Breast Imaging and Intervention during Pregnancy and Lactation.

Physiologic changes that occur in the breast during pregnancy and lactation create challenges for breast cancer screening and diagnosis. Despite these challenges, imaging evaluation should not be deferred, because delayed diagnosis of pregnancy-associated breast cancer contributes to poor outcomes. Both screening and diagnostic imaging can be safely performed using protocols based on age, breast cancer risk, and whether the patient is pregnant or lactating. US is the preferred initial imaging modality for the evaluation of clinical symptoms in pregnant women, followed by mammography if the US findings are suspicious for malignancy or do not show the cause of the clinical symptom. Breast MRI is not recommended during pregnancy because of the use of intravenous gadolinium-based contrast agents. Diagnostic imaging for lactating women is the same as that for nonpregnant nonlactating individuals, beginning with US for patients younger than 30 years old and mammography followed by US for patients aged 30 years and older. MRI can be performed for high-risk screening and local-regional staging in lactating women. The radiologist may encounter a wide variety of breast abnormalities, some specific to pregnancy and lactation, including normal physiologic changes, benign disorders, and malignant neoplasms. Although most masses encountered are benign, biopsy should be performed if the imaging characteristics are suspicious for cancer or if the finding does not resolve after a short period of clinical follow-up. Knowledge of the expected imaging appearance of physiologic changes and common benign conditions of pregnancy and lactation is critical for differentiating these findings from pregnancy-associated breast cancer. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.

Utilization and Cancer Yield of Probably Benign Assessment Category in the National Mammography Database: 2009 to 2018.

PURPOSE: Data on utilization rate and cancer yield of BI-RADS® category 3 in routine clinical practice in diagnostic mammography are sparse. The aim of this study was to determine utilization rate and cancer yield of BI-RADS 3 in diagnostic mammography in the ACR National Mammography Database (NMD). METHODS: Retrospective analysis of NMD mammograms from January 1, 2009, to June 30, 2018, was performed. BI-RADS 3 utilization rate in diagnostic setting was calculated and stratified by patient, facility, and examination-level variables. Patient-level cancer yield was calculated among women with BI-RADS 3 assessment and adequate follow-up (imaging follow-up ≥24 months or biopsy). Logistic regression was performed to assess the odds of utilization of BI-RADS 3, with respect to facility, examination, and patient variables, and the odds of malignancy among patients with probably benign findings. Chi-square and t tests were used to determine significance (P < .05). RESULTS: Data from 19,443,866 mammograms from 500 NMD facilities across 31 states were analyzed, of which 3,039,952 were diagnostic mammograms. Utilization rate of BI-RADS 3 was 15.5% (470,155 of 3,039,952) in the diagnostic setting. There was a statistically significant difference in BI-RADS 3 utilization rate across all collected variables (P < .001). Patient-level cancer yield at 2-year follow-up was 0.91% (2,009 of 220,672; 95% confidence interval [CI], 0.87%-0.95%) in the diagnostic setting. Patient and examination variables associated with significantly higher likelihood of malignancy included calcifications (odds ratio, 4.27; 95% CI, 2.43-7.51), patient age > 70 years (odds ratio, 3.77; 95% CI, 2.49-5.7), and presence of prior comparisons (odds ratio, 1.23; 95% CI, 1.07-1.42). CONCLUSIONS: In the NMD, BI-RADS 3 assessment was common in diagnostic mammography (15.5%), with an overall cancer yield of 0.91%, less than the benchmark of 2%. Utilization trends in diagnostic mammography warrant further research for optimization of use.

Image-based screening for men at high risk for breast cancer: Benefits and drawbacks.

Male breast cancer is a rare malignancy. Due to low prevalence and limited data to support male breast cancer screening, there are currently no recommendations for image-based screening in asymptomatic men and few recommendations for men at high risk for breast cancer. However, symptomatically diagnosed cancers in men are typically advanced, suggesting that earlier detection may improve outcomes. In this article we briefly review the risk factors for male breast cancer, and discuss the potential benefits and possible drawbacks of routine image-based screening for men at high risk for breast cancer.

BRCA Mutation Carriers: Breast and Ovarian Cancer Screening Guidelines and Imaging Considerations.

Patients who carry the BRCA1 and BRCA2 gene mutations have an underlying genetic predisposition for breast and ovarian cancers. These deleterious genetic mutations are the most common genes implicated in hereditary breast and ovarian cancers. This monograph summarizes the evidence behind current screening recommendations, reviews imaging protocols specific to this patient population, and illustrates some of the imaging nuances of breast and ovarian cancers in this clinical setting.

Comparison of screening full-field digital mammography and digital breast tomosynthesis technical recalls.

Enhancing quality using the inspection program (EQUIP) augments the FDA/MQSA program ensuring image quality review and implementation of corrective processes. We compared technical recalls between digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM). Prospectively recorded technical recalls of consecutive screening mammograms (10/2013 - 12/2017) were compared for imaging modality [FFDM, DBT + FFDM, DBT + synthesized mammography (SynM)], images requested, and indication(s) (motion, positioning, technical/artifact). Chi-squared tests evaluated statistical significance between proportions. Of 48,324 screening mammograms, 277 (0.57%) patients were recalled for 360 indications with 371 repeated views. DBT exams had significantly less recalls compared to FFDM ( X 2 = 25.239 ; p = 0 < 0.001 ). 98 (27.2%) recalls were for motion, 192 (53.3%) positioning, and 70 (19.4%) technique/artifacts. Theses indications for technical recall were compared for FFDM, DBT + FFDM, and DBT + SynM. There were significant differences in the indications for technical recall prior to and after implementing DBT + SynM ( X 2 = 18.719 ; p < 0.001 ). Technical recalls declined significantly with the inclusion of DBT (SynM/FFDM) compared to FFDM alone. Recalls for motion demonstrated the greatest decrease. Positioning remains a dominant factor for technical recall regardless of modality, supporting the opportunity for continued technologist education in positioning to decrease technical recalls.

ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database.

Purpose To determine the utilization and positive predictive value (PPV) of the American College of Radiology (ACR) Breast Imaging Data and Reporting System (BI-RADS) category 4 subdivisions in diagnostic mammography in the National Mammography Database (NMD). Materials and Methods This study involved retrospective review of diagnostic mammography data submitted to the NMD from January 1, 2008 to December 30, 2014. Utilization rates of BI-RADS category 4 subdivisions were compared by year, facility (type, location, census region), and examination (indication, finding type) characteristics. PPV3 (positive predictive value for biopsies performed) was calculated overall and according to category 4 subdivision. The χ2 test was used to test for significant associations. Results Of 1 309 950 diagnostic mammograms, 125 447 (9.6%) were category 4, of which 33.3% (41 841 of 125 447) were subdivided. Subdivision utilization rates were higher (P < .001) in practices that were community, suburban, or in the West; for examination indication of prior history of breast cancer; and for the imaging finding of architectural distortion. Of 41 841 category 4 subdivided examinations, 4A constituted 55.6% (23 258 of 41 841) of the examinations; 4B, 31.8% (13 302 of 41 841) of the examinations; and 4C, 12.6% (5281 of 41 841) of the examinations. Pathologic outcomes were available in 91 563 examinations, and overall category 4 PPV3 was 21.1% (19 285 of 91 563). There was a statistically significant difference in PPV3 according to category 4 subdivision (P < .001): The PPV of 4A was 7.6% (1274 of 16 784), that of 4B was 22% (2317 of 10 408), and that of 4C was 69.3% (2839 of 4099). Conclusion Although BI-RADS suggests their use, subdivisions were utilized in the minority (33.3% [41 841 of 125 447]) of category 4 diagnostic mammograms, with variability based on facility and examination characteristics. When subdivisions were used, PPV3s were in BI-RADS-specified malignancy ranges. This analysis supports the use of subdivisions in broad practice and, given benefits for patient care, should motivate increased utilization. © RSNA, 2018 Online supplemental material is available for this article.

Utility of BI-RADS Assessment Category 4 Subdivisions for Screening Breast MRI.

OBJECTIVE: BI-RADS for mammography and ultrasound subdivides category 4 assessments by likelihood of malignancy into categories 4A (> 2% to ≤ 10%), 4B (> 10% to ≤ 50%), and 4C (> 50% to < 95%). Category 4 is not subdivided for breast MRI because of a paucity of data. The purpose of the present study is to determine the utility of categories 4A, 4B, and 4C for MRI by calculating their positive predictive values (PPVs) and comparing them with BI-RADS-specified rates of malignancy for mammography and ultrasound. MATERIALS AND METHODS: All screening breast MRI examinations performed from July 1, 2010, through June 30, 2013, were included in this study. We identified in medical records prospectively assigned MRI BI-RADS categories, including category 4 subdivisions, which are used routinely in our practice. Benign versus malignant outcomes were determined by pathologic analysis, findings from 12 months or more clinical or imaging follow-up, or a combination of these methods. Distribution of BI-RADS categories and positive predictive value level 2 (PPV2; based on recommendation for tissue diagnosis) for categories 4 (including its subdivisions) and 5 were calculated. RESULTS: Of 860 screening breast MRI examinations performed for 566 women (mean age, 47 years), 82 with a BI-RADS category 4 assessment were identified. A total of 18 malignancies were found among 84 category 4 and 5 assessments, for an overall PPV2 of 21.4% (18/84). For category 4 subdivisions, PPV2s were as follows: for category 4A, 2.5% (1/40); for category 4B, 27.6% (8/29); for category 4C, 83.3% (5/6); and for category 4 (not otherwise specified), 28.6% (2/7). CONCLUSION: Category 4 subdivisions for MRI yielded malignancy rates within BI-RADS-specified ranges, supporting their use for benefits to patient care and more meaningful practice audits.

Screening Breast MRI Outcomes in Routine Clinical Practice: Comparison to BI-RADS Benchmarks.

RATIONALE AND OBJECTIVES: The BI-RADS Atlas 5th Edition includes screening breast magnetic resonance imaging (MRI) outcome benchmarks. However, the metrics are from expert practices and clinical trials of women with hereditary breast cancer predispositions, and it is unknown if they are appropriate for routine practice. We evaluated screening breast MRI audit outcomes in routine practice across a spectrum of elevated risk patients. MATERIALS AND METHODS: This Institutional Review Board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study included all consecutive screening breast MRI examinations from July 1, 2010 to June 30, 2013. Examination indications were categorized as gene mutation carrier (GMC), personal history (PH) breast cancer, family history (FH) breast cancer, chest radiation, and atypia/lobular carcinoma in situ (LCIS). Outcomes were determined by pathology and/or ≥12 months clinical and/or imaging follow-up. We calculated abnormal interpretation rate (AIR), cancer detection rate (CDR), positive predictive value of recommendation for tissue diagnosis (PPV2) and biopsy performed (PPV3), and median size and percentage of node-negative invasive cancers. RESULTS: Eight hundred and sixty examinations were performed in 566 patients with a mean age of 47 years. Indications were 367 of 860 (42.7%) FH, 365 of 860 (42.4%) PH, 106 of 860 (12.3%) GMC, 14 of 860 (1.6%) chest radiation, and 8 of 22 (0.9%) atypia/LCIS. The AIR was 134 of 860 (15.6%). Nineteen cancers were identified (13 invasive, 4 DCIS, two lymph nodes), resulting in CDR of 19 of 860 (22.1 per 1000), PPV2 of 19 of 88 (21.6%), and PPV3 of 19 of 80 (23.8%). Of 13 invasive breast cancers, median size was 10 mm, and 8 of 13 were node negative (61.5%). CONCLUSIONS: Performance outcomes of screening breast MRI in routine clinical practice across a spectrum of elevated risk patients met the American College of Radiology Breast Imaging Reporting and Data System benchmarks, supporting broad application of these metrics. The indication of a personal history of treated breast cancer accounted for a large proportion (42%) of our screening examinations, with breast MRI performance in this population at least comparable to that of other screening indications.

Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems.

While the use of machine learning methods in clinical decision support has great potential for improving patient care, acquiring standardized, complete, and sufficient training data presents a major challenge for methods relying exclusively on machine learning techniques. Domain experts possess knowledge that can address these challenges and guide model development. We present Advice-Based-Learning (ABLe), a framework for incorporating expert clinical knowledge into machine learning models, and show results for an example task: estimating the probability of malignancy following a non-definitive breast core needle biopsy. By applying ABLe to this task, we demonstrate a statistically significant improvement in specificity (24.0% with p=0.004) without missing a single malignancy.

Developing a comprehensive database management system for organization and evaluation of mammography datasets.

We aimed to design and develop a comprehensive mammography database system (CMDB) to collect clinical datasets for outcome assessment and development of decision support tools. A Health Insurance Portability and Accountability Act (HIPAA) compliant CMDB was created to store multi-relational datasets of demographic risk factors and mammogram results using the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The CMDB collected both biopsy pathology outcomes, in a breast pathology lexicon compiled by extending BI-RADS, and our institutional breast cancer registry. The audit results derived from the CMDB were in accordance with Mammography Quality Standards Act (MQSA) audits and national benchmarks. The CMDB has managed the challenges of multi-level organization demanded by the complexity of mammography practice and lexicon development in pathology. We foresee that the CMDB will be useful for efficient quality assurance audits and development of decision support tools to improve breast cancer diagnosis. Our procedure of developing the CMDB provides a framework to build a detailed data repository for breast imaging quality control and research, which has the potential to augment existing resources.

Addressing the challenge of assessing physician-level screening performance: mammography as an example.

BACKGROUND: Motivated by the challenges in assessing physician-level cancer screening performance and the negative impact of misclassification, we propose a method (using mammography as an example) that enables confident assertion of adequate or inadequate performance or alternatively recognizes when more data is required. METHODS: Using established metrics for mammography screening performance-cancer detection rate (CDR) and recall rate (RR)-and observed benchmarks from the Breast Cancer Surveillance Consortium (BCSC), we calculate the minimum volume required to be 95% confident that a physician is performing at or above benchmark thresholds. We graphically display the minimum observed CDR and RR values required to confidently assert adequate performance over a range of interpretive volumes. We use a prospectively collected database of consecutive mammograms from a clinical screening program outside the BCSC to illustrate how this method classifies individual physician performance as volume accrues. RESULTS: Our analysis reveals that an annual interpretive volume of 2770 screening mammograms, above the United States' (US) mandatory (480) and average (1777) annual volumes but below England's mandatory (5000) annual volume is necessary to confidently assert that a physician performed adequately. In our analyzed US practice, a single year of data uniformly allowed confident assertion of adequate performance in terms of RR but not CDR, which required aggregation of data across more than one year. CONCLUSION: For individual physician quality assessment in cancer screening programs that target low incidence populations, considering imprecision in observed performance metrics due to small numbers of patients with cancer is important.