Faculty Bibliography

The type of nipple discharge dictates the appropriate imaging study. Physiologic nipple discharge is common and does not require diagnostic imaging. Pathologic nipple discharge in women, men, and transgender patients necessitates breast imaging. Evidence-based guidelines were used to evaluate breast imaging modalities for appropriateness based on patient age and gender. For an adult female or male 40 years of age or greater, mammography or digital breast tomosynthesis (DBT) is performed initially. Breast ultrasound is usually performed at the same time with rare exception. For males or females 30 to 39 years of age, mammography/DBT or breast ultrasound is performed based on institutional preference and individual patient considerations. For young women less than 30 years of age, ultrasound is performed first with mammography/DBT added if there are suspicious findings or if the patient is at elevated lifetime risk for developing breast cancer. There is a high incidence of breast cancer in males with pathologic discharge. Men 25 years and older should be evaluated using mammography/DBT and ultrasound added when indicted. In transfeminine (male-to-female) patients, mammography/DBT and ultrasound are useful due to the increased incidence of breast cancer. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer-reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances in which peer-reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a high sensitivity in detecting breast cancer but often leads to unnecessary biopsies and patient workup. We used a deep learning (DL) system to improve the overall accuracy of breast cancer diagnosis and personalize management of patients undergoing DCE-MRI. On the internal test set (n = 3936 exams), our system achieved an area under the receiver operating characteristic curve (AUROC) of 0.92 (95% CI: 0.92 to 0.93). In a retrospective reader study, there was no statistically significant difference (P = 0.19) between five board-certified breast radiologists and the DL system (mean ΔAUROC, +0.04 in favor of the DL system). Radiologists' performance improved when their predictions were averaged with DL's predictions [mean ΔAUPRC (area under the precision-recall curve), +0.07]. We demonstrated the generalizability of the DL system using multiple datasets from Poland and the United States. An additional reader study on a Polish dataset showed that the DL system was as robust to distribution shift as radiologists. In subgroup analysis, we observed consistent results across different cancer subtypes and patient demographics. Using decision curve analysis, we showed that the DL system can reduce unnecessary biopsies in the range of clinically relevant risk thresholds. This would lead to avoiding biopsies yielding benign results in up to 20% of all patients with BI-RADS category 4 lesions. Last, we performed an error analysis, investigating situations where DL predictions were mostly incorrect. This exploratory work creates a foundation for deployment and prospective analysis of DL-based models for breast MRI.

PURPOSE/OBJECTIVE:The aim of this study was to quantify the initial decline and subsequent rebound in breast cancer screening metrics throughout the coronavirus disease 2019 (COVID-19) pandemic. METHODS:Screening and diagnostic mammographic examinations, biopsies performed, and cancer diagnoses were extracted from the ACR National Mammography Database from March 1, 2019, through May 31, 2021. Patient (race and age) and facility (regional location, community type, and facility type) demographics were collected. Three time periods were used for analysis: pre-COVID-19 (March 1, 2019, to May 31, 2019), peak COVID-19 (March 1, 2020, to May 31, 2020), and COVID-19 recovery (March 1, 2021, to May 31, 2021). Analysis was performed at the facility level and overall between time periods. RESULTS:In total, 5,633,783 screening mammographic studies, 1,282,374 diagnostic mammographic studies, 231,390 biopsies, and 69,657 cancer diagnoses were analyzed. All peak COVID-19 metrics were less than pre-COVID-19 volumes: 36.3% of pre-COVID-19 for screening mammography, 57.9% for diagnostic mammography, 47.3% for biopsies, and 48.7% for cancer diagnoses. There was some rebound during COVID-19 recovery as a percentage of pre-COVID-19 volumes: 85.3% of pre-COVID-19 for screening mammography, 97.8% for diagnostic mammography, 91.5% for biopsies, and 92.0% for cancer diagnoses. Across various metrics, there was a disproportionate negative impact on older women, Asian women, facilities in the Northeast, and facilities affiliated with academic medical centers. CONCLUSIONS:COVID-19 had the greatest impact on screening mammography volumes, which have not returned to pre-COVID-19 levels. Cancer diagnoses declined significantly in the acute phase and have not fully rebounded, emphasizing the need to increase outreach efforts directed at specific patient population and facility types.