Breast imaging with an ultra-low field MRI scanner: a pilot study.

Breast cancer screening is necessary to reduce mortality due to undetected breast cancer. Current methods have limitations, and as a result many women forego regular screening. Magnetic resonance imaging (MRI) can overcome most of these limitations, but access to conventional MRI is not widely available for routine annual screening. Here, we used an MRI scanner operating at ultra-low field (ULF) to image the left breasts of 11 women (mean age, 35 years ±13 years) in the prone position. Three breast radiologists reviewed the imaging and were able to discern the breast outline and distinguish fibroglandular tissue (FGT) from intramammary adipose tissue. Additionally, the expert readers agreed on their assessment of the breast tissue pattern including fatty, scattered FGT, heterogeneous FGT, and extreme FGT. This preliminary work demonstrates that ULF breast MRI is feasible and may be a potential option for comfortable, widely deployable, and low-cost breast cancer diagnosis and screening.

Incidence, Timing, and Long-Term Outcomes of COVID-19 Vaccine-Related Lymphadenopathy on Screening Mammography.

OBJECTIVE: To determine the incidence, timing, and long-term outcomes of unilateral axillary lymphadenopathy ipsilateral to vaccine site (UIAL) on screening mammography after COVID-19 vaccination. METHODS: This retrospective, multisite study included consecutive patients undergoing screening mammography February 8, 2021, to January 31, 2022, with at least 1 year of follow-up. UIAL was typically considered benign (BI-RADS 1 or 2) in the setting of recent (≤6 weeks) vaccination or BI-RADS 0 (ultrasound recommended) when accompanied by a breast finding or identified >6 weeks postvaccination. Vaccination status and manufacturer were obtained from regional registries. Lymphadenopathy rates in vaccinated patients with and without UIAL were compared using Pearson's χ2 test. RESULTS: There were 44,473 female patients (mean age 60.4 ± 11.4 years) who underwent screening mammography at five sites, and 40,029 (90.0%) received at least one vaccine dose. Ninety-four (0.2%) presented with UIAL, 1 to 191 days postvaccination (median 13.5 [interquartile range: 5.0-31.0]). Incidence declined from 2.1% to 0.9% to ≤0.5% after 1, 2, and 3 weeks and persisted up to 36 weeks (P < .001). UIAL did not vary across manufacturer (P = .15). Of 94, 77 (81.9%) were BI-RADS 1 or 2 at screening. None were diagnosed with malignancy at 1-year follow-up. Seventeen (18.1%) were BI-RADS 0 at screening. At diagnostic workup, 13 (76.5%) were BI-RADS 1 or 2, 2 (11.8%) were BI-RADS 3, and 2 (11.8%) were BI-RADS 4. Both BI-RADS 4 patients had malignant status and ipsilateral breast malignancies. Of BI-RADS 3 patients, at follow-up, one was biopsied yielding benign etiology, and one was downgraded to BI-RADS 2. DISCUSSION: Isolated UIAL on screening mammography performed within 6 months of COVID-19 vaccination can be safely assessed as benign.

Comparison of the Diagnostic Accuracy of Mammogram-based Deep Learning and Traditional Breast Cancer Risk Models in Patients Who Underwent Supplemental Screening with MRI.

Background Access to supplemental screening breast MRI is determined using traditional risk models, which are limited by modest predictive accuracy. Purpose To compare the diagnostic accuracy of a mammogram-based deep learning (DL) risk assessment model to that of traditional breast cancer risk models in patients who underwent supplemental screening with MRI. Materials and Methods This retrospective study included consecutive patients undergoing breast cancer screening MRI from September 2017 to September 2020 at four facilities. Risk was assessed using the Tyrer-Cuzick (TC) and National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT) 5-year and lifetime models as well as a DL 5-year model that generated a risk score based on the most recent screening mammogram. A risk score of 1.67% or higher defined increased risk for traditional 5-year models, a risk score of 20% or higher defined high risk for traditional lifetime models, and absolute scores of 2.3 or higher and 6.6 or higher defined increased and high risk, respectively, for the DL model. Model accuracy metrics including cancer detection rate (CDR) and positive predictive values (PPVs) (PPV of abnormal findings at screening [PPV1], PPV of biopsies recommended [PPV2], and PPV of biopsies performed [PPV3]) were compared using logistic regression models. Results This study included 2168 women who underwent 4247 high-risk screening MRI examinations (median age, 54 years [IQR, 48-60 years]). CDR (per 1000 examinations) was higher in patients at high risk according to the DL model (20.6 [95% CI: 11.8, 35.6]) than according to the TC (6.0 [95% CI: 2.9, 12.3]; P < .01) and BCRAT (6.8 [95% CI: 2.9, 15.8]; P = .04) lifetime models. PPV1, PPV2, and PPV3 were higher in patients identified as high risk by the DL model (PPV1, 14.6%; PPV2, 32.4%; PPV3, 36.4%) than those identified as high risk with the TC (PPV1, 5.0%; PPV2, 12.7%; PPV3, 13.5%; P value range, .02-.03) and BCRAT (PPV1, 5.5%; PPV2, 11.1%; PPV3, 12.5%; P value range, .02-.05) lifetime models. Conclusion Patients identified as high risk by a mammogram-based DL risk assessment model showed higher CDR at breast screening MRI than patients identified as high risk with traditional risk models. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae in this issue.

Predicting ipsilateral recurrence in women treated for ductal carcinoma in situ using machine learning and multivariable logistic regression models.

PURPOSE: To develop machine learning (ML) and multivariable regression models to predict ipsilateral breast event (IBE) risk after ductal carcinoma in situ (DCIS) treatment. METHODS: A retrospective investigation was conducted of patients diagnosed with DCIS from 2007 to 2014 who were followed for a minimum of five years after treatment. Data about each patient were extracted from the medical records. Two ML models (penalized logistic regression and random forest) and a multivariable logistic regression model were developed to evaluate recurrence-related variables. RESULTS: 650 women (mean age 56 years, range 27-87 years) underwent treatment for DCIS and were followed for at least five years after treatment (mean 8.0 years). 5.5% (n = 36) experienced an IBE. With multivariable analysis, the variables associated with higher IBE risk were younger age (adjusted odds ratio [aOR] 0.96, p = 0.02), dense breasts at mammography (aOR 3.02, p = 0.02), and < 5 years of endocrine therapy (aOR 4.48, p = 0.02). The multivariable regression model to predict IBE risk achieved an area under the receiver operating characteristic curve (AUC) of 0.75 (95% CI 0.67-0.84). The penalized logistic regression and random forest models achieved mean AUCs of 0.52 (95% CI 0.42-0.61) and 0.54 (95% CI 0.43-0.65), respectively. CONCLUSION: Variables associated with higher IBE risk after DCIS treatment include younger age, dense breasts, and <5 years of adjuvant endocrine therapy. The multivariable logistic regression model attained the highest AUC (0.75), suggesting that regression models have a critical role in risk prediction for patients with DCIS.

Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening.

BACKGROUND: Deep learning breast cancer risk models demonstrate improved accuracy compared with traditional risk models but have not been prospectively tested. We compared the accuracy of a deep learning risk score derived from the patient's prior mammogram to traditional risk scores to prospectively identify patients with cancer in a cohort due for screening. METHODS: We collected data on 119 139 bilateral screening mammograms in 57 617 consecutive patients screened at 5 facilities between September 18, 2017, and February 1, 2021. Patient demographics were retrieved from electronic medical records, cancer outcomes determined through regional tumor registry linkage, and comparisons made across risk models using Wilcoxon and Pearson χ2 2-sided tests. Deep learning, Tyrer-Cuzick, and National Cancer Institute Breast Cancer Risk Assessment Tool (NCI BCRAT) risk models were compared with respect to performance metrics and area under the receiver operating characteristic curves. RESULTS: Cancers detected per thousand patients screened were higher in patients at increased risk by the deep learning model (8.6, 95% confidence interval [CI] = 7.9 to 9.4) compared with Tyrer-Cuzick (4.4, 95% CI = 3.9 to 4.9) and NCI BCRAT (3.8, 95% CI = 3.3 to 4.3) models (P < .001). Area under the receiver operating characteristic curves of the deep learning model (0.68, 95% CI = 0.66 to 0.70) was higher compared with Tyrer-Cuzick (0.57, 95% CI = 0.54 to 0.60) and NCI BCRAT (0.57, 95% CI = 0.54 to 0.60) models. Simulated screening of the top 50th percentile risk by the deep learning model captured statistically significantly more patients with cancer compared with Tyrer-Cuzick and NCI BCRAT models (P < .001). CONCLUSIONS: A deep learning model to assess breast cancer risk can support feasible and effective risk-based screening and is superior to traditional models to identify patients destined to develop cancer in large screening cohorts.

Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice.

OBJECTIVE: Legislation in 38 states requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit mammographic sensitivity. Because radiologist density assessments vary widely, our objective was to implement and measure the impact of a deep learning (DL) model on mammographic breast density assessments in clinical practice. METHODS: This institutional review board-approved prospective study identified consecutive screening mammograms performed across three clinical sites over two periods: 2017 period (January 1, 2017, through September 30, 2017) and 2019 period (January 1, 2019, through September 30, 2019). The DL model was implemented at sites A (academic practice) and B (community practice) in 2018 for all screening mammograms. Site C (community practice) was never exposed to the DL model. Prospective densities were evaluated, and multivariable logistic regression models evaluated the odds of a dense mammogram classification as a function of time and site. RESULTS: We identified 85,124 consecutive screening mammograms across the three sites. Across time intervals, odds of a dense classification decreased at sites exposed to the DL model, site A (adjusted odds ratio [aOR], 0.93; 95% confidence interval [CI], 0.86-0.99; P = .024) and site B (aOR, 0.81 [95% CI, 0.70-0.93]; P = .003), and odds increased at the site unexposed to the model (site C) (aOR, 1.13 [95% CI, 1.01-1.27]; P = .033). DISCUSSION: A DL model reduces the odds of screening mammograms categorized as dense. Accurate density assessments could help health care systems more appropriately use limited supplemental screening resources and help better inform traditional clinical risk models.

Artificial Intelligence (AI) for Screening Mammography, From the AJR Special Series on AI Applications.

Artificial intelligence (AI) applications for screening mammography are being marketed for clinical use in the interpretative domains of lesion detection and diagnosis, triage, and breast density assessment and in the noninterpretive domains of breast cancer risk assessment, image quality control, image acquisition, and dose reduction. Evidence in support of these nascent applications, particularly for lesion detection and diagnosis, is largely based on multireader studies with cancer-enriched datasets rather than rigorous clinical evaluation aligned with the application's specific intended clinical use. This article reviews commercial AI algorithms for screening mammography that are currently available for clinical practice, their use, and evidence supporting their performance. Clinical implementation considerations, such as workflow integration, governance, and ethical issues, are also described. In addition, the future of AI for screening mammography is discussed, including the development of interpretive and noninterpretive AI applications and strategic priorities for research and development.

Evidence-Based Pragmatic Approach to the Management of Borderline or High-Risk Breast Lesions.

The management of borderline or high-risk breast lesions (HRLs) remains controversial. We propose a pragmatic evidence-based approach based on lesion type. For lobular carcinoma in situ with pleomorphism, papilloma with atypia, atypical ductal hyperplasia, and fibroepithelial lesions, surgical consultation and excision are recommended. Patients with other borderline or HRLs are referred for surgical consultation to discuss excision versus surveillance. Our recommendations align with American Society of Breast Surgeons guidelines, which aim to reduce unnecessary surgery and ensure appropriate follow-up.

Disparities in Same-Day Diagnostic Imaging in Breast Cancer Screening: Impact of an Immediate-Read Screening Mammography Program Implemented During the COVID-19 Pandemic.

BACKGROUND. The need for second visits between screening mammography and diagnostic imaging contributes to disparities in the time to breast cancer diagnosis. During the COVID-19 pandemic, an immediate-read screening mammography program was implemented to reduce patient visits and decrease time to diagnostic imaging. OBJECTIVE. The purpose of this study was to measure the impact of an immediate-read screening program with focus on disparities in same-day diagnostic imaging after abnormal findings are made at screening mammography. METHODS. In May 2020, an immediate-read screening program was implemented whereby a dedicated breast imaging radiologist interpreted all screening mammograms in real time; patients received results before discharge; and efforts were made to perform any recommended diagnostic imaging during the visit (performed by different radiologists). Screening mammographic examinations performed from June 1, 2019, through October 31, 2019 (preimplementation period), and from June 1, 2020, through October 31, 2020 (postimplementation period), were retrospectively identified. Patient characteristics were recorded from the electronic medical record. Multivariable logistic regression models incorporating patient age, race and ethnicity, language, and insurance type were estimated to identify factors associated with same-day diagnostic imaging. Screening metrics were compared between periods. RESULTS. A total of 8222 preimplementation and 7235 postimplementation screening examinations were included; 521 patients had abnormal screening findings before implementation, and 359 after implementation. Before implementation, 14.8% of patients underwent same-day diagnostic imaging after abnormal screening mammograms. This percentage increased to 60.7% after implementation. Before implementation, patients who identified their race as other than White had significantly lower odds than patients who identified their race as White of undergoing same-day diagnostic imaging after receiving abnormal screening results (adjusted odds ratio, 0.30; 95% CI, 0.10-0.86; p = .03). After implementation, the odds of same-day diagnostic imaging were not significantly different between patients of other races and White patients (adjusted odds ratio, 0.92; 95% CI, 0.50-1.71; p = .80). After implementation, there was no significant difference in race and ethnicity between patients who underwent and those who did not undergo same-day diagnostic imaging after receiving abnormal results of screening mammography (p > .05). The rate of abnormal interpretation was significantly lower after than it was before implementation (5.0% vs 6.3%; p < .001). Cancer detection rate and PPV1 (PPV based on positive findings at screening examination) were not significantly different before and after implementation (p > .05). CONCLUSION. Implementation of the immediate-read screening mammography program reduced prior racial and ethnic disparities in same-day diagnostic imaging after abnormal screening mammograms. CLINICAL IMPACT. An immediate-read screening program provides a new paradigm for improved screening mammography workflow that allows more rapid diagnostic workup with reduced disparities in care.

Screening Mammography Recovery After COVID-19 Pandemic Facility Closures: Associations of Facility Access and Racial and Ethnic Screening Disparities.

BACKGROUND. Screening mammography facilities closed during the COVID-19 pandemic in spring 2020. Recovery of screening volumes has varied across patient subgroups and facilities. OBJECTIVE. We compared screening mammography volumes and patient and facility characteristics between periods before COVID-19 and early and later postclosure recovery periods. METHODS. This retrospective study included screening mammograms performed in the same 2-month period (May 26-July 26) in 2019 (pre-COVID-19), 2020 (early recovery), and 2021 (late recovery after targeted interventions to expand access) and across multiple facility types (urban, suburban, community health center). Suburban sites had highest proportion of White patients and the greatest scheduling flexibility and expanded appointments during initial reopening. Findings were compared across years. RESULTS. For White patients, volumes decreased 36.6% from 6550 in 2019 (4384 in 2020) and then increased 61.0% to 6579 in 2021; for patients with races other than White, volumes decreased 53.9% from 1321 in 2019 (609 in 2020) and then increased 136.8% to 1442 in 2021. The percentage of mammograms in patients with races other than White was 16.8% in 2019, 12.2% in 2020, and 18.0% in 2021. The proportion performed at the urban center was 55.3% in 2019, 42.2% in 2020, and 45.9% in 2021; the proportion at suburban sites was 34.0% in 2019, 49.2% in 2020, and 43.5% in 2021. Pre-COVID-19 volumes were reached by the sixth week after reopening for suburban sites but were not reached during early recovery for the other sites. The proportion that were performed on Saturday for suburban sites was similar across periods, whereas the proportion performed on Saturday for the urban site was 7.6% in 2019, 5.3% in 2020, and 8.8% in 2021; the community health center did not offer Saturday appointments during recovery. CONCLUSION. After reopening, screening shifted from urban to suburban settings, with a disproportionate screening decrease in patients with races other than White. Initial delayed access at facilities serving underserved populations exacerbated disparities. Interventions to expand access resulted in late recovery volumes exceeding prepandemic volumes in patients with races other than White. CLINICAL IMPACT. Interventions to support equitable access across facilities serving diverse patient populations may mitigate potential widening disparities in breast cancer diagnosis during the pandemic.

Contralateral breast cancer after curative-intent treatment for ductal carcinoma in situ: Rate and associated clinicopathological and imaging risk factors.

PURPOSE: Patients who have ductal carcinoma in situ (DCIS) are undergoing bilateral mastectomy at increasing rates. One of the reasons is to minimize contralateral breast cancer (CBC) risk. The purpose of this study is to determine the rate of and risk factors associated with CBC in women treated for DCIS. METHODS: A retrospective study was performed of women with DCIS at surgery from 2007 to 2014 who had at least five-year follow-up. Patient attributes, imaging findings, histopathology results, and surgical and long-term outcomes were collected. Features associated with a CBC were assessed with multivariable logistic regression models. RESULTS: 613 women (mean 56 years, range 30-87) with DCIS underwent breast-conserving surgery (BCS) (n = 426), unilateral mastectomy (n = 101), or bilateral mastectomy (n = 86), with mean follow-up of 7.9 years. Of the 527 women who had BCS or unilateral mastectomy, 7.4% (n = 39) developed a CBC (DCIS in 12 and invasive cancer in 27). 4.1% (5/122) of women treated with adjuvant endocrine therapy developed a CBC, compared to 8.4% (34/405) who were not treated (p = .11). Features associated with CBC risk were younger age at menarche (adjusted odds ratio [aOR] of 0.76, p = .03) and low nuclear grade of DCIS (aOR of 5.43 for grade 1 versus 3, p = .01). CONCLUSION: In women treated for DCIS, the overall rate of CBC was low at 7.4%. Younger age at menarche and low nuclear grade of DCIS had significant associations with higher CBC risk.

Are English-language online patient education materials related to breast cancer risk assessment understandable, readable, and actionable?

PURPOSE: To evaluate the readability, understandability, and actionability of online patient education materials (OPEM) related to breast cancer risk assessment. MATERIAL AND METHODS: We queried seven English-language search terms related to breast cancer risk assessment: breast cancer high-risk, breast cancer risk factors, breast cancer family history, BRCA, breast cancer risk assessment, Tyrer-Cuzick, and Gail model. Websites were categorized as: academic/hospital-based, commercial, government, non-profit or academic based on the organization hosting the site. Grade-level readability of qualifying websites and categories was determined using readability metrics and generalized estimating equations based on written content only. Readability scores were compared to the recommended parameters set by the American Medical Association (AMA). Understandability and actionability of OPEM related to breast cancer high-risk were evaluated using the Patient Education Materials Assessment Tool (PEMAT) and compared to criteria set at ≥70%. Descriptive statistics and inter-rater reliability analysis were utilized. RESULTS: 343 websites were identified, of which 162 met study inclusion criteria. The average grade readability score was 12.1 across all websites (range 10.8-13.4). No website met the AMA recommendation. Commercial websites demonstrated the highest overall average readability of 13.1. Of the 26 websites related to the search term breast cancer high-risk, the average understandability and actionability scores were 62% and 34% respectively, both below criteria. CONCLUSIONS: OPEM on breast cancer risk assessment available to the general public do not meet criteria for readability, understandability, or actionability. To ensure patient comprehension of medical information online, future information should be published in simpler, more appropriate terms.

An Algorithm for Management of Cosmetic and Reconstructive Patients with Textured Implants: An Institutional Experience.

SUMMARY: Textured anatomic silicone gel breast implants have advantages of gel cohesivity and a natural slope between the upper and lower poles of the breast; however, they have fallen out of favor secondary to their risk of breast implant-associated anaplastic large cell lymphoma. For patients who choose to keep their textured devices, there remains controversy over the best long-term follow-up. This article presents an algorithm from plastic surgery, surgical oncology, and radiology at the Massachusetts General Hospital for management of these patients.

Unilateral Lymphadenopathy After COVID-19 Vaccination: A Practical Management Plan for Radiologists Across Specialties.

Reports are rising of patients with unilateral axillary lymphadenopathy, visible on diverse imaging examinations, after recent coronavirus disease 2019 vaccination. With less than 10% of the US population fully vaccinated, we can prepare now for informed care of patients imaged after recent vaccination. The authors recommend documenting vaccination information (date[s] of vaccination[s], injection site [left or right, arm or thigh], type of vaccine) on intake forms and having this information available to the radiologist at the time of examination interpretation. These recommendations are based on three key factors: the timing and location of the vaccine injection, clinical context, and imaging findings. The authors report isolated unilateral axillary lymphadenopathy (i.e., no imaging findings outside of visible lymphadenopathy), which is ipsilateral to recent (prior 6 weeks) vaccination, as benign with no further imaging indicated. Clinical management is recommended, with ultrasound if clinical concern persists 6 weeks after the final vaccination dose. In the clinical setting to stage a recent cancer diagnosis or assess response to therapy, the authors encourage prompt recommended imaging and vaccination (possibly in the thigh or contralateral arm according to the location of the known cancer). Management in this clinical context of a current cancer diagnosis is tailored to the specific case, ideally with consultation between the oncology treatment team and the radiologist. The aim of these recommendations is to (1) reduce patient anxiety, provider burden, and costs of unnecessary evaluation of enlarged nodes in the setting of recent vaccination and (2) avoid further delays in vaccinations and recommended imaging for best patient care during the pandemic.

Mitigating the Impact of Coronavirus Disease (COVID-19) Vaccinations on Patients Undergoing Breast Imaging Examinations: A Pragmatic Approach.

Reports of patients with axillary adenopathy identified on breast imaging after coronavirus disease (COVID-19) vaccination are rising. We propose a pragmatic management approach based on clinical presentation, vaccination delivery, and imaging findings. In the settings of screening mammography, screening MRI, and diagnostic imaging workup of breast symptoms, with no imaging findings beyond unilateral axillary adenopathy ipsilateral to recent (within the past 6 weeks) vaccination, we report the adenopathy as benign with no further imaging indicated if no nodes are palpable 6 weeks after the last dose. For patients with palpable axillary adenopathy in the setting of ipsilateral recent vaccination, clinical follow-up of the axilla is recommended. In all these scenarios, axillary ultrasound is recommended if clinical concern persists 6 weeks after vaccination. In patients with a recent breast cancer diagnosis in the pre- or peritreatment setting, prompt recommended imaging is encouraged as well as vaccination (in the thigh or contralateral arm). Our recommendations align with the ACR BI-RADS Atlas and aim to reduce patient anxiety, provider burden, and costs of unnecessary evaluation of enlarged nodes in the setting of recent vaccinations and, also, to avoid further delays in vaccinations and breast cancer screening during the pandemic.

Noncalcified Ductal Carcinoma In Situ (DCIS): Rate and Predictors of Upgrade to Invasive Carcinoma.

RATIONALE AND OBJECTIVES: To determine the upgrade rate of noncalcified ductal carcinoma in situ (DCIS) and features that are associated with risk of upgrade to invasive disease at surgery. MATERIALS AND METHODS: A retrospective review was conducted of consecutive women who were diagnosed with noncalcified DCIS from January 2007 to December 2016. Patient demographics, imaging findings, biopsy pathology results, and surgical outcomes were reviewed. The unpaired t test, chi-square test, and Fisher's exact test were used to compare features between the cases of DCIS that did and did not upgrade to invasive carcinoma at surgery. RESULTS: Over a 10-year period, 78 women (mean age 62 years, range 30-88 years) were diagnosed with noncalcified DCIS. Two-thirds (67.9%, 53/78) of cases were detected on screening mammography, and 15.4% (12/78) of diagnoses were made after presentation with an area of palpable concern. The most common mammographic presentations of noncalcified DCIS were mass (51.3%, 40/78) and asymmetry (30.8%, 24/78). Seventeen cases (21.8%, 17/78) were upgraded to invasive ductal carcinoma (IDC) at surgery. Features associated with upgrade risk included older patient age (68.1 versus 60.3 years, OR 1.08, p < 0.01) and family history of breast cancer in a first-degree relative (41.2% [7/17] versus 16.4% [10/61], OR 3.57, p = 0.03). CONCLUSION: In our study cohort, the upgrade rate of noncalcified DCIS to IDC at surgery is 21.8%. Upgrade risk is associated with older patient age and family history of breast cancer in a first-degree relative.

Opportunities for Radiology Trainee Education Amid the COVID-19 Pandemic: Lessons From an Academic Breast Imaging Program.

The COVID-19 pandemic required restructuring of Radiology trainee education across US institutions. While reduced clinical imaging volume and mandates to maintain physical distancing presented new challenges to traditional medical education during this period, new opportunities developed to support our division in providing high-quality training for residents and fellows. The Accreditation Council for Graduate Medical Education (ACGME) Core Competencies for Diagnostic Radiology helped guide division leadership in restructuring and reframing breast imaging education during this time of drastic change and persistent uncertainty. Here, we reflect on the educational challenges and opportunities faced by our academic breast imaging division during the height of the COVID-19 pandemic across each of the ACGME Core Competencies. We also discuss how systems and processes developed out of necessity during the first peak of the pandemic may continue to support radiology training during phased reopening and beyond.

Predictors of Reexcision following Breast-Conserving Surgery for Ductal Carcinoma In Situ.

BACKGROUND: Reexcision following breast-conserving surgery (BCS) in women with ductal carcinoma in situ (DCIS) results in adjuvant treatment delays, higher health care costs, and undesirable cosmetic outcomes. The purpose of this study is to determine patient, imaging, pathological, and surgical predictors of reexcision following BCS for DCIS. PATIENTS AND METHODS: A retrospective review of women with DCIS who had BCS from 2007 to 2016 was conducted. Patient, imaging, pathological, and surgical features, in addition to surgical outcomes, were collected from medical records. Standard statistical tests were used to compare features between patients who did and did not undergo at least one reexcision. A multivariable logistic regression model was fit to assess features associated with reexcision. RESULTS: A total of 547 women (mean age 59 years; range 30-88 years) diagnosed with DCIS at core needle biopsy underwent BCS. Of all women, 31.6% (173/547) had at least one reexcision. With multivariable analysis, features associated with reexcision included younger patient age (adjusted odds ratio [aOR] 0.98, 95% confidence interval [CI] 0.97-1.0, p = 0.049), African-American race (aOR 2.66, 95% CI 1.13-6.26, p = 0.03), biopsy modality of ultrasound (aOR 2.35, 95% CI 1.22-4.53, p = 0.01), and earlier year of surgery (aOR 0.92, 95% CI 0.86-0.98, p = 0.01). No pathological features of DCIS were associated with reexcision risk. CONCLUSIONS: In our cohort of nearly 550 women with DCIS who underwent BCS, 31.6% had at least one reexcision. Features associated with reexcision include younger patient age, African-American race, biopsy modality of ultrasound, and earlier year of surgery.

Retrospective Review of Preoperative Radiofrequency Tag Localization of Breast Lesions in 848 Patients.

BACKGROUND. Advantages of radiofrequency tags for preoperative breast lesion localization include decoupling of tag placement from surgical schedules and improved patient comfort. OBJECTIVE. The purpose of this study was to evaluate the feasibility of a preoperative localization radiofrequency tag system for breast lesions requiring surgical excision. METHODS. The cohort for this retrospective study included consecutive patients who underwent image-guided needle localization with radiofrequency tags before surgical excision from July 12, 2018, to July 31, 2019. Images and medical records were reviewed to evaluate the pathologic diagnoses serving as indications for tag placement, imaging guidance for tag placement, number of tags placed, and target lesion type. Tag placement technical accuracy rate (defined as deployment of the tag within 1 cm of the edge of the target), success (defined as technical accuracy without complication), and surgical margin and reexcision status were evaluated. RESULTS. A total of 1013 tags were placed under imaging guidance in 848 patients (mean age, 60 years; range, 23-96 years) and 847 subsequently underwent surgical excision. Tags were most commonly placed for invasive carcinoma (537/1013, 53.0%), ductal carcinoma in situ (138/1013, 13.6%), and high-risk lesions (289/1013, 28.5%). A total of 673 (66.4%) tags were deployed under mammographic guidance, whereas 340 (33.6%) were placed under sonographic guidance. Two or more tags were placed in 149 of 848 patients (17.6%). Targeted lesion types primarily included masses (448/1013, 44.2%), biopsy clip markers (331/1013, 32.7%), and calcifications (155/1013, 15.3%). Technical accuracy of placement was achieved in 1004 (99.1%) tags. Of the nine inaccurate tag placements, seven (77.8%) required an additional tag or wire placement. Seven (0.7%) biopsy clip markers were displaced within the breast or removed by the tag device during placement. No complications were reported intraoperatively. Therefore, success was achieved in 997 (98.4%) tags. Tags were successfully retrieved in all 847 patients who underwent surgery. Of the 568 patients with a preoperative diagnosis of carcinoma, 86 (15.1%) had positive or close surgical margins requiring surgical reexcision. CONCLUSION. Preoperative image-guided localization with radiofrequency tags is a safe and feasible technique for breast lesions requiring surgery. CLINICAL IMPACT. Radiofrequency tag localization is an acceptable alternative to needle or wire localization, offering the potential for improved patient workflow and experience.