Mammographic density and the risk and detection of breast cancer

Value of automated breast ultrasound in screening: Standalone and as a supplemental to digital breast tomosynthesis

We aimed to determine the value of standalone and supplemental automated breast ultrasound (ABUS) in detecting cancers in an opportunistic screening setting with digital breast tomosynthesis (DBT) and compare this combined screening method to DBT and ABUS alone in women older than 39 years with BI-RADS B-D density categories. In this prospective opportunistic screening study, 3466 women aged 39 or older with BI-RADS B-D density categories and with a mean age of 50 were included. The screening protocol consisted of DBT mediolateral-oblique views, 2D craniocaudal views, and ABUS with three projections for both breasts. ABUS was evaluated blinded to mammography findings. Statistical analysis evaluated diagnostic performance for DBT, ABUS, and combined workflows. Twenty-nine cancers were screen-detected. ABUS and DBT exhibited the same cancer detection rates (CDR) at 7.5/1000 whereas DBT + ABUS showed 8.4/1000, with ABUS contributing an additional CDR of 0.9/1000. Standalone ABUS outperformed DBT in detecting 12.5% more invasive cancers. DBT displayed better accuracy (95%) compared to ABUS (88%) and combined approach (86%). Sensitivities for DBT and ABUS were the same (84%), with DBT + ABUS showing a higher rate (94%). DBT outperformed ABUS in specificity (95% vs. 88%). DBT + ABUS exhibited a higher recall rate (14.89%) compared to ABUS (12.38%) and DBT (6.03%) (p < .001). Standalone ABUS detected more invasive cancers compared to DBT, with a higher recall rate. The combined approach showed a higher CDR by detecting one additional cancer per thousand.

A comparative analysis of risk factor associations with interval and screen-detected breast cancers: A large UK prospective study

The associations of certain factors, such as age and menopausal hormone therapy, with breast cancer risk are known to differ for interval and screen-detected cancers. However, the extent to which associations of other established breast cancer risk factors differ by mode of detection is unclear. We investigated associations of a wide range of risk factors using data from a large UK cohort with linkage to the National Health Service Breast Screening Programme, cancer registration, and other health records. We used Cox regression to estimate adjusted relative risks (RRs) and 95% confidence intervals (CIs) for associations between risk factors and breast cancer risk. A total of 9421 screen-detected and 5166 interval cancers were diagnosed in 517,555 women who were followed for an average of 9.72 years. We observed the following differences in risk factor associations by mode of detection: greater body mass index (BMI) was associated with a smaller increased risk of interval (RR per 5 unit increase 1.07, 95% CI 1.03-1.11) than screen-detected cancer (RR 1.27, 1.23-1.30); having a first-degree family history was associated with a greater increased risk of interval (RR 1.81, 1.68-1.95) than screen-detected cancer (RR 1.52, 1.43-1.61); and having had previous breast surgery was associated with a greater increased risk of interval (RR 1.85, 1.72-1.99) than screen-detected cancer (RR 1.34, 1.26-1.42). As these differences in associations were relatively unchanged after adjustment for tumour grade, and are in line with the effects of these factors on mammographic density, they are likely to reflect the effects of these risk factors on screening sensitivity.

The prognostic potential of mammographic growth rate of invasive breast cancer in the Nijmegen breast cancer screening cohort

OBJECTIVES

Insight into the aggressiveness of potential breast cancers found in screening may optimize recall decisions. Specific growth rate (SGR), measured on mammograms, may provide valuable prognostic information. This study addresses the association of SGR with prognostic factors and overall survival in patients with invasive carcinoma of no special type (NST) from a screened population.

METHODS

In this historic cohort study, 293 women with NST were identified from all participants in the Nijmegen screening program (2003-2007). Information on clinicopathological factors was retrieved from patient files and follow-up on vital status through municipalities. On consecutive mammograms, tumor volumes were estimated. After comparing five growth functions, SGR was calculated using the best-fitting function. Regression and multivariable survival analyses described associations between SGR and prognostic factors as well as overall survival.

RESULTS

Each one standard deviation increase in SGR was associated with an increase in the Nottingham prognostic index by 0.34 [95% confidence interval (CI): 0.21-0.46]. Each one standard deviation increase in SGR increased the odds of a tumor with an unfavorable subtype (based on histologic grade and hormone receptors; odds ratio 2.14 [95% CI: 1.45-3.15]) and increased the odds of diagnosis as an interval cancer (versus screen-detected; odds ratio 1.57 [95% CI: 1.20-2.06]). After a median of 12.4 years of follow-up, 78 deaths occurred. SGR was not associated with overall survival (hazard ratio 1.12 [95% CI: 0.87-1.43]).

CONCLUSIONS

SGR may indicate prognostically relevant differences in tumor aggressiveness if serial mammograms are available. A potential association with cause-specific survival could not be determined and is of interest for future research.

SHG Fiberscopy Assessment of Collagen Morphology and Its Potential for Breast Cancer Optical Histology

OBJECTIVE

This study is to investigate the feasibility of our recently developed nonlinear fiberscope for label-free in situ breast tumor detection and lymph node status assessment based on second harmonic generation (SHG) imaging of fibrillar collagen matrix with histological details. The long-term goal is to improve the current biopsy-based cancer paradigm with reduced sampling errors.

METHODS

In this pilot study we undertook retrospective SHG imaging study of ex vivo invasive ductal carcinoma human biopsy tissue samples, and carried out quantitative image analysis to search for collagen structural signatures that are associated with the malignance of breast cancer.

RESULTS

SHG fiberscopy image-based quantitative assessment of collagen fiber morphology reveals that: 1) cancerous tissues contain generally less extracellular collagen fibers compared with tumor-adjacent normal tissues, and 2) collagen fibers in lymph node positive biopsies are more aligned than lymph node negative counterparts.

CONCLUSION/SIGNIFICANCE

The results demonstrate the promising potential of our SHG fiberscope for in situ breast tumor detection and lymph node involvement assessment and for offering real-time guidance during ongoing tissue biopsy.

Screening mammography frequency following dense breast notification among a predominantly Hispanic/Latina screening cohort

PURPOSE

Nationally legislated dense breast notification (DBN) informs women of their breast density (BD) and the impact of BD on breast cancer risk and detection, but consequences for screening participation are unclear. We evaluated the association of DBN in New York State (NYS) with subsequent screening mammography in a largely Hispanic/Latina cohort.

METHODS

Women aged 40-60 were surveyed in their preferred language (33% English, 67% Spanish) during screening mammography from 2016 to 2018. We used clinical BD classification from mammography records from 2013 (NYS DBN enactment) through enrollment (baseline) to create a 6-category variable capturing prior and new DBN receipt (sent only after clinically dense mammograms). We used this variable to compare the number of subsequent mammograms (0, 1, ≥ 2) from 10 to 30 months after baseline using ordinal logistic regression.

RESULTS

In a sample of 728 women (78% foreign-born, 72% Hispanic, 46% high school education or less), first-time screeners and women who received DBN for the first time after prior non-dense mammograms had significantly fewer screening mammograms within 30 months of baseline (Odds Ratios range: 0.33 (95% Confidence Interval (CI) 0.12-0.85) to 0.38 (95% CI 0.17-0.82)) compared to women with prior mammography but no DBN. There were no differences in subsequent mammogram frequency between women with multiple DBN and those who never received DBN. Findings were consistent across age, language, health literacy, and education groups.

CONCLUSION

Women receiving their first DBN after previous non-dense mammograms have lower mammography participation within 2.5 years. DBN has limited influence on screening participation of first-time screeners and those with persistent dense mammograms.

Relationship between Volpara Density Grade and Compressed Breast Thickness in Japanese Patients with Breast Cancer

BACKGROUND

High breast density found using mammographs (MGs) reduces positivity rates and is considered a risk factor for breast cancer. Research on the relationship between Volpara density grade (VDG) and compressed breast thickness (CBT) in the Japanese population is still lacking. Moreover, little attention has been paid to pseudo-dense breasts with CBT < 30 mm among high-density breasts. We investigated VDG, CBT, and apparent high breast density in patients with breast cancer.

METHODS

Women who underwent MG and breast cancer surgery at our institution were included. VDG and CBT were measured. VDG was divided into a non-dense group (NDG) and a dense group (DG).

RESULTS

This study included 419 patients. VDG was negatively correlated with CBT. The DG included younger patients with lower body mass index (BMI) and thinner CBT. In the DG, patients with CBT < 30 mm had lower BMI and higher VDG; however, no significant difference was noted in the positivity rate of the two groups.

CONCLUSIONS

Younger women tend to have higher breast density, resulting in thinner CBT, which may pose challenges in detecting breast cancer on MGs. However, there was no significant difference in the breast cancer detection rate between CBT < 30 mm and CBT ≥ 30 mm.

Radiation exposure and screening yield by digital breast tomosynthesis compared to mammography: results of the TOSYMA Trial breast density related

OBJECTIVES

The randomized TOmosynthesis plus SYnthesized MAmmography (TOSYMA) screening trial has shown that digital breast tomosynthesis plus synthesized mammography (DBT + SM) is superior to digital mammography (DM) in invasive breast cancer detection varying with breast density. On the other hand, the overall average glandular dose (AGD) of DBT is higher than that of DM. Comparing the DBT + SM and DM trial arm, we analyzed here the mean AGD and their determinants per breast density category and related them to the respective invasive cancer detection rates (iCDR).

METHODS

TOSYMA screened 99,689 women aged 50 to 69 years. Compression force, resulting breast thickness, the calculated AGD obtained from each mammography device, and previously published iCDR were used for comparisons across breast density categories in the two trial arms.

RESULTS

There were 196,622 exposures of 49,227 women (DBT + SM) and 197,037 exposures of 49,132 women (DM) available for analyses. Mean breast thicknesses declined from breast density category A (fatty) to D (extremely dense) in both trial arms. However, while the mean AGD in the DBT + SM arm declined concomitantly from category A (2.41 mGy) to D (1.89 mGy), it remained almost unchanged in the DM arm (1.46 and 1.51 mGy, respectively). In relative terms, the AGD elevation in the DBT + SM arm (64.4% (A), by 44.5% (B), 27.8% (C), and 26.0% (D)) was lowest in dense breasts where, however, the highest iCDR were observed.

CONCLUSION

Women with dense breasts may specifically benefit from DBT + SM screening as high cancer detection is achieved with only moderate AGD elevations.

CLINICAL RELEVANCE STATEMENT

TOSYMA suggests a favorable constellation for screening with digital breast tomosynthesis plus synthesized mammography (DBT + SM) in dense breasts when weighing average glandular dose elevation against raised invasive breast cancer detection rates. There is potential for density-, i.e., risk-adapted population-wide breast cancer screening with DBT + SM.

KEY POINTS

Breast thickness declines with visually increasing density in digital mammography (DM) and digital breast tomosynthesis (DBT). Average glandular doses of DBT decrease with increasing density; digital mammography shows lower and more constant values. With the smallest average glandular dose difference in dense breasts, DBT plus SM had the highest difference in invasive breast cancer detection rates.

Baseline breast tissue characteristics determine the effect of tamoxifen on mammographic density change

Tamoxifen prevents recurrence of breast cancer and is also approved for preventive, risk-reducing, therapy. Tamoxifen alters the breast tissue composition and decreases the mammographic density. We aimed to test if baseline breast tissue composition influences tamoxifen-associated density change. This biopsy-based study included 83 participants randomised to 6 months daily intake of placebo, 20, 10, 5, 2.5, or 1 mg tamoxifen. The study is nested within the double-blinded tamoxifen dose-determination trial Karolinska Mammography Project for Risk Prediction of Breast Cancer Intervention (KARISMA) Study. Ultrasound-guided core-needle breast biopsies were collected at baseline before starting treatment. Biopsies were quantified for epithelial, stromal, and adipose distributions, and epithelial and stromal expression of proliferation marker Ki67, oestrogen receptor (ER) and progesterone receptor (PR). Mammographic density was measured using STRATUS. We found that greater mammographic density at baseline was positively associated with stromal area and inversely associated with adipose area and stromal expression of ER. Premenopausal women had greater mammographic density and epithelial tissue, and expressed more epithelial Ki67, PR, and stromal PR, compared to postmenopausal women. In women treated with tamoxifen (1-20 mg), greater density decrease was associated with higher baseline density, epithelial Ki67, and stromal PR. Women who responded to tamoxifen with a density decrease had on average 17% higher baseline density and a 2.2-fold higher PR expression compared to non-responders. Our results indicate that features in the normal breast tissue before tamoxifen exposure influences the tamoxifen-associated density decrease, and that the age-associated difference in density change may be related to age-dependant differences in expression of Ki67 and PR.

Associations between quantitative measures of mammographic density and terminal ductal lobular unit involution in Chinese breast cancer patients

BACKGROUND

Higher mammographic density (MD), a radiological measure of the proportion of fibroglandular tissue in the breast, and lower terminal duct lobular unit (TDLU) involution, a histological measure of the amount of epithelial tissue in the breast, are independent breast cancer risk factors. Previous studies among predominantly white women have associated reduced TDLU involution with higher MD.

METHODS

In this cohort of 611 invasive breast cancer patients (ages 23-91 years [58.4% ≥ 50 years]) from China, where breast cancer incidence rates are lower and the prevalence of dense breasts is higher compared with Western countries, we examined the associations between TDLU involution assessed in tumor-adjacent normal breast tissue and quantitative MD assessed in the contralateral breast obtained from the VolparaDensity software. Associations were estimated using generalized linear models with MD measures as the outcome variables (log-transformed), TDLU measures as explanatory variables (categorized into quartiles or tertiles), and adjusted for age, body mass index, parity, age at menarche and breast cancer subtype.

RESULTS

We found that, among all women, percent dense volume (PDV) was positively associated with TDLU count (highest tertile vs. zero: Expbeta = 1.28, 95% confidence interval [CI] 1.08-1.51, ptrend =  < .0001), TDLU span (highest vs. lowest tertile: Expbeta = 1.23, 95% CI 1.11-1.37, ptrend =  < .0001) and acini count/TDLU (highest vs. lowest tertile: Expbeta = 1.22, 95% CI 1.09-1.37, ptrend = 0.0005), while non-dense volume (NDV) was inversely associated with these measures. Similar trend was observed for absolute dense volume (ADV) after the adjustment of total breast volume, although the associations for ADV were in general weaker than those for PDV. The MD-TDLU associations were generally more pronounced among breast cancer patients ≥ 50 years and those with luminal A tumors compared with patients < 50 years and with luminal B tumors.

CONCLUSIONS

Our findings based on quantitative MD and TDLU involution measures among Chinese breast cancer patients are largely consistent with those reported in Western populations and may provide additional insights into the complexity of the relationship, which varies by age, and possibly breast cancer subtype.

AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial

Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagnosed after negative screens have shown that many cancers are missed. Supplemental screening using magnetic resonance imaging (MRI) can reduce the number of missed cancers. However, as qualified MRI staff are lacking, the equipment is expensive to purchase and cost-effectiveness for screening may not be convincing, the utilization of MRI is currently limited. An effective method for triaging individuals to supplemental MRI screening is therefore needed. We conducted a randomized clinical trial, ScreenTrustMRI, using a recently developed artificial intelligence (AI) tool to score each mammogram. We offered trial participation to individuals with a negative screening mammogram and a high AI score (top 6.9%). Upon agreeing to participate, individuals were assigned randomly to one of two groups: those receiving supplemental MRI and those not receiving MRI. The primary endpoint of ScreenTrustMRI is advanced breast cancer defined as either interval cancer, invasive component larger than 15 mm or lymph node positive cancer, based on a 27-month follow-up time from the initial screening. Secondary endpoints, prespecified in the study protocol to be reported before the primary outcome, include cancer detected by supplemental MRI, which is the focus of the current paper. Compared with traditional breast density measures used in a previous clinical trial, the current AI method was nearly four times more efficient in terms of cancers detected per 1,000 MRI examinations (64 versus 16.5). Most additional cancers detected were invasive and several were multifocal, suggesting that their detection was timely. Altogether, our results show that using an AI-based score to select a small proportion (6.9%) of individuals for supplemental MRI after negative mammography detects many missed cancers, making the cost per cancer detected comparable with screening mammography. ClinicalTrials.gov registration: NCT04832594 .

Effect of testosterone therapy on breast tissue composition and mammographic breast density in trans masculine individuals

BACKGROUND

The effect of gender-affirming testosterone therapy (TT) on breast cancer risk is unclear. This study investigated the association between TT and breast tissue composition and breast tissue density in trans masculine individuals (TMIs).

METHODS

Of the 444 TMIs who underwent chest-contouring surgeries between 2013 and 2019, breast tissue composition was assessed in 425 TMIs by the pathologists (categories of lobular atrophy and stromal composition) and using our automated deep-learning algorithm (% epithelium, % fibrous stroma, and % fat). Forty-two out of 444 TMIs had mammography prior to surgery and their breast tissue density was read by a radiologist. Mammography digital files, available for 25/42 TMIs, were analyzed using the LIBRA software to obtain percent density, absolute dense area, and absolute non-dense area. Linear regression was used to describe the associations between duration of TT use and breast tissue composition or breast tissue density measures, while adjusting for potential confounders. Analyses stratified by body mass index were also conducted.

RESULTS

Longer duration of TT use was associated with increasing degrees of lobular atrophy (p < 0.001) but not fibrous content (p = 0.82). Every 6 months of TT was associated with decreasing amounts of epithelium (exp(β) = 0.97, 95% CI 0.95,0.98, adj p = 0.005) and fibrous stroma (exp(β) = 0.99, 95% CI 0.98,1.00, adj p = 0.05), but not fat (exp(β) = 1.01, 95%CI 0.98,1.05, adj p = 0.39). The effect of TT on breast epithelium was attenuated in overweight/obese TMIs (exp(β) = 0.98, 95% CI 0.95,1.01, adj p = 0.14). When comparing TT users versus non-users, TT users had 28% less epithelium (exp(β) = 0.72, 95% CI 0.58,0.90, adj p = 0.003). There was no association between TT and radiologist's breast density assessment (p = 0.58) or LIBRA measurements (p > 0.05).

CONCLUSIONS

TT decreases breast epithelium, but this effect is attenuated in overweight/obese TMIs. TT has the potential to affect the breast cancer risk of TMIs. Further studies are warranted to elucidate the effect of TT on breast density and breast cancer risk.

Effects of vitamin D supplementation on a deep learning-based mammographic evaluation in SWOG S0812

Deep learning-based mammographic evaluations could noninvasively assess response to breast cancer chemoprevention. We evaluated change in a convolutional neural network-based breast cancer risk model applied to mammograms among women enrolled in SWOG S0812, which randomly assigned 208 premenopausal high-risk women to receive oral vitamin D3 20 000 IU weekly or placebo for 12 months. We applied the convolutional neural network model to mammograms collected at baseline (n = 109), 12 months (n = 97), and 24 months (n = 67) and compared changes in convolutional neural network-based risk score between treatment groups. Change in convolutional neural network-based risk score was not statistically significantly different between vitamin D and placebo groups at 12 months (0.005 vs 0.002, P = .875) or at 24 months (0.020 vs 0.001, P = .563). The findings are consistent with the primary analysis of S0812, which did not demonstrate statistically significant changes in mammographic density with vitamin D supplementation compared with placebo. There is an ongoing need to evaluate biomarkers of response to novel breast cancer chemopreventive agents.

Mammographic density assessment: comparison of radiologists, automated volumetric measurement, and artificial intelligence-based computer-assisted diagnosis

BACKGROUND

Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated.

PURPOSE

To assess the inter-modality agreement between radiologists, automated volumetric density measurement program (Volpara), and AI-CAD system in breast density categorization using the Breast Imaging-Reporting and Data System (BI-RADS) density categories.

MATERIAL AND METHODS

A retrospective review was conducted on 1015 screening digital mammograms that were performed in Asian female patients (mean age = 56 years ± 10 years) in our health examination center between December 2022 and January 2023. Four radiologists with two different levels of experience (expert and general radiologists) performed density assessments. Agreement between the radiologists, Volpara, and AI-CAD (Lunit INSIGHT MMG) was evaluated using weighted kappa statistics and matched rates.

RESULTS

Inter-reader agreement between expert and general radiologists was substantial (k = 0.65) with a matched rate of 72.8%. The agreement was substantial between expert or general radiologists and Volpara (k = 0.64-0.67) with a matched rate of 72.0% but moderate between expert or general radiologists and AI-CAD (k = 0.45-0.58) with matched rates of 56.7%-67.0%. The agreement between Volpara and AI-CAD was moderate (k = 0.53) with a matched rate of 60.8%.

CONCLUSION

The agreement in breast density categorization between radiologists and automated volumetric density measurement program (Volpara) was higher than the agreement between radiologists and AI-CAD (Lunit INSIGHT MMG).

Background Parenchymal Enhancement: A Comprehensive Update

Breast MR imaging is a complementary screening tool for patients at high risk for breast cancer and has been used in the diagnostic setting. Normal enhancement of breast tissue on MR imaging is called breast parenchymal enhancement (BPE), which occurs after administration of an intravenous contrast agent. BPE varies widely due to menopausal status, use of exogenous hormones, and breast cancer treatment. Degree of BPE has also been shown to influence breast cancer risk and may predict treatment outcomes. The authors provide a comprehensive update on BPE with review of the recent literature.

Abbreviated Breast MRI for Supplemental Screening in Patients With Dense Breasts: Comparison of Baseline Versus Subsequent-Round Examinations

BACKGROUND. Abbreviated breast MRI (AB-MRI) achieves a higher cancer detection rate (CDR) than digital breast tomosynthesis when applied for baseline (i.e., first-round) supplemental screening of individuals with dense breasts. Limited literature has evaluated subsequent (i.e., sequential) AB-MRI screening rounds. OBJECTIVE. This study aimed to compare outcomes between baseline and subsequent rounds of screening AB-MRI in individuals with dense breasts who otherwise had an average risk for breast cancer. METHODS. This retrospective study included patients with dense breasts who otherwise had an average risk for breast cancer and underwent AB-MRI for supplemental screening between December 20, 2016, and May 10, 2023. The clinical interpretations and results of recommended biopsies for AB-MRI examinations were extracted from the EMR. Baseline and subsequent-round AB-MRI examinations were compared. RESULTS. The final sample included 2585 AB-MRI examinations (2007 baseline and 578 subsequent-round examinations) performed for supplemental screening of 2007 women (mean age, 57.1 years old) with dense breasts. Of 2007 baseline examinations, 1658 (82.6%) were assessed as BI-RADS category 1 or 2, 171 (8.5%) as BI-RADS category 3, and 178 (8.9%) as BI-RADS category 4 or 5. Of 578 subsequent-round examinations, 533 (92.2%) were assessed as BI-RADS category 1 or 2, 20 (3.5%) as BI-RADS category 3, and 25 (4.3%) as BI-RADS category 4 or 5 (p < .001). The abnormal interpretation rate (AIR) was 17.4% (349/2007) for baseline examinations versus 7.8% (45/578) for subsequent-round examinations (p < .001). For baseline examinations, PPV2 was 21.3% (38/178), PPV3 was 26.6% (38/143), and the CDR was 18.9 cancers per 1000 examinations (38/2007). For subsequent-round examinations, PPV2 was 28.0% (7/25) (p = .45), PPV3 was 29.2% (7/24) (p = .81), and the CDR was 12.1 cancers per 1000 examinations (7/578) (p = .37). All 45 cancers diagnosed by baseline or subsequent-round AB-MRI were stage 0 or 1. Seven cancers diagnosed by subsequent-round AB-MRI had a mean interval of 872 ± 373 (SD) days since prior AB-MRI and node-negative status at surgical axillary evaluation; six had an invasive component, all measuring 1.2 cm or less. CONCLUSION. Subsequent rounds of AB-MRI screening of individuals with dense breasts had lower AIR than baseline examinations while maintaining a high CDR. All cancers detected by subsequent-round examinations were early-stage node-negative cancers. CLINICAL IMPACT. The findings support sequential AB-MRI for supplemental screening in individuals with dense breasts. Further investigations are warranted to optimize the screening interval.

Imaging the dense breast

The sensitivity of mammography reduces as breast density increases, which impacts breast screening and locoregional staging in breast cancer. Supplementary imaging with other modalities can offer improved cancer detection, but this often comes at the cost of more false positives. Magnetic resonance imaging and contrast-enhanced mammography, which assess tumour enhancement following contrast administration, are more sensitive than digital breast tomosynthesis and ultrasound, which predominantly rely on the assessment of tumour morphology.

Breast Density: Where Are We Now?

Breast density refers to the amount of fibroglandular tissue relative to fat on mammography and is determined either qualitatively through visual assessment or quantitatively. It is a heritable and dynamic trait associated with age, race/ethnicity, body mass index, and hormonal factors. Increased breast density has important clinical implications including the potential to mask malignancy and as an independent risk factor for the development of breast cancer. Breast density has been incorporated into breast cancer risk models. Given the impact of dense breasts on the interpretation of mammography, supplemental screening may be indicated.

AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human Performance

Purpose To compare two deep learning-based commercially available artificial intelligence (AI) systems for mammography with digital breast tomosynthesis (DBT) and benchmark them against the performance of radiologists. Materials and Methods This retrospective study included consecutive asymptomatic patients who underwent mammography with DBT (2019-2020). Two AI systems (Transpara 1.7.0 and ProFound AI 3.0) were used to evaluate the DBT examinations. The systems were compared using receiver operating characteristic (ROC) analysis to calculate the area under the ROC curve (AUC) for detecting malignancy overall and within subgroups based on mammographic breast density. Breast Imaging Reporting and Data System results obtained from standard-of-care human double-reading were compared against AI results with use of the DeLong test. Results Of 419 female patients (median age, 60 years [IQR, 52-70 years]) included, 58 had histologically proven breast cancer. The AUC was 0.86 (95% CI: 0.85, 0.91), 0.93 (95% CI: 0.90, 0.95), and 0.98 (95% CI: 0.96, 0.99) for Transpara, ProFound AI, and human double-reading, respectively. For Transpara, a rule-out criterion of score 7 or lower yielded 100% (95% CI: 94.2, 100.0) sensitivity and 60.9% (95% CI: 55.7, 66.0) specificity. The rule-in criterion of higher than score 9 yielded 96.6% sensitivity (95% CI: 88.1, 99.6) and 78.1% specificity (95% CI: 73.8, 82.5). For ProFound AI, a rule-out criterion of lower than score 51 yielded 100% sensitivity (95% CI: 93.8, 100) and 67.0% specificity (95% CI: 62.2, 72.1). The rule-in criterion of higher than score 69 yielded 93.1% (95% CI: 83.3, 98.1) sensitivity and 82.0% (95% CI: 77.9, 86.1) specificity. Conclusion Both AI systems showed high performance in breast cancer detection but lower performance compared with human double-reading. Keywords: Mammography, Breast, Oncology, Artificial Intelligence, Deep Learning, Digital Breast Tomosynthesis © RSNA, 2024.

Automated Breast Density Assessment in MRI Using Deep Learning and Radiomics: Strategies for Reducing Inter-Observer Variability

BACKGROUND

Accurate breast density evaluation allows for more precise risk estimation but suffers from high inter-observer variability.

PURPOSE

To evaluate the feasibility of reducing inter-observer variability of breast density assessment through artificial intelligence (AI) assisted interpretation.

STUDY TYPE

Retrospective.

POPULATION

Six hundred and twenty-one patients without breast prosthesis or reconstructions were randomly divided into training (N = 377), validation (N = 98), and independent test (N = 146) datasets.

FIELD STRENGTH/SEQUENCE

1.5 T and 3.0 T; T1-weighted spectral attenuated inversion recovery.

ASSESSMENT

Five radiologists independently assessed each scan in the independent test set to establish the inter-observer variability baseline and to reach a reference standard. Deep learning and three radiomics models were developed for three classification tasks: (i) four Breast Imaging-Reporting and Data System (BI-RADS) breast composition categories (A-D), (ii) dense (categories C, D) vs. non-dense (categories A, B), and (iii) extremely dense (category D) vs. moderately dense (categories A-C). The models were tested against the reference standard on the independent test set. AI-assisted interpretation was performed by majority voting between the models and each radiologist's assessment.

STATISTICAL TESTS

Inter-observer variability was assessed using linear-weighted kappa (κ) statistics. Kappa statistics, accuracy, and area under the receiver operating characteristic curve (AUC) were used to assess models against reference standard.

RESULTS

In the independent test set, five readers showed an overall substantial agreement on tasks (i) and (ii), but moderate agreement for task (iii). The best-performing model showed substantial agreement with reference standard for tasks (i) and (ii), but moderate agreement for task (iii). With the assistance of the AI models, almost perfect inter-observer variability was obtained for tasks (i) (mean κ = 0.86), (ii) (mean κ = 0.94), and (iii) (mean κ = 0.94).

DATA CONCLUSION

Deep learning and radiomics models have the potential to help reduce inter-observer variability of breast density assessment.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 1.

Spatial N-glycomics of the normal breast microenvironment reveals fucosylated and high-mannose N-glycan signatures related to BI-RADS density and ancestry

Higher breast cancer mortality rates continue to disproportionally affect black women (BW) compared to white women (WW). This disparity is largely due to differences in tumor aggressiveness that can be related to distinct ancestry-associated breast tumor microenvironments (TMEs). Yet, characterization of the normal microenvironment (NME) in breast tissue and how they associate with breast cancer risk factors remains unknown. N-glycans, a glucose metabolism-linked post-translational modification, has not been characterized in normal breast tissue. We hypothesized that normal female breast tissue with distinct Breast Imaging and Reporting Data Systems (BI-RADS) categories have unique microenvironments based on N-glycan signatures that varies with genetic ancestries. Profiles of N-glycans were characterized in normal breast tissue from BW (n = 20) and WW (n = 20) at risk for breast cancer using matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). A total of 176 N-glycans (32 core-fucosylated and 144 noncore-fucosylated) were identified in the NME. We found that certain core-fucosylated, outer-arm fucosylated and high-mannose N-glycan structures had specific intensity patterns and histological distributions in the breast NME dependent on BI-RADS densities and ancestry. Normal breast tissue from BW, and not WW, with heterogeneously dense breast densities followed high-mannose patterns as seen in invasive ductal and lobular carcinomas. Lastly, lifestyles factors (e.g. age, menopausal status, Gail score, BMI, BI-RADS) differentially associated with fucosylated and high-mannose N-glycans based on ancestry. This study aims to decipher the molecular signatures in the breast NME from distinct ancestries towards improving the overall disparities in breast cancer burden.

Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial

OBJECTIVES

Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI.

METHODS

This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating.

RESULTS

The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE).

CONCLUSION

It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories.

[Structured screening for sporadic breast cancer]

BACKGROUND

The aim of secondary prevention of breast cancer is to detect the disease at the earliest curable stage and thus to reduce breast cancer-specific mortality. To this end, the nationwide population-based mammography screening program (MSP) was set up in Germany in 2005 in addition to an interdisciplinary prevention project for high-risk groups.

OBJECTIVE

Overview of the current state of the MSP, the upcoming age expansion, and potential further developments.

MATERIAL AND METHODS

Narrative review article with topic-guided literature and data search.

RESULTS

Approximately 50% of the 70,500 new cases of breast cancer that occur each year are related to the age group of the MSP. 10 years after introduction of the MSP, the incidence of advanced breast cancer stages and breast cancer-related mortality of the screening target group have steadily decreased by about one quarter, while no relevant trends were seen in the neighboring age groups at the population level.

CONCLUSION

The MSP has effectively contributed to a reduction of breast cancer mortality. With the expansion of the age groups to 45-75 years, more women have access to structured, quality assured screening. With the use of advanced stratifications and diagnostics as well as artificial intelligence, the MSP could be further optimized.

Comparison of Mammography and Mammography with Supplemental Whole-Breast US Tomography for Cancer Detection in Patients with Dense Breasts

BACKGROUND

Women with dense breasts benefit from supplemental cancer screening with US, but US has low specificity.

PURPOSE

To evaluate the performance of breast US tomography (UST) combined with full-field digital mammography (FFDM) compared with FFDM alone for breast cancer screening in women with dense breasts.

MATERIALS AND METHODS

This retrospective multireader multicase study included women with dense breasts who underwent FFDM and UST at 10 centers between August 2017 and October 2019 as part of a prospective case collection registry. All patients in the registry with cancer were included; patients with benign biopsy or negative follow-up imaging findings were randomly selected for inclusion. Thirty-two Mammography Quality Standards Act-qualified radiologists independently evaluated FFDM followed immediately by FFDM plus UST for suspicious findings and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. The superiority of FFDM plus UST versus FFDM alone for cancer detection (assessed with area under the receiver operating characteristic curve [AUC]), BI-RADS 4 sensitivity, and BI-RADS 3 sensitivity and specificity were evaluated using the two-sided significance level of α = .05. Noninferiority of BI-RADS 4 specificity was evaluated at the one-sided significance level of α = .025 with a -10% margin.

RESULTS

Among 140 women (mean age, 56 years ±10 [SD]; 36 with cancer, 104 without), FFDM plus UST achieved superior performance compared with FFDM alone (AUC, 0.60 [95% CI: 0.51, 0.69] vs 0.54 [95% CI: 0.45, 0.64]; P = .03). For FFDM plus UST versus FFDM alone, BI-RADS 4 mean sensitivity was superior (37% [428 of 1152] vs 30% [343 of 1152]; P = .03) and BI-RADS 4 mean specificity was noninferior (82% [2741 of 3328] vs 88% [2916 of 3328]; P = .004). For FFDM plus UST versus FFDM, no difference in BI-RADS 3 mean sensitivity was observed (40% [461 of 1152] vs 33% [385 of 1152]; P = .08), but BI-RADS 3 mean specificity was superior (75% [2491 of 3328] vs 69% [2299 of 3328]; P = .04).

CONCLUSION

In women with dense breasts, FFDM plus UST improved cancer detection by radiologists versus FFDM alone. Clinical trial registration nos. NCT03257839 and NCT04260620 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Mann in this issue.

The Usefulness of the Breast Density Assessment Application Used by Breast Radiologists

Breast density determined by breast radiologists and also automatically estimated by applications has been widely investigated. However, no study has yet clarified whether the use of these applications by breast radiologists improves reading efficacy. Therefore, this study aimed to assess the usefulness of applications when used by breast radiologists. A Breast Density Assessment application (App) developed by Konica Minolta, Inc. (Tokyo, Japan) was used. Independent and sequential tests were conducted to assess the usefulness of the concurrent- and second-look modes. Fifty and 100 cases were evaluated using sequential and independent tests, respectively. Each dataset was configured based on the evaluation by an expert breast radiologist who developed the Japanese guidelines for breast density. Nine breast radiologists evaluated the mammary gland content ratio and breast density; the inter-observer and expert-to-observer variability were calculated. The time required to complete the experiments was also recorded. The inter-observer variability was significant with the App, as revealed by the independent test. The use of the App significantly improved the agreement between the responses of the observers for the mammary gland content ratio and those of the expert by 6.6% and led to a reduction of 186.9 seconds in the average time required by the observers to evaluate 100 cases. However, the results of the sequential test did not suggest the effectiveness of the App. These findings suggest that the concurrent use of the App improves reading efficiency.

The distribution of breast density in women aged 18 years and older

PURPOSE

Age and body mass index (BMI) are critical considerations when assessing individual breast cancer risk, particularly for women with dense breasts. However, age- and BMI-standardized estimates of breast density are not available for screen-aged women, and little is known about the distribution of breast density in women aged < 40. This cross-sectional study uses three different modalities: optical breast spectroscopy (OBS), dual-energy X-ray absorptiometry (DXA), and mammography, to describe the distributions of breast density across categories of age and BMI.

METHODS

Breast density measures were estimated for 1,961 Australian women aged 18-97 years using OBS (%water and %water + %collagen). Of these, 935 women had DXA measures (percent and absolute fibroglandular dense volume, %FGV and FGV, respectively) and 354 had conventional mammographic measures (percent and absolute dense area). The distributions for each breast density measure were described across categories of age and BMI.

RESULTS

The mean age was 38 years (standard deviation = 15). Median breast density measures decreased with age and BMI for all three modalities, except for DXA-FGV, which increased with BMI and decreased after age 30. The variation in breast density measures was largest for younger women and decreased with increasing age and BMI.

CONCLUSION

This unique study describes the distribution of breast density measures for women aged 18-97 using alternative and conventional modalities of measurement. While this study is the largest of its kind, larger sample sizes are needed to provide clinically useful age-standardized measures to identify women with high breast density for their age or BMI.

Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study

The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736-0.903), along with an F1 score of 0.798 (0.594-0.905), precision of 0.806 (0.596-0.896), recall of 0.830 (0.650-0.946), and a Cohen's Kappa (κ) of 0.708 (0.562-0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model's competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes.

Patient Experience of Women With Dense Breasts Undergoing Screening Contrast-Enhanced Mammography

OBJECTIVE

We investigated patient experience with screening contrast-enhanced mammography (CEM) to determine whether a general population of women with dense breasts would accept CEM in a screening setting.

METHODS

In this institutional review board-approved prospective study, patients with heterogeneous and extremely dense breasts on their mammogram were invited to undergo screening CEM and complete pre-CEM and post-CEM surveys. On the pre-CEM survey, patients were asked about their attitudes regarding supplemental screening in general. On the post-CEM survey, patients were asked about their experience undergoing screening CEM, including causes and severity of any discomfort and whether they would consider undergoing screening CEM again in the future or recommend it to a friend.

RESULTS

One hundred sixty-three women were surveyed before and after screening CEM. Most patients, 97.5% (159/163), reported minimal or no unpleasantness associated with undergoing screening CEM. In addition, 91.4% (149/163) said they would probably or very likely undergo screening CEM in the future if it cost the same as a traditional screening mammogram, and 95.1% (155/163) said they would probably or very likely recommend screening CEM to a friend. Patients in this study, who were all willing to undergo CEM, more frequently reported a family history of breast cancer than a comparison cohort of women with dense breasts (58.2% vs 47.1%, P = .027).

CONCLUSION

Patients from a general population of women with dense breasts reported a positive experience undergoing screening CEM, suggesting screening CEM might be well received by this patient population, particularly if the cost was comparable with traditional screening mammography.

Comparison of the effectiveness of contrast-enhanced mammography in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population

Purpose

To assess the effectiveness of contrast-enhanced mammography (CEM) recombinant images in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population.

Material and methods

792 patients with 808 breast lesions, in whom the final decision on core-needle biopsy was made based on CEM, and who received the result of histopathological examination, were qualified for a single-centre, retrospective study. Patient electronic records and imaging examinations were reviewed to establish demographics, clinical and imaging findings, and histopathology results. The CEM images were reassessed and assigned to the appropriate American College of Radiology (ACR) density categories.

Results

Extremely dense breasts were present in 86 (10.9%) patients. Histopathological examination confirmed the presence of malignant lesions in 52.6% of cases in the entire group of patients and 43% in the group of extremely dense breasts. CEM incorrectly classified the lesion as false negative in 16/425 (3.8%) cases for the whole group, and in 1/37 (2.7%) cases for extremely dense breasts. The sensitivity of CEM for the group of all patients was 96.2%, specificity - 60%, positive predictive values (PPV) - 72.8%, and negative predictive values (NPV) - 93.5%. In the group of patients with extremely dense breasts, the sensitivity of the method was 97.3%, specificity - 59.2%, PPV - 64.3%, and NPV - 96.7%.

Conclusions

CEM is characterised by high sensitivity and NPV in detecting malignant lesions regardless of the type of breast density. In patients with extremely dense breasts, CEM could serve as a complementary or additional examination in the absence or low availability of MRI.

Association of area- and volumetric-mammographic density and breast cancer risk in women of Asian descent: a case control study

BACKGROUND

Mammographic density (MD) has been shown to be a strong and independent risk factor for breast cancer in women of European and Asian descent. However, the majority of Asian studies to date have used BI-RADS as the scoring method and none have evaluated area and volumetric densities in the same cohort of women. This study aims to compare the association of MD measured by two automated methods with the risk of breast cancer in Asian women, and to investigate if the association is different for premenopausal and postmenopausal women.

METHODS

In this case-control study of 531 cases and 2297 controls, we evaluated the association of area-based MD measures and volumetric-based MD measures with breast cancer risk in Asian women using conditional logistic regression analysis, adjusting for relevant confounders. The corresponding association by menopausal status were assessed using unconditional logistic regression.

RESULTS

We found that both area and volume-based MD measures were associated with breast cancer risk. Strongest associations were observed for percent densities (OR (95% CI) was 2.06 (1.42-2.99) for percent dense area and 2.21 (1.44-3.39) for percent dense volume, comparing women in highest density quartile with those in the lowest quartile). The corresponding associations were significant in postmenopausal but not premenopausal women (premenopausal versus postmenopausal were 1.59 (0.95-2.67) and 1.89 (1.22-2.96) for percent dense area and 1.24 (0.70-2.22) and 1.96 (1.19-3.27) for percent dense volume). However, the odds ratios were not statistically different by menopausal status [p difference = 0.782 for percent dense area and 0.486 for percent dense volume].

CONCLUSIONS

This study confirms the associations of mammographic density measured by both area and volumetric methods and breast cancer risk in Asian women. Stronger associations were observed for percent dense area and percent dense volume, and strongest effects were seen in postmenopausal individuals.

Targets in the Tumour Matrisome to Promote Cancer Therapy Response

The extracellular matrix (ECM) is composed of complex fibrillar proteins, proteoglycans, and macromolecules, generated by stromal, immune, and cancer cells. The components and organisation of the matrix evolves as tumours progress to invasive disease and metastasis. In many solid tumours, dense fibrotic ECM has been hypothesised to impede therapy response by limiting drug and immune cell access. Interventions to target individual components of the ECM, collectively termed the matrisome, have, however, revealed complex tumour-suppressor, tumour-promoter, and immune-modulatory functions, which have complicated clinical translation. The degree to which distinct components of the matrisome can dictate tumour phenotypes and response to therapy is the subject of intense study. A primary aim is to identify therapeutic opportunities within the matrisome, which might support a better response to existing therapies. Many matrix signatures have been developed which can predict prognosis, immune cell content, and immunotherapy responses. In this review, we will examine key components of the matrisome which have been associated with advanced tumours and therapy resistance. We have primarily focussed here on targeting matrisome components, rather than specific cell types, although several examples are described where cells of origin can dramatically affect tumour roles for matrix components. As we unravel the complex biochemical, biophysical, and intracellular transduction mechanisms associated with the ECM, numerous therapeutic opportunities will be identified to modify tumour progression and therapy response.

The incidence of breast cancer in Egyptian females in correlation to different mammographic ACR densities

INTRODUCTION

The density of breast tissue, radiologically referred to as fibroglandular mammary tissue, was found to be a predisposing factor for breast cancer (BC). However, the stated degree of elevated BC risk varies widely in the literature.

An exploration of causal relationships between nine neurological diseases and the risk of breast cancer: a Mendelian randomization study

BACKGROUND

Some preceding researches have observed that certain neurological disorders, such as Alzheimer's disease and multiple sclerosis, may affect breast cancer risk. However, whether there are causal relationships between these neurological conditions and breast cancer is inconclusive. This study was designed to explore whether neurological disorders affected the risks of breast cancer overall and of the two subtypes (ER+ and ER-).

METHODS

In the course of this study, genome-wide association study (GWAS) data for nine neurological diseases (Alzheimer's disease, multiple sclerosis, Parkinson's disease, myasthenia gravis, generalized epilepsy, intracerebral haemorrhage, cerebral atherosclerosis, brain glioblastoma, and benign meningeal tumour) were collected from the Complex Trait Genetics lab and the MRC Integrative Epidemiology Unit, and single-nucleotide polymorphisms (SNPs) extensively associated with these neurological ailments had been recognized as instrumental variables (IVs). GWAS data on breast cancer were collected from the Breast Cancer Association Consortium (BCAC). Two-sample Mendelian randomization (MR) analyses as well as multivariable MR analyses were performed to determine whether these SNPs contributed to breast cancer risk. Additionally, the accuracy of the results was evaluated using the false discovery rate (FDR) multiple correction method. Both heterogeneity and pleiotropy were evaluated by analyzing sensitivities.

RESULTS

According to the results of two-sample MR analyses, Alzheimer's disease significantly reduced the risks of overall (OR 0.925, 95% CI [0.871-0.982], P = 0.011) and ER+ (OR 0.912, 95% CI [0.853-0.975], P = 0.007) breast cancer, but there was a negative result in ER- breast cancer. However, after multiple FDR corrections, the effect of Alzheimer's disease on overall breast cancer was not statistically significant. In contrast, multiple sclerosis significantly increased ER+ breast cancer risk (OR 1.007, 95% CI [1.003-1.011], P = 0.001). In addition, the multivariable MR analyses showed that Alzheimer's disease significantly reduced the risk of ER+ breast cancer (IVW: OR 0.929, 95% CI [0.864-0.999], P=0.047; MR-Egger: OR 0.916, 95% CI [0.846-0.992], P=0.031); however, multiple sclerosis significantly increased the risk of ER+ breast cancer (IVW: OR 1.008, 95% CI [1.003-1.012], P=4.35×10-4; MR-Egger: OR 1.008, 95% CI [1.003-1.012], P=5.96×10-4). There were no significant associations between the remainder of the neurological diseases and breast cancer.

CONCLUSIONS

This study found the trends towards a decreased risk of ER+ breast cancer in patients with Alzheimer's disease and an increased risk in patients with multiple sclerosis. However, due to the limitations of Mendelian randomization, we cannot determine whether there are definite causal relationships between neurological diseases and breast cancer risk. For conclusive evidences, more prospective randomized controlled trials will be needed in the future.

Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection

BACKGROUND

Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women.

METHODS

We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A-D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories.

RESULTS

Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7-77.3] vs. 67.1% [95% CI, 58.8-74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9-93.2] vs. 77.6% [95% CI, 61.7-77.9]), PPV (1.5% [95% CI, 1.2-1.9] vs. 0.5% [95% CI, 0.4-0.6]), recall rate (7.1% [95% CI, 6.9-7.2] vs. 22.5% [95% CI, 22.2-22.7]), and AUC values (0.8 [95% CI, 0.76-0.84] vs. 0.74 [95% CI, 0.7-0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category.

CONCLUSIONS

AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.

Re-attendance in supplemental breast MRI screening rounds of the DENSE trial for women with extremely dense breasts

OBJECTIVES

Supplemental MRI screening improves early breast cancer detection and reduces interval cancers in women with extremely dense breasts in a cost-effective way. Recently, the European Society of Breast Imaging recommended offering MRI screening to women with extremely dense breasts, but the debate on whether to implement it in breast cancer screening programs is ongoing. Insight into the participant experience and willingness to re-attend is important for this discussion.

METHODS

We calculated the re-attendance rates of the second and third MRI screening rounds of the DENSE trial. Moreover, we calculated age-adjusted odds ratios (ORs) to study the association between characteristics and re-attendance. Women who discontinued MRI screening were asked to provide one or more reasons for this.

RESULTS

The re-attendance rates were 81.3% (3458/4252) and 85.2% (2693/3160) in the second and third MRI screening round, respectively. A high age (> 65 years), a very low BMI, lower education, not being employed, smoking, and no alcohol consumption were correlated with lower re-attendance rates. Moderate or high levels of pain, discomfort, or anxiety experienced during the previous MRI screening round were correlated with lower re-attendance rates. Finally, a plurality of women mentioned an examination-related inconvenience as a reason to discontinue screening (39.1% and 34.8% in the second and third screening round, respectively).

CONCLUSIONS

The willingness of women with dense breasts to re-attend an ongoing MRI screening study is high. However, emphasis should be placed on improving the MRI experience to increase the re-attendance rate if widespread supplemental MRI screening is implemented.

CLINICAL RELEVANCE STATEMENT

For many women, MRI is an acceptable screening method, as re-attendance rates were high - even for screening in a clinical trial setting. To further enhance the (re-)attendance rate, one possible approach could be improving the overall MRI experience.

KEY POINTS

• The willingness to re-attend in an ongoing MRI screening study is high. • Pain, discomfort, and anxiety in the previous MRI screening round were related to lower re-attendance rates. • Emphasis should be placed on improving MRI experience to increase the re-attendance rate in supplemental MRI screening.

Modeling correlated pairs of mammogram images

Mammography remains the primary screening strategy for breast cancer, which continues to be the most prevalent cancer diagnosis among women globally. Because screening mammograms capture both the left and right breast, there is a nonnegligible correlation between the pair of images. Previous studies have explored the concept of averaging between the pair of images after proper image registration; however, no comparison has been made in directly utilizing the paired images. In this paper, we extend the bivariate functional principal component analysis over triangulations to jointly characterize the pair of imaging data bounded in an irregular domain and then nest the extracted features within the survival model to predict the onset of breast cancer. The method is applied to our motivating data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our findings indicate that there was no statistically significant difference in model discrimination performance between averaging the pair of images and jointly modeling the two images. Although the breast cancer study did not reveal any significant difference, it is worth noting that the methods proposed here can be readily extended to other studies involving paired or multivariate imaging data.

Use of a commercial artificial intelligence-based mammography analysis software for improving breast ultrasound interpretations

OBJECTIVES

To evaluate the use of a commercial artificial intelligence (AI)-based mammography analysis software for improving the interpretations of breast ultrasound (US)-detected lesions.

METHODS

A retrospective analysis was performed on 1109 breasts that underwent both mammography and US-guided breast biopsy. The AI software processed mammograms and provided an AI score ranging from 0 to 100 for each breast, indicating the likelihood of malignancy. The performance of the AI score in differentiating mammograms with benign outcomes from those revealing cancers following US-guided breast biopsy was evaluated. In addition, prediction models for benign outcomes were constructed based on clinical and imaging characteristics with and without AI scores, using logistic regression analysis.

RESULTS

The AI software had an area under the receiver operating characteristics curve (AUROC) of 0.79 (95% CI, 0.79-0.82) in differentiating between benign and cancer cases. The prediction models that did not include AI scores (non-AI model), only used AI scores (AI-only model), and included AI scores (integrated model) had AUROCs of 0.79 (95% CI, 0.75-0.83), 0.78 (95% CI, 0.74-0.82), and 0.85 (95% CI, 0.81-0.88) in the development cohort, and 0.75 (95% CI, 0.68-0.81), 0.82 (95% CI, 0.76-0.88), and 0.84 (95% CI, 0.79-0.90) in the validation cohort, respectively. The integrated model outperformed the non-AI model in the development and validation cohorts (p < 0.001 for both).

CONCLUSION

The commercial AI-based mammography analysis software could be a valuable adjunct to clinical decision-making for managing US-detected breast lesions.

CLINICAL RELEVANCE STATEMENT

The commercial AI-based mammography analysis software could potentially reduce unnecessary biopsies and improve patient outcomes.

KEY POINTS

• Breast US has high rates of false-positive interpretations. • A commercial AI-based mammography analysis software could distinguish mammograms having benign outcomes from those revealing cancers after US-guided breast biopsy. • A commercial AI-based mammography analysis software may improve interpretations for breast US-detected lesions.

Breast percent density changes in digital mammography pre- and post-radiotherapy

INTRODUCTION

Breast cancer (BC), the most frequently diagnosed malignancy among women worldwide, presents a public health challenge and affects mortality rates. Breast-conserving therapy (BCT) is a common treatment, but the risk from residual disease necessitates radiotherapy. Digital mammography monitors treatment response by identifying post-operative and radiotherapy tissue alterations, but accurate assessment of mammographic density remains a challenge. This study used OpenBreast to measure percent density (PD), offering insights into changes in mammographic density before and after BCT with radiation therapy.

METHODS

This retrospective analysis included 92 female patients with BC who underwent BCT, chemotherapy, and radiotherapy, excluding those who received hormonal therapy or bilateral BCT. Percent/percentage density measurements were extracted using OpenBreast, an automated software that applies computational techniques to density analyses. Data were analysed at baseline, 3 months, and 15 months post-treatment using standardised mean difference (SMD) with Cohen's d, chi-square, and paired sample t-tests. The predictive power of PD changes for BC was measured based on the receiver operating characteristic (ROC) curve analysis.

RESULTS

The mean age was 53.2 years. There were no significant differences in PD between the periods. Standardised mean difference analysis revealed no significant changes in the SMD for PD before treatment compared with 3- and 15-months post-treatment. Although PD increased numerically after radiotherapy, ROC analysis revealed optimal sensitivity at 15 months post-treatment for detecting changes in breast density.

CONCLUSIONS

This study utilised an automated breast density segmentation tool to assess the changes in mammographic density before and after BC treatment. No significant differences in the density were observed during the short-term follow-up period. However, the results suggest that quantitative density assessment could be valuable for long-term monitoring of treatment effects. The study underscores the necessity for larger and longitudinal studies to accurately measure and validate the effectiveness of quantitative methods in clinical BC management.

Increased matrix stiffness enhances pro-tumorigenic traits in a physiologically relevant breast tissue- monocyte 3D model

High mammographic density, associated with increased tissue stiffness, is a strong risk factor for breast cancer per se. In postmenopausal women there is no differences in the occurrence of ductal carcinoma in situ (DCIS) depending on breast density. Preliminary data suggest that dense breast tissue is associated with a pro-inflammatory microenvironment including infiltrating monocytes. However, the underlying mechanism(s) remains largely unknown. A major roadblock to understanding this risk factor is the lack of relevant in vitro models. A biologically relevant 3D model with tunable stiffness was developed by cross-linking hyaluronic acid. Breast cancer cells were cultured with and without freshly isolated human monocytes. In a unique clinical setting, extracellular proteins were sampled using microdialysis in situ from women with various breast densities. We show that tissue stiffness resembling high mammographic density increases the attachment of monocytes to the cancer cells, increase the expression of adhesion molecules and epithelia-mesenchymal-transition proteins in estrogen receptor (ER) positive breast cancer. Increased tissue stiffness results in increased secretion of similar pro-tumorigenic proteins as those found in human dense breast tissue including inflammatory cytokines, proteases, and growth factors. ER negative breast cancer cells were mostly unaffected suggesting that diverse cancer cell phenotypes may respond differently to tissue stiffness. We introduce a biological relevant model with tunable stiffness that resembles the densities found in normal breast tissue in women. The model will be key for further mechanistic studies. Additionally, our data revealed several pro-tumorigenic pathways that may be exploited for prevention and therapy against breast cancer. STATEMENT OF SIGNIFICANCE: Women with mammographic high-density breasts have a 4-6-fold higher risk of breast cancer than low-density breasts. Biological mechanisms behind this increase are not fully understood and no preventive therapeutics are available. One major reason being a lack of suitable experimental models. Having such models available would greatly enhance the discovery of relevant targets for breast cancer prevention. We present a biologically relevant 3D-model for studies of human dense breasts, providing a platform for investigating both biophysical and biochemical properties that may affect cancer progression. This model will have a major scientific impact on studies for identification of novel targets for breast cancer prevention.

Tamoxifen Dose De-Escalation: An Effective Strategy for Reducing Adverse Effects?

Tamoxifen, a cornerstone in the adjuvant treatment of estrogen receptor-positive breast cancer, significantly reduces breast cancer recurrence and breast cancer mortality; however, its standard adjuvant dose of 20 mg daily presents challenges due to a broad spectrum of adverse effects, contributing to high discontinuation rates. Dose reductions of tamoxifen might be an option to reduce treatment-related toxicity, but large randomized controlled trials investigating the tolerability and, more importantly, efficacy of low-dose tamoxifen in the adjuvant setting are lacking. We conducted an extensive literature search to explore evidence on the tolerability and clinical efficacy of reduced doses of tamoxifen. In this review, we discuss two important topics regarding low-dose tamoxifen: (1) the incidence of adverse effects and quality of life among women using low-dose tamoxifen; and (2) the clinical efficacy of low-dose tamoxifen examined in the preventive setting and evaluated through the measurement of several efficacy derivatives. Moreover, practical tools for tamoxifen dose reductions in the adjuvant setting are provided and further research to establish optimal dosing strategies for individual patients are discussed.

Abbreviated breast magnetic resonance imaging (MRI) or digital breast tomosynthesis for breast cancer detection in dense breasts? A retrospective preliminary study with comparable results

AIM

To compare the diagnostic performance of abbreviated breast magnetic resonance (AB-MR) imaging (MRI) and digital breast tomosynthesis (DBT) for breast cancer detection in Malaysian women with dense breasts, using histopathology as the reference standard.

MATERIALS AND METHODS

This was a single-centre cross-sectional study of 115 women with American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BIRADS) breast density C and D on DBT with breast lesions who underwent AB-MR from June 2018 to December 2021. AB-MR was performed on a 3 T MRI system with an imaging protocol consisting of three sequences: axial T1 fat-saturated unenhanced; axial first contrast-enhanced; and subtracted first contrast-enhanced with maximum intensity projection (MIP). DBT and AB-MR images were evaluated by two radiologists blinded to the histopathology and patient outcomes. Diagnostic accuracy (sensitivity, specificity, positive predictive value [PPV] and negative predictive value [NPV]) was assessed.

RESULT

Of the 115 women, the mean age was 50.6 years. There were 48 (41.7%) Malay, 54 (47%) Chinese, and 12 (10.4%) Indian women. The majority (n=87, 75.7%) were from the diagnostic population. Sixty-one (53.1%) were premenopausal and 54 (46.9%) postmenopausal. Seventy-eight (72.4%) had an increased risk of developing breast cancer. Ninety-one (79.1%) women had density C and 24 (20.9%) had density D. There were 164 histopathology-proven lesions; 69 (42.1%) were malignant and 95 (57.9%) were benign. There were 62.8% (n=103/164) lesions detected at DBT. All the malignant lesions 100% (n=69) and 35.7% (n=34) of benign lesions were detected. Of the 61 lesions that were not detected, 46 (75.4%) were in density C, and 15 (24.6%) were in density D. The sensitivity, specificity, PPV, and NPV for DBT were 98.5%, 34.6%, 66.3%, and 94.7%, respectively. There were 65.2% (n=107/164) lesions detected on AB-MR, with 98.6% (n=68) malignant and 41.1% (39) benign lesions detected. The sensitivity, specificity, PPV, and NPV for AB-MR were 98.5%, 43.9%, 67.2%, and 96.2%, respectively. One malignant lesion (0.6%), which was a low-grade ductal carcinoma in-situ (DCIS), was missed on AB-MR.

CONCLUSION

The present findings suggest that both DBT and AB-MR have comparable effectiveness as an imaging method for detecting breast cancer and have high NPV for low-risk lesions in women with dense breasts.

Comparative Effectiveness of Mammography, Ultrasound, and MRI in the Detection of Breast Carcinoma in Dense Breast Tissue: A Systematic Review

This systematic review aimed to critically assess the effectiveness of mammography, ultrasound, and magnetic resonance imaging (MRI) in the detection of breast carcinoma within dense breast tissue. An exhaustive search of contemporary literature was undertaken, focusing on the diagnostic accuracy, false positive and negative rates, and clinical implications of the aforementioned imaging modalities. Each modality was assessed in isolation and side by side against the others to draw comparative inferences. While mammography remains a foundational imaging modality, its effectiveness waned within the context of dense breast tissue. Ultrasound demonstrated a strong differentiation prowess, especially among specific demographic cohorts. MRI, despite its exceptional precision and differentiation capabilities, exhibited a tendency for slightly elevated false positive rates. No single modality emerged as singularly superior for all cases. Instead, an integrated approach, combining the strengths of each modality based on individual patient profiles and clinical scenarios, is recommended. This tailored approach ensures optimized detection rates and minimizes diagnostic ambiguities, underscoring the significance of individualized patient care in the field of diagnostic radiology.

Use of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer Screening

Background Mammographic density measurements are used to identify patients who should undergo supplemental imaging for breast cancer detection, but artificial intelligence (AI) image analysis may be more effective. Purpose To assess whether AISmartDensity-an AI-based score integrating cancer signs, masking, and risk-surpasses measurements of mammographic density in identifying patients for supplemental breast imaging after a negative screening mammogram. Materials and Methods This retrospective study included randomly selected individuals who underwent screening mammography at Karolinska University Hospital between January 2008 and December 2015. The models in AISmartDensity were trained and validated using nonoverlapping data. The ability of AISmartDensity to identify future cancer in patients with a negative screening mammogram was evaluated and compared with that of mammographic density models. Sensitivity and positive predictive value (PPV) were calculated for the top 8% of scores, mimicking the proportion of patients in the Breast Imaging Reporting and Data System "extremely dense" category. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and was compared using the DeLong test. Results The study population included 65 325 examinations (median patient age, 53 years [IQR, 47-62 years])-64 870 examinations in healthy patients and 455 examinations in patients with breast cancer diagnosed within 3 years of a negative screening mammogram. The AUC for detecting subsequent cancers was 0.72 and 0.61 (P < .001) for AISmartDensity and the best-performing density model (age-adjusted dense area), respectively. For examinations with scores in the top 8%, AISmartDensity identified 152 of 455 (33%) future cancers with a PPV of 2.91%, whereas the best-performing density model (age-adjusted dense area) identified 57 of 455 (13%) future cancers with a PPV of 1.09% (P < .001). AISmartDensity identified 32% (41 of 130) and 34% (111 of 325) of interval and next-round screen-detected cancers, whereas the best-performing density model (dense area) identified 16% (21 of 130) and 9% (30 of 325), respectively. Conclusion AISmartDensity, integrating cancer signs, masking, and risk, outperformed traditional density models in identifying patients for supplemental imaging after a negative screening mammogram. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kim and Chang in this issue.

Surgical and Radiology Trainees' Proficiency in Reading Mammograms: the Importance of Education for Cancer Localisation

Medical imaging with mammography plays a very important role in screening and diagnosis of breast cancer, Australia's most common female cancer. The visualisation of cancers on mammograms often forms a diagnosis and guidance for radiologists and breast surgeons, and education platforms that provide real cases in a simulated testing environment have been shown to improve observer performance for radiologists. This study reports on the performance of surgical and radiology trainees in locating breast cancers. An enriched test set of 20 mammography cases (6 cancer and 14 cancer free) was created, and 18 surgical trainees and 32 radiology trainees reviewed the cases via the Breast Screen Reader Assessment Strategy (BREAST) platform and marked any lesions identifiable. Further analysis of performance with high- and low-density cases was undertaken, and standard metrics including sensitivity and specificity. Radiology trainees performed significantly better than surgical trainees in terms of specificity (0.72 vs. 0.35; P < 0.01). No significant differences were observed between the surgical and radiology trainees in sensitivity or lesion sensitivity. Mixed results were obtained with participants regarding breast density, with higher density cases generally having lower performance. The higher specificity of the radiology trainees compared to the surgical trainees likely represents less exposure to negative mammography cases. The use of high-fidelity simulated self-test environments like BREAST is able to benchmark, understand and build strategies for improving cancer education in a safe environment, including identifying challenging scenarios like breast density for enhanced training.

Cigarette smoking and mammographic breast density in post-menopausal women from the EPIC Florence cohort

Introduction

Cigarette smoking has been recognized as a risk factor for breast cancer (BC) also if the biological mechanism remains poorly understood. High mammographic breast density (MBD) is associated with BC risk and many BC risk factors, such as genetic, anthropometric, reproductive and lifestyle factors and age, are also able to modulate MBD. The aim of the present study was to prospectively explore, in post-menopausal women, the association between smoking habits and MBD, assessed using an automated software, considering duration and intensity of smoking.

Methods

The analysis was carried out in 3,774 women enrolled in the European Prospective Investigation into Cancer and Nutrition (EPIC) Florence cohort in 1993-98, participating in the 2004-06 follow up (FU) and with at least one full-field digital mammography (FFDM) performed after FU. For each woman, detailed information on smoking habits, anthropometry, lifestyle and reproductive history was collected at enrollment and at FU. Smoking information at baseline and at FU was integrated. The fully automated Volpara™ software was used to obtain total breast volume (cm3), absolute breast dense volume (DV, cm3) and volumetric percent density (VPD, %) from the first available FFDM (average 5.3 years from FU). Multivariable linear regression models were applied to evaluate the associations between smoking habits and VPD or DV.

Results

An inverse association between smoking exposure and VPD emerged (Diff% -7.96%, p <0.0001 for current smokers and -3.92%, p 0.01 for former smokers, compared with non-smokers). An inverse dose-response relationship with number of cigarettes/day, years of smoking duration and lifetime smoking exposure (pack-years) and a direct association with time since smoking cessation among former smokers emerged. Similar associations, with an attenuated effect, emerged when DV was considered as the outcome variable.

Discussion

This longitudinal study confirms the inverse association between active smoking, a known risk factor for BC, and MBD among post-menopausal women. The inclusion of smoking habits in the existing BC risk prediction models could be evaluated in future studies.

Breast density in NF1 women: a retrospective study

Neurofibromatosis type 1 (NF1) is an autosomal dominant condition caused by neurofibromin haploinsufficiency due to pathogenic variants in the NF1 gene. Tumor predisposition has long been associated with NF1, and an increased breast cancer (BC) incidence and reduced survival have been reported in recent years for women with NF1. As breast density is another known independent risk factor for BC, this study aims to evaluate the variability of breast density in patients with NF1 compared to the general population. Mammograms from 98 NF1 women affected by NF1, and enrolled onto our monocentric BC screening program, were compared with those from 300 healthy subjects to verify differences in breast density. Mammograms were independently reviewed and scored by a radiologist and using a Computer-Aided Detection (CAD) software. The comparison of breast density between NF1 patients and controls was performed through Chi-squared test and with multivariable ordinal logistic models adjusted for age, body mass index (BMI), number of pregnancies, and menopausal status.breast density was influenced by BMI and menopausal status in both NF1 patients and healthy subjects. No difference in breast density was observed between NF1 patients and the healthy female population, even after considering the potential confounding factors.Although NF1 and a highly fibroglandular breast are known risk factors of BC, in this study, NF1 patients were shown to have comparable breast density to healthy subjects. The presence of pathogenic variants in the NF1 gene does not influence the breast density value.

Gynecologic Care of Black Breast Cancer Survivors

Purpose of Review

Black patients suffer from breast cancer-related racial health disparities, which could have implications on their gynecologic care. This review explores considerations in the gynecologic care of Black breast cancer survivors.

Recent Findings

Black people have a higher risk of leiomyoma and endometrial cancer, which could confound bleeding patterns such as in the setting of tamoxifen use. As Black people are more likely to have early-onset breast cancer, this may have implications on long-term bone and heart health. Black patients may be more likely to have menopausal symptoms at baseline and as a result of breast cancer treatment. Furthermore, Black patients are less likely to utilize assisted reproductive technology and genetic testing services.

Summary

It is important for healthcare providers to be well-versed in the intersections of breast cancer and gynecologic care. Black breast cancer survivors may have unique considerations for which practitioners should be knowledgeable.

Trends in patient dose and compression force for digital (DR) mammography systems over an eleven-year period

This study evaluated trends in patient dose and compression force for screening digital (DR) mammography systems. The results of five audits (carried out in 2011, 2014, 2018, 2020 and 2022) were compared. For every audit, anonymised screening examinations from each system consisting of the standard craniocaudal (CC) and mediolateral oblique (MLO) views of both breasts were analysed. Exposure parameters were extracted from the Digital Imaging and Communications in Medicine (DICOM) header and the mean glandular dose (MGD) for each image was calculated. Trends in the distribution of MGD, compressed breast thickness, compression force and compression force per radiographer were investigated. The mean MGD per image (and mean compressed breast thickness) was 1.20 mGy (58 mm), 1.53 mGy (59 mm), 1.83 mGy (61 mm), 1.94 mGy (60 mm) and 2.11 mGy (61 mm) for 2011, 2014, 2018, 2020 and 2022 respectively. The mean (and standard deviation) compression force was 114 (32) N, 112 (29) N, 108 (27) N, 104 (24) N and 100 (23) N for 2011, 2014, 2018, 2020 and 2022 respectively. The mean MGD per image has increased over time but remains below internationally established Diagnostic Reference Levels (DRLs). This increase is primarily due to a change in the distribution of the different manufacturers and digital detector technologies, rather than an increase in the dose of the individual systems over time. The mean compression force has decreased over time in response to client feedback surveys. The standard deviation has also reduced, indicating more consistent application of force.

Automated Breast Volume Scanner Is More Valuable Than Hand-Held Ultrasound in Diagnosis of Small Breast cancer: An Analysis of 725 Patients With 912 Lesions Evaluations

ABSTRACT

This study aimed to evaluate the clinical value of automated breast volume scanner (ABVS) compared with hand-held ultrasound (HHUS). From January 2015 to May 2019, a total of 912 breast lesions in 725 consecutive patients were included in this study. κ statistics were calculated to identify interobserver agreement of ABVS and HHUS. The diagnostic performance for ABVS and HHUS was expressed as the area under the receiver operating characteristic curve, as well as the corresponding 95% confidence interval, sensitivity, and specificity. The sensitivities of ABVS and HHUS were 95.95% and 93.69%, and the specificities were 85.47% and 81.20%, respectively. A difference that nearly reached statistical significance was observed in sensitivities between ABVS and HHUS (P = 0.0525). The specificity of ABVS was significantly higher than that of HHUS (P = 0.006). When lesions were classified according to their maximum diameter, the sensitivity and specificity of ABVS were significantly higher than HHUS for lesions ≤20 mm, while they made no statistical significance between ABVS and HHUS for lesions >20 mm. The interobserver agreement for ABVS was better than that of HHUS. Automated breast volume scanner was more valuable than HHUS in diagnosing breast cancer, especially for lesions ≤20 mm, and it could be a valuable diagnostic tool for breast cancer.

A Public Health Emergency: Breast Cancer Among Black Communities in the United States

While Black people have a similar incidence of breast cancer compared to White people, they have a 40% increased death rate. Black people are more likely to be diagnosed with aggressive subtypes such as triple-negative breast cancer. However, despite biological factors, systemic racism and social determinants of health create delays in care and barriers to treatment. While genetic testing holds incredible promise for Black people, uptake remains low and results may be challenging to interpret. There is a need for more robust, multidisciplinary, and antiracist interventions to reverse breast cancer-related racial disparities.

Investigation of Genetic Alterations Associated With Interval Breast Cancer

Importance

Breast cancers (BCs) diagnosed between 2 screening examinations are called interval cancers (ICs), and they have worse clinicopathological characteristics and poorer prognosis than screen-detected cancers (SDCs). However, the association of rare germline genetic variants with IC have not been studied.

Objective

To evaluate whether rare germline deleterious protein-truncating variants (PTVs) can be applied to discriminate between IC and SDC while considering mammographic density.

Design, Setting, and Participants

This population-based genetic association study was based on women aged 40 to 76 years who were attending mammographic screening in Sweden. All women with a diagnosis of BC between January 2001 and January 2016 were included, together with age-matched controls. Patients with BC were followed up for survival until 2021. Statistical analysis was performed from September 2021 to December 2022.

Exposure

Germline PTVs in 34 BC susceptibility genes as analyzed by targeted sequencing.

Main Outcomes and Measures

Odds ratios (ORs) were used to compare IC with SDC using logistic regression. Hazard ratios were used to investigate BC-specific survival using Cox regression.

Results

All 4121 patients with BC (IC, n = 1229; SDC, n = 2892) were female, with a mean (SD) age of 55.5 (7.1) years. There were 5631 age-matched controls. The PTVs of the ATM, BRCA1, BRCA2, CHEK2, and PALB2 genes were more common in patients with IC compared with SDC (OR, 1.48; 95% CI, 1.06-2.05). This association was primarily influenced by BRCA1/2 and PALB2 variants. A family history of BC together with PTVs of any of these genes synergistically increased the probability of receiving a diagnosis of IC rather than SDC (OR, 3.95; 95% CI, 1.97-7.92). Furthermore, 10-year BC-specific survival revealed that if a patient received a diagnosis of an IC, carriers of PTVs in any of these 5 genes had significantly worse survival compared with patients not carrying any of them (hazard ratio, 2.04; 95% CI, 1.06-3.92). All of these associations were further pronounced in a subset of patients with IC who had a low mammographic density at prior screening examination.

Conclusions and Relevance

The results of this study may be helpful in future optimizations of screening programs that aim to lower mortality as well as the clinical treatment of patients with BC.