Impact of the COVID-19 Pandemic on Breast Imaging Education.

OBJECTIVE: To determine the impact of the COVID-19 pandemic on breast imaging education. METHODS: A 22-item survey addressing four themes during the early pandemic (time on service, structured education, clinical training, future plans) was emailed to Society of Breast Imaging members and members-in-training in July 2020. Responses were compared using McNemar's and Mann-Whitney U tests; a general linear model was used for multivariate analysis. RESULTS: Of 136 responses (136/2824, 4.8%), 96 U.S. responses from radiologists with trainees, residents, and fellows were included. Clinical exposure declined during the early pandemic, with almost no medical students on service (66/67, 99%) and fewer clinical days for residents (78/89, 88%) and fellows (48/68, 71%). Conferences shifted to remote live format (57/78, 73%), with some canceled (15/78, 19%). Compared to pre-pandemic, resident diagnostic (75/78, 96% vs 26/78, 33%) (P < 0.001) and procedural (73/78, 94% vs 21/78, 27%) (P < 0.001) participation fell, as did fellow diagnostic (60/61, 98% vs 47/61, 77%) (P = 0.001) and procedural (60/61, 98% vs 43/61, 70%) (P < 0.001) participation. Most thought that the pandemic negatively influenced resident and fellow screening (64/77, 83% and 43/60, 72%, respectively), diagnostic (66/77, 86% and 37/60, 62%), and procedural (71/77, 92% and 37/61, 61%) education. However, a majority thought that decreased time on service (36/67, 54%) and patient contact (46/79, 58%) would not change residents' pursuit of a breast imaging fellowship. CONCLUSION: The pandemic has had a largely negative impact on breast imaging education, with reduction in exposure to all aspects of breast imaging. However, this may not affect career decisions.

Adaptations of Breast Imaging Centers to the COVID-19 Pandemic: A Survey of California and Texas.

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 Fisher's tests, and timeframes with McNemar's 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.

A probabilistic expert system that provides automated mammographic-histologic correlation: initial experience.

OBJECTIVE: We sought to determine whether a probabilistic expert system can provide accurate automated imaging-histologic correlations to aid radiologists in assessing the concordance of mammographic findings with the results of imaging-guided breast biopsies. MATERIALS AND METHODS: We created a Bayesian network in which Breast Imaging Reporting and Data System (BI-RADS) descriptors are used to convey the level of suspicion of mammographic abnormalities. Our system is a computer model that links BI-RADS descriptors with diseases of the breast using probabilities derived from the literature. Mammographic findings are used to update pretest probabilities (prevalence of disease) into posttest probabilities applying Bayes' theorem. We evaluated the histologic results of 92 consecutive imaging-guided breast biopsies for concordance with the mammographic findings during radiology-pathology review sessions. First, radiologists with no knowledge of the biopsy results chose BI-RADS descriptors for the mammographic findings. After the histologic diagnosis was revealed, the radiologists assessed concordance between the pathologic results and the mammographic findings. We then input the information gathered from these sessions into the Bayesian network to produce an automated mammographic-histologic correlation. RESULTS: We had a sampling error rate of 1.1% (1/92 biopsies). Our expert system was able to integrate pathologic diagnoses and mammographic findings to obtain probabilities of sampling error, thereby enabling us to identify the incorrect pathologic diagnosis with 100% sensitivity while maintaining a specificity of 91%. CONCLUSION: Our probabilistic expert system has the potential to help radiologists in identifying breast biopsy results that are discordant with mammographic findings and discovering cases in which biopsy sampling errors may have occurred.

Differential value of comparison with previous examinations in diagnostic versus screening mammography.

OBJECTIVE: The purpose of our study was to analyze the differences in clinical outcomes of diagnostic and screening mammography depending on whether comparison is made with previous examinations. MATERIALS AND METHODS: We analyzed 48,281 consecutive mammography examinations for which previous mammography (9825 diagnostic, 38,456 screening) had been performed between 1997 and 2001, collecting data on demographics, whether comparison actually was made with previous examinations, abnormal findings (recall for screening mammography or biopsy recommendation for diagnostic mammography), biopsy yield of cancer, cancer detection rate, size of invasive cancers, axillary nodal status, and cancer stage. RESULTS: Comparison with previous examinations in the incidence screening setting decreases the recall rate from 4.9% to 3.8% (p < 0.0001) but does not significantly affect the biopsy yield (40-44%, p = 0.56) or the cancer detection rate (5.5-5.2/1000, p = 0.87). In the diagnostic setting, comparison with previous examinations increases the biopsy-recommended rate from 4.3% to 9.4% (p < 0.0001), the biopsy yield from 38% to 51% (p = 0.12), and the overall cancer detection rate from 11/1000 to 39/1000 (p < 0.0001). Comparison with previous examinations is not associated with a significant difference in mean tumor size. However, it is associated with a significant decrease in the frequency of axillary node metastasis and the cancer stage for screening mammography, but not for diagnostic mammography. CONCLUSION: For screening mammography, comparison with previous examinations significantly decreases false-positive but not true-positive findings and permits detection of cancers at an earlier stage. For diagnostic mammography, comparison with previous examinations increases true-positive findings.

Interpreting data from audits when screening and diagnostic mammography outcomes are combined.

OBJECTIVE: The objective of this study was to use mathematic models to aid mammography practices in interpreting outcomes data derived from a combination of screening and diagnostic examinations, and in interpreting diagnostic mammography outcomes data that are not segregated by indication for examination. MATERIALS AND METHODS: We analyzed outcomes from 51,805 consecutive mammography examinations. Screening and diagnostic examinations were audited separately. Diagnostic examinations were audited by indication for examination. Extrapolating from our known mix of screening (79%) and diagnostic (21%) examinations, we determined expected combined outcomes for various mixes that might be encountered in clinical practice. Similarly, we determined the expected overall diagnostic mammography outcomes for various clinically relevant mixes of indications for examination. RESULTS: Outcomes vary substantially depending on the mix of screening and diagnostic examinations performed. For example, expected outcomes for practices with screening-diagnostic mixes of 90-10% and 50-50% are, respectively: rate of abnormal findings, 6% versus 11%; rate of positive biopsy findings, 38% versus 42%; cancer detection rate, 10 per 1,000 versus 30 per 1,000; mean invasive cancer size, 14.4 mm versus 16.0 mm; nodal metastasis rate, 8% versus 11%; and rate of stage 0 and stage I cancers, 87% versus 82%. Diagnostic outcomes also vary substantially according to indication for examination, with a higher rate of abnormal findings, a higher rate of positive biopsy findings, and a larger mean invasive cancer size expected for mixes involving a high percentage of workups for palpable lesions. CONCLUSION: When screening and diagnostic mammography outcomes are not segregated during auditing, and when diagnostic outcomes are not segregated by indication for examination, analysis of combined audit data should be based on extrapolations from known outcomes.