using Fisher's exact test.

RESULTS

Of 121 women with complete follow-up, all had family (35.5%) or personal (20.7%) history of breast cancer, or both (43.8%). Twenty-four women (19.8%) were recalled by at least one modality. Recalls increased from 5.0% to 13.2% (p = 0.002) when ABUS was added to FFDM and from 3.3% to 10.7% (p = 0.004) when ABUS was added to DBT. Findings recalled by both readers were more likely to result in a recommendation for short term follow-up imaging or tissue biopsy compared to findings recalled by only one reader (100% vs. 42.1%, p = 0.041). The cancer detection rate was 8.3 per 1000 screens (1/121); mode of detection: FFDM and DBT.

CONCLUSIONS

Adding ABUS significantly increased the recall rate of both FFDM and DBT screening. Double reading of ABUS during early phase adoption may reduce false positive recalls.

Digital Breast Tomosynthesis: Radiologist Learning Curve

Background There is growing evidence that digital breast tomosynthesis (DBT) results in lower recall rates and higher cancer detection rates when compared with digital mammography. However, whether DBT interpretative performance changes with experience (learning curve effect) is unknown. Purpose To evaluate screening DBT performance by cumulative DBT volume within 2 years after adoption relative to digital mammography (DM) performance 1 year before DBT adoption. Materials and Methods This prospective study included 106 126 DBT and 221 248 DM examinations in 271 362 women (mean age, 57.5 years) from 2010 to 2017 that were interpreted by 104 radiologists from 53 facilities in the Breast Cancer Surveillance Consortium. Conditional logistic regression was used to estimate within-radiologist effects of increasing cumulative DBT volume on recall and cancer detection rates relative to DM and was adjusted for examination-level characteristics. Changes were also evaluated by subspecialty and breast density. Results Before DBT adoption, DM recall rate was 10.4% (95% confidence interval [CI]: 9.5%, 11.4%) and cancer detection rate was 4.0 per 1000 screenings (95% CI: 3.6 per 1000 screenings, 4.5 per 1000 screenings); after DBT adoption, DBT recall rate was lower (9.4%; 95% CI: 8.2%, 10.6%; P = .02) and cancer detection rate was similar (4.6 per 1000 screenings; 95% CI: 4.0 per 1000 screenings, 5.2 per 1000 screenings; P = .12). Relative to DM, DBT recall rate decreased for a cumulative DBT volume of fewer than 400 studies (odds ratio [OR] = 0.83; 95% CI: 0.78, 0.89) and remained lower as volume increased (400-799 studies, OR = 0.8 [95% CI: 0.75, 0.85]; 800-1199 studies, OR = 0.81 [95% CI: 0.76, 0.87]; 1200-1599 studies, OR = 0.78 [95% CI: 0.73, 0.84]; 1600-2000 studies, OR = 0.81 [95% CI: 0.75, 0.88]; P < .001). Improvements were sustained for breast imaging subspecialists (OR range, 0.67-0.85; P < .02) and readers who were not breast imaging specialists (OR range, 0.80-0.85; P < .001). Recall rates decreased more in women with nondense breasts (OR range, 0.68-0.76; P < .001) than in those with dense breasts (OR range, 0.86-0.90; P ≤ .05; P interaction < .001). Cancer detection rates for DM and DBT were similar, regardless of DBT volume (P ≥ .10). Conclusion Early performance improvements after digital breast tomosynthesis (DBT) adoption were sustained regardless of DBT volume, radiologist subspecialty, or breast density. © RSNA, 2019 See also the editorial by Hooley in this issue.

Representation of Women on Radiology Journal Editorial Boards: A 40-Year Analysis

RATIONALE AND OBJECTIVES

We examined female representation on editorial boards of four prominent radiology journals. We compared editorial board representation to female academic radiology career advancement and the proportion of female authorship in three journals over four decades.

METHODS

We collected data on the gender of editorial board members as listed on mastheads of Radiology, American Journal of Roentgenology (AJR), Academic Radiology, and the Journal of the American College of Radiology in 5-year intervals plus the most recent year available (1973-2017), and the gender of their editors-in-chief for all years since each journal's inception. We compared Radiology, AJR, and Academic Radiology data to published data on gender of the journals' authors, all US medical students, and academic radiologists over time.

RESULTS

Gender was determined for 171 editors-in-chief (100%) and 2139 (100%) editorial board members listed in the selected journals for each of the study years. The proportion of women on editorial boards increased from 1.4% (1 of 69) in 1978 to 18.8% (73 of 388) in 2013 (P < .001), but remained below the proportion of female first authors (7.5% in 1978 and 27.1% in 2013) and female faculty in radiology (11.5% in 1978 and 28.1% in 2013). None of the four general radiology journals had a female editor-in-chief during the study period.

CONCLUSIONS

Female representation on editorial boards has increased over time, but still lags behind increases seen in female first authorship in radiology journals and radiology faculty appointments over the last four decades. There was no female editor-in-chief during the study period.