Breast Cancer Risk Assessment Research Articles

Oral Contraceptive Use and Assessment of Breast Cancer Risk among Premenopausal Women via Molecular Characteristics: Systematic Review with Meta-Analysis.

Breast cancer is divided into four molecular subtypes. Each one has distinct clinical features. The aim of this study was to assess individual breast cancer subtype risk in premenopausal women taking oral contraceptives (OCs). Databases (MEDLINE; PubMed, EMBASE, and the Cochrane Library) were searched to January 2022 to identify case-control studies meeting the inclusion criteria. The influence of OCs intake on the risk of ER-positive breast cancer (ER+BC) was revealed to be non-significant with regard to reduction: OR = 0.9134, 95% CI: 0.8128 to 1.0265, p = 0.128. Assessment of ER-negative subtype breast cancer (ER-BC) risk indicated that OCs use significantly increased the risk: OR = 1.3079, 95% CI: 1.0003 to 1.7100, p = 0.050. Analysis for HER2-positive breast cancer (HER2+BC) risk showed that OCs use statistically non-significantly lowered the risk: OR = 0.8810, 95% CI: 0.5977 to 1.2984, p = 0.522. Meta-analysis with regard to Triplet-negative breast cancer (TNBC) risk showed non-statistically significant increased risk: OR = 1.553, 95% CI: 0.99 to 2.43, p = 0.055. The findings of the meta-analysis suggest that breast cancer risk in premenopausal women may vary with respect to molecular subtypes. Extensive scientific work is still necessary in order to understand the impact of OCs use on breast cancer risk in young women.

22 (PB-022) Poster Spotlight - An individualized breast cancer risk assessment model for women attending screening in BreastScreen Norway

Background: The results of the effectiveness of population-based screening are controversial in terms of the balance between mortality reduction and adverse effects. In order to improve it, studies have proposed personalized screening strategies based on woman’s individual breast cancer (BC) risk. There is, therefore, a need to create individual risk prediction models through the analysis of large population-based databases. We developed a model that could be used to classify women targeted for mammography screening according to individual BC risk.

Mass Spectrometry-Based Proteomics of Human Milk to Identify Differentially Expressed Proteins in Women with Breast Cancer versus Controls.

It is thought that accurate risk assessment and early diagnosis of breast cancer (BC) can help reduce cancer-related mortality. Proteomics analysis of breast milk may provide biomarkers of risk and occult disease. Our group works on the analysis of human milk samples from women with BC and controls to investigate alterations in protein patterns of milk that could be related to BC. In the current study, we used mass spectrometry (MS)-based proteomics analysis of 12 milk samples from donors with BC and matched controls. Specifically, we used one-dimensional (1D)-polyacrylamide gel electrophoresis (PAGE) coupled with nanoliquid chromatography tandem MS (nanoLC-MS/MS), followed by bioinformatics analysis. We confirmed the dysregulation of several proteins identified previously in a different set of milk samples. We also identified additional dysregulations in milk proteins shown to play a role in cancer development, such as Lactadherin isoform A, O-linked N-acetylglucosamine (GlcNAc) transferase, galactosyltransferase, recoverin, perilipin-3 isoform 1, histone-lysine methyltransferase, or clathrin heavy chain. Our results expand our current understanding of using milk as a biological fluid for identification of BC-related dysregulated proteins. Overall, our results also indicate that milk has the potential to be used for BC biomarker discovery, early detection and risk assessment in young, reproductively active women.

Breast Cancer Among Transgender and Nonbinary Patients: Paradigms for Improving Data Collection and Inclusion in Breast Imaging Settings.

Breast cancer incidence among transgender and nonbinary (TGNB) individuals is not well characterized owing to the absence of robust data collection among this patient population. Consequently, breast cancer risks are largely unknown, and screening guidelines are not based on robust evidence. Additionally, TGNB patients experience barriers to access health care. A first step in improving data collection, research, and ultimately care of TGNB individuals is the identification of group members and demonstration to patients that our breast imaging centers are champions of LGBTQ+ health. At our institution, patients who present for breast imaging complete an iPad-administered breast imaging history and breast cancer risk assessment survey. Using the modified Tyrer-Cuzick model, the lifetime risk of developing breast cancer is estimated, and additional key history that may impact breast care and future breast imaging is collected. Under the previous clinic workflow, patients are identified as either "male" or "female" and complete a corresponding gender-specific survey. To improve care, we revised the survey using gender-inclusive language and developed four versions to allow patients to separately self-report their sex assigned at birth and gender identity. Relevant queries relating to hormone use and gender-affirming chest/breast surgery that are concordant with six gender-identity groups were added. Long-term collection of these inclusive data by imaging centers has the potential to enhance the data set available to improve breast care and better understand breast cancer risk and outcomes among TGNB populations.

Burden of lymphedema in long-term breast cancer survivors by race and age.

Risk assessment for breast cancer-related lymphedema has emphasized upper-limb symptoms and treatment-related risk factors. This article examined breast cancer-related lymphedema after surgery, overall and in association with broader demographic and clinical features. The Carolina Breast Cancer Study phase 3 followed participants for breast cancer-related lymphedema from baseline (on average, 5months after breast cancer diagnosis) to 7years after diagnosis. Among 2645 participants, 552 self-reported lymphedema cases were identified. Time-to-lymphedema curves and inverse probability weighted conditional Cox proportional hazards model were used to evaluate whether demographics and clinical features were associated with breast cancer-related lymphedema. Point prevalence of breast cancer-related lymphedema was 6.8% at baseline, and 19.9% and 23.8% at 2 and 7 years after diagnosis, respectively. Most cases had lymphedema in the arm (88%-93%), whereas 14% to 27% presented in the trunk and/or breast. Beginning approximately 10months after diagnosis, younger Black women had the highest risk of breast cancer-related lymphedema and older non-Black women had the lowest risk. Positive lymph node status, larger tumor size (>5cm), and estrogen receptor-negative breast cancer, as well as established risk factors such as higher body mass index, removal of more than five lymph nodes, mastectomy, chemotherapy, and radiation therapy, were significantly associated with increased hazard (1.5- to 3.5-fold) of lymphedema. Findings highlight that hazard of breast cancer-related lymphedema differs by demographic characteristics and clinical features. These factors could be used to identify those at greatest need of lymphedema prevention and early intervention. In this study, the aim was to investigate breast cancer-related lymphedema (BCRL) burden. This study found that risk of BCRL differs by race, age, and other characteristics.

T064: Doxorubicin exposure and breast cancer risk in adolescent and adult Hodgkin lymphoma survivors

Figure 1: Doxorubicin dose-response plot. Dose-response curve for continuous doxorubicin dose and breast cancer risk, adjusted for age at HL treatment, chest radiotherapy and gonadotoxic treatment. Adjusted HRs for doxorubicin dose categories are added. Background: Female Hodgkin lymphoma (HL) survivors treated with chest radiotherapy at a young age have a strongly increased risk of breast cancer (BC). Recent studies in childhood cancer survivors have shown that doxorubicin may also increase BC risk. So far, the association between doxorubicin and BC risk has not been examined in cancer survivors treated at adolescent/adult ages. Methods: We assessed BC risk in a cohort of 1964 female five year HL survivors, treated at ages 15–50 years in 20 Dutch hospitals between 1975 and 2008. Cumulative BC incidence was estimated in the presence of death as a competing risk. Treatment factors were time-dependently included in the multivariable Cox regression analysis, focusing on the effect of doxorubicin exposure on BC risk. Results: HL survivors were treated at a median age of 27.8 years (interquartile range (IQR) 21.9–35.2 years). After a median follow-up of 18.3 years (IQR 12.9–24.7) years, 200 women had developed invasive BC (n=190) and/or ductal carcinoma in situ (n=49). The 30-year cumulative incidence was 19.4% (95% confidence Interval (CI) 16.6–22.3%). Among patients treated with chemotherapy (n=1113), receipt of doxorubicin-containing chemotherapy increased from 32.6% in 1975–1986 to 84.5% in 1998–2008. In multivariable analysis a cumulative dose of >200 mg/m2 was associated with increased BC risk (HR 1.7; 95% CI 1.1–2.4), compared to patients not treated with doxorubicin. BC risk increased 19% (HR 1.19; 95% CI 1.1–1.3) per additional 100 mg/m2 doxorubicine (ptrend=0.003). Receipt of mantle or axillary field irradiation or gonadotoxic therapy did not modify the association between doxorubicin and BC risk. Among patients who received >200 mg/m2 doxorubicin, the HR was 1.6 (95% CI 1.1–2.5) for patients treated with and 2.0 (95% CI 0.9–4.2) for patients treated without mantle or axillary field irradiation (Pinteraction=0.35). Gonadotoxic treatment (>8.4 g/m2 procarbazine or pelvic irradiation) significantly decreased the risk of BC. Conclusion: This study shows that doxorubicin is associated with an increased BC risk among adolescent and adult HL survivors. Now that radiotherapy doses and volumes have decreased and doxorubicin increasingly forms the backbone of HL treatment, the potential association of doxorubicin with increased BC risk is an important issue.

The Landmark Series-Addressing Disparities in Breast Cancer Screening: New Recommendations for Black Women.

Randomized, clinical trials have established the efficacy of screening mammography in improving survival from breast cancer for women through detection of early, asymptomatic disease. However, disparities in survival rates between black women and women from other racial and ethnic groups following breast cancer diagnosis persist. Various professional groups have different, somewhat conflicting, guidelines with regards to recommended age for commencing screening as well as recommended frequency of screening exams, but the trials upon which these recommendations are based were not specifically designed to examine benefit among black women. Furthermore, these recommendations do not appear to incorporate the unique epidemiological circumstances of breast cancer among black women, including higher rates of diagnosis before age 40 years and greater likelihood of advanced stage at diagnosis, into their formulation. In this review, we examined the epidemiologic and socioeconomic factors that are associated with breast cancer among black women and assess the implications of these factors for screening in this population. Specifically, we recommend that by no later than age 25 years, all black women should undergo baseline assessment for future risk of breast cancer utilizing a model that incorporates race (e.g., Breast Cancer Risk Assessment Tool [BCRAT], formerly the Gail model) and that this assessment should be conducted by a breast specialist or a healthcare provider (e.g., primary care physician or gynecologist) who is trained to assess breast cancer risk and is aware of the increased risks of early (i.e., premenopausal) and biologically aggressive (e.g., late-stage, triple-negative) breast cancer among black women.

Association of patients' primary language, race, and location of care with decision-making for personalized breast cancer risk assessment.

331 Background: Personalized breast cancer risk assessment (CRA) and genetic counseling/testing for women have been shown to improve interventional and clinical outcomes. However, despite CRA being routinely offered to all women at our institution, not all women choose to opt into the program. This study evaluated the demographic distribution of patients that opted out of the CRA to identify potential components influencing this substantial decision. Methods: Our medical center, located in four urban and suburban locations, piloted a clinical framework to provide CRA to all women between the ages of 25 and 75 with a qualifying mammogram. In this single institution retrospective study, we analyzed differences among patients who opted out of the program between July 20, 2020and July 19, 2021. Data elements extracted from the electronic medical record include race, primary language, location of care, and decision to partake in CRA. Overall Chi-square tests and all pairwise comparisons with Bonferroni correction were used to statistically determine the impact of various demographics on opting out. Results: 18726 women met criteria for inclusion and 2717 (14.5%) declined CRA. Within their respective racial groups, 122 (17.6%) Asian, 1175 (16.3%) Black, 455 (14.2%) Hispanic, 112 (15%) Other, and 853 (12.4%) White opted out. Within their identified primary language, 2370 (13.7%) English, 231 (23.1%) Spanish, 35 (19.7%) Bilingual, and 81 (35.2%) Other opted out. Based on their setting of care, 837 (16.8%) Location A, 1436 (14.2%) Location B, 270 (13.8%) Location C, and 174 (10.6%) Location D opted out. The differences between demographics and opting out were statistically significant for all analyses (p <.0001). Results shown in Table below. Conclusions: The main variables mediating the decision to opt out of CRA are a non-English primary language, non-White race, and location of care, potentially due to inadequate interpretation services, barriers to education, and staffing shortages. Thus, these women are less likely to receive genetic counseling/testing or supplemental screening. Our study is the first to investigate the factors contributing to the uptake of risk assessment. Further study is needed to determine the clinical impact of this striking disparity.[Table: see text]

External Validation of a Mammography-Derived AI-Based Risk Model in a U.S. Breast Cancer Screening Cohort of White and Black Women.

Simple SummaryThe aim of this study was to perform an external validation in a U.S. screening cohort of a mammography-derived AI risk model that was originally developed in a European study setting. The AI risk model was designed to predict short-term breast cancer risk toward identifying women who could benefit from supplemental screening and/or a shorter screening interval due to their high risk of breast cancer. The AI risk model showed a discriminatory performance of AUC 0.68, comparable to previously reported European validation results (AUC = 0.73). The discriminatory performance of the AI risk model was non-significantly different by race (AUC for White women = 0.67 and for Black women = 0.70), p = 0.20. In relation to a clinically used lifestyle–family-based risk model, the AI risk model showed a significantly higher discriminatory performance (AUCs 0.68 vs. 0.55, p < 0.01).Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation on a U.S. screening cohort of a mammography-derived AI breast cancer risk model originally developed for European screening cohorts. We retrospectively identified 176 breast cancers with exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4963 controls from women with at least one-year negative follow-up. A risk score for each woman was calculated via the AI risk model. Age-adjusted areas under the ROC curves (AUCs) were estimated for the entire cohort and separately for White and Black women. The Gail 5-year risk model was also evaluated for comparison. The overall AUC was 0.68 (95% CIs 0.64–0.72) for all women, 0.67 (0.61–0.72) for White women, and 0.70 (0.65–0.76) for Black women. The AI risk model significantly outperformed the Gail risk model for all women p < 0.01 and for Black women p < 0.01, but not for White women p = 0.38. The performance of the mammography-derived AI risk model was comparable to previously reported European validation results; non-significantly different when comparing White and Black women; and overall, significantly higher than that of the Gail model.

Breast cancer diagnosis in Inner-City African American and Hispanic women: The importance of early screening

PurposeTo evaluate the diagnosis of breast cancer in inner-city African-American and Hispanic women under age 50 to support the importance of screening in this population. MethodsThis retrospective chart review included women newly diagnosed with breast cancer from 1/1/2015 to 1/1/2019 in a city hospital mainly serving minority patients. Chi-square and Fisher's exact tests were used for analysis. ResultsIn this cohort of 108 newly diagnosed African-American (63%) and Hispanic (31%) women, 60/108 (56%) presented with a site of palpable concern for diagnostic workup, and the remaining were diagnosed via asymptomatic screening. Women ages 30–49 were significantly more likely to present with a site of palpable concern when compared to women ages 50–69 (68% vs. 44%, p = 0.045). Additionally, women ages 30–49 were more likely to have triple-negative breast cancer (TNBC) than women ages 50–69 (20% vs. 10%, p = 0.222). However, women ages 30–49 were less likely to have prior mammogram than women ages 50–69 (24% vs. 46%, p = 0.062). ConclusionAfrican-American and Hispanic women ages 30–49 were more likely to present with a site of palpable concern and TNBC than those ages 50–69. However, these young minority women ages 30–49 were less likely to have prior screening mammograms when compared to those ages 50–69. Our data highlights the importance of starting screening mammography no later than age 40 in African-American and Hispanic women. In addition, these women should have risk assessment for breast cancer no later than age 30 and be screened appropriately.

Implementing Risk-Stratified Breast Screening in England: An Agenda Setting Meeting.

Simple SummaryA risk-stratified approach to breast cancer screening has gained international interest as a mechanism to better balance the benefits, harms and costs of screening programmes. The aim of our agenda setting meeting was to identify key uncertainties to resolve in order to implement a risk-stratified breast screening programme within England. Findings from a 5-year programme of research assessing the feasibility of such an approach in England were presented before individual discussion groups and an open plenary session regarding what preparation needs to take place. Key findings highlight the need to further develop risk modelling to ensure equity of access to breast cancer risk assessment and ensure a risk-stratified programme is cost-effective. Other issues highlighted related to capacity and capability of the health services to offer an integrated risk assessment pathway that is accessible. Attendees identified ways in which risk-stratification could be implemented to minimise inequity of access to screening.It is now possible to accurately assess breast cancer risk at routine NHS Breast Screening Programme (NHSBSP) appointments, provide risk feedback and offer risk management strategies to women at higher risk. These strategies include National Institute for Health and Care Excellence (NICE) approved additional breast screening and risk-reducing medication. However, the NHSBSP invites nearly all women three-yearly, regardless of risk. In March 2022, a one-day agenda setting meeting took place in Manchester to discuss the feasibility and desirability of implementation of risk-stratified screening in the NHSBSP. Fifty-eight individuals participated (38 face-to-face, 20 virtual) with relevant expertise from academic, clinical and/or policy-making perspectives. Key findings were presented from the PROCAS2 NIHR programme grant regarding feasibility of risk-stratified screening in the NHSBSP. Participants discussed key uncertainties in seven groups, followed by a plenary session. Discussions were audio-recorded and thematically analysed to produce descriptive themes. Five themes were developed: (i) risk and health economic modelling; (ii) health inequalities and communication with women; (iii); extending screening intervals for low-risk women; (iv) integration with existing NHSBSP; and (v) potential new service models. Most attendees expected some form of risk-stratified breast screening to be implemented in England and collectively identified key issues to be resolved to facilitate this.

ASO Author Reflections: Optimizing Breast Cancer Risk Assessment and Screening Among Black Women.

ASO Author Reflections: Optimizing Breast Cancer Risk Assessment and Screening Among Black Women.

Assessment of breast dose and cancer risk for young females during CT chest and abdomen examinations

CT scanning deliver much higher radiation doses than planar radiological procedures, which puts patients to high risks. This study measures and evaluates patient doses during chest and abdomen computed tomography procedures. Particular attention is given to measuring the dose to the equivalent breast (mSv) and to estimate the associated risks of breast cancer to young female patients (15–35 years). Data was obtained from standard examinations from three hospitals. The measured values of CT dose indexes, CTDI (mGy) as well as exposure-related parameters were used for assessment. Breast and effective doses were extrapolated using a software. The results showed remarkable variations of the mean organ equivalent doses for similar CT examinations in the studied hospitals. This could be attributed to the variation in CT scanning imaging technique, and clinical indications. The average effective dose to the chest was 7.9 mSv (2.3–47.0 mSv) and for the abdomen the mean dose was 6.6 mSv, ranging from (3.3–27 mSv). The breast received equivalent doses from chest and abdomen procedures as follows: 10.2 (1.6–33 mSv) and 10.1(2.3–19 mS) Sv respectively. Each procedure yielded high risks of breast cancer for young females. Implementation of accurate referral criteria is recommended to avoid unnecessary breast radiation exposure.

Fully automated breast segmentation on spiral breast computed tomography images.

IntroductionThe quantification of the amount of the glandular tissue and breast density is important to assess breast cancer risk. Novel photon‐counting breast computed tomography (CT) technology has the potential to quantify them. For accurate analysis, a dedicated method to segment the breast components—the adipose and glandular tissue, skin, pectoralis muscle, skinfold section, rib, and implant—is required. We propose a fully automated breast segmentation method for breast CT images.MethodsThe framework consists of four parts: (1) investigate, (2) segment the components excluding adipose and glandular tissue, (3) assess the breast density, and (4) iteratively segment the glandular tissue according to the estimated density. For the method, adapted seeded watershed and region growing algorithm were dedicatedly developed for the breast CT images and optimized on 68 breast images. The segmentation performance was qualitatively (five‐point Likert scale) and quantitatively (Dice similarity coefficient [DSC] and difference coefficient [DC]) demonstrated according to human reading by experienced radiologists.ResultsThe performance evaluation on each component and overall segmentation for 17 breast CT images resulted in DSCs ranging 0.90–0.97 and in DCs 0.01–0.08. The readers rated 4.5–4.8 (5 highest score) with an excellent inter‐reader agreement. The breast density varied by 3.7%–7.1% when including mis‐segmented muscle or skin.ConclusionThe automatic segmentation results coincided with the human expert's reading. The accurate segmentation is important to avoid the significant bias in breast density analysis. Our method enables accurate quantification of the breast density and amount of the glandular tissue that is directly related to breast cancer risk.

Patient, primary care provider, and stakeholder perspectives on mammography screening frequency: lessons learned from a qualitative study

BackgroundU.S. professional organizations have provided conflicting recommendations on annual vs. biennial mammography screening. Potential harms of more frequent screening include increased anxiety and costs of false positive results, including unnecessary breast biopsies and overdiagnosis.ObjectiveTo characterize current practices and beliefs surrounding mammography screening frequency and perspectives on using risk-based screening to inform screening intervals.DesignSemi-structured interviews informed by the Consolidated Framework for Implementation Research (CFIR).ParticipantsPatients, primary care providers (PCPs), third-party stakeholders (breast radiologists, radiology administrators, patient advocates).Main measuresQualitative data, with a codebook developed based upon prespecified implementation science constructs.Key resultsWe interviewed 25 patients, 11 PCPs, and eight key stakeholders, including three radiologists, two radiology administrators, and three patient advocates. Most patients reported having annual mammograms, however, half believed having mammograms every two years was acceptable. Some women were worried early breast cancer would be missed if undergoing biennial screening. PCPs were equally split between recommending annual and biennial mammograms. Although PCPs were interested in using breast cancer risk models to inform screening decisions, concerns raised include time burden and lack of familiarity with breast cancer risk assessment tools. All breast radiologists believed patients should receive annual mammograms, while patient advocates and radiology administrators were split between annual vs. biennial. Radiologists were worried about missing breast cancer diagnoses when mammograms are not performed yearly. Patient advocates and radiology administrators were more open to biennial mammograms and utilizing risk-based screening.ConclusionsUncertainty remains across stakeholder groups regarding appropriate mammogram screening intervals. Radiologists recommend annual mammography, whereas patients and PCPs were evenly split between annual vs. biennial screening, although both favored annual screening among higher-risk women. Breast cancer risk assessment tools may help facilitate decisions about screening intervals, but face barriers to widespread implementation in the primary care setting. These results will inform future implementation strategies to adopt risk-stratified breast cancer screening.

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

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. 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. 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 &lt; .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 &lt; .001). 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.

Microinvasive breast cancer and the role of sentinel lymph node biopsy

Whether sentinel lymph node biopsy (SLNB) should be performed in patients with microinvasive breast cancer (MIBC) has been a matter of debate over the last decade. MIBC has a favorable prognosis and while metastasis to the axilla is rare, it can impact treatment recommendations. In this study we evaluated clinical and histological features in both MIBC and background DCIS including ER, PR, and HER-2, number of foci of MIBC, the extent of the DCIS, nuclear grade, presence of comedo necrosis, as well as surgical procedures, adjuvant treatment and follow up to identify variables which predict disease free survival (DFS), as well as the factors which influence clinical decision making. Our study included 72 MIBC patients with a mean patient follow-up time of 55 months. Three patients with MIBC had recurrence, and two deceased, leaving five patients in total with poor long-term outcomes and a DFS rate of 93.1%. Performing mastectomy, high nuclear grade, and negativity for ER and HER-2 were found to be associated with the use of SLNB, although none of these variables were found to be associated with DFS. One positive lymph node case was discovered following SLNB in our study. This suggests the use of SLNB may provide diagnostic information to some patients, although these are the anomalies. When comparing patients who had undergone SLNB to those which had not there was no difference in DFS. Certainly, the use of SLNB in MIBC is quite the conundrum. It is important to acknowledge that surgical complications have been reported, and traditional metrics used for risk assessment in invasive breast cancer may not hold true in the setting of microinvasion.

Black Women Are Less Likely to Be Classified as High-Risk for Breast Cancer Using the Tyrer-Cuzick 8 Model

Breast cancer risk assessment is a powerful tool that guides recommendations for supplemental breast cancer screening and genetic counseling. The Tyrer-Cuzick 8 (TC8) model is widely used for calculating breast cancer risk and thus helps determine if women qualify for supplemental screening or genetic counseling. However, the TC8 model may underestimate breast cancer risk in Black women. This study sought to assess this disparity. Data on race, breast density, body mass index (BMI), and TC8 scores were retrospectively extracted from the electronic medical record (EMR). Logistic regressions were run to evaluate racial differences in TC8 scores. Summary and correlation statistics determined relationships between BMI, breast density, and race. Rank biserial correlations were employed to explore the impact of breast density and BMI on TC8 scores. Of 15,356 patients, 5796 were White and 5813 were Black. Black patients had higher rates of BMI ≥ 27 compared with White women (79.2% vs. 45.7%), lower rates of breast density (35.1% vs. 56.2%), and lower rates of high-risk TC8 scores (10.7% vs. 17.5%, OR = 1.6646). There was an inverse relationship between TC8 score and BMI (rrb = - 0.04) and a direct relationship between TC8 score and breast density (rrb = 0.37). Black women are less likely to have high-risk TC8 scores despite having only marginally lower breast cancer incidence rates and higher breast cancer mortality rates than White women. This suggests that the TC8 model underestimates breast cancer risk in Black women, possibly due to lower rates of breast density and higher BMIs among Black women.

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.

Breast Density Legislation Impact on Breast Cancer Screening and Risk Assessment.

To evaluate breast density notification legislation (BDNL) on breast imaging practice patterns, risk assessment, and supplemental screening. A 20-question anonymous web-based survey was administered to practicing Society of Breast Imaging radiologists in the U.S. between February and April 2021 regarding breast cancer risk assessment, supplemental screening, and density measurements. Results were compared between facilities with and without BDNL using the two-sided Fisher's exact test. One hundred and ninety-seven radiologists from 41 U.S. states, with (187/197, 95%) or without (10/197, 5%) BDNL, responded. Fifty-seven percent (113/197) performed breast cancer risk assessment, and 93% (183/197) offered supplemental screening for women with dense breasts. Between facilities with or without BDNL, there was no significant difference in whether risk assessment was (P = 0.19) or was not performed (P = 0.20). There was no significant difference in supplemental screening types (P > 0.05) between BDNL and non-BDNL facilities. Thirty-five percent (69/197) of facilities offered no supplemental screening studies, and 25% (49/197) had no future plans to offer supplemental screening. A statistically significant greater proportion of non-BDNL facilities offered no supplemental screening (P < 0.03) and had no plans to offer supplemental screening compared to BDNL facilities (P < 0.02). Facilities in BDNL states often offer supplemental screening compared to facilities in non-BDNL states. Compared to BDNL facilities, a statistically significant proportion of non-BDNL facilities had no supplemental screening nor plans for implementation. Our data suggest that upcoming federal BDNL will impact how supplemental screening is addressed in currently non-BDNL states.